diff --git a/.env.example b/.env.example index f17b822..6866357 100644 --- a/.env.example +++ b/.env.example @@ -6,17 +6,18 @@ CLAUDE_BACKEND=anthropic # Anthropic API (used when CLAUDE_BACKEND=anthropic) ANTHROPIC_API_KEY=your_key_here -CLAUDE_MODEL=claude-sonnet-4-6 # override default model +CLAUDE_MODEL=claude-sonnet-5-5 # override default model # Vertex AI (used when CLAUDE_BACKEND=vertex) -# Prerequisites: pip install anthropic[vertex] && gcloud auth application-default login +# Prerequisites: uv sync --extra vertex && gcloud auth application-default login GOOGLE_CLOUD_PROJECT=your_gcp_project_id -VERTEX_REGION=us-east5 # region where Claude is deployed in your project -VERTEX_CLAUDE_MODEL=claude-sonnet-4-0@20250514 # override Vertex model +VERTEX_REGION=global # global, a multi-region (us / eu), or a specific region +VERTEX_CLAUDE_MODEL=claude-sonnet-5-5 # override Vertex model # Embeddings — choose one mode -EMBEDDING_MODE=local # local | openai -OLLAMA_BASE_URL=http://localhost:11434 +EMBEDDING_MODE=fastembed # fastembed (in-process) | ollama | openai +FASTEMBED_CACHE=./data/models # where fastembed keeps its model (~520 MB) +OLLAMA_BASE_URL=http://localhost:11434 # only needed if EMBEDDING_MODE=ollama OPENAI_API_KEY=your_key_here # only needed if EMBEDDING_MODE=openai # Storage paths @@ -28,3 +29,7 @@ MAX_ARTICLES=120 # cap on total articles to crawl UMAP_N_NEIGHBORS=15 KMEANS_K=auto # auto | integer (e.g. 7) RAG_TOP_K=15 +CHAT_EFFORT=medium # low | medium | high: reasoning before chat answers +RECOVER_ACADEMIC=true # look up open-access copies of pages academic publishers block +SEARCH_PROVIDER=brave # brave | searxng +SEARXNG_URL= # e.g. http://localhost:8888 when SEARCH_PROVIDER=searxng diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..e70565d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,4 @@ +# Keep line endings consistent across Windows and Linux (CI, tooling) +* text=auto eol=lf +*.png binary +*.svg text eol=lf diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..21b32d1 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,75 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: true + +jobs: + backend: + name: Backend (lint, tests) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + - run: uv python install 3.12 + - run: uv sync --locked + - run: uv run ruff check backend tests scripts + - run: uv run pytest -q + + frontend: + name: Frontend (types, lint, unit tests, build) + runs-on: ubuntu-latest + defaults: + run: + working-directory: frontend + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-node@v4 + with: + node-version: 22 + cache: npm + cache-dependency-path: frontend/package-lock.json + # npm 10's resolver crashes on this dependency tree; npm 11 is fine + - run: npm install -g npm@11 + - run: npm ci + - run: npm run check + - run: npm run lint + - run: npx vitest run + - run: npm run build + + e2e: + name: End-to-end (demo map, no API keys) + runs-on: ubuntu-latest + needs: [backend, frontend] + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + - run: uv python install 3.12 + - run: uv sync --locked + - uses: actions/setup-node@v4 + with: + node-version: 22 + cache: npm + cache-dependency-path: frontend/package-lock.json + - working-directory: frontend + run: | + npm install -g npm@11 + npm ci + npx playwright install --with-deps chromium + npm run build + npx playwright test + - uses: actions/upload-artifact@v4 + if: failure() + with: + name: playwright-traces + path: frontend/test-results/ + retention-days: 7 diff --git a/.gitignore b/.gitignore index a3d14a4..a6200ef 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,6 @@ frontend/build/ # Claude .claude/ +.venv/ +.playwright-mcp/ +.e2e/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md index 63e2e30..1574338 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,14 @@ # discourse-lens -Discourse Lens takes a topic string as input, crawls ~100 relevant articles from the web, and surfaces patterns in the discourse through semantic visualization. You get five views — a cluster map, temporal drift, outlier finder, consensus bars, and a full-width chat interface — all running locally. +Discourse Lens takes a topic, reads ~100 relevant articles from the web, and maps the conversation: which camps exist, how coverage has moved over time, which articles don't fit, and what each camp talks about. You can then ask questions answered only from those articles. Everything runs locally except web search and the Claude API calls. --- ## Requirements -- Python 3.10+ -- Node.js 18+ -- [Ollama](https://ollama.com) (for local embeddings) — or an OpenAI API key -- A [Brave Search API key](https://brave.com/search/api) (free tier: 2000 queries/month) +- Python 3.12 and [uv](https://docs.astral.sh/uv/) +- Node.js 20.19+ (Vite 8) +- A [Brave Search API key](https://brave.com/search/api) (free tier: 2000 queries/month), or a self-hosted [SearXNG](https://docs.searxng.org) instance with the JSON format enabled (`SEARCH_PROVIDER=searxng`, `SEARXNG_URL=http://localhost:8888`) - An [Anthropic API key](https://console.anthropic.com) (for chat and cluster labelling) --- @@ -21,27 +20,24 @@ Discourse Lens takes a topic string as input, crawls ~100 relevant articles from ```bash git clone https://github.com/Meerav29/discourse-lens.git cd discourse-lens -python -m venv venv -source venv/Scripts/activate # Windows Git Bash -# source venv/bin/activate # Mac/Linux -pip install -r requirements.txt +uv sync # creates .venv from uv.lock +uv run playwright install chromium ``` -**2. Install frontend dependencies** +Using Vertex AI for Claude? `uv sync --extra vertex` instead. + +**2. Build the frontend** ```bash cd frontend npm install +npm run build cd .. ``` -**3. Pull the embedding model** (skip if using OpenAI) - -```bash -ollama pull nomic-embed-text -``` +Embeddings run in-process with [fastembed](https://github.com/qdrant/fastembed) (nomic-embed-text v1.5, no GPU or PyTorch needed). The first map downloads the model once (about 520 MB) to `data/models`. Already running [Ollama](https://ollama.com)? `EMBEDDING_MODE=ollama` uses its `nomic-embed-text` instead. It's the same model with the same vectors, so maps work with either. `EMBEDDING_MODE=openai` uses OpenAI's API, but maps made that way can only be searched with OpenAI. -**4. Configure API keys** +**3. Configure API keys** Copy `.env.example` to `.env` and fill in your keys: @@ -60,39 +56,68 @@ ANTHROPIC_API_KEY=your_key_here ## Running -Open two terminals. +```bash +uv run uvicorn backend.api.main:app --port 8001 +``` + +Then open [http://localhost:8001](http://localhost:8001). The backend serves the built app and the API from one address. -**Terminal 1 — Backend:** +**No API keys yet?** Load a small synthetic demo map to explore the interface: ```bash -source venv/Scripts/activate # Windows Git Bash / source venv/bin/activate on Mac/Linux -python -m uvicorn backend.api.main:app --reload --port 8001 +uv run python -m scripts.seed_demo ``` -**Terminal 2 — Frontend:** +### Working on the frontend + +Run the backend as above, plus the Vite dev server with hot reload (it forwards `/api` to port 8001): ```bash cd frontend npm run dev ``` -Then open [http://localhost:5173](http://localhost:5173) in your browser. +and open [http://localhost:5173](http://localhost:5173). After changing the backend API, regenerate the typed client: + +```bash +uv run python -m scripts.export_openapi +cd frontend && npm run gen:api +``` + +How answers are found: `backend/synthesis/chat.py` (hybrid search: vector similarity plus BM25 keyword search, fused by reciprocal rank). + +How maps are built: `backend/topics.py` (article vectors → UMAP → HDBSCAN → c-TF-IDF, BERTopic's recipe without PyTorch) and `backend/embedding/project.py` (naming topics with Claude). + +Checks (CI runs all of these on every push and pull request): + +```bash +uv run ruff check backend tests scripts +uv run pytest +cd frontend && npm run check && npm run lint && npx vitest run +``` + +End-to-end tests drive the real app (backend + built frontend) against the demo map in a throwaway `.e2e/` folder, with no network or API keys: + +```bash +cd frontend && npm run build && npx playwright install chromium && npm run test:e2e +``` --- ## Using it -1. Type a topic into the input field (e.g. `mechanistic interpretability in large language models`) and click **Analyze Discourse** -2. The pipeline runs in the background — crawling, embedding, and clustering ~100 articles. This takes **3–8 minutes** depending on your connection and machine -3. A progress bar shows the current stage. Once complete, five tabs appear: - - **Cluster Map** — semantic clusters of the discourse, plotted in 2D - - **Temporal Drift** — how the discourse has shifted over time - - **Outliers** — articles furthest from any cluster centroid - - **Consensus** — most common terms per cluster, via TF-IDF - - **Ask** — full-width chat interface for natural language queries against the indexed corpus; answers render as formatted markdown with cited sources -4. Click any point on the cluster map to open the source article. Click a cluster label to filter the outliers sidebar. Use the **Ask** tab to query the corpus with natural language -5. Click **New analysis** in the top bar to start over with a different topic. You'll be asked to confirm — this clears the current corpus entirely before the new pipeline runs -6. Click **Export** in the top bar to download an Obsidian-compatible zip — a Canvas file with all articles plotted at their UMAP positions, and a vault folder with one Markdown note per article +1. On the home page, type a topic (e.g. `mechanistic interpretability in large language models`) and choose **Map this topic**. Tick **Let me check the sources before they're read** to see the searches and pages first: edit the searches, untick pages, or add your own before anything is read. +2. A map takes **3–8 minutes**. Academic publishers often block automated reading; for those pages (and for PDF links) Discourse Lens looks up an open-access copy or the abstract through OpenAlex and Europe PMC, and the article reader says where the text came from. You'll see each step as it happens: the search queries, pages crawled, articles kept or skipped (and why), and clusters as they're named. You can leave the page, or **Cancel this map**. +3. When it's done you get six views of the same map: + - **Map**: articles that make similar arguments sit close together, grouped into topics (camps) with contour lines and a name for each. Switch to **Table** for a plain list. + - **Timeline**: articles per month or year, by cluster. + - **Outliers**: articles furthest from the middle of their cluster. + - **Key terms**: each topic's one-line summary and the words it uses far more than the others. + - **Claims**: the points two or more articles take a position on, and whether each article agrees, qualifies or disputes them, with the most debated first. Click a point to see each article's claim and open the article. This view runs only when you choose **Find claims**, because it costs Claude requests: about half a cent per article (roughly $0.30 for a 50-article map; about a minute). + - **Ask**: questions answered only from this map's articles. Answers stream in as they're written, with numbered citations like **[3]**; click one to read that article. Follow-up questions build on earlier answers, and a citation number means the same article for the whole conversation. Conversations are saved with the map. +4. Pick a topic in the **Clusters** panel to focus every view on it (and read its summary). Articles that don't fit any topic well are grouped as **Unclustered** rather than forced into one. Click any article to read it in a side panel. The address bar keeps the view, cluster and open article, so links and reloads land in the same place. +5. **Export to Obsidian** downloads a vault: a Canvas of the map plus one note per article. +6. Every map is kept. **All maps** returns to the list, where you can open or delete them. --- @@ -111,12 +136,22 @@ Avoid topics that are too broad (`AI`, `climate change`) or too narrow (fewer th ## Troubleshooting +**Don't want the open-access lookups?** Set `RECOVER_ACADEMIC=false` in `.env`; academic pages a publisher blocks are then skipped. + **Pipeline stalls at "Crawling"** — Check your `BRAVE_API_KEY` in `.env`. **Cluster labels show as "cluster 0", "cluster 1"...** — The Anthropic API call for labelling failed. Check your `ANTHROPIC_API_KEY`. -**Embeddings are slow** — You're running Ollama on CPU. Switch to OpenAI embeddings by setting `EMBEDDING_MODE=openai` and `OPENAI_API_KEY=your_key` in `.env`. +**Embeddings are slow** — Local embedding runs on the CPU: about 3 seconds per 10 chunks, so a few minutes for a typical map. For speed, set `EMBEDDING_MODE=openai` and `OPENAI_API_KEY=your_key` in `.env` (new maps only; see Setup). + +**Answers are slow to start or too brief** — `CHAT_EFFORT` in `.env` sets how much the model reasons before answering (`low`, `medium` (default), `high`). In testing, `medium` started answering in about a second, same as `low`. + +**Chat says "This analysis has no indexed corpus yet"** — The analysis hasn't completed, or no articles passed the quality filter. Try re-running with a different topic. -**Chat returns "No corpus indexed yet"** — The analysis hasn't completed, or no articles passed the quality filter. Try re-running with a different topic. +**Upgrading from an older version** — On first start the backend migrates the database. Maps made before topic modelling keep their old clusters, without key terms or summaries, until you rebuild them from the stored embeddings (no re-crawl; one Claude request per map): + +```bash +uv run python -m scripts.recluster --all +``` -**Second analysis returns stale data** — This should no longer happen. Each new job deletes all rows from `articles`, `chunks`, `projections` and drops the LanceDB vectors table before running. If you see stale data, stop the backend, delete `data/discourse.db` and `data/vectors/`, and restart. +The very first migration (from the single-corpus version) copies `data/discourse.db` and `data/vectors/` to `*.v1.bak`; delete those once you're happy. diff --git a/backend/api/main.py b/backend/api/main.py index 250eec6..27b1958 100644 --- a/backend/api/main.py +++ b/backend/api/main.py @@ -1,33 +1,48 @@ import asyncio -import hashlib import io import json as json_lib -import os import re import sqlite3 +import sys import time import uuid import zipfile +from contextlib import asynccontextmanager from pathlib import Path -from dotenv import load_dotenv -from fastapi import BackgroundTasks, FastAPI, HTTPException +from typing import Literal + +from fastapi import FastAPI, Header, HTTPException, Response from fastapi.middleware.cors import CORSMiddleware -from pydantic import BaseModel -import asyncio -import sys +from fastapi.responses import StreamingResponse +from pydantic import BaseModel, Field + +from backend import pipeline +from backend.config import settings +from backend.db import RUNNING_STATUSES, connect, init_db +from backend.embedding.store import drop_embeddings if sys.platform == "win32": asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy()) -load_dotenv() -DB_PATH = os.getenv("DB_PATH", "./data/discourse.db") +@asynccontextmanager +async def lifespan(_app: FastAPI): + init_db() + yield + pipeline.cancel_all() + -app = FastAPI(title="Discourse Lens API") +app = FastAPI( + title="Discourse Lens API", + lifespan=lifespan, + # Operation ids = route function names, so the generated client reads + # listAnalyses() rather than listAnalysesApiAnalysesGet(). + generate_unique_id_function=lambda route: route.name, +) app.add_middleware( CORSMiddleware, - allow_origins=["http://localhost:5173"], + allow_origins=settings.cors_origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], @@ -35,50 +50,193 @@ # --------------------------------------------------------------------------- -# DB helpers +# Models # --------------------------------------------------------------------------- -def get_db() -> sqlite3.Connection: - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - return conn +AnalysisStatus = Literal[ + "queued", "crawling", "reviewing", "processing", "embedding", "projecting", "done", "error", + "cancelled", +] -def init_db() -> None: - schema = (Path(__file__).parent.parent / "db" / "schema.sql").read_text() - conn = get_db() - conn.executescript(schema) - try: - conn.execute("ALTER TABLE jobs ADD COLUMN warning TEXT") - conn.commit() - except sqlite3.OperationalError: - pass # column already exists (pre-existing db file) - conn.commit() - conn.close() +class AnalysisCreate(BaseModel): + topic: str = Field(min_length=1, max_length=500) + # Pause after searching so the user can edit queries and choose pages. + review: bool = False + + +class PlanResult(BaseModel): + url: str + title: str + snippet: str + query: str + + +class Plan(BaseModel): + """What a paused analysis found: its queries and the pages they returned.""" + + queries: list[str] + results: list[PlanResult] + + +class PlanSearch(BaseModel): + queries: list[str] = Field(min_length=1, max_length=12) + + +class PlanApprove(BaseModel): + urls: list[str] = Field(min_length=1) + + +class Analysis(BaseModel): + id: str + topic: str + status: AnalysisStatus + progress: int + stage_detail: str | None + article_count: int + error_msg: str | None + warning: str | None + created_ts: int + updated_ts: int + + +class AnalysisEvent(BaseModel): + id: int + ts: int + kind: Literal["stage", "query", "article", "skip", "cluster", "warning", "error"] + message: str + url: str | None + + +class MapArticle(BaseModel): + """An article as placed on the map.""" + + article_id: str + url: str + title: str | None + domain: str | None + publish_date: str | None + word_count: int | None + x: float + y: float + cluster_id: int # -1 = unclustered + distance: float # cosine distance to its topic's centre: the outlier score -def update_job(job_id: str, **kwargs) -> None: - kwargs["updated_ts"] = int(time.time()) - sets = ", ".join(f"{k}=?" for k in kwargs) - vals = list(kwargs.values()) + [job_id] - conn = get_db() - conn.execute(f"UPDATE jobs SET {sets} WHERE id=?", vals) - conn.commit() - conn.close() +class Term(BaseModel): + term: str + weight: float + + +class Cluster(BaseModel): + cluster_id: int + label: str + description: str | None + article_count: int + terms: list[Term] + + +class Article(BaseModel): + id: str + analysis_id: str + url: str + domain: str | None + title: str | None + publish_date: str | None + crawl_ts: int | None + word_count: int | None + language: str | None + raw_text: str | None + status: str + source_note: str | None = None # set when the text came from an open-access copy + + +class PropositionClaim(BaseModel): + claim_id: str + article_id: str + text: str + stance: Literal["agrees", "qualifies", "disputes"] + + +class Proposition(BaseModel): + id: int + statement: str + claims: list[PropositionClaim] + + +class Claims(BaseModel): + """Where the articles agree and disagree. status is null until claims are first found.""" + + status: Literal["running", "done", "error"] | None + detail: str | None + claim_count: int + propositions: list[Proposition] + + +class ChatRequest(BaseModel): + question: str = Field(min_length=1, max_length=2000) + + +class ChatSource(BaseModel): + """An excerpt the answer could cite as [n].""" + + n: int + article_id: str + url: str + title: str + domain: str + publish_date: str | None + cited: bool | None = None # set once the answer is complete + + +class ChatMessage(BaseModel): + id: int + role: Literal["user", "assistant"] + text: str + sources: list[ChatSource] | None + created_ts: int # --------------------------------------------------------------------------- -# Startup +# Helpers # --------------------------------------------------------------------------- -@app.on_event("startup") -async def startup() -> None: - Path("data").mkdir(exist_ok=True) - init_db() +def _get_analysis(conn: sqlite3.Connection, analysis_id: str) -> sqlite3.Row: + row = conn.execute("SELECT * FROM analyses WHERE id=?", (analysis_id,)).fetchone() + if row is None: + raise HTTPException(status_code=404, detail="Analysis not found") + return row + + +def fetch_events(conn: sqlite3.Connection, analysis_id: str, after: int = 0) -> list[AnalysisEvent]: + rows = conn.execute( + "SELECT id, ts, kind, message, url FROM analysis_events " + "WHERE analysis_id = ? AND id > ? ORDER BY id", + (analysis_id, after), + ).fetchall() + return [AnalysisEvent(**dict(r)) for r in rows] + + +def fetch_clusters(conn: sqlite3.Connection, analysis_id: str) -> list[Cluster]: + rows = conn.execute( + "SELECT cluster_id, label, description, article_count, terms FROM clusters " + "WHERE analysis_id = ? ORDER BY cluster_id", + (analysis_id,), + ).fetchall() + return [ + Cluster( + cluster_id=r["cluster_id"], + label=r["label"], + description=r["description"], + article_count=r["article_count"], + terms=[Term(term=t, weight=w) for t, w in json_lib.loads(r["terms"])], + ) + for r in rows + ] # --------------------------------------------------------------------------- -# Endpoints +# Analyses # --------------------------------------------------------------------------- @app.get("/api/health") @@ -86,435 +244,430 @@ def health(): return {"status": "ok"} -class JobRequest(BaseModel): - topic: str - - -@app.post("/api/jobs") -def create_job(req: JobRequest, background_tasks: BackgroundTasks): - job_id = str(uuid.uuid4()) +@app.post("/api/analyses", status_code=201) +async def create_analysis(req: AnalysisCreate) -> Analysis: + analysis_id = str(uuid.uuid4()) now = int(time.time()) - conn = get_db() - conn.execute( - "INSERT INTO jobs (id, topic, status, progress, created_ts, updated_ts) VALUES (?,?,?,?,?,?)", - (job_id, req.topic, "queued", 0, now, now), - ) - conn.commit() - conn.close() - background_tasks.add_task(run_pipeline, job_id, req.topic) - return {"job_id": job_id} - - -@app.get("/api/corpus") -def get_corpus(): - conn = get_db() + topic = req.topic.strip() + conn = connect() try: - rows = conn.execute( - """ - SELECT - p.chunk_id, - c.article_id, - c.child_text, - a.url, - a.title, - a.domain, - a.publish_date, - p.x, - p.y, - p.cluster_id, - p.cluster_label, - p.distance_from_centroid, - a.word_count - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - JOIN articles a ON a.id = c.article_id - WHERE a.status = 'ok' - ORDER BY p.cluster_id, p.distance_from_centroid - """ - ).fetchall() + conn.execute( + "INSERT INTO analyses (id, topic, status, progress, created_ts, updated_ts) " + "VALUES (?,?,?,?,?,?)", + (analysis_id, topic, "queued", 0, now, now), + ) + conn.commit() + row = _get_analysis(conn, analysis_id) finally: conn.close() - return [dict(r) for r in rows] + pipeline.start(analysis_id, topic, review=req.review) + return Analysis(**dict(row)) -@app.get("/api/clusters") -def get_clusters(): - conn = get_db() +@app.get("/api/analyses") +def list_analyses() -> list[Analysis]: + conn = connect() try: - rows = conn.execute( - """ - SELECT p.cluster_id, p.cluster_label, - COUNT(DISTINCT c.article_id) AS article_count - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - GROUP BY p.cluster_id, p.cluster_label - ORDER BY p.cluster_id - """ - ).fetchall() + rows = conn.execute("SELECT * FROM analyses ORDER BY created_ts DESC").fetchall() finally: conn.close() - return [dict(r) for r in rows] + return [Analysis(**dict(r)) for r in rows] -@app.get("/api/outliers") -def get_outliers(cluster_id: int | None = None): - conn = get_db() +@app.get("/api/analyses/{analysis_id}") +def get_analysis(analysis_id: str) -> Analysis: + conn = connect() try: - if cluster_id is not None: - rows = conn.execute( - """ - SELECT p.chunk_id, p.cluster_id, p.cluster_label, - p.distance_from_centroid, - c.article_id, a.title, a.domain, a.publish_date - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - JOIN articles a ON a.id = c.article_id - WHERE a.status = 'ok' AND p.cluster_id = ? - ORDER BY p.distance_from_centroid DESC - LIMIT 50 - """, - (cluster_id,), - ).fetchall() - else: - rows = conn.execute( - """ - SELECT p.chunk_id, p.cluster_id, p.cluster_label, - p.distance_from_centroid, - c.article_id, a.title, a.domain, a.publish_date - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - JOIN articles a ON a.id = c.article_id - WHERE a.status = 'ok' - ORDER BY p.distance_from_centroid DESC - LIMIT 50 - """ - ).fetchall() + return Analysis(**dict(_get_analysis(conn, analysis_id))) finally: conn.close() - return [dict(r) for r in rows] -@app.get("/api/articles/{article_id}") -def get_article(article_id: str): - conn = get_db() +@app.delete("/api/analyses/{analysis_id}", status_code=204) +def delete_analysis(analysis_id: str) -> Response: + conn = connect() try: - row = conn.execute( - "SELECT * FROM articles WHERE id = ?", (article_id,) - ).fetchone() + row = _get_analysis(conn, analysis_id) + if row["status"] in RUNNING_STATUSES: + raise HTTPException(status_code=409, detail="Analysis is still running") + conn.execute("DELETE FROM analyses WHERE id=?", (analysis_id,)) + conn.commit() finally: conn.close() - if row is None: - raise HTTPException(status_code=404, detail={"error": "Article not found"}) - return dict(row) - + drop_embeddings(analysis_id) + return Response(status_code=204) -class ChatRequest(BaseModel): - query: str - - -@app.post("/api/chat") -def chat(req: ChatRequest): - from backend.synthesis.rag import query_corpus - return query_corpus(req.query) +@app.post("/api/analyses/{analysis_id}/cancel", status_code=202) +def cancel_analysis(analysis_id: str) -> Analysis: + """Ask a running analysis to stop; it reaches status 'cancelled' shortly after.""" + conn = connect() + try: + row = _get_analysis(conn, analysis_id) + finally: + conn.close() + if row["status"] not in RUNNING_STATUSES or not pipeline.cancel(analysis_id): + raise HTTPException(status_code=409, detail="Analysis is not running") + return Analysis(**dict(row)) -@app.get("/api/export") -def export_obsidian(): - from fastapi.responses import StreamingResponse - from backend.export import build_canvas, build_vault_files - conn = get_db() +def _awaiting_review(analysis_id: str) -> pipeline.RunContext: + conn = connect() try: - job_row = conn.execute( - "SELECT topic FROM jobs WHERE status='done' ORDER BY updated_ts DESC LIMIT 1" - ).fetchone() - topic = job_row["topic"] if job_row else "discourse" + _get_analysis(conn, analysis_id) + finally: + conn.close() + ctx = pipeline.context(analysis_id) + if ctx is None or not ctx.awaiting_review(): + raise HTTPException(status_code=409, detail="This analysis isn't waiting for review") + return ctx - rows = conn.execute( - """ - SELECT - a.id AS article_id, a.title, a.url, a.domain, - a.publish_date, a.word_count, - AVG(p.x) AS x, AVG(p.y) AS y, - MIN(p.cluster_id) AS cluster_id, - MIN(p.cluster_label) AS cluster_label - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - JOIN articles a ON a.id = c.article_id - WHERE a.status = 'ok' - GROUP BY a.id, a.title, a.url, a.domain, a.publish_date, a.word_count - ORDER BY MIN(p.cluster_id) - """ - ).fetchall() - rows = [dict(r) for r in rows] - clusters = conn.execute( - """ - SELECT p.cluster_label, COUNT(DISTINCT c.article_id) AS article_count - FROM projections p - JOIN chunks c ON c.id = p.chunk_id - JOIN articles a ON a.id = c.article_id - WHERE a.status = 'ok' - GROUP BY p.cluster_label - ORDER BY MIN(p.cluster_id) - """ - ).fetchall() - clusters = [dict(r) for r in clusters] +@app.get("/api/analyses/{analysis_id}/plan") +def get_plan(analysis_id: str) -> Plan: + conn = connect() + try: + _get_analysis(conn, analysis_id) finally: conn.close() - - if not rows: + plan = pipeline.load_plan(analysis_id) + if plan is None: + raise HTTPException(status_code=404, detail="This analysis has no search plan") + return Plan(**plan) + + +@app.post("/api/analyses/{analysis_id}/plan/search") +def search_plan(analysis_id: str, req: PlanSearch) -> Plan: + """Run edited queries again while the analysis waits for review.""" + from backend.ingestion.search import search_all + + _awaiting_review(analysis_id) + queries = [q.strip() for q in req.queries if q.strip()] + if not queries: + raise HTTPException(status_code=422, detail="Give at least one search query") + results = search_all(queries) + plan = pipeline.save_plan(analysis_id, queries, results) + from backend.db import add_event, update_analysis + + update_analysis(analysis_id, stage_detail=f"Waiting for you to review {len(results)} pages") + add_event(analysis_id, "stage", f"Searched again: {len(results)} pages") + return Plan(**plan) + + +@app.post("/api/analyses/{analysis_id}/plan/approve", status_code=202) +def approve_plan(analysis_id: str, req: PlanApprove) -> Analysis: + """Continue the paused analysis with the chosen pages (search results or your own URLs).""" + from urllib.parse import urlparse + + ctx = _awaiting_review(analysis_id) + urls = list(dict.fromkeys(u.strip() for u in req.urls if u.strip())) + bad = [u for u in urls if urlparse(u).scheme not in ("http", "https") or not urlparse(u).netloc] + if bad: + raise HTTPException(status_code=422, detail=f"Not a web address: {bad[0]}") + if len(urls) > settings.max_articles: raise HTTPException( - status_code=404, - detail={"error": "No corpus found. Run an analysis first."}, + status_code=422, + detail=f"Choose at most {settings.max_articles} pages (you chose {len(urls)})", ) + ctx.approve(urls) + conn = connect() + try: + return Analysis(**dict(_get_analysis(conn, analysis_id))) + finally: + conn.close() - canvas = build_canvas(rows) - vault_files = build_vault_files(rows, clusters, topic) - - slug = re.sub(r"[^a-z0-9]+", "-", topic.lower()).strip("-")[:40] - zip_name = f"discourse-{slug}.zip" - folder = f"discourse-{slug}" - buf = io.BytesIO() - with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf: - zf.writestr( - f"{folder}/cluster-map.canvas", - json_lib.dumps(canvas, ensure_ascii=False, indent=2), - ) - for path, content in vault_files.items(): - zf.writestr(f"{folder}/vault/{path}", content) - buf.seek(0) - - return StreamingResponse( - buf, - media_type="application/zip", - headers={"Content-Disposition": f'attachment; filename="{zip_name}"'}, - ) +@app.get("/api/analyses/{analysis_id}/events") +def get_events(analysis_id: str, after: int = 0) -> list[AnalysisEvent]: + conn = connect() + try: + _get_analysis(conn, analysis_id) + return fetch_events(conn, analysis_id, after) + finally: + conn.close() -@app.get("/api/jobs/{job_id}") -def get_job(job_id: str): - conn = get_db() - row = conn.execute("SELECT * FROM jobs WHERE id=?", (job_id,)).fetchone() - conn.close() - if row is None: - raise HTTPException( - status_code=404, - detail={"error": "Job not found", "code": "NOT_FOUND"}, - ) - return dict(row) +STREAM_POLL_SECONDS = 0.5 -@app.get("/api/jobs/{job_id}/stream") -async def stream_job(job_id: str): - from fastapi.responses import StreamingResponse +@app.get("/api/analyses/{analysis_id}/stream") +async def stream_analysis(analysis_id: str, last_event_id: int = Header(0)): + """ + Server-sent events until the analysis stops running: + default `message` events — the Analysis row, whenever it changes + `log` events (with `id:`) — new rows from the event log; EventSource + resends Last-Event-ID on reconnect, so the log resumes where it left off. + """ + conn = connect() + try: + _get_analysis(conn, analysis_id) + finally: + conn.close() async def event_generator(): last_payload = None + last_id = last_event_id while True: - conn = get_db() - row = conn.execute("SELECT * FROM jobs WHERE id=?", (job_id,)).fetchone() - conn.close() + conn = connect() + try: + row = conn.execute("SELECT * FROM analyses WHERE id=?", (analysis_id,)).fetchone() + events = fetch_events(conn, analysis_id, last_id) if row else [] + finally: + conn.close() - if row is None: - yield f"event: error\ndata: {json_lib.dumps({'error': 'Job not found', 'code': 'NOT_FOUND'})}\n\n" + if row is None: # deleted while streaming + yield f"event: error\ndata: {json_lib.dumps({'detail': 'Analysis not found'})}\n\n" return - payload = json_lib.dumps(dict(row)) + for ev in events: + yield f"event: log\nid: {ev.id}\ndata: {ev.model_dump_json()}\n\n" + last_id = ev.id + + payload = Analysis(**dict(row)).model_dump_json() if payload != last_payload: yield f"data: {payload}\n\n" last_payload = payload - if row["status"] in ("done", "error"): + if row["status"] not in RUNNING_STATUSES: return - await asyncio.sleep(1) + await asyncio.sleep(STREAM_POLL_SECONDS) return StreamingResponse(event_generator(), media_type="text/event-stream") # --------------------------------------------------------------------------- -# Ingestion pipeline (background task) +# Analysis data # --------------------------------------------------------------------------- -async def run_pipeline(job_id: str, topic: str) -> None: - """Full ingestion + processing pipeline: search → crawl → extract → chunk → dedup.""" - from backend.ingestion.search import collect_urls - from backend.ingestion.crawler import crawl_urls - from backend.ingestion.extractor import extract_article - from backend.processing.metadata import extract_publish_date - from backend.processing.chunker import chunk_article - from backend.processing.dedup import mark_duplicates +@app.get("/api/analyses/{analysis_id}/articles") +def list_articles(analysis_id: str) -> list[MapArticle]: + """Every article on the map, with its position, topic and outlier score.""" + conn = connect() + try: + _get_analysis(conn, analysis_id) + rows = conn.execute( + """ + SELECT p.article_id, a.url, a.title, a.domain, a.publish_date, a.word_count, + p.x, p.y, p.cluster_id, p.distance + FROM placements p + JOIN articles a ON a.id = p.article_id + WHERE p.analysis_id = ? + ORDER BY p.cluster_id, p.distance + """, + (analysis_id,), + ).fetchall() + finally: + conn.close() + return [MapArticle(**dict(r)) for r in rows] + +@app.get("/api/analyses/{analysis_id}/clusters") +def get_clusters(analysis_id: str) -> list[Cluster]: + """Topics, largest first, with their key terms. Unclustered articles have none.""" + conn = connect() try: - # Clear previous corpus before starting fresh - conn = get_db() - conn.execute("DELETE FROM projections") - conn.execute("DELETE FROM chunks") - conn.execute("DELETE FROM articles") - conn.commit() + _get_analysis(conn, analysis_id) + return fetch_clusters(conn, analysis_id) + finally: conn.close() - from backend.embedding.store import clear_embeddings - clear_embeddings() - # Step 1: generate queries and search Brave - update_job(job_id, status="crawling", progress=5) - urls = await asyncio.to_thread(collect_urls, topic) - # Step 2: Playwright scrape all URLs — runs in a thread with a fresh event loop - update_job(job_id, progress=20) - crawled = await asyncio.to_thread(crawl_urls, urls) +@app.get("/api/analyses/{analysis_id}/articles/{article_id}") +def get_article(analysis_id: str, article_id: str) -> Article: + conn = connect() + try: + row = conn.execute( + "SELECT * FROM articles WHERE id = ? AND analysis_id = ?", (article_id, analysis_id) + ).fetchone() + finally: + conn.close() + if row is None: + raise HTTPException(status_code=404, detail="Article not found") + return Article(**dict(row)) - # Step 3: extract clean text, filter, persist to SQLite - update_job(job_id, status="processing", progress=60) - def _extract_and_store() -> tuple[int, list[dict]]: - ok_count = 0 - ok_articles: list[dict] = [] - conn = sqlite3.connect(DB_PATH) - try: - for item in crawled: - url = item["url"] - html = item["html"] - try: - article = extract_article(url, html) - if article is None: - aid = hashlib.sha256(url.encode()).hexdigest()[:16] - conn.execute( - "INSERT OR IGNORE INTO articles " - "(id, url, status, crawl_ts) VALUES (?,?,?,?)", - (aid, url, "filtered", int(time.time())), - ) - continue - - publish_date = extract_publish_date(html, url) - conn.execute( - "INSERT OR IGNORE INTO articles " - "(id, url, domain, title, publish_date, crawl_ts, " - " word_count, language, raw_text, status) " - "VALUES (?,?,?,?,?,?,?,?,?,?)", - ( - article["id"], article["url"], article["domain"], - article["title"], publish_date, article["crawl_ts"], - article["word_count"], article["language"], - article["raw_text"], "ok", - ), - ) - ok_count += 1 - ok_articles.append({"id": article["id"], "raw_text": article["raw_text"]}) - except Exception: - continue # individual failure — log and keep going - conn.commit() - finally: - conn.close() - return ok_count, ok_articles +def _claims(analysis_id: str) -> Claims: + from backend.synthesis import claims - ok_count, ok_articles = await asyncio.to_thread(_extract_and_store) + conn = connect() + try: + row = _get_analysis(conn, analysis_id) + finally: + conn.close() + return Claims( + status=row["claims_status"], + detail=row["claims_detail"], + claim_count=claims.claim_count(analysis_id), + propositions=[Proposition(**p) for p in claims.load(analysis_id)], + ) - # Step 4: chunk + dedup all successfully extracted articles - update_job(job_id, progress=75) - def _chunk_and_dedup(articles: list[dict]) -> None: - conn = sqlite3.connect(DB_PATH) - try: - for art in articles: - if not art.get("raw_text"): - continue - chunks = chunk_article(art["id"], art["raw_text"]) - chunks = mark_duplicates(chunks) - conn.executemany( - "INSERT OR IGNORE INTO chunks " - "(id, article_id, parent_id, parent_text, child_text, " - " chunk_index, token_count, is_duplicate) " - "VALUES (:id, :article_id, :parent_id, :parent_text, :child_text, " - " :chunk_index, :token_count, :is_duplicate)", - chunks, - ) - conn.commit() - finally: - conn.close() +@app.get("/api/analyses/{analysis_id}/claims") +def get_claims(analysis_id: str) -> Claims: + return _claims(analysis_id) - await asyncio.to_thread(_chunk_and_dedup, ok_articles) - # Step 5: embed non-duplicate child chunks - update_job(job_id, status="embedding", progress=80) +@app.post("/api/analyses/{analysis_id}/claims", status_code=202) +def find_claims(analysis_id: str) -> Claims: + """Find each article's claims and where they agree or disagree (Claude requests).""" + from backend.synthesis import claims - def _embed_and_store() -> None: - from backend.embedding.embed import embed_chunks - from backend.embedding.store import write_embeddings + conn = connect() + try: + row = _get_analysis(conn, analysis_id) + finally: + conn.close() + if row["status"] != "done": + raise HTTPException(status_code=409, detail="Finish the map before finding claims") + try: + claims.start(analysis_id) + except claims.AlreadyRunning: + raise HTTPException(status_code=409, detail="Already finding claims") from None + return _claims(analysis_id) - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - try: - rows = conn.execute( - "SELECT c.id, c.child_text, c.article_id, " - " a.domain, a.publish_date " - "FROM chunks c " - "JOIN articles a ON a.id = c.article_id " - "WHERE c.is_duplicate = 0" - ).fetchall() - finally: - conn.close() - if not rows: - return +@app.get("/api/analyses/{analysis_id}/chat") +def get_chat(analysis_id: str) -> list[ChatMessage]: + """The saved conversation for this analysis, oldest first.""" + from backend.synthesis.chat import load_history - chunks_for_embed = [ - { - "id": r["id"], - "child_text": r["child_text"], - "article_id": r["article_id"], - "domain": r["domain"] or "", - "publish_date": r["publish_date"] or "", - } - for r in rows - ] - - embeddings = embed_chunks(chunks_for_embed) - - # Build full rows for LanceDB (merge embed result with metadata) - meta_by_id = {c["id"]: c for c in chunks_for_embed} - lance_rows = [] - for e in embeddings: - m = meta_by_id[e["chunk_id"]] - lance_rows.append( - { - "chunk_id": e["chunk_id"], - "article_id": m["article_id"], - "vector": e["vector"], - "child_text": m["child_text"], - "domain": m["domain"], - "publish_date": m["publish_date"], - "cluster_id": -1, - } - ) - write_embeddings(lance_rows) - - await asyncio.to_thread(_embed_and_store) - - # Step 6: UMAP + k-means + cluster labels → projections table - update_job(job_id, status="projecting", progress=90) - - def _project() -> None: - from backend.embedding.project import run_projection - run_projection(db_path=DB_PATH) - - await asyncio.to_thread(_project) - - warning = None - if ok_count < 30: - warning = ( - f"Only {ok_count} articles were successfully indexed. " - "Results may be sparse — try a broader or more general topic." - ) - - update_job( - job_id, status="done", progress=100, - article_count=ok_count, warning=warning, - ) + conn = connect() + try: + _get_analysis(conn, analysis_id) + finally: + conn.close() + return [ChatMessage(**m) for m in load_history(analysis_id)] + +@app.delete("/api/analyses/{analysis_id}/chat", status_code=204) +def clear_chat(analysis_id: str) -> Response: + from backend.synthesis.chat import clear_history + + conn = connect() + try: + _get_analysis(conn, analysis_id) + finally: + conn.close() + clear_history(analysis_id) + return Response(status_code=204) + + +def _sse(event: str, data: object) -> str: + return f"event: {event}\ndata: {json_lib.dumps(data)}\n\n" + + +@app.post( + "/api/analyses/{analysis_id}/chat", + response_class=StreamingResponse, + responses={200: {"content": {"text/event-stream": {}}}}, +) +def ask(analysis_id: str, req: ChatRequest): + """ + Ask a question; the answer streams as server-sent events: + sources — the numbered excerpts the answer may cite (ChatSource[]) + delta — {"text": ...}, a piece of the answer as it's written + done — the saved answer (ChatMessage), with each source's `cited` set + error — {"detail": ...}; nothing is saved + The question and answer are saved together only when the answer completes. + """ + from dataclasses import asdict + + from backend.synthesis.chat import NoCorpusError, answer_stream, load_history, save_turn + + conn = connect() + try: + _get_analysis(conn, analysis_id) + finally: + conn.close() + + question = req.question.strip() + history = load_history(analysis_id) + try: + sources, text_stream = answer_stream(analysis_id, question, history) + except NoCorpusError as e: + raise HTTPException(status_code=409, detail=str(e)) from e except Exception as e: - import traceback - traceback.print_exc() - update_job(job_id, status="error", error_msg=str(e)) + raise HTTPException(status_code=502, detail=f"Couldn't search the corpus: {e}") from e + + def events(): + yield _sse("sources", [{k: v for k, v in asdict(s).items() if k != "excerpt"} + for s in sources]) + parts: list[str] = [] + try: + for piece in text_stream: + parts.append(piece) + yield _sse("delta", {"text": piece}) + except Exception as e: + yield _sse("error", {"detail": f"The answer stopped early: {e}"}) + return + message = save_turn(analysis_id, question, "".join(parts).strip(), sources) + yield _sse("done", message) + + return StreamingResponse(events(), media_type="text/event-stream") + + +@app.get("/api/analyses/{analysis_id}/export") +def export_obsidian(analysis_id: str): + from backend.export import build_canvas, build_vault_files + + conn = connect() + try: + topic = _get_analysis(conn, analysis_id)["topic"] + rows = conn.execute( + """ + SELECT a.id AS article_id, a.title, a.url, a.domain, a.publish_date, + a.word_count, p.x, p.y, p.cluster_id, + COALESCE(c.label, 'Unclustered') AS cluster_label + FROM placements p + JOIN articles a ON a.id = p.article_id + LEFT JOIN clusters c + ON c.analysis_id = p.analysis_id AND c.cluster_id = p.cluster_id + WHERE p.analysis_id = ? + ORDER BY p.cluster_id < 0, p.cluster_id + """, + (analysis_id,), + ).fetchall() + rows = [dict(r) for r in rows] + + counts: dict[str, int] = {} + for r in rows: + counts[r["cluster_label"]] = counts.get(r["cluster_label"], 0) + 1 + clusters = [{"cluster_label": k, "article_count": n} for k, n in counts.items()] + finally: + conn.close() + + if not rows: + raise HTTPException(status_code=404, detail="This analysis has no corpus to export.") + + canvas = build_canvas(rows) + vault_files = build_vault_files(rows, clusters, topic) + + slug = re.sub(r"[^a-z0-9]+", "-", topic.lower()).strip("-")[:40] + folder = f"discourse-{slug}" + + buf = io.BytesIO() + with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf: + zf.writestr( + f"{folder}/cluster-map.canvas", + json_lib.dumps(canvas, ensure_ascii=False, indent=2), + ) + for path, content in vault_files.items(): + zf.writestr(f"{folder}/vault/{path}", content) + buf.seek(0) + + return StreamingResponse( + buf, + media_type="application/zip", + headers={"Content-Disposition": f'attachment; filename="{folder}.zip"'}, + ) + + +# --------------------------------------------------------------------------- +# Frontend (registered last so it never shadows an API route) +# --------------------------------------------------------------------------- + +if (Path(settings.frontend_build)).is_dir(): + from backend.api.spa import spa_router + + app.include_router(spa_router(Path(settings.frontend_build))) diff --git a/backend/api/spa.py b/backend/api/spa.py new file mode 100644 index 0000000..32d230f --- /dev/null +++ b/backend/api/spa.py @@ -0,0 +1,30 @@ +"""Serve the built frontend (frontend/build) from the API server.""" + +from __future__ import annotations + +from pathlib import Path + +from fastapi import APIRouter, HTTPException +from fastapi.responses import FileResponse + + +def spa_router(build_dir: Path) -> APIRouter: + """ + Static files from the SvelteKit build, with every other non-API path + falling back to index.html so client-side routes (/analysis//...) + work on reload and as shared links. + """ + root = build_dir.resolve() + router = APIRouter(include_in_schema=False) + + @router.get("/{path:path}") + def serve(path: str) -> FileResponse: + if path.startswith("api/") or path == "api": + raise HTTPException(status_code=404, detail="Not found") + if path: + candidate = (root / path).resolve() + if candidate.is_file() and root in candidate.parents: + return FileResponse(candidate) + return FileResponse(root / "index.html") + + return router diff --git a/backend/claude_client.py b/backend/claude_client.py index deac72d..290b911 100644 --- a/backend/claude_client.py +++ b/backend/claude_client.py @@ -1,39 +1,131 @@ """ -Shared Claude client factory. +Shared Claude client. -Supports two backends, selected by the CLAUDE_BACKEND env var: +Supports two backends, selected by CLAUDE_BACKEND: anthropic (default) — Direct Anthropic API, authenticated via ANTHROPIC_API_KEY vertex — Google Cloud Vertex AI, authenticated via Application Default Credentials Vertex AI setup: - 1. pip install anthropic[vertex] (adds google-auth) + 1. uv sync --extra vertex (adds google-auth) 2. gcloud auth application-default login 3. Set GOOGLE_CLOUD_PROJECT and VERTEX_REGION in .env """ from __future__ import annotations -import os +from collections.abc import Iterator +from functools import lru_cache +from typing import Literal -CLAUDE_BACKEND = os.getenv("CLAUDE_BACKEND", "anthropic") +from backend.config import settings -# Model IDs differ between backends — Vertex uses @ format. -_ANTHROPIC_MODEL = os.getenv("CLAUDE_MODEL", "claude-sonnet-4-6") -_VERTEX_MODEL = os.getenv("VERTEX_CLAUDE_MODEL", "claude-sonnet-4-0@20250514") +Effort = Literal["low", "medium", "high"] +class ClaudeRefusal(RuntimeError): + """Claude declined the request (stop_reason == 'refusal').""" + + +@lru_cache(maxsize=1) def get_client(): """Return an Anthropic-compatible messages client for the configured backend.""" - if CLAUDE_BACKEND == "vertex": + if settings.claude_backend == "vertex": from anthropic import AnthropicVertex # requires anthropic[vertex] + if not settings.google_cloud_project: + raise RuntimeError("GOOGLE_CLOUD_PROJECT must be set when CLAUDE_BACKEND=vertex") return AnthropicVertex( - project_id=os.environ["GOOGLE_CLOUD_PROJECT"], - region=os.getenv("VERTEX_REGION", "us-east5"), + project_id=settings.google_cloud_project, + region=settings.vertex_region, ) from anthropic import Anthropic - return Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]) + return Anthropic(api_key=settings.anthropic_api_key) def get_model() -> str: - """Return the correct model ID string for the configured backend.""" - return _VERTEX_MODEL if CLAUDE_BACKEND == "vertex" else _ANTHROPIC_MODEL + """Return the model ID for the configured backend.""" + if settings.claude_backend == "vertex": + return settings.vertex_claude_model + return settings.claude_model + + +def complete( + prompt: str, + *, + system: str | None = None, + max_tokens: int = 16000, + effort: Effort = "medium", + json_schema: dict | None = None, +) -> str: + """ + Single-turn request; returns the concatenated text blocks. + + Current models think by default, so content[0] may be a thinking block — + always collect the text blocks. On the Anthropic API, server-side fallback + re-runs a refused request on a suitable model within the same call. + + With json_schema, the response is constrained to JSON matching it + (structured outputs); parse the returned text with json.loads. + """ + output_config: dict = {"effort": effort} + if json_schema is not None: + output_config["format"] = {"type": "json_schema", "schema": json_schema} + kwargs = dict( + model=get_model(), + max_tokens=max_tokens, + messages=[{"role": "user", "content": prompt}], + output_config=output_config, + ) + if system: + kwargs["system"] = system + + client = get_client() + if settings.claude_backend == "anthropic": + response = client.beta.messages.create( + betas=["server-side-fallback-2026-07-01"], + fallbacks="default", + **kwargs, + ) + else: + response = client.messages.create(**kwargs) + + _raise_on_refusal(response) + return "".join(b.text for b in response.content if b.type == "text").strip() + + +def stream_text( + messages: list[dict], + *, + system: str | None = None, + max_tokens: int = 16000, + effort: Effort = "low", +) -> Iterator[str]: + """ + Stream a reply's text as it's generated (thinking happens before the first + text and isn't shown). Raises ClaudeRefusal at the end if the model declined. + """ + kwargs = dict( + model=get_model(), + max_tokens=max_tokens, + messages=messages, + output_config={"effort": effort}, + ) + if system: + kwargs["system"] = system + + client = get_client() + if settings.claude_backend == "anthropic": + manager = client.beta.messages.stream( + betas=["server-side-fallback-2026-07-01"], fallbacks="default", **kwargs + ) + else: + manager = client.messages.stream(**kwargs) + with manager as stream: + yield from stream.text_stream + final = stream.get_final_message() + _raise_on_refusal(final) + + +def _raise_on_refusal(message) -> None: + if message.stop_reason == "refusal": + category = message.stop_details.category if message.stop_details else None + raise ClaudeRefusal(f"Claude declined the request (category: {category})") diff --git a/backend/config.py b/backend/config.py new file mode 100644 index 0000000..eaf8223 --- /dev/null +++ b/backend/config.py @@ -0,0 +1,63 @@ +""" +All configuration, read once from the environment / .env file. + +Import `settings` and read attributes at call time (not at import time) so +tests can monkeypatch them. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): + model_config = SettingsConfigDict(env_file=".env", extra="ignore") + + # Search: brave (needs BRAVE_API_KEY) or searxng (self-hosted, needs SEARXNG_URL) + search_provider: Literal["brave", "searxng"] = "brave" + brave_api_key: str = "" + searxng_url: str = "" + max_articles: int = 120 + + # Look up open-access copies of academic pages the crawler can't read (OpenAlex, Europe PMC) + recover_academic: bool = True + + # Claude + claude_backend: Literal["anthropic", "vertex"] = "anthropic" + claude_model: str = "claude-sonnet-5-5" + anthropic_api_key: str | None = None # SDK also resolves ANTHROPIC_API_KEY itself + google_cloud_project: str | None = None + vertex_region: str = "global" + vertex_claude_model: str = "claude-sonnet-5-5" + + # Embeddings + # fastembed runs nomic-embed-text in-process; ollama serves the same model + # (identical vectors, so maps move freely between the two). "local" is the + # old name for ollama. openai vectors are a different size: maps made with + # them can only be searched with openai. + embedding_mode: Literal["fastembed", "ollama", "local", "openai"] = "fastembed" + fastembed_cache: str = "./data/models" + ollama_base_url: str = "http://localhost:11434" + openai_api_key: str | None = None + + # Storage + db_path: str = "./data/discourse.db" + lance_path: str = "./data/vectors" + + # Tuning + umap_n_neighbors: int = 15 + kmeans_k: str = "auto" # "auto" | integer string + rag_top_k: int = 15 + # Thinking effort for chat answers: low answers sooner, medium reasons more. + chat_effort: Literal["low", "medium", "high"] = "medium" + + # Built frontend served at / (cd frontend && npm run build) + frontend_build: str = "./frontend/build" + + # Frontend dev server origin allowed by CORS + cors_origins: list[str] = ["http://localhost:5173"] + + +settings = Settings() diff --git a/backend/db/__init__.py b/backend/db/__init__.py new file mode 100644 index 0000000..00f57ef --- /dev/null +++ b/backend/db/__init__.py @@ -0,0 +1,265 @@ +""" +SQLite access: connections, schema creation and migration. + +Every connection enables foreign keys (so deleting an analysis cascades) and a +busy timeout, and the database runs in WAL mode so a pipeline writing in a +worker thread does not block API reads. +""" + +from __future__ import annotations + +import logging +import shutil +import sqlite3 +import time +from pathlib import Path + +from backend.config import settings + +log = logging.getLogger(__name__) + +SCHEMA_VERSION = 8 +_SCHEMA = Path(__file__).parent / "schema.sql" + +# Chunk-level map rows used by schema v2/v3, before placements + clusters. +_LEGACY_PROJECTIONS = """ +CREATE TABLE IF NOT EXISTS projections ( + chunk_id TEXT PRIMARY KEY, analysis_id TEXT NOT NULL, x REAL, y REAL, + cluster_id INTEGER, cluster_label TEXT, distance_from_centroid REAL +) +""" + +# Analyses still in one of these states are being worked on by a pipeline. +RUNNING_STATUSES = ("queued", "crawling", "reviewing", "processing", "embedding", "projecting") + + +def connect(db_path: str | None = None) -> sqlite3.Connection: + conn = sqlite3.connect(db_path or settings.db_path, timeout=30) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA foreign_keys = ON") + return conn + + +def init_db(db_path: str | None = None) -> None: + """Create or migrate the schema, then fail any analysis a restart interrupted.""" + path = db_path or settings.db_path + Path(path).parent.mkdir(parents=True, exist_ok=True) + conn = connect(path) + try: + conn.execute("PRAGMA journal_mode = WAL") + version = conn.execute("PRAGMA user_version").fetchone()[0] + if version < 2 and _table_exists(conn, "jobs"): + conn.close() + _migrate_v1_to_v2(path) + conn = connect(path) + # v2 -> v3: new events table (created by the schema script) + stage_detail. + # v3 -> v4: clusters + placements tables (schema script), projections converted. + # v4 -> v5: chat_messages table (schema script). + conn.executescript(_SCHEMA.read_text()) + _ensure_column(conn, "analyses", "stage_detail", "TEXT") + _ensure_column(conn, "articles", "source_note", "TEXT") # v5 -> v6 + _ensure_column(conn, "analyses", "plan", "TEXT") # v6 -> v7: review step + # v7 -> v8: claim finding (claims/propositions tables come from the schema script) + _ensure_column(conn, "analyses", "claims_status", "TEXT") + _ensure_column(conn, "analyses", "claims_detail", "TEXT") + if _table_exists(conn, "projections"): + _migrate_projections(conn) + conn.execute(f"PRAGMA user_version = {SCHEMA_VERSION}") + placeholders = ",".join("?" for _ in RUNNING_STATUSES) + conn.execute( + f"UPDATE analyses SET status='error', error_msg=?, updated_ts=? " + f"WHERE status IN ({placeholders})", + ("Interrupted by a server restart", int(time.time()), *RUNNING_STATUSES), + ) + conn.execute( + "UPDATE analyses SET claims_status='error', " + "claims_detail='Interrupted by a server restart' WHERE claims_status='running'" + ) + conn.commit() + finally: + conn.close() + + +def _schema_statements() -> list[str]: + """ + schema.sql as individual statements, for running inside an explicit + transaction (executescript would commit first). Comments are stripped + before splitting so a ';' in a comment can't break a statement. + """ + lines = [line.split("--", 1)[0] for line in _SCHEMA.read_text().splitlines()] + return [s.strip() for s in "\n".join(lines).split(";") if s.strip()] + + +def _migrate_projections(conn: sqlite3.Connection) -> None: + """ + v3 -> v4: chunk-level projections become article placements plus a + clusters table. Each article takes the cluster most of its chunks were in + (ties: lower id), sits at the mean of those chunks, and keeps their largest + distance. Key terms and descriptions are empty until the analysis is + re-clustered (scripts/recluster.py). + """ + rows = conn.execute( + "SELECT p.analysis_id, c.article_id, p.x, p.y, p.cluster_id, p.cluster_label, " + "p.distance_from_centroid AS distance " + "FROM projections p JOIN chunks c ON c.id = p.chunk_id" + ).fetchall() + by_article: dict[str, list[sqlite3.Row]] = {} + for r in rows: + by_article.setdefault(r["article_id"], []).append(r) + + placements, labels, sizes = [], {}, {} + for article_id, chunks in by_article.items(): + votes: dict[int, int] = {} + for c in chunks: + votes[c["cluster_id"]] = votes.get(c["cluster_id"], 0) + 1 + cluster = min(votes, key=lambda k: (-votes[k], k)) + mine = [c for c in chunks if c["cluster_id"] == cluster] + analysis_id = mine[0]["analysis_id"] + placements.append(( + article_id, analysis_id, + sum(c["x"] for c in mine) / len(mine), sum(c["y"] for c in mine) / len(mine), + cluster, max(c["distance"] for c in mine), + )) + key = (analysis_id, cluster) + labels.setdefault(key, mine[0]["cluster_label"] or f"Cluster {cluster + 1}") + sizes[key] = sizes.get(key, 0) + 1 + + conn.executemany( + "INSERT OR REPLACE INTO placements (article_id, analysis_id, x, y, cluster_id, distance) " + "VALUES (?,?,?,?,?,?)", + placements, + ) + conn.executemany( + "INSERT OR REPLACE INTO clusters (analysis_id, cluster_id, label, article_count) " + "VALUES (?,?,?,?)", + [(a, c, labels[(a, c)], n) for (a, c), n in sizes.items()], + ) + conn.execute("DROP TABLE projections") + log.info("Converted %d articles' map positions to placements", len(placements)) + + +def _ensure_column(conn: sqlite3.Connection, table: str, column: str, decl: str) -> None: + columns = {r["name"] for r in conn.execute(f"PRAGMA table_info({table})")} + if column not in columns: + conn.execute(f"ALTER TABLE {table} ADD COLUMN {column} {decl}") + + +def _table_exists(conn: sqlite3.Connection, name: str) -> bool: + row = conn.execute( + "SELECT 1 FROM sqlite_master WHERE type='table' AND name=?", (name,) + ).fetchone() + return row is not None + + +def _migrate_v1_to_v2(path: str) -> None: + """ + v1 had a single global corpus plus a `jobs` table. The corpus becomes one + analysis, keyed by the most recent finished job. The old database file and + vector directory are kept alongside as backups. + """ + from backend.embedding import store + + backup = f"{path}.v1.bak" + shutil.copy2(path, backup) + if Path(settings.lance_path).exists(): + shutil.copytree(settings.lance_path, f"{settings.lance_path}.v1.bak", dirs_exist_ok=True) + log.info("Migrating %s to schema v2 (backup at %s)", path, backup) + + conn = connect(path) + try: + has_corpus = conn.execute("SELECT COUNT(*) FROM articles").fetchone()[0] > 0 + job = conn.execute( + "SELECT * FROM jobs WHERE status='done' ORDER BY updated_ts DESC LIMIT 1" + ).fetchone() + # Older v1 databases lack columns that were added later (e.g. `warning`). + job = dict(job) if job else {} + analysis_id = None + if has_corpus: + now = int(time.time()) + analysis_id = job.get("id", "imported-corpus") + analysis = { + "id": analysis_id, + "topic": job.get("topic") or "Imported corpus", + "status": "done", + "progress": 100, + "article_count": job.get("article_count") or 0, + "warning": job.get("warning"), + "created_ts": job.get("created_ts") or now, + "updated_ts": job.get("updated_ts") or now, + } + # Copy vectors first: non-destructive, and re-runnable if we crash + # before the SQL migration commits. + store.adopt_legacy_table(analysis_id) + + conn.execute("PRAGMA foreign_keys = OFF") + conn.execute("BEGIN") + for table in ("articles", "chunks", "projections"): + conn.execute(f"ALTER TABLE {table} RENAME TO {table}_v1") + for statement in _schema_statements(): + conn.execute(statement) + # v2/v3 kept chunk-level map rows; the v3 -> v4 step converts them. + conn.execute(_LEGACY_PROJECTIONS) + + if analysis_id: + conn.execute( + "INSERT INTO analyses (id, topic, status, progress, article_count, warning, " + "created_ts, updated_ts) VALUES (:id, :topic, :status, :progress, " + ":article_count, :warning, :created_ts, :updated_ts)", + analysis, + ) + conn.execute( + "INSERT INTO articles (id, analysis_id, url, domain, title, publish_date, " + "crawl_ts, word_count, language, raw_text, status) " + "SELECT id, ?, url, domain, title, publish_date, crawl_ts, word_count, " + "language, raw_text, status FROM articles_v1", + (analysis_id,), + ) + conn.execute( + "INSERT INTO chunks (id, analysis_id, article_id, parent_id, parent_text, " + "child_text, chunk_index, token_count, is_duplicate) " + "SELECT id, ?, article_id, parent_id, parent_text, child_text, chunk_index, " + "token_count, is_duplicate FROM chunks_v1", + (analysis_id,), + ) + conn.execute( + "INSERT INTO projections (chunk_id, analysis_id, x, y, cluster_id, " + "cluster_label, distance_from_centroid) " + "SELECT chunk_id, ?, x, y, cluster_id, cluster_label, distance_from_centroid " + "FROM projections_v1 WHERE chunk_id IN (SELECT id FROM chunks)", + (analysis_id,), + ) + + for table in ("projections_v1", "chunks_v1", "articles_v1", "jobs"): + conn.execute(f"DROP TABLE {table}") + conn.execute("PRAGMA user_version = 2") + conn.commit() + except Exception: + conn.rollback() + raise + finally: + conn.close() + + store.drop_legacy_table() + + +def add_event(analysis_id: str, kind: str, message: str, url: str | None = None) -> None: + conn = connect() + try: + conn.execute( + "INSERT INTO analysis_events (analysis_id, ts, kind, message, url) VALUES (?,?,?,?,?)", + (analysis_id, int(time.time()), kind, message, url), + ) + conn.commit() + finally: + conn.close() + + +def update_analysis(analysis_id: str, **fields) -> None: + fields["updated_ts"] = int(time.time()) + sets = ", ".join(f"{k}=?" for k in fields) + conn = connect() + try: + conn.execute(f"UPDATE analyses SET {sets} WHERE id=?", [*fields.values(), analysis_id]) + conn.commit() + finally: + conn.close() diff --git a/backend/db/schema.sql b/backend/db/schema.sql index 4b52832..8675e36 100644 --- a/backend/db/schema.sql +++ b/backend/db/schema.sql @@ -1,7 +1,29 @@ --- Articles: one row per crawled URL +-- Schema version 8 (PRAGMA user_version = 8): every row belongs to an analysis. +-- Deleting an analysis cascades to everything it collected +-- (requires PRAGMA foreign_keys = ON, set by backend.db.connect). + +-- Analyses: one row per topic run (formerly "jobs") +CREATE TABLE IF NOT EXISTS analyses ( + id TEXT PRIMARY KEY, -- uuid4 + topic TEXT NOT NULL, + status TEXT NOT NULL, -- queued | crawling | reviewing | processing | embedding | projecting | done | error | cancelled + progress INTEGER DEFAULT 0, -- 0-100 + stage_detail TEXT, -- e.g. "Crawled 47 of 112 pages" + plan TEXT, -- review step: JSON {queries, results[{url,title,snippet,query}]} + claims_status TEXT, -- claim finding: NULL (never run) | running | done | error + claims_detail TEXT, -- its progress, summary or error message + article_count INTEGER DEFAULT 0, + error_msg TEXT, + warning TEXT, -- non-fatal warning, e.g. low article yield + created_ts INTEGER NOT NULL, + updated_ts INTEGER NOT NULL +); + +-- Articles: one row per crawled URL per analysis CREATE TABLE IF NOT EXISTS articles ( - id TEXT PRIMARY KEY, -- sha256(url)[:16] - url TEXT NOT NULL UNIQUE, + id TEXT PRIMARY KEY, -- sha256(analysis_id:url)[:16] + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + url TEXT NOT NULL, domain TEXT, title TEXT, publish_date TEXT, -- ISO date string, nullable @@ -9,13 +31,17 @@ CREATE TABLE IF NOT EXISTS articles ( word_count INTEGER, language TEXT DEFAULT 'en', raw_text TEXT, - status TEXT DEFAULT 'ok' -- ok | filtered | error + status TEXT DEFAULT 'ok', -- ok | filtered | duplicate | error + source_note TEXT, -- where recovered text came from, e.g. "Full text from Europe PMC" + UNIQUE (analysis_id, url) ); +CREATE INDEX IF NOT EXISTS idx_articles_analysis ON articles(analysis_id); -- Chunks: one row per child chunk CREATE TABLE IF NOT EXISTS chunks ( id TEXT PRIMARY KEY, -- {article_id}#p{i}c{j} - article_id TEXT REFERENCES articles(id), + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + article_id TEXT REFERENCES articles(id) ON DELETE CASCADE, parent_id TEXT, -- {article_id}#p{i} parent_text TEXT, -- full parent chunk text (for RAG context) child_text TEXT, @@ -23,26 +49,76 @@ CREATE TABLE IF NOT EXISTS chunks ( token_count INTEGER, is_duplicate INTEGER DEFAULT 0 ); +CREATE INDEX IF NOT EXISTS idx_chunks_analysis ON chunks(analysis_id); +CREATE INDEX IF NOT EXISTS idx_chunks_article ON chunks(article_id); --- Projections: 2D UMAP coords + cluster info per chunk -CREATE TABLE IF NOT EXISTS projections ( - chunk_id TEXT PRIMARY KEY REFERENCES chunks(id), - x REAL, - y REAL, - cluster_id INTEGER, - cluster_label TEXT, -- LLM-generated 2-3 word label - distance_from_centroid REAL +-- Clusters: one row per topic found in an analysis (cluster_id -1 = unclustered, +-- which has no row here) +CREATE TABLE IF NOT EXISTS clusters ( + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + cluster_id INTEGER NOT NULL, -- 0 = largest + label TEXT NOT NULL, -- short name from Claude + description TEXT, -- one sentence from Claude + article_count INTEGER NOT NULL, + terms TEXT NOT NULL DEFAULT '[]', -- JSON [[term, weight], ...] by c-TF-IDF + PRIMARY KEY (analysis_id, cluster_id) ); --- Jobs: track ingestion pipeline state -CREATE TABLE IF NOT EXISTS jobs ( - id TEXT PRIMARY KEY, -- uuid4 - topic TEXT, - status TEXT, -- queued | crawling | processing | embedding | projecting | done | error - progress INTEGER DEFAULT 0, -- 0-100 - article_count INTEGER DEFAULT 0, - error_msg TEXT, - warning TEXT, -- non-fatal warning, e.g. low article yield - created_ts INTEGER, - updated_ts INTEGER +-- Placements: where each article sits on the map and which topic it belongs to +CREATE TABLE IF NOT EXISTS placements ( + article_id TEXT PRIMARY KEY REFERENCES articles(id) ON DELETE CASCADE, + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + x REAL NOT NULL, + y REAL NOT NULL, + cluster_id INTEGER NOT NULL, -- -1 = unclustered + distance REAL NOT NULL -- cosine distance to its topic's centre (outlier score) +); +CREATE INDEX IF NOT EXISTS idx_placements_analysis ON placements(analysis_id); + +-- Live log of what a pipeline run is doing; streamed to the UI over SSE. +CREATE TABLE IF NOT EXISTS analysis_events ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + ts INTEGER NOT NULL, + kind TEXT NOT NULL, -- stage | query | article | skip | cluster | warning | error + message TEXT NOT NULL, + url TEXT +); +CREATE INDEX IF NOT EXISTS idx_events_analysis ON analysis_events(analysis_id, id); + +-- Ask: saved conversation per analysis (a turn is stored once its answer completes) +CREATE TABLE IF NOT EXISTS chat_messages ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + role TEXT NOT NULL, -- user | assistant + text TEXT NOT NULL, + sources TEXT, -- assistant only: JSON [{n, article_id, url, title, domain, publish_date, cited}] + created_ts INTEGER NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_chat_analysis ON chat_messages(analysis_id, id); + +-- Claims: what each article argues or reports (found on demand by Claude) +CREATE TABLE IF NOT EXISTS claims ( + id TEXT PRIMARY KEY, -- {article_id}:{position} + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + article_id TEXT NOT NULL REFERENCES articles(id) ON DELETE CASCADE, + position INTEGER NOT NULL, + text TEXT NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_claims_analysis ON claims(analysis_id); + +-- Propositions: points two or more articles take a position on +CREATE TABLE IF NOT EXISTS propositions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + analysis_id TEXT NOT NULL REFERENCES analyses(id) ON DELETE CASCADE, + position INTEGER NOT NULL, -- 0 = most important + statement TEXT NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_propositions_analysis ON propositions(analysis_id); + +CREATE TABLE IF NOT EXISTS proposition_claims ( + proposition_id INTEGER NOT NULL REFERENCES propositions(id) ON DELETE CASCADE, + claim_id TEXT NOT NULL REFERENCES claims(id) ON DELETE CASCADE, + stance TEXT NOT NULL, -- agrees | qualifies | disputes + PRIMARY KEY (proposition_id, claim_id) ); diff --git a/backend/embedding/embed.py b/backend/embedding/embed.py index 8a579fa..ef66489 100644 --- a/backend/embedding/embed.py +++ b/backend/embedding/embed.py @@ -1,6 +1,7 @@ """ -Embed child chunks using either: - - local: Ollama nomic-embed-text +Embed child chunks using one of: + - fastembed: nomic-embed-text v1.5 in-process (ONNX, downloaded once to FASTEMBED_CACHE) + - ollama (or "local"): the same model served by Ollama - openai: text-embedding-3-small Always batched at max 32 chunks at a time. @@ -9,15 +10,24 @@ from __future__ import annotations -import os -from typing import Generator +import threading +from collections.abc import Callable, Generator +from functools import cache import httpx +import numpy as np + +from backend.config import settings BATCH_SIZE = 32 -EMBEDDING_MODE = os.getenv("EMBEDDING_MODE", "local") -OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434") OLLAMA_MODEL = "nomic-embed-text" +# The full-precision model: its vectors match Ollama's. The quantized "-Q" +# variant is smaller but drifts (cosine ~0.9), which would break old maps. +FASTEMBED_MODEL = "nomic-ai/nomic-embed-text-v1.5" + +# nomic-embed-text is trained with task prefixes; without them retrieval and +# clustering quality drop. OpenAI models take raw text. +_NOMIC_PREFIXES = {"document": "search_document: ", "query": "search_query: "} def _batched(items: list, size: int) -> Generator[list, None, None]: @@ -25,9 +35,27 @@ def _batched(items: list, size: int) -> Generator[list, None, None]: yield items[i : i + size] +_fastembed_lock = threading.Lock() + + +@cache +def _fastembed_model(): + from fastembed import TextEmbedding + + return TextEmbedding(FASTEMBED_MODEL, cache_dir=settings.fastembed_cache) + + +def _embed_fastembed(texts: list[str]) -> list[list[float]]: + """Embed in-process. Unit-length like Ollama's, so search distances agree.""" + with _fastembed_lock: # one load, and onnxruntime sessions aren't re-entrant here + vectors = np.array(list(_fastembed_model().embed(texts, batch_size=BATCH_SIZE))) + vectors /= np.linalg.norm(vectors, axis=1, keepdims=True) + return vectors.tolist() + + def _embed_local(texts: list[str]) -> list[list[float]]: """Call Ollama embed endpoint. Tries /api/embed (>=0.1.33) then falls back to /api/embeddings.""" - url = f"{OLLAMA_BASE_URL}/api/embed" + url = f"{settings.ollama_base_url}/api/embed" resp = httpx.post( url, json={"model": OLLAMA_MODEL, "input": texts}, @@ -42,7 +70,7 @@ def _embed_local(texts: list[str]) -> list[list[float]]: def _embed_local_legacy(texts: list[str]) -> list[list[float]]: """Ollama <0.1.33 fallback: /api/embeddings, one text at a time.""" - url = f"{OLLAMA_BASE_URL}/api/embeddings" + url = f"{settings.ollama_base_url}/api/embeddings" results = [] for text in texts: resp = httpx.post( @@ -59,7 +87,7 @@ def _embed_openai(texts: list[str]) -> list[list[float]]: """Call OpenAI embeddings API for a batch of texts.""" from openai import OpenAI - client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + client = OpenAI(api_key=settings.openai_api_key) resp = client.embeddings.create( model="text-embedding-3-small", input=texts, @@ -67,20 +95,42 @@ def _embed_openai(texts: list[str]) -> list[list[float]]: return [item.embedding for item in resp.data] -def embed_chunks(chunks: list[dict]) -> list[dict]: +def prepare_texts(texts: list[str], kind: str) -> list[str]: + """Apply the model's task prefix. kind is 'document' or 'query'.""" + if kind not in _NOMIC_PREFIXES: + raise ValueError(f"kind must be 'document' or 'query', got {kind!r}") + if settings.embedding_mode == "openai": + return texts + prefix = _NOMIC_PREFIXES[kind] + return [prefix + t for t in texts] + + +def embed_chunks( + chunks: list[dict], + kind: str = "document", + on_batch: Callable[[int, int], None] | None = None, +) -> list[dict]: """ Takes a list of chunk dicts (must have 'id' and 'child_text'). + kind: 'document' for corpus chunks, 'query' for search queries. Returns list of dicts: {chunk_id, vector}. - Processes in batches of BATCH_SIZE. + Processes in batches of BATCH_SIZE; on_batch(done, total) runs after each. """ results: list[dict] = [] - embed_fn = _embed_local if EMBEDDING_MODE == "local" else _embed_openai + embed_fn = { + "fastembed": _embed_fastembed, + "ollama": _embed_local, + "local": _embed_local, + "openai": _embed_openai, + }[settings.embedding_mode] for batch in _batched(chunks, BATCH_SIZE): - texts = [c["child_text"] for c in batch] + texts = prepare_texts([c["child_text"] for c in batch], kind) vectors = embed_fn(texts) - for chunk, vector in zip(batch, vectors): + for chunk, vector in zip(batch, vectors, strict=True): results.append({"chunk_id": chunk["id"], "vector": vector}) + if on_batch: + on_batch(len(results), len(chunks)) return results diff --git a/backend/embedding/project.py b/backend/embedding/project.py index c765ae9..8a3513c 100644 --- a/backend/embedding/project.py +++ b/backend/embedding/project.py @@ -1,150 +1,182 @@ """ -UMAP projection + k-means clustering + cluster labelling. - -Steps: -1. Load all vectors from LanceDB. -2. Run UMAP (2D). -3. Try k-means for k in {5, 7, 10}; pick best silhouette score. -4. For each chunk compute Euclidean distance to its cluster centroid. -5. Call Claude once per cluster (top-10 central chunks) to get a 2-3 word label. -6. Write (chunk_id, x, y, cluster_id, cluster_label, distance_from_centroid) - to the SQLite projections table. +Map + topics for one analysis. + +1. Load the analysis's chunk vectors; average them into one vector per article. +2. Topic model (backend.topics): UMAP → HDBSCAN → merge/assign/cap → 2D map. +3. Key terms per topic by c-TF-IDF over the articles' text. +4. One Claude call names every topic at once, so names are distinct. +5. Replace the analysis's rows in `placements` and `clusters`. """ from __future__ import annotations -import math -import os -import sqlite3 +import json +import logging +from collections.abc import Callable import numpy as np -from umap import UMAP -from sklearn.cluster import KMeans -from sklearn.metrics import silhouette_score - -from backend.embedding.store import read_all_embeddings, update_cluster_ids - -DB_PATH = os.getenv("DB_PATH", "./data/discourse.db") -UMAP_N_NEIGHBORS = int(os.getenv("UMAP_N_NEIGHBORS", "15")) -KMEANS_K = os.getenv("KMEANS_K", "auto") # "auto" | integer string - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- -def _best_k(vectors: np.ndarray) -> int: - if KMEANS_K != "auto": - return int(KMEANS_K) - - candidates = [k for k in (5, 7, 10) if k < len(vectors)] - if not candidates: - return max(2, len(vectors) // 10) - - best_k, best_score = candidates[0], -1.0 - for k in candidates: - km = KMeans(n_clusters=k, n_init=10, random_state=42) - labels = km.fit_predict(vectors) - score = silhouette_score(vectors, labels) - if score > best_score: - best_k, best_score = k, score - return best_k - - -def _euclidean(a: np.ndarray, b: np.ndarray) -> float: - return float(np.linalg.norm(a - b)) +from backend import topics +from backend.db import connect +from backend.embedding.store import read_all_embeddings + +log = logging.getLogger(__name__) + +TERMS_PER_TOPIC = 10 +EXCERPT_CHARS = 400 + +LABEL_SCHEMA = { + "type": "object", + "properties": { + "topics": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": {"type": "integer"}, + "label": {"type": "string"}, + "description": {"type": "string"}, + }, + "required": ["id", "label", "description"], + "additionalProperties": False, + }, + } + }, + "required": ["topics"], + "additionalProperties": False, +} + + +def run_projection( + analysis_id: str, + on_step: Callable[[float, str], None] | None = None, + on_label: Callable[[str, int], None] | None = None, +) -> None: + """ + Build the map and topics for an analysis whose chunks are embedded. + on_step(fraction_done, detail) reports progress; on_label(label, n_articles) + fires once per topic after naming. + """ -def _label_cluster(cluster_id: int, top_chunks: list[str]) -> str: - """Ask Claude for a 2-3 word label for this cluster.""" - from backend.claude_client import get_client, get_model + def step(fraction: float, detail: str) -> None: + if on_step: + on_step(fraction, detail) - excerpts = "\n\n".join(f"- {t[:300]}" for t in top_chunks) - prompt = ( - f"Below are excerpts from articles in a semantic cluster (cluster {cluster_id}).\n\n" - f"{excerpts}\n\n" - "Give a 2-3 word descriptive label that captures the main theme of this cluster. " - "Respond with ONLY the label, nothing else." - ) - msg = get_client().messages.create( - model=get_model(), - max_tokens=20, - messages=[{"role": "user", "content": prompt}], + rows = read_all_embeddings(analysis_id) + if not rows: + raise RuntimeError("No embeddings found for this analysis — run the embedding step first.") + ids, X = topics.article_vectors( + np.array([r["vector"] for r in rows], dtype=np.float32), + [r["article_id"] for r in rows], ) - return msg.content[0].text.strip() - -# --------------------------------------------------------------------------- -# Main entry point -# --------------------------------------------------------------------------- + step(0.0, f"Finding topics among {len(ids)} articles") + labels, coords, dist = topics.model_topics(X) -def run_projection(db_path: str | None = None) -> None: - """ - Full projection pipeline. Writes rows to the SQLite projections table. - """ - _db_path = db_path or DB_PATH - - # 1. Load embeddings - rows = read_all_embeddings() - if not rows: - raise RuntimeError("No embeddings found in LanceDB — run embedding step first.") - - chunk_ids = [r["chunk_id"] for r in rows] - vectors = np.array([r["vector"] for r in rows], dtype=np.float32) - child_texts = {r["chunk_id"]: r["child_text"] for r in rows} - - # 2. UMAP - n_neighbors = min(UMAP_N_NEIGHBORS, len(vectors) - 1) - reducer = UMAP(n_components=2, n_neighbors=n_neighbors, min_dist=0.1, random_state=42) - coords_2d = reducer.fit_transform(vectors) # shape (N, 2) - - # 3. K-means - k = _best_k(vectors) - km = KMeans(n_clusters=k, n_init=10, random_state=42) - cluster_labels_arr = km.fit_predict(vectors) # int labels, shape (N,) - centroids = km.cluster_centers_ # shape (k, dim) - - # 4. Distance from centroid - distances = np.array( - [_euclidean(vectors[i], centroids[cluster_labels_arr[i]]) for i in range(len(vectors))] + step(0.6, "Finding each topic's key terms") + articles = _load_articles(analysis_id, ids) + terms = topics.topic_terms( + [articles[i]["raw_text"] or "" for i in ids], labels, top_n=TERMS_PER_TOPIC + ) + reps = topics.representatives(X, labels, k=3) + + step(0.75, f"Naming {len(terms)} topics") + named = name_topics( + { + t: { + "terms": [w for w, _ in terms.get(t, [])], + "examples": [articles[ids[i]] for i in reps.get(t, [])], + "size": int(np.sum(labels == t)), + } + for t in sorted(terms) + } ) - # 5. Generate cluster text labels via Claude - cluster_text_labels: dict[int, str] = {} - for cid in range(k): - mask = cluster_labels_arr == cid - idxs = np.where(mask)[0] - # Sort by distance (ascending = most central) - sorted_idxs = idxs[np.argsort(distances[idxs])][:10] - top_texts = [child_texts[chunk_ids[i]] for i in sorted_idxs] - try: - cluster_text_labels[cid] = _label_cluster(cid, top_texts) - except Exception: - cluster_text_labels[cid] = f"cluster {cid}" - - # 6. Update LanceDB cluster_ids - chunk_cluster_map = {chunk_ids[i]: int(cluster_labels_arr[i]) for i in range(len(chunk_ids))} - update_cluster_ids(chunk_cluster_map) - - # 7. Write projections to SQLite - conn = sqlite3.connect(_db_path) + conn = connect() try: + conn.execute("DELETE FROM placements WHERE analysis_id = ?", (analysis_id,)) + conn.execute("DELETE FROM clusters WHERE analysis_id = ?", (analysis_id,)) conn.executemany( - "INSERT OR REPLACE INTO projections " - "(chunk_id, x, y, cluster_id, cluster_label, distance_from_centroid) " - "VALUES (?, ?, ?, ?, ?, ?)", + "INSERT INTO placements (article_id, analysis_id, x, y, cluster_id, distance) " + "VALUES (?,?,?,?,?,?)", [ - ( - chunk_ids[i], - float(coords_2d[i, 0]), - float(coords_2d[i, 1]), - int(cluster_labels_arr[i]), - cluster_text_labels[int(cluster_labels_arr[i])], - float(distances[i]), - ) - for i in range(len(chunk_ids)) + (aid, analysis_id, float(coords[i, 0]), float(coords[i, 1]), + int(labels[i]), float(dist[i])) + for i, aid in enumerate(ids) + ], + ) + conn.executemany( + "INSERT INTO clusters (analysis_id, cluster_id, label, description, article_count, terms) " + "VALUES (?,?,?,?,?,?)", + [ + (analysis_id, t, named[t][0], named[t][1], int(np.sum(labels == t)), + json.dumps([[w, round(s, 6)] for w, s in terms.get(t, [])])) + for t in sorted(terms) ], ) conn.commit() finally: conn.close() + + if on_label: + for t in sorted(terms): + on_label(named[t][0], int(np.sum(labels == t))) + + +def _load_articles(analysis_id: str, ids: list[str]) -> dict[str, dict]: + conn = connect() + try: + rows = conn.execute( + "SELECT id, title, domain, raw_text FROM articles WHERE analysis_id = ?", + (analysis_id,), + ).fetchall() + finally: + conn.close() + found = {r["id"]: dict(r) for r in rows} + return {i: found.get(i, {"id": i, "title": None, "domain": None, "raw_text": ""}) for i in ids} + + +def name_topics(topics_in: dict[int, dict]) -> dict[int, tuple[str, str | None]]: + """ + Name every topic in one request so the names contrast with each other — + naming topics one at a time gave near-identical names to neighbours. + Falls back to the top key terms if the request fails. + """ + from backend.claude_client import complete + + fallback = { + t: (", ".join(info["terms"][:3]).capitalize() or f"Topic {t + 1}", None) + for t, info in topics_in.items() + } + if not topics_in: + return {} + + blocks = [] + for t, info in topics_in.items(): + examples = "\n".join( + f" - {(a['title'] or a['domain'] or 'Untitled')}: " + f"{' '.join((a['raw_text'] or '').split())[:EXCERPT_CHARS]}" + for a in info["examples"] + ) + blocks.append( + f"Topic {t} ({info['size']} articles)\n" + f"Key terms: {', '.join(info['terms'])}\n" + f"Most typical articles:\n{examples}" + ) + prompt = ( + "These topics were found by clustering articles about one subject. For each topic, " + "write a label of 2 to 4 words naming the angle or camp it represents, and one " + "sentence (under 25 words) describing what its articles argue or cover.\n\n" + "Labels must be clearly different from each other: they will sit side by side on a " + "map, so name what sets each topic apart rather than the subject they all share. " + "Use sentence case.\n\n" + "\n\n".join(blocks) + ) + try: + data = json.loads(complete(prompt, max_tokens=8000, effort="low", json_schema=LABEL_SCHEMA)) + named = {int(item["id"]): (item["label"].strip(), item["description"].strip()) + for item in data["topics"]} + except Exception: + log.warning("Naming topics failed; using key terms instead", exc_info=True) + return fallback + return {t: named.get(t) or fallback[t] for t in topics_in} diff --git a/backend/embedding/store.py b/backend/embedding/store.py index 4ac14ae..6f8c714 100644 --- a/backend/embedding/store.py +++ b/backend/embedding/store.py @@ -1,138 +1,119 @@ """ -LanceDB read/write for embeddings. +LanceDB read/write for embeddings — one table per analysis. -Table schema (per PLANNING.md §4.2): - chunk_id, article_id, vector, child_text, domain, publish_date, cluster_id +Table schema: + chunk_id, article_id, vector, child_text, domain, publish_date """ from __future__ import annotations -import os - import lancedb import pyarrow as pa -LANCE_PATH = os.getenv("LANCE_PATH", "./data/vectors") -TABLE_NAME = "embeddings" -MAX_ARTICLES = int(os.getenv("MAX_ARTICLES", "120")) +from backend.config import settings + +# Single global table used before analyses existed (schema v1). +LEGACY_TABLE_NAME = "embeddings" + +_COLUMNS = ["chunk_id", "article_id", "vector", "child_text", "domain", "publish_date"] + + +def table_name(analysis_id: str) -> str: + return "emb_" + analysis_id.replace("-", "") + + +def _connect() -> lancedb.DBConnection: + return lancedb.connect(settings.lance_path) + + +def table_exists(db: lancedb.DBConnection, name: str) -> bool: + """list_tables() is paginated — walk every page.""" + page_token = None + while True: + resp = db.list_tables(page_token=page_token) + if name in resp.tables: + return True + page_token = resp.page_token + if not page_token: + return False + +def open_table(analysis_id: str) -> lancedb.table.Table | None: + db = _connect() + name = table_name(analysis_id) + return db.open_table(name) if table_exists(db, name) else None -def _get_table(db: lancedb.DBConnection) -> lancedb.table.Table: - names = db.table_names() - if TABLE_NAME in names: - return db.open_table(TABLE_NAME) - return None + +def _schema(dim: int) -> pa.Schema: + return pa.schema( + [ + pa.field("chunk_id", pa.string()), + pa.field("article_id", pa.string()), + pa.field("vector", pa.list_(pa.float32(), dim)), + pa.field("child_text", pa.string()), + pa.field("domain", pa.string()), + pa.field("publish_date", pa.string()), + ] + ) -def write_embeddings(rows: list[dict]) -> None: +def write_embeddings(analysis_id: str, rows: list[dict]) -> None: """ - rows: list of dicts with keys: - chunk_id, article_id, vector, child_text, domain, publish_date, cluster_id - Creates or appends to the LanceDB embeddings table. + rows: dicts with chunk_id, article_id, vector, child_text, domain, publish_date. + Creates the analysis's table or appends to it. """ if not rows: return - - db = lancedb.connect(LANCE_PATH) - table = _get_table(db) - + normalised = _normalise(rows) + table = open_table(analysis_id) if table is None: - # Infer dim from first row - dim = len(rows[0]["vector"]) - schema = pa.schema( - [ - pa.field("chunk_id", pa.string()), - pa.field("article_id", pa.string()), - pa.field("vector", pa.list_(pa.float32(), dim)), - pa.field("child_text", pa.string()), - pa.field("domain", pa.string()), - pa.field("publish_date", pa.string()), - pa.field("cluster_id", pa.int32()), - ] + _connect().create_table( + table_name(analysis_id), data=normalised, schema=_schema(len(rows[0]["vector"])) ) - # Normalise rows to correct types - normalised = _normalise(rows) - db.create_table(TABLE_NAME, data=normalised, schema=schema) else: - normalised = _normalise(rows) table.add(normalised) -def read_all_embeddings() -> list[dict]: - """Return all rows from the embeddings table as a list of dicts.""" - db = lancedb.connect(LANCE_PATH) - table = _get_table(db) +def read_all_embeddings(analysis_id: str) -> list[dict]: + """Return all rows of the analysis's table as a list of dicts.""" + table = open_table(analysis_id) if table is None: return [] return table.to_pandas().to_dict(orient="records") -def update_cluster_ids(chunk_cluster_map: dict[str, int]) -> None: - """ - Update the cluster_id column for each chunk_id. - LanceDB doesn't support in-place updates; we reload and overwrite. - """ - if not chunk_cluster_map: - return +def drop_embeddings(analysis_id: str) -> None: + db = _connect() + name = table_name(analysis_id) + if table_exists(db, name): + db.drop_table(name) - db = lancedb.connect(LANCE_PATH) - table = _get_table(db) - if table is None: + +def adopt_legacy_table(analysis_id: str) -> None: + """Copy the pre-analyses global table into the analysis's own table (idempotent).""" + db = _connect() + if not table_exists(db, LEGACY_TABLE_NAME): return + data = db.open_table(LEGACY_TABLE_NAME).to_arrow().select(_COLUMNS) + db.create_table(table_name(analysis_id), data=data, mode="overwrite") - df = table.to_pandas() - df["cluster_id"] = df["chunk_id"].map( - lambda cid: chunk_cluster_map.get(cid, df.loc[df["chunk_id"] == cid, "cluster_id"].iloc[0]) - ) - # Overwrite table - db.drop_table(TABLE_NAME) - dim = len(df["vector"].iloc[0]) - schema = pa.schema( - [ - pa.field("chunk_id", pa.string()), - pa.field("article_id", pa.string()), - pa.field("vector", pa.list_(pa.float32(), dim)), - pa.field("child_text", pa.string()), - pa.field("domain", pa.string()), - pa.field("publish_date", pa.string()), - pa.field("cluster_id", pa.int32()), - ] - ) - import pyarrow as pa2 - records = [] - for _, row in df.iterrows(): - records.append({ - "chunk_id": str(row["chunk_id"]), - "article_id": str(row["article_id"]), - "vector": [float(v) for v in row["vector"]], - "child_text": str(row["child_text"]), - "domain": str(row["domain"]) if row["domain"] else "", - "publish_date": str(row["publish_date"]) if row["publish_date"] else "", - "cluster_id": int(row["cluster_id"]) if row["cluster_id"] is not None else -1, - }) - db.create_table(TABLE_NAME, data=records, schema=schema) - - -def clear_embeddings() -> None: - """Drop and recreate the embeddings table, clearing all vectors.""" - db = lancedb.connect(LANCE_PATH) - if TABLE_NAME in db.table_names(): - db.drop_table(TABLE_NAME) + +def drop_legacy_table() -> None: + db = _connect() + if table_exists(db, LEGACY_TABLE_NAME): + db.drop_table(LEGACY_TABLE_NAME) def _normalise(rows: list[dict]) -> list[dict]: """Ensure all fields have correct Python types for Arrow ingestion.""" - out = [] - for r in rows: - out.append( - { - "chunk_id": str(r.get("chunk_id", "")), - "article_id": str(r.get("article_id", "")), - "vector": [float(v) for v in r["vector"]], - "child_text": str(r.get("child_text", "")), - "domain": str(r.get("domain", "") or ""), - "publish_date": str(r.get("publish_date", "") or ""), - "cluster_id": int(r.get("cluster_id", -1)), - } - ) - return out + return [ + { + "chunk_id": str(r.get("chunk_id", "")), + "article_id": str(r.get("article_id", "")), + "vector": [float(v) for v in r["vector"]], + "child_text": str(r.get("child_text", "")), + "domain": str(r.get("domain", "") or ""), + "publish_date": str(r.get("publish_date", "") or ""), + } + for r in rows + ] diff --git a/backend/export.py b/backend/export.py index 3df192a..05e79ff 100644 --- a/backend/export.py +++ b/backend/export.py @@ -7,7 +7,6 @@ import re import statistics - CANVAS_SCALE = 200 # UMAP units → canvas pixels NODE_WIDTH = 200 NODE_HEIGHT = 80 diff --git a/backend/ingestion/crawler.py b/backend/ingestion/crawler.py index 6383881..2f86744 100644 --- a/backend/ingestion/crawler.py +++ b/backend/ingestion/crawler.py @@ -1,36 +1,49 @@ import asyncio +from collections.abc import Callable from playwright.async_api import async_playwright _MAX_ATTEMPTS = 2 # 1 retry on transient failure _RETRY_BACKOFF_SECONDS = 2.0 +_CONCURRENCY = 5 -async def _crawl_async(urls: list[str]) -> list[dict]: - semaphore = asyncio.Semaphore(5) +async def _crawl_async( + urls: list[str], + on_page: Callable[[str, bool], None] | None, + should_stop: Callable[[], bool] | None, +) -> list[dict]: + semaphore = asyncio.Semaphore(_CONCURRENCY) async with async_playwright() as p: browser = await p.chromium.launch(headless=True) - async def _scrape(url: str) -> dict: + async def _fetch(url: str) -> str | None: if url.lower().endswith(".pdf"): - return {"url": url, "html": None} + return None async with semaphore: for attempt in range(_MAX_ATTEMPTS): + if should_stop and should_stop(): + return None page = None try: page = await browser.new_page() await page.goto(url, timeout=10_000, wait_until="domcontentloaded") - html = await page.content() - return {"url": url, "html": html} + return await page.content() except Exception: if attempt + 1 == _MAX_ATTEMPTS: - return {"url": url, "html": None} + return None await asyncio.sleep(_RETRY_BACKOFF_SECONDS) finally: if page: await page.close() - return {"url": url, "html": None} + return None + + async def _scrape(url: str) -> dict: + html = await _fetch(url) + if on_page and not (should_stop and should_stop()): + on_page(url, html is not None) + return {"url": url, "html": html} results = await asyncio.gather(*[_scrape(url) for url in urls]) await browser.close() @@ -38,10 +51,17 @@ async def _scrape(url: str) -> dict: return [r for r in results if r["html"] is not None] -def crawl_urls(urls: list[str]) -> list[dict]: +def crawl_urls( + urls: list[str], + on_page: Callable[[str, bool], None] | None = None, + should_stop: Callable[[], bool] | None = None, +) -> list[dict]: """ Sync entry point — safe to call from asyncio.to_thread(). Runs Playwright in a fresh event loop isolated from FastAPI's loop, which avoids the Windows NotImplementedError from subprocess transport. + + on_page(url, ok) is called as each page finishes. Once should_stop() + returns True, pages not yet started are skipped. """ - return asyncio.run(_crawl_async(urls)) + return asyncio.run(_crawl_async(urls, on_page, should_stop)) diff --git a/backend/ingestion/extractor.py b/backend/ingestion/extractor.py index 7f2147a..5e64130 100644 --- a/backend/ingestion/extractor.py +++ b/backend/ingestion/extractor.py @@ -3,29 +3,39 @@ from urllib.parse import urlparse import trafilatura -from langdetect import detect, LangDetectException +from langdetect import LangDetectException, detect +MIN_WORDS = 200 -def extract_article(url: str, html: str) -> dict | None: + +class SkipArticle(Exception): + """The page failed a quality filter; str(exc) is a human-readable reason.""" + + +def article_id(analysis_id: str, url: str) -> str: + """Stable per-analysis id, so the same URL can appear in several analyses.""" + return hashlib.sha256(f"{analysis_id}:{url}".encode()).hexdigest()[:16] + + +def extract_article(analysis_id: str, url: str, html: str) -> dict: """ Extract clean article text from raw HTML using trafilatura. - Returns an article dict, or None if the article fails filtering: - - extraction failed - - word count < 200 + Raises SkipArticle if the page fails filtering: + - no article text could be extracted + - fewer than MIN_WORDS words - language is not English """ text = trafilatura.extract( html, include_comments=False, include_tables=True, - no_fallback=False, ) if not text: - return None + raise SkipArticle("no article text found") words = text.split() - if len(words) < 200: - return None + if len(words) < MIN_WORDS: + raise SkipArticle(f"too short ({len(words)} words)") try: lang = detect(text[:2000]) @@ -33,15 +43,14 @@ def extract_article(url: str, html: str) -> dict | None: lang = "unknown" if lang != "en": - return None + raise SkipArticle(f"not English ({lang})") meta = trafilatura.extract_metadata(html, default_url=url) title = meta.title if meta and meta.title else None domain = urlparse(url).netloc - article_id = hashlib.sha256(url.encode()).hexdigest()[:16] return { - "id": article_id, + "id": article_id(analysis_id, url), "url": url, "domain": domain, "title": title, diff --git a/backend/ingestion/scholarly.py b/backend/ingestion/scholarly.py new file mode 100644 index 0000000..b1df3c5 --- /dev/null +++ b/backend/ingestion/scholarly.py @@ -0,0 +1,258 @@ +""" +Recover academic articles the crawler couldn't read. + +Publishers (ScienceDirect, PMC, MDPI, Wiley, …) often serve a headless browser +a bot check or paywall instead of the article. Many of those articles are +openly available elsewhere, and scholarly indexes know where: + +1. Identify the work: a DOI, PMC id, PubMed id or Elsevier PII in the URL, + MDPI's ISSN/volume/issue/article URL scheme, or citation meta tags in the + page we did get. +2. Look it up in OpenAlex (free, no key). Elsevier PIIs go via Crossref. +3. Get text, best first: Europe PMC full text (PMC articles); an open-access + PDF from OpenAlex's locations; otherwise the abstract. + +Measured on the 126 pages real maps skipped as "too short", about 40% are +recoverable this way (mostly full text). +""" + +from __future__ import annotations + +import io +import logging +import re +import xml.etree.ElementTree as ET +from dataclasses import dataclass +from urllib.parse import unquote, urlparse + +import httpx + +log = logging.getLogger(__name__) +logging.getLogger("pypdf").setLevel(logging.ERROR) # font-encoding chatter, irrelevant to text + +OPENALEX = "https://api.openalex.org" +CROSSREF = "https://api.crossref.org" +EUROPE_PMC = "https://www.ebi.ac.uk/europepmc/webservices/rest" +USER_AGENT = "discourse-lens/0.2 (open-source research tool)" + +MIN_ABSTRACT_WORDS = 60 +MAX_PDF_PAGES = 40 +MAX_WORDS = 8000 # a paper's substance; keeps a few long papers from dominating answers +MAX_PDF_ATTEMPTS = 3 + + +@dataclass +class Identifier: + kind: str # doi | pmcid | pmid | pii | mdpi + value: str + + +@dataclass +class Recovered: + title: str | None + text: str + publish_date: str | None # YYYY-MM-DD + note: str # where the text came from, shown to the reader + + +def new_client() -> httpx.Client: + return httpx.Client(timeout=25, follow_redirects=True, headers={"User-Agent": USER_AGENT}) + + +# ── identify ───────────────────────────────────────────────────────────────── + +_DOI = re.compile(r"(10\.\d{4,9}/[^\s?#&\"'<>]+)") +_DOI_SUFFIXES = re.compile(r"(/(full|abstract|pdf|xml|epdf|meta|html|fulltext))+/?$", re.I) +_META = re.compile( + r"]+name=[\"'](citation_doi|dc\.identifier|prism\.doi|citation_pmid)[\"'][^>]*" + r"content=[\"']([^\"']+)[\"']", + re.I, +) + + +def _clean_doi(doi: str) -> str: + doi = re.sub(r"^(https?://(dx\.)?doi\.org/|doi:)", "", doi.strip(), flags=re.I) + return _DOI_SUFFIXES.sub("", doi.rstrip("/.")) + + +def identify(url: str, html: str | None = None) -> Identifier | None: + """Find a scholarly identifier in the URL, else in the page's citation meta tags.""" + u = unquote(url) + host = urlparse(u).netloc.lower() + if m := _DOI.search(u): + return Identifier("doi", _clean_doi(m.group(1))) + if m := re.search(r"\b(PMC\d{4,9})\b", u): + return Identifier("pmcid", m.group(1)) + if "pubmed.ncbi.nlm.nih.gov" in host and (m := re.search(r"/(\d{5,9})\b", u)): + return Identifier("pmid", m.group(1)) + if m := re.search(r"/pii/(S?[0-9X]{15,17})", u, re.I): + return Identifier("pii", m.group(1).upper()) + if "arxiv.org" in host and (m := re.search(r"(\d{4}\.\d{4,5})", u)): + return Identifier("doi", f"10.48550/arXiv.{m.group(1)}") + if m := re.search(r"mdpi\.com/(\d{4}-\d{3}[\dX])/(\d+)/(\d+)/(\d+)", u): + return Identifier("mdpi", "/".join(m.groups())) + for name, content in _META.findall(html or ""): + if name.lower() == "citation_pmid" and content.strip().isdigit(): + return Identifier("pmid", content.strip()) + if (m := _DOI.search(content)) is not None: + return Identifier("doi", _clean_doi(m.group(1))) + return None + + +# ── resolve ────────────────────────────────────────────────────────────────── + +def lookup_work(ident: Identifier, client: httpx.Client) -> dict | None: + """The OpenAlex work for an identifier, or None.""" + if ident.kind == "pii": + r = client.get(f"{CROSSREF}/works", params={"filter": f"alternative-id:{ident.value}", "rows": 1}) + items = r.json().get("message", {}).get("items", []) if r.status_code == 200 else [] + if not items: + return None + ident = Identifier("doi", items[0]["DOI"]) + if ident.kind == "mdpi": + issn, vol, issue, art = ident.value.split("/") + r = client.get(f"{OPENALEX}/works", params={"filter": ( + f"primary_location.source.issn:{issn},biblio.volume:{vol}," + f"biblio.issue:{issue},biblio.first_page:{art}")}) + results = r.json().get("results", []) if r.status_code == 200 else [] + return results[0] if results else None + r = client.get(f"{OPENALEX}/works/{ident.kind}:{ident.value}") + return r.json() if r.status_code == 200 else None + + +def abstract_text(inverted_index: dict | None) -> str: + """OpenAlex stores abstracts as {word: [positions]}; rebuild the text.""" + if not inverted_index: + return "" + slots: dict[int, str] = {} + for word, positions in inverted_index.items(): + for p in positions: + slots[p] = word + return " ".join(slots[i] for i in sorted(slots)) + + +# ── full text ──────────────────────────────────────────────────────────────── + +def europe_pmc_text(pmcid: str, client: httpx.Client) -> tuple[str | None, str] | None: + """(title, body text) from Europe PMC's open-access XML, or None.""" + r = client.get(f"{EUROPE_PMC}/{pmcid}/fullTextXML") + if r.status_code != 200 or not r.text.lstrip().startswith("<"): + return None + try: + root = ET.fromstring(r.text) + except ET.ParseError: + return None + title_el = root.find(".//article-meta//article-title") + title = " ".join("".join(title_el.itertext()).split()) if title_el is not None else None + body = root.find(".//body") + if body is None: + return None + paragraphs = [] + for el in body.iter(): + if el.tag in ("title", "p"): + text = " ".join("".join(el.itertext()).split()) + if text: + paragraphs.append(text) + return title, "\n\n".join(paragraphs) + + +def pdf_text(url: str, client: httpx.Client) -> str | None: + """Text of an open-access PDF, or None if it's blocked or unreadable.""" + from pypdf import PdfReader + + try: + r = client.get(url) + except httpx.HTTPError: + return None + if r.status_code != 200 or not r.content.startswith(b"%PDF"): + return None + try: + reader = PdfReader(io.BytesIO(r.content)) + pages = [(p.extract_text() or "") for p in reader.pages[:MAX_PDF_PAGES]] + except Exception: + log.warning("Couldn't read PDF %s", url, exc_info=True) + return None + return clean_pdf_text("\n".join(pages)) + + +_REFERENCES = re.compile(r"^\s*(references|bibliography|literature cited)\s*$", re.I | re.M) + + +def clean_pdf_text(text: str) -> str: + """Rejoin hyphenated line breaks, collapse lines, drop the reference list.""" + text = re.sub(r"(\w)-\n(\w)", r"\1\2", text) + matches = list(_REFERENCES.finditer(text)) + if matches and matches[-1].start() > len(text) * 0.5: + text = text[: matches[-1].start()] + text = re.sub(r"[ \t]*\n[ \t]*", "\n", text) + # PDF line breaks are layout, not structure: join them; blank lines stay paragraph breaks. + text = re.sub(r"(? str: + words = text.split() + return text if len(words) <= MAX_WORDS else " ".join(words[:MAX_WORDS]) + + +def _date(work: dict) -> str | None: + d = work.get("publication_date") + return d if d and re.match(r"\d{4}-\d{2}-\d{2}$", d) else None + + +def recover(url: str, html: str | None = None, client: httpx.Client | None = None) -> Recovered | None: + """Best available text for a blocked academic page or a PDF link, or None.""" + ident = identify(url, html) + is_pdf = urlparse(url).path.lower().endswith(".pdf") + if ident is None and not is_pdf: + return None + own_client = client is None + client = client or new_client() + try: + found = _recover(ident, client) if ident else None + if found is None and is_pdf: + # The crawler doesn't open PDFs; read the file itself. + text = pdf_text(url, client) + if text and len(text.split()) >= 500: + return Recovered(None, _cap(text), None, "Read from the PDF") + return found + except (httpx.HTTPError, ValueError): + log.warning("Recovering %s failed", url, exc_info=True) + return None + finally: + if own_client: + client.close() + + +def _recover(ident: Identifier, client: httpx.Client) -> Recovered | None: + if ident.kind == "pmcid": + found = europe_pmc_text(ident.value, client) + if found and found[1]: + return Recovered(found[0], _cap(found[1]), None, "Full text from Europe PMC") + work = lookup_work(ident, client) + else: + work = lookup_work(ident, client) + pmcid = ((work or {}).get("ids") or {}).get("pmcid") + if pmcid: + found = europe_pmc_text(pmcid.rsplit("/", 1)[-1], client) + if found and found[1]: + return Recovered(found[0] or work.get("title"), _cap(found[1]), _date(work), + "Full text from Europe PMC") + if not work: + return None + + title = work.get("title") or work.get("display_name") + pdfs = [] + for loc in [work.get("best_oa_location") or {}, *(work.get("locations") or [])]: + if loc.get("is_oa") is not False and loc.get("pdf_url") and loc["pdf_url"] not in pdfs: + pdfs.append(loc["pdf_url"]) + for pdf_url in pdfs[:MAX_PDF_ATTEMPTS]: + text = pdf_text(pdf_url, client) + if text and len(text.split()) >= 500: + host = urlparse(pdf_url).netloc.removeprefix("www.") + return Recovered(title, _cap(text), _date(work), f"Open-access PDF from {host}") + + abstract = abstract_text(work.get("abstract_inverted_index")) + if len(abstract.split()) >= MIN_ABSTRACT_WORDS: + return Recovered(title, abstract, _date(work), "Abstract only: the full text isn't openly available") + return None diff --git a/backend/ingestion/search.py b/backend/ingestion/search.py index 5254960..436fdeb 100644 --- a/backend/ingestion/search.py +++ b/backend/ingestion/search.py @@ -1,84 +1,148 @@ -import os +""" +Turn a topic into candidate pages: Claude writes search queries, a search +provider runs them, and results are deduplicated up to the article cap. + +Providers (SEARCH_PROVIDER): "brave" (Brave Search API, needs BRAVE_API_KEY) +or "searxng" (a SearXNG instance with the JSON format enabled, at SEARXNG_URL; +self-hostable, no key). +""" + +from __future__ import annotations + +import logging import time +from collections.abc import Callable +from dataclasses import dataclass import httpx -from dotenv import load_dotenv -from backend.claude_client import get_client, get_model +from backend.claude_client import complete +from backend.config import settings + +log = logging.getLogger(__name__) -load_dotenv() +RESULTS_PER_QUERY = 20 -_BRAVE_API_KEY = os.getenv("BRAVE_API_KEY") -_MAX_ARTICLES = int(os.getenv("MAX_ARTICLES", "120")) + +@dataclass +class SearchResult: + url: str + title: str + snippet: str + query: str def generate_queries(topic: str) -> list[str]: """Use Claude to generate 6 diverse search queries for the topic.""" - response = get_client().messages.create( - model=get_model(), - max_tokens=512, - messages=[{ - "role": "user", - "content": ( - f"Generate 6 diverse search queries to comprehensively cover the topic: '{topic}'.\n" - "The queries should approach the topic from different angles: historical context, " - "current debates, key figures, recent research, criticism, and applications.\n" - "Return ONLY the queries, one per line, no numbering or extra text." - ), - }], + text = complete( + f"Generate 6 diverse search queries to comprehensively cover the topic: '{topic}'.\n" + "The queries should approach the topic from different angles: historical context, " + "current debates, key figures, recent research, criticism, and applications.\n" + "Return ONLY the queries, one per line, no numbering or extra text.", + max_tokens=4096, + effort="low", ) - text = response.content[0].text.strip() queries = [q.strip() for q in text.split("\n") if q.strip()] return queries[:6] -def brave_search(query: str, count: int = 20, max_retries: int = 3) -> list[str]: - """Call Brave Search API and return a list of result URLs. - - Retries with exponential backoff on rate limiting (429) and transient - server/network errors; other 4xx errors fail immediately. - """ - headers = { - "Accept": "application/json", - "Accept-Encoding": "gzip", - "X-Subscription-Token": _BRAVE_API_KEY, - } - params = {"q": query, "count": count, "text_decorations": "false"} - +def _get_with_retries( + url: str, *, params: dict, headers: dict, max_retries: int = 3 +) -> httpx.Response: + """GET with exponential backoff on rate limiting (429), 5xx and network errors.""" for attempt in range(max_retries + 1): try: with httpx.Client(timeout=15) as client: - resp = client.get( - "https://api.search.brave.com/res/v1/web/search", - headers=headers, - params=params, - ) + resp = client.get(url, headers=headers, params=params) if resp.status_code == 429 or resp.status_code >= 500: raise httpx.HTTPStatusError( f"retryable status {resp.status_code}", request=resp.request, response=resp ) resp.raise_for_status() - data = resp.json() - results = data.get("web", {}).get("results", []) - return [r["url"] for r in results if "url" in r] - except (httpx.HTTPStatusError, httpx.TransportError): + return resp + except httpx.HTTPStatusError as e: + code = e.response.status_code + if attempt == max_retries or not (code == 429 or code >= 500): + raise + except httpx.TransportError: if attempt == max_retries: raise - time.sleep(2 ** attempt) # 1s, 2s, 4s - return [] + time.sleep(2**attempt) # 1s, 2s, 4s + raise AssertionError("unreachable") -def collect_urls(topic: str) -> list[str]: - """Generate queries, search each, return deduplicated URLs up to MAX_ARTICLES cap.""" - queries = generate_queries(topic) +def brave_search(query: str, count: int = RESULTS_PER_QUERY) -> list[SearchResult]: + resp = _get_with_retries( + "https://api.search.brave.com/res/v1/web/search", + params={"q": query, "count": count, "text_decorations": "false"}, + headers={"Accept": "application/json", "X-Subscription-Token": settings.brave_api_key}, + ) + return [ + SearchResult(r["url"], r.get("title") or "", r.get("description") or "", query) + for r in resp.json().get("web", {}).get("results", []) + if "url" in r + ][:count] + + +def searxng_search(query: str, count: int = RESULTS_PER_QUERY) -> list[SearchResult]: + if not settings.searxng_url: + raise RuntimeError("SEARCH_PROVIDER=searxng needs SEARXNG_URL (e.g. http://localhost:8888)") + resp = _get_with_retries( + f"{settings.searxng_url.rstrip('/')}/search", + params={"q": query, "format": "json", "language": "en"}, + headers={"Accept": "application/json"}, + ) + return [ + SearchResult(r["url"], r.get("title") or "", r.get("content") or "", query) + for r in resp.json().get("results", []) + if "url" in r + ][:count] + + +def web_search(query: str) -> list[SearchResult]: + if settings.search_provider == "searxng": + return searxng_search(query) + return brave_search(query) + + +def search_all( + queries: list[str], + on_query: Callable[[int, int, str, int | None], None] | None = None, +) -> list[SearchResult]: + """ + Run each query; deduplicate by URL (first query wins) up to the article cap. + on_query(done, total, query, new_results) fires after each search; + new_results is None when that search failed. + """ seen: set[str] = set() - urls: list[str] = [] - for query in queries: + results: list[SearchResult] = [] + for i, query in enumerate(queries, start=1): + new: int | None = 0 try: - for url in brave_search(query): - if url not in seen: - seen.add(url) - urls.append(url) + for r in web_search(query): + if r.url not in seen: + seen.add(r.url) + results.append(r) + new += 1 except Exception: - continue # failed query — skip, don't crash - return urls[:_MAX_ARTICLES] + log.warning("Search failed for %r", query, exc_info=True) + new = None # failed query: skip, don't crash + if on_query: + on_query(i, len(queries), query, new) + return results[: settings.max_articles] + + +def collect_results( + topic: str, + on_query: Callable[[int, int, str, int | None], None] | None = None, +) -> tuple[list[str], list[SearchResult]]: + queries = generate_queries(topic) + return queries, search_all(queries, on_query) + + +def collect_urls( + topic: str, + on_query: Callable[[int, int, str, int | None], None] | None = None, +) -> list[str]: + """Generate queries, search each, return deduplicated URLs up to the article cap.""" + return [r.url for r in collect_results(topic, on_query)[1]] diff --git a/backend/pipeline.py b/backend/pipeline.py new file mode 100644 index 0000000..279819e --- /dev/null +++ b/backend/pipeline.py @@ -0,0 +1,534 @@ +""" +The analysis pipeline: search → crawl → extract → chunk/dedup → embed → project. + +Each run writes only rows tagged with its own analysis_id, so analyses are +independent and several can exist side by side. + +Runs are cancellable. Work happens in worker threads, which can't be killed, +so cancellation is cooperative: each stage checks the run's cancel flag +between units of work (a search query, a page, an article, an embedding +batch, a cluster label) and stops there. A cancelled run's partial data is +deleted; the analysis row stays, with status 'cancelled'. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import threading +import time + +from backend.config import settings +from backend.db import add_event, connect, update_analysis + +log = logging.getLogger(__name__) + +LOW_YIELD_THRESHOLD = 30 + +# Progress bands (percent) for each stage. +SEARCH = (0, 10) +CRAWL = (10, 55) +EXTRACT = (55, 62) +RECOVER = (62, 66) +CHUNK = (66, 68) +EMBED = (68, 88) +PROJECT = (88, 99) + +_PROGRESS_WRITE_INTERVAL = 0.5 # seconds between throttled progress writes + + +class Cancelled(BaseException): # noqa: N818 — mirrors asyncio.CancelledError + """ + Raised inside a stage when the run is cancelled. A BaseException, like + asyncio.CancelledError, so the stages' `except Exception` handlers (which + skip a single bad page or query) don't swallow it. + """ + + +class RunContext: + """Progress reporting, event logging, the cancel flag and the review pause for one run.""" + + def __init__(self, analysis_id: str, review: bool = False): + self.analysis_id = analysis_id + self.review = review + self._cancel = threading.Event() + self._last_write = 0.0 + # Review pause: the pipeline awaits _resume; approve() or cancel() sets it. + # Both may be called from other threads, hence call_soon_threadsafe. + self._loop: asyncio.AbstractEventLoop | None = None + self._resume: asyncio.Event | None = None + self._plan_ready = False # set (in the search thread) once the plan is saved + self.approved_urls: list[str] | None = None + + def prepare_review(self) -> None: + """Create the pause before searching, so it exists by the time anyone can approve.""" + self._loop = asyncio.get_running_loop() + self._resume = asyncio.Event() + + def plan_ready(self) -> None: + self._plan_ready = True + + async def wait_for_review(self) -> list[str]: + assert self._resume is not None, "prepare_review() first" + if self.cancelled(): + raise Cancelled + await self._resume.wait() + self.check() + return self.approved_urls or [] + + def awaiting_review(self) -> bool: + return self._plan_ready and self._resume is not None and not self._resume.is_set() + + def approve(self, urls: list[str]) -> None: + self.approved_urls = urls + self._wake() + + def _wake(self) -> None: + if self._loop is not None and self._resume is not None: + self._loop.call_soon_threadsafe(self._resume.set) + + # -- cancellation -- + def cancel(self) -> None: + self._cancel.set() + self._wake() + + def cancelled(self) -> bool: + return self._cancel.is_set() + + def check(self) -> None: + if self._cancel.is_set(): + raise Cancelled + + # -- reporting -- + def stage(self, status: str, band: tuple[int, int], detail: str) -> None: + self.check() + update_analysis(self.analysis_id, status=status, progress=band[0], stage_detail=detail) + self.event("stage", detail) + + def progress(self, band: tuple[int, int], fraction: float, detail: str) -> None: + """Map fraction (0-1) of a stage onto its band; writes are throttled.""" + now = time.monotonic() + if fraction < 1 and now - self._last_write < _PROGRESS_WRITE_INTERVAL: + return + self._last_write = now + lo, hi = band + pct = lo + (hi - lo) * min(max(fraction, 0.0), 1.0) + update_analysis(self.analysis_id, progress=int(pct), stage_detail=detail) + + def event(self, kind: str, message: str, url: str | None = None) -> None: + add_event(self.analysis_id, kind, message, url) + + +# --------------------------------------------------------------------------- +# Registry of running analyses (this process only) +# --------------------------------------------------------------------------- + +_runs: dict[str, tuple[asyncio.Task, RunContext]] = {} + + +def start(analysis_id: str, topic: str, review: bool = False) -> None: + """Start a run on the current event loop.""" + ctx = RunContext(analysis_id, review) + task = asyncio.get_running_loop().create_task(run_pipeline(ctx, topic)) + _runs[analysis_id] = (task, ctx) + task.add_done_callback(lambda _t: _runs.pop(analysis_id, None)) + + +def context(analysis_id: str) -> RunContext | None: + entry = _runs.get(analysis_id) + return entry[1] if entry else None + + +def cancel(analysis_id: str) -> bool: + """Ask a running analysis to stop. Returns False if it isn't running here.""" + entry = _runs.get(analysis_id) + if entry is None: + return False + entry[1].cancel() + return True + + +def cancel_all() -> None: + for _task, ctx in list(_runs.values()): + ctx.cancel() + + +# --------------------------------------------------------------------------- +# Pipeline +# --------------------------------------------------------------------------- + +async def run_pipeline(ctx: RunContext, topic: str) -> None: + analysis_id = ctx.analysis_id + try: + if ctx.review: + ctx.prepare_review() + await asyncio.to_thread(_search_for_review, ctx, topic) + urls = await ctx.wait_for_review() + if not urls: + raise RuntimeError("No pages were approved for reading.") + ctx.event("stage", f"Approved {len(urls)} pages") + else: + urls = await asyncio.to_thread(_search, ctx, topic) + crawled = await asyncio.to_thread(_crawl, ctx, urls) + ok_articles = await asyncio.to_thread(_extract_and_store, ctx, crawled, urls) + await asyncio.to_thread(_chunk_and_store, ctx, ok_articles) + await asyncio.to_thread(_embed_and_store, ctx) + await asyncio.to_thread(_project, ctx) + + ok_count = len(ok_articles) + warning = None + if ok_count < LOW_YIELD_THRESHOLD: + warning = ( + f"Only {ok_count} articles were successfully indexed. " + "Results may be sparse — try a broader or more general topic." + ) + ctx.event("warning", warning) + update_analysis( + analysis_id, status="done", progress=100, article_count=ok_count, + warning=warning, stage_detail=f"Indexed {ok_count} articles", + ) + ctx.event("stage", "Done") + except Cancelled: + await asyncio.to_thread(_discard_partial_data, analysis_id) + update_analysis(analysis_id, status="cancelled", stage_detail="Cancelled") + ctx.event("stage", "Cancelled") + except Exception as e: + log.exception("Pipeline failed for analysis %s", analysis_id) + update_analysis(analysis_id, status="error", error_msg=str(e)) + ctx.event("error", str(e)) + + +def _search(ctx: RunContext, topic: str) -> list[str]: + from backend.ingestion.search import collect_urls + + ctx.stage("crawling", SEARCH, "Generating search queries") + + def on_query(done: int, total: int, query: str, new_urls: int | None) -> None: + if new_urls is None: + ctx.event("warning", f"Search failed: {query}") + else: + ctx.event("query", f"{query} ({new_urls} new results)") + ctx.progress(SEARCH, done / total, f"Searched {done} of {total} queries") + ctx.check() + + urls = collect_urls(topic, on_query=on_query) + if not urls: + raise RuntimeError("Search returned no results — check BRAVE_API_KEY or try another topic.") + return urls + + +def _search_for_review(ctx: RunContext, topic: str) -> None: + """Search, then save the plan and pause until the user approves pages.""" + from backend.ingestion.search import collect_results + + ctx.stage("crawling", SEARCH, "Generating search queries") + + def on_query(done: int, total: int, query: str, new: int | None) -> None: + ctx.event("warning" if new is None else "query", + f"Search failed: {query}" if new is None else f"{query} ({new} new results)") + ctx.progress(SEARCH, done / total, f"Searched {done} of {total} queries") + ctx.check() + + queries, results = collect_results(topic, on_query=on_query) + save_plan(ctx.analysis_id, queries, results) + ctx.plan_ready() # before the status flips, so "reviewing" always means "ready to approve" + update_analysis(ctx.analysis_id, status="reviewing", progress=SEARCH[1], + stage_detail=f"Waiting for you to review {len(results)} pages") + ctx.event("stage", f"Found {len(results)} pages. Waiting for your review") + + +def save_plan(analysis_id: str, queries: list[str], results: list) -> dict: + from dataclasses import asdict + + plan = {"queries": queries, "results": [asdict(r) for r in results]} + update_analysis(analysis_id, plan=json.dumps(plan)) + return plan + + +def load_plan(analysis_id: str) -> dict | None: + conn = connect() + try: + row = conn.execute("SELECT plan FROM analyses WHERE id = ?", (analysis_id,)).fetchone() + finally: + conn.close() + return json.loads(row["plan"]) if row and row["plan"] else None + + +def _crawl(ctx: RunContext, urls: list[str]) -> list[dict]: + from backend.ingestion.crawler import crawl_urls + + ctx.stage("crawling", CRAWL, f"Crawling {len(urls)} pages") + done = 0 + lock = threading.Lock() + + # Called from the crawler's own event loop: must not raise. + def on_page(url: str, ok: bool) -> None: + nonlocal done + with lock: + done += 1 + n = done + if not ok: + ctx.event("skip", "Could not load page", url) + ctx.progress(CRAWL, n / len(urls), f"Crawled {n} of {len(urls)} pages") + + crawled = crawl_urls(urls, on_page=on_page, should_stop=ctx.cancelled) + ctx.check() + return crawled + + +def _extract_and_store(ctx: RunContext, crawled: list[dict], urls: list[str]) -> list[dict]: + from backend.ingestion.extractor import SkipArticle, article_id, extract_article + from backend.processing.metadata import extract_publish_date + + analysis_id = ctx.analysis_id + ctx.stage("processing", EXTRACT, f"Extracting text from {len(crawled)} pages") + ok_articles: list[dict] = [] + # Pages worth asking the scholarly indexes about: unreadable or too short + # (often a publisher's bot check), and pages the crawler couldn't open. + crawled_urls = {item["url"] for item in crawled} + unreadable: list[tuple[str, str | None]] = [(u, None) for u in urls if u not in crawled_urls] + conn = connect() + try: + for i, item in enumerate(crawled, start=1): + ctx.check() + url, html = item["url"], item["html"] + try: + article = extract_article(analysis_id, url, html) + except SkipArticle as skip: + conn.execute( + "INSERT OR IGNORE INTO articles (id, analysis_id, url, status, crawl_ts) " + "VALUES (?,?,?,?,?)", + (article_id(analysis_id, url), analysis_id, url, "filtered", + int(time.time())), + ) + # Commit before reporting: events and progress are written on + # their own connections and would wait on this transaction. + conn.commit() + ctx.event("skip", str(skip).capitalize(), url) + if not str(skip).startswith("not English"): + unreadable.append((url, html)) + continue + except Exception: + log.warning("Extraction failed for %s", url, exc_info=True) + ctx.event("skip", "Extraction failed", url) + unreadable.append((url, html)) + continue + + conn.execute( + "INSERT OR IGNORE INTO articles " + "(id, analysis_id, url, domain, title, publish_date, crawl_ts, " + " word_count, language, raw_text, status) " + "VALUES (?,?,?,?,?,?,?,?,?,?,?)", + ( + article["id"], analysis_id, article["url"], article["domain"], + article["title"], extract_publish_date(html, url), article["crawl_ts"], + article["word_count"], article["language"], article["raw_text"], "ok", + ), + ) + conn.commit() + ok_articles.append({ + "id": article["id"], "url": url, "raw_text": article["raw_text"], + "title": article["title"] or article["domain"], + }) + ctx.event("article", article["title"] or article["domain"], url) + ctx.progress(EXTRACT, i / len(crawled), f"Extracted {i} of {len(crawled)} pages") + finally: + conn.close() + if settings.recover_academic: + ok_articles += _recover_academic(ctx, unreadable) + if not ok_articles: + raise RuntimeError("No pages passed the quality filters — try a broader topic.") + return _drop_duplicate_articles(ctx, ok_articles) + + +RECOVERY_WORKERS = 8 + + +def _recover_academic(ctx: RunContext, pages: list[tuple[str, str | None]]) -> list[dict]: + """ + Find open-access full text (or an abstract) for academic pages that came + back blocked or empty. Runs a few lookups at a time; cancellable between them. + """ + from concurrent.futures import ThreadPoolExecutor, as_completed + from urllib.parse import urlparse + + from backend.ingestion import scholarly + from backend.ingestion.extractor import article_id + + candidates = [ + (url, html) for url, html in pages + if scholarly.identify(url, html) or urlparse(url).path.lower().endswith(".pdf") + ] + if not candidates: + return [] + ctx.stage("processing", RECOVER, + f"Looking for open-access copies of {len(candidates)} academic pages") + recovered: list[dict] = [] + client = scholarly.new_client() + try: + with ThreadPoolExecutor(max_workers=RECOVERY_WORKERS) as pool: + futures = { + pool.submit(scholarly.recover, url, html, client): url for url, html in candidates + } + for i, future in enumerate(as_completed(futures), start=1): + if ctx.cancelled(): + for f in futures: + f.cancel() + ctx.check() + url = futures[future] + found = future.result() + ctx.progress(RECOVER, i / len(candidates), + f"Checked {i} of {len(candidates)} academic pages") + if found is None: + continue + article = _store_recovered(ctx.analysis_id, article_id(ctx.analysis_id, url), + url, found) + recovered.append(article) + ctx.event("article", f"{article['title']} ({found.note.lower()})", url) + finally: + client.close() + ctx.check() + if recovered: + ctx.event("stage", f"Recovered {len(recovered)} academic articles from open sources") + return recovered + + +def _store_recovered(analysis_id: str, aid: str, url: str, found) -> dict: + from urllib.parse import urlparse + + domain = urlparse(url).netloc + title = found.title or domain + words = len(found.text.split()) + conn = connect() + try: + # The page may already be recorded as filtered; replace that row. + conn.execute( + "INSERT INTO articles (id, analysis_id, url, domain, title, publish_date, crawl_ts, " + " word_count, language, raw_text, status, source_note) " + "VALUES (?,?,?,?,?,?,?,?,?,?,?,?) " + "ON CONFLICT(id) DO UPDATE SET domain=excluded.domain, title=excluded.title, " + " publish_date=excluded.publish_date, word_count=excluded.word_count, " + " language=excluded.language, raw_text=excluded.raw_text, status=excluded.status, " + " source_note=excluded.source_note", + (aid, analysis_id, url, domain, title, found.publish_date, int(time.time()), + words, "en", found.text, "ok", found.note), + ) + conn.commit() + finally: + conn.close() + return {"id": aid, "url": url, "raw_text": found.text, "title": title} + + +def _drop_duplicate_articles(ctx: RunContext, articles: list[dict]) -> list[dict]: + """Mark near-copies of an earlier article (e.g. syndicated stories) and drop them.""" + from backend.processing.dedup import find_duplicate_articles + + duplicates = find_duplicate_articles(articles) + if not duplicates: + return articles + by_id = {a["id"]: a for a in articles} + conn = connect() + try: + conn.executemany( + "UPDATE articles SET status = 'duplicate' WHERE id = ?", [(d,) for d in duplicates] + ) + conn.commit() + finally: + conn.close() + for dup, original in duplicates.items(): + ctx.event("skip", f"Near-copy of “{by_id[original]['title']}”", by_id[dup]["url"]) + return [a for a in articles if a["id"] not in duplicates] + + +def _chunk_and_store(ctx: RunContext, articles: list[dict]) -> None: + from backend.processing.dedup import chunk_and_dedup + + ctx.stage("processing", CHUNK, f"Chunking and deduplicating {len(articles)} articles") + chunks = [{**c, "analysis_id": ctx.analysis_id} for c in chunk_and_dedup(articles)] + dupes = sum(c["is_duplicate"] for c in chunks) + if dupes: + ctx.event("stage", f"Removed {dupes} near-duplicate chunks") + conn = connect() + try: + conn.executemany( + "INSERT OR IGNORE INTO chunks " + "(id, analysis_id, article_id, parent_id, parent_text, child_text, " + " chunk_index, token_count, is_duplicate) " + "VALUES (:id, :analysis_id, :article_id, :parent_id, :parent_text, :child_text, " + " :chunk_index, :token_count, :is_duplicate)", + chunks, + ) + conn.commit() + finally: + conn.close() + + +def _embed_and_store(ctx: RunContext) -> None: + from backend.embedding.embed import embed_chunks + from backend.embedding.store import write_embeddings + + conn = connect() + try: + rows = conn.execute( + "SELECT c.id, c.child_text, c.article_id, a.domain, a.publish_date " + "FROM chunks c JOIN articles a ON a.id = c.article_id " + "WHERE c.analysis_id = ? AND c.is_duplicate = 0", + (ctx.analysis_id,), + ).fetchall() + finally: + conn.close() + + ctx.stage("embedding", EMBED, f"Embedding {len(rows)} chunks") + if not rows: + return + chunks = [ + { + "id": r["id"], + "child_text": r["child_text"], + "article_id": r["article_id"], + "domain": r["domain"] or "", + "publish_date": r["publish_date"] or "", + } + for r in rows + ] + + def on_batch(done: int, total: int) -> None: + ctx.progress(EMBED, done / total, f"Embedded {done} of {total} chunks") + ctx.check() + + embeddings = embed_chunks(chunks, kind="document", on_batch=on_batch) + meta_by_id = {c["id"]: c for c in chunks} + write_embeddings( + ctx.analysis_id, + [{**meta_by_id[e["chunk_id"]], "chunk_id": e["chunk_id"], "vector": e["vector"]} + for e in embeddings], + ) + + +def _project(ctx: RunContext) -> None: + from backend.embedding.project import run_projection + + ctx.stage("projecting", PROJECT, "Mapping and clustering") + + def on_step(fraction: float, detail: str) -> None: + ctx.check() + ctx.progress(PROJECT, fraction, detail) + + def on_label(label: str, n_articles: int) -> None: + ctx.event("cluster", f"{label} ({n_articles} articles)") + + run_projection(ctx.analysis_id, on_step=on_step, on_label=on_label) + + +def _discard_partial_data(analysis_id: str) -> None: + from backend.embedding.store import drop_embeddings + + conn = connect() + try: + for table in ("placements", "clusters", "chunks", "articles"): + conn.execute(f"DELETE FROM {table} WHERE analysis_id = ?", (analysis_id,)) + conn.commit() + finally: + conn.close() + drop_embeddings(analysis_id) diff --git a/backend/processing/chunker.py b/backend/processing/chunker.py index 87d8104..2cd65cf 100644 --- a/backend/processing/chunker.py +++ b/backend/processing/chunker.py @@ -9,7 +9,7 @@ from __future__ import annotations import re -from typing import Iterator +from collections.abc import Iterator import tiktoken diff --git a/backend/processing/dedup.py b/backend/processing/dedup.py index bd2829c..8fc674b 100644 --- a/backend/processing/dedup.py +++ b/backend/processing/dedup.py @@ -11,7 +11,6 @@ from datasketch import MinHash, MinHashLSH - NUM_PERM = 128 THRESHOLD = 0.85 @@ -57,3 +56,59 @@ def mark_duplicates(chunks: list[dict]) -> list[dict]: lsh.insert(cid, m) return chunks + + +# Word 3-shingle Jaccard. Measured across real maps (~7,000 article pairs): +# republished copies scored 0.52-1.0 (an edited WLRN reprint of a Conversation +# piece: 0.77; two arXiv versions of one paper: 0.52); distinct pages that +# merely share a site template stayed below 0.4. +ARTICLE_THRESHOLD = 0.5 + + +def _word_shingles(text: str, k: int = 3) -> set[bytes]: + words = re.sub(r"\s+", " ", text.lower()).split(" ") + return {" ".join(words[i : i + k]).encode("utf-8") for i in range(max(1, len(words) - k + 1))} + + +def find_duplicate_articles( + articles: list[dict], threshold: float = ARTICLE_THRESHOLD +) -> dict[str, str]: + """ + Whole-article near-duplicates (dicts with 'id' and 'raw_text'), e.g. one + wire story syndicated across sites with different intros or footers — + chunk dedup misses these because shifted text misaligns every chunk. + Returns {duplicate_id: id_of_the_earlier_article_it_copies}. + """ + lsh = MinHashLSH(threshold=threshold, num_perm=NUM_PERM) + kept: dict[str, MinHash] = {} + duplicates: dict[str, str] = {} + for art in articles: + text = art.get("raw_text") or "" + if len(text.split()) < 20: + continue + m = MinHash(num_perm=NUM_PERM) + for shingle in _word_shingles(text): + m.update(shingle) + # LSH buckets are approximate; confirm candidates by their estimated Jaccard. + matches = [c for c in lsh.query(m) if m.jaccard(kept[c]) >= threshold] + if matches: + duplicates[art["id"]] = max(matches, key=lambda c: m.jaccard(kept[c])) + else: + lsh.insert(art["id"], m) + kept[art["id"]] = m + return duplicates + + +def chunk_and_dedup(articles: list[dict]) -> list[dict]: + """ + Chunk every article (dicts with 'id' and 'raw_text'), then dedup across the + whole corpus in one pass so syndicated copies of the same story on different + sites are caught. Earlier articles keep their chunks; later copies are marked. + """ + from backend.processing.chunker import chunk_article + + chunks: list[dict] = [] + for art in articles: + if art.get("raw_text"): + chunks.extend(chunk_article(art["id"], art["raw_text"])) + return mark_duplicates(chunks) diff --git a/backend/processing/metadata.py b/backend/processing/metadata.py index aad39a2..93b8d4e 100644 --- a/backend/processing/metadata.py +++ b/backend/processing/metadata.py @@ -2,7 +2,6 @@ import trafilatura - # URL date slug patterns, most specific first _URL_DATE_PATTERNS = [ r"/(\d{4})/(\d{1,2})/(\d{1,2})", # /2024/03/15/ diff --git a/backend/synthesis/chat.py b/backend/synthesis/chat.py new file mode 100644 index 0000000..09d3014 --- /dev/null +++ b/backend/synthesis/chat.py @@ -0,0 +1,287 @@ +""" +Ask the corpus: retrieval-augmented chat with numbered citations. + +1. Retrieve: hybrid search over the analysis's chunks — vector similarity plus + BM25 full-text, fused by reciprocal rank — keeping each article's best + chunk. For follow-ups, the vector query includes the previous question. +2. Number sources across the whole conversation: the excerpts the previous + answer cited come along again under the same numbers, and newly found + articles continue the count, so "[6]" means one article throughout. +3. Prompt: recent conversation turns, then the numbered excerpts and the + question. The model cites excerpts as [n]. +3. Stream the answer; the caller saves the finished turn. +""" + +from __future__ import annotations + +import json +import re +import time +from collections.abc import Iterator +from dataclasses import asdict, dataclass + +from backend.config import settings +from backend.db import connect +from backend.embedding.embed import embed_chunks +from backend.embedding.store import open_table + +MAX_SOURCES = 8 # articles per answer +EXCERPT_CHARS = 3000 # parent chunks are ~1500 tokens; cap keeps prompts bounded +HISTORY_TURNS = 4 # previous question/answer pairs sent with a new question + +SYSTEM_PROMPT = """You help a researcher understand a collection of articles on one subject. + +Answer only from the numbered excerpts provided with each question. After each claim, cite the excerpts it rests on with bracketed numbers, like [2] or [1][4]. Don't write URLs or titles in place of numbers. Numbers are stable for the whole conversation: an excerpt you cited before is shown again under the same number, and new excerpts get new numbers. + +If the excerpts don't cover the question, say so plainly and say what they do cover instead. Don't speculate beyond them. + +Be analytical: identify camps, tensions, evidence and gaps rather than summarising article by article. Use short headings and lists only when an answer is long enough to need them.""" + + +class NoCorpusError(LookupError): + """The analysis has no indexed vectors to search.""" + + +class EmbeddingMismatchError(NoCorpusError): + """The map's vectors came from a different embedding model than the current one.""" + + +@dataclass +class Source: + n: int # the number answers cite; stable for the whole conversation + chunk_id: str # which excerpt (lets a follow-up show the model the same text) + article_id: str + url: str + title: str + domain: str + publish_date: str | None + excerpt: str + + +def order_and_dedupe_by_article(rows: list[dict], ranked_chunk_ids: list[str]) -> list[dict]: + """ + Put SQLite rows (which come back in arbitrary order) into search rank + order, then keep only the highest-ranked chunk per article. + """ + rank = {cid: i for i, cid in enumerate(ranked_chunk_ids)} + ordered = sorted(rows, key=lambda r: rank.get(r["chunk_id"], len(rank))) + seen_articles: set[str] = set() + deduped = [] + for row in ordered: + if row["article_id"] not in seen_articles: + seen_articles.add(row["article_id"]) + deduped.append(row) + return deduped + + +def _ensure_fts_index(table) -> None: + """Full-text index on chunk text; built on first use (well under a second).""" + from lancedb.index import FTS + + if not any(i.index_type == "FTS" for i in table.list_indices()): + table.create_index("child_text", config=FTS(), replace=True) + + +def retrieve( + analysis_id: str, + question: str, + previous_question: str | None = None, + exclude_articles: frozenset[str] = frozenset(), + first_n: int = 1, +) -> list[Source]: + """Best excerpt per article, skipping `exclude_articles`, numbered from `first_n`.""" + from lancedb.rerankers import RRFReranker + + table = open_table(analysis_id) + if table is None: + raise NoCorpusError("This analysis has no indexed corpus yet.") + _ensure_fts_index(table) + + # Follow-ups ("what about the second one?") need the earlier question's context. + semantic_query = f"{previous_question}\n{question}" if previous_question else question + vector = embed_chunks([{"id": "q", "child_text": semantic_query}], kind="query")[0]["vector"] + stored = table.schema.field("vector").type.list_size + if len(vector) != stored: + raise EmbeddingMismatchError( + f"This map was embedded with a different model ({stored}-number vectors, " + f"the current EMBEDDING_MODE={settings.embedding_mode} makes {len(vector)}). " + + ("Set EMBEDDING_MODE=openai to ask about it." if stored == 1536 else + "Set EMBEDDING_MODE=fastembed or ollama to ask about it.") + ) + hits = ( + table.search(query_type="hybrid") + .vector(vector) + .text(question) + .rerank(RRFReranker()) + .limit(settings.rag_top_k) + .to_pandas() + ) + if hits.empty: + raise NoCorpusError("This analysis has no indexed corpus yet.") + chunk_ids = hits["chunk_id"].tolist() + rows = [r for r in _chunk_rows(chunk_ids) if r["article_id"] not in exclude_articles] + best = order_and_dedupe_by_article(rows, chunk_ids)[:MAX_SOURCES] + return [_to_source(n, r) for n, r in enumerate(best, start=first_n)] + + +def _chunk_rows(chunk_ids: list[str]) -> list[dict]: + if not chunk_ids: + return [] + conn = connect() + try: + rows = conn.execute( + f""" + SELECT c.id AS chunk_id, c.parent_text, c.article_id, + a.url, a.title, a.domain, a.publish_date + FROM chunks c JOIN articles a ON a.id = c.article_id + WHERE c.id IN ({",".join("?" for _ in chunk_ids)}) + """, + chunk_ids, + ).fetchall() + finally: + conn.close() + return [dict(r) for r in rows] + + +def _to_source(n: int, r: dict) -> Source: + return Source( + n=n, + chunk_id=r["chunk_id"], + article_id=r["article_id"], + url=r["url"], + title=r["title"] or r["domain"] or "Untitled", + domain=r["domain"] or "", + publish_date=r["publish_date"], + excerpt=(r["parent_text"] or "")[:EXCERPT_CHARS], + ) + + +def carried_sources(history: list[dict]) -> list[Source]: + """ + The excerpts the previous answer cited, under their original numbers, so + a follow-up can build on (and check) them. Older stored answers without + chunk ids can't be carried. + """ + last = next((m for m in reversed(history) if m["role"] == "assistant"), None) + if not last or not last.get("sources"): + return [] + keep = [s for s in last["sources"] if s.get("cited", True) and s.get("chunk_id")] + rows = {r["chunk_id"]: r for r in _chunk_rows([s["chunk_id"] for s in keep])} + return [_to_source(s["n"], rows[s["chunk_id"]]) for s in keep if s["chunk_id"] in rows] + + +def next_source_number(history: list[dict]) -> int: + used = [s["n"] for m in history for s in (m.get("sources") or [])] + return max(used, default=0) + 1 + + +def build_messages(history: list[dict], question: str, sources: list[Source]) -> list[dict]: + """ + Previous turns as plain text (their excerpts aren't resent — the answers + already summarise them), then this turn's numbered excerpts and question. + """ + messages = [] + for turn in history[-2 * HISTORY_TURNS :]: + messages.append({"role": turn["role"], "content": turn["text"]}) + excerpts = "\n\n".join( + f"[{s.n}] {s.title} ({s.domain}{', ' + s.publish_date if s.publish_date else ''})\n{s.excerpt}" + for s in sources + ) + messages.append({ + "role": "user", + "content": f"Excerpts:\n\n{excerpts}\n\n---\n\nQuestion: {question}", + }) + return messages + + +_CITATION = re.compile(r"\[(\d+(?:\s*[,–-]\s*\d+)*)\]") + + +def cited_numbers(text: str, available: set[int]) -> list[int]: + """Source numbers the answer cites, in first-cited order: [2], [1][3], [1, 4], [2-4].""" + seen: list[int] = [] + for group in _CITATION.findall(text): + for part in re.split(r"\s*,\s*", group): + bounds = [int(x) for x in re.split(r"\s*[–-]\s*", part)] + for n in range(bounds[0], bounds[-1] + 1): + if n in available and n not in seen: + seen.append(n) + return seen + + +def answer_stream( + analysis_id: str, question: str, history: list[dict] +) -> tuple[list[Source], Iterator[str]]: + """Retrieve now (so errors surface before streaming starts); stream the answer lazily.""" + from backend.claude_client import stream_text + + previous = next((t["text"] for t in reversed(history) if t["role"] == "user"), None) + carried = carried_sources(history) + fresh = retrieve( + analysis_id, + question, + previous, + exclude_articles=frozenset(s.article_id for s in carried), + first_n=next_source_number(history), + ) + sources = carried + fresh + text = stream_text( + build_messages(history, question, sources), + system=SYSTEM_PROMPT, + max_tokens=8000, + effort=settings.chat_effort, + ) + return sources, text + + +# ── persistence ────────────────────────────────────────────────────────────── + +def load_history(analysis_id: str) -> list[dict]: + conn = connect() + try: + rows = conn.execute( + "SELECT id, role, text, sources, created_ts FROM chat_messages " + "WHERE analysis_id = ? ORDER BY id", + (analysis_id,), + ).fetchall() + finally: + conn.close() + return [ + {**dict(r), "sources": json.loads(r["sources"]) if r["sources"] else None} for r in rows + ] + + +def save_turn(analysis_id: str, question: str, answer: str, sources: list[Source]) -> dict: + """Store a finished question/answer pair; returns the stored answer message.""" + now = int(time.time()) + used = set(cited_numbers(answer, {s.n for s in sources})) + stored = [asdict(s) | {"cited": s.n in used} for s in sources] + for s in stored: + del s["excerpt"] # the reader fetches the full article; keep history small + conn = connect() + try: + conn.execute( + "INSERT INTO chat_messages (analysis_id, role, text, sources, created_ts) " + "VALUES (?, 'user', ?, NULL, ?)", + (analysis_id, question, now), + ) + cur = conn.execute( + "INSERT INTO chat_messages (analysis_id, role, text, sources, created_ts) " + "VALUES (?, 'assistant', ?, ?, ?)", + (analysis_id, answer, json.dumps(stored), now), + ) + conn.commit() + message_id = cur.lastrowid + finally: + conn.close() + return {"id": message_id, "role": "assistant", "text": answer, "sources": stored, + "created_ts": now} + + +def clear_history(analysis_id: str) -> None: + conn = connect() + try: + conn.execute("DELETE FROM chat_messages WHERE analysis_id = ?", (analysis_id,)) + conn.commit() + finally: + conn.close() diff --git a/backend/synthesis/claims.py b/backend/synthesis/claims.py new file mode 100644 index 0000000..fb5f7d7 --- /dev/null +++ b/backend/synthesis/claims.py @@ -0,0 +1,323 @@ +""" +Claim-level consensus: where the sources agree and where they disagree. + +Runs on demand (it costs Claude requests, so maps don't do it automatically): + 1. extract — Claude reads each article's opening, BATCH articles per request, + and lists up to MAX_CLAIMS claims the article argues or reports. + 2. group — one request gathers claims from different articles into shared + propositions and marks each claim as agreeing with, qualifying or + disputing its proposition. Only propositions at least two articles take a + position on are kept. + +Progress and the outcome live on the analysis row (claims_status, +claims_detail); a server restart marks a running job as interrupted. +""" + +from __future__ import annotations + +import json +import logging +import threading +from concurrent.futures import ThreadPoolExecutor + +from backend.claude_client import complete +from backend.db import connect, update_analysis + +log = logging.getLogger(__name__) + +BATCH = 10 +WORKERS = 4 +MAX_CLAIMS = 4 +OPENING_WORDS = 500 +MAX_PROPOSITIONS = 15 +STANCES = ("agrees", "qualifies", "disputes") + +EXTRACT_SCHEMA = { + "type": "object", + "properties": { + "articles": { + "type": "array", + "items": { + "type": "object", + "properties": { + "article": {"type": "string"}, + "claims": {"type": "array", "items": {"type": "string"}}, + }, + "required": ["article", "claims"], + "additionalProperties": False, + }, + } + }, + "required": ["articles"], + "additionalProperties": False, +} + +GROUP_SCHEMA = { + "type": "object", + "properties": { + "propositions": { + "type": "array", + "items": { + "type": "object", + "properties": { + "statement": {"type": "string"}, + "claims": { + "type": "array", + "items": { + "type": "object", + "properties": { + # A number, not a label: given "C17" the model + # tends to echo the claim's text back instead. + "claim": {"type": "integer"}, + "stance": {"type": "string", "enum": list(STANCES)}, + }, + "required": ["claim", "stance"], + "additionalProperties": False, + }, + }, + }, + "required": ["statement", "claims"], + "additionalProperties": False, + }, + } + }, + "required": ["propositions"], + "additionalProperties": False, +} + +_running: set[str] = set() +_lock = threading.Lock() + + +class AlreadyRunning(RuntimeError): + pass + + +def start(analysis_id: str) -> None: + """Find claims in a background thread. Raises AlreadyRunning if it's underway.""" + with _lock: + if analysis_id in _running: + raise AlreadyRunning + _running.add(analysis_id) + update_analysis(analysis_id, claims_status="running", claims_detail="Starting") + _launch(analysis_id) + + +def _launch(analysis_id: str) -> None: + threading.Thread(target=_run, args=(analysis_id,), daemon=True).start() + + +def is_running(analysis_id: str) -> bool: + return analysis_id in _running + + +def _run(analysis_id: str) -> None: + try: + articles = _load_articles(analysis_id) + if len(articles) < 2: + raise RuntimeError("This map needs at least two readable articles to compare.") + + def on_batch(done: int) -> None: + update_analysis(analysis_id, claims_detail=f"Read {done} of {len(articles)} articles") + + claims = extract_claims(articles, on_batch) + update_analysis(analysis_id, claims_detail=f"Comparing {len(claims)} claims") + propositions = group_claims(claims) + _save(analysis_id, claims, propositions) + update_analysis(analysis_id, claims_status="done", + claims_detail=f"{len(propositions)} shared claims from {len(claims)}") + except Exception as e: + log.exception("Finding claims failed for %s", analysis_id) + update_analysis(analysis_id, claims_status="error", claims_detail=str(e) or type(e).__name__) + finally: + with _lock: + _running.discard(analysis_id) + + +def _load_articles(analysis_id: str) -> list[dict]: + """Articles on the map (placed, so duplicates and filtered pages are left out).""" + conn = connect() + try: + rows = conn.execute( + "SELECT a.id, a.title, a.domain, a.raw_text FROM articles a " + "JOIN placements p ON p.article_id = a.id " + "WHERE a.analysis_id = ? AND a.raw_text IS NOT NULL ORDER BY a.id", + (analysis_id,), + ).fetchall() + finally: + conn.close() + return [dict(r) for r in rows] + + +def opening(text: str, words: int = OPENING_WORDS) -> str: + parts = text.split() + return " ".join(parts[:words]) + (" …" if len(parts) > words else "") + + +# ── 1. extract ─────────────────────────────────────────────────────────────── + +EXTRACT_PROMPT = """Below are the openings of {n} articles about the same subject. + +For each article, list up to {max_claims} claims it argues for or reports as findings: \ +the points a reader should come away with. Write each claim as one plain, self-contained \ +sentence (under 30 words) that names its subject, so it can be compared with claims from \ +other articles. Leave out background facts nobody would dispute, and don't invent claims \ +the text doesn't make. An article with nothing substantive gets an empty list. + +Return every article by its label. + +{articles}""" + + +def extract_claims(articles: list[dict], on_batch=None) -> list[dict]: + """[{id, article_id, position, text}] for every article, BATCH per request.""" + batches = [articles[i : i + BATCH] for i in range(0, len(articles), BATCH)] + claims: list[dict] = [] + done = 0 + with ThreadPoolExecutor(WORKERS) as pool: + for batch, found in zip(batches, pool.map(_extract_batch, batches), strict=True): + for article in batch: + for i, text in enumerate(found.get(article["id"], [])[:MAX_CLAIMS]): + claims.append({"id": f"{article['id']}:{i}", "article_id": article["id"], + "position": i, "text": text}) + done += len(batch) + if on_batch: + on_batch(done) + return claims + + +def _extract_batch(batch: list[dict]) -> dict[str, list[str]]: + labels = {f"A{i + 1}": a for i, a in enumerate(batch)} + blocks = "\n\n".join( + f'
\nTitle: {a["title"] or "(untitled)"} ({a["domain"]})\n' + f"{opening(a['raw_text'])}\n
" + for label, a in labels.items() + ) + prompt = EXTRACT_PROMPT.format(n=len(batch), max_claims=MAX_CLAIMS, articles=blocks) + data = json.loads(complete(prompt, max_tokens=8000, effort="low", json_schema=EXTRACT_SCHEMA)) + out: dict[str, list[str]] = {} + for item in data["articles"]: + article = labels.get(item["article"].strip()) + if article: + out[article["id"]] = [c.strip() for c in item["claims"] if c.strip()] + return out + + +# ── 2. group ───────────────────────────────────────────────────────────────── + +GROUP_PROMPT = """These claims come from different articles on one subject. Each line is \ +a claim number, the article it came from, and the claim. + +Find the questions the articles actually take sides on, or the points several of them \ +independently make. For each, write a proposition: one neutral, specific sentence \ +that a claim can agree with, qualify (agree in part, or with conditions) or dispute. \ +Then list the claims that take a position on it, by number, with their stance. + +Rules: +- A proposition needs claims from at least two different articles. +- Use each claim at most once, and only where it directly addresses the proposition. +- Prefer propositions where articles disagree, then those many articles share. +- At most {max_props} propositions; most important first. + +{claims}""" + + +def group_claims(claims: list[dict]) -> list[dict]: + """[{statement, claims: [{claim_id, stance}]}] — validated against the input.""" + if not claims: + return [] + labels = {i + 1: c for i, c in enumerate(claims)} + article_labels: dict[str, str] = {} + for c in claims: + article_labels.setdefault(c["article_id"], f"A{len(article_labels) + 1}") + lines = "\n".join( + f"{n}. [{article_labels[c['article_id']]}] {c['text']}" for n, c in labels.items() + ) + prompt = GROUP_PROMPT.format(max_props=MAX_PROPOSITIONS, claims=lines) + data = json.loads(complete(prompt, max_tokens=16000, effort="medium", json_schema=GROUP_SCHEMA)) + return validate_groups(data["propositions"], labels) + + +def validate_groups(raw: list[dict], labels: dict[int, dict]) -> list[dict]: + """Drop unknown or reused claims and propositions fewer than two articles address.""" + used: set[str] = set() + out = [] + for p in raw: + members = [] + for m in p["claims"]: + claim = labels.get(m["claim"]) + if claim is None or claim["id"] in used or m["stance"] not in STANCES: + continue + members.append({"claim_id": claim["id"], "article_id": claim["article_id"], + "stance": m["stance"]}) + # One stance per article: keep each article's first claim. + seen_articles: set[str] = set() + members = [m for m in members + if not (m["article_id"] in seen_articles or seen_articles.add(m["article_id"]))] + statement = p["statement"].strip() + if len(members) >= 2 and statement: + used.update(m["claim_id"] for m in members) + out.append({"statement": statement, "claims": members}) + return out[:MAX_PROPOSITIONS] + + +# ── storage ────────────────────────────────────────────────────────────────── + +def _save(analysis_id: str, claims: list[dict], propositions: list[dict]) -> None: + """Replace the analysis's claims and propositions in one transaction.""" + conn = connect() + try: + with conn: + conn.execute("DELETE FROM propositions WHERE analysis_id = ?", (analysis_id,)) + conn.execute("DELETE FROM claims WHERE analysis_id = ?", (analysis_id,)) + conn.executemany( + "INSERT INTO claims (id, analysis_id, article_id, position, text) " + "VALUES (?,?,?,?,?)", + [(c["id"], analysis_id, c["article_id"], c["position"], c["text"]) + for c in claims], + ) + for position, p in enumerate(propositions): + cur = conn.execute( + "INSERT INTO propositions (analysis_id, position, statement) VALUES (?,?,?)", + (analysis_id, position, p["statement"]), + ) + conn.executemany( + "INSERT INTO proposition_claims (proposition_id, claim_id, stance) " + "VALUES (?,?,?)", + [(cur.lastrowid, m["claim_id"], m["stance"]) for m in p["claims"]], + ) + finally: + conn.close() + + +def load(analysis_id: str) -> list[dict]: + """Saved propositions in order, each with its claims (and their articles).""" + conn = connect() + try: + rows = conn.execute( + "SELECT p.id, p.statement, pc.stance, c.id AS claim_id, c.article_id, c.text " + "FROM propositions p " + "JOIN proposition_claims pc ON pc.proposition_id = p.id " + "JOIN claims c ON c.id = pc.claim_id " + "WHERE p.analysis_id = ? ORDER BY p.position, pc.rowid", + (analysis_id,), + ).fetchall() + finally: + conn.close() + by_id: dict[int, dict] = {} + for r in rows: + p = by_id.setdefault(r["id"], {"id": r["id"], "statement": r["statement"], "claims": []}) + p["claims"].append({"claim_id": r["claim_id"], "article_id": r["article_id"], + "text": r["text"], "stance": r["stance"]}) + return list(by_id.values()) + + +def claim_count(analysis_id: str) -> int: + conn = connect() + try: + return conn.execute( + "SELECT COUNT(*) FROM claims WHERE analysis_id = ?", (analysis_id,) + ).fetchone()[0] + finally: + conn.close() + diff --git a/backend/synthesis/prompts.py b/backend/synthesis/prompts.py deleted file mode 100644 index 2ea6bf6..0000000 --- a/backend/synthesis/prompts.py +++ /dev/null @@ -1,27 +0,0 @@ -SYSTEM_PROMPT = """You are an analytical research assistant helping a researcher understand a curated corpus of articles on a specific topic. - -Your rules: -- Answer ONLY using information present in the provided article excerpts. -- When making a claim, cite the source article by including its URL in parentheses, e.g. (https://example.com/article). -- If the answer to the question cannot be found in the provided corpus, say exactly: "This topic does not appear to be covered in the indexed corpus." -- Be concise and analytical — identify patterns, tensions, and gaps rather than just summarizing. -- Do not speculate beyond what the corpus says. -- Structure longer answers with brief headers when appropriate.""" - -RAG_USER_TEMPLATE = """Here are the most relevant excerpts from the indexed corpus: - -{context} - ---- - -Question: {query}""" - -CONTEXT_ITEM_TEMPLATE = """[{index}] {title} — {domain} ({publish_date}) -URL: {url} -Excerpt: {text}""" - -PRESET_QUERIES = [ - "What are the main camps or schools of thought in this discourse?", - "What topics or angles appear underexplored in the current coverage?", - "How has the conversation shifted or evolved over time?", -] diff --git a/backend/synthesis/rag.py b/backend/synthesis/rag.py deleted file mode 100644 index 022c3b1..0000000 --- a/backend/synthesis/rag.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -RAG retrieval pipeline. - -Steps: -1. Embed the user query using the same embedding mode as the corpus. -2. Search LanceDB for top-k most similar child chunks. -3. Fetch parent_text + article metadata from SQLite. -4. Deduplicate by article (max one excerpt per article). -5. Assemble context string and call Claude. -6. Return {"answer": str, "citations": [{"url", "title", "domain"}]}. -""" - -from __future__ import annotations - -import os -import sqlite3 - -DB_PATH = os.getenv("DB_PATH", "./data/discourse.db") -LANCE_PATH = os.getenv("LANCE_PATH", "./data/vectors") -TABLE_NAME = "embeddings" - - -def query_corpus(query: str, db_path: str | None = None, top_k: int | None = None) -> dict: - _db_path = db_path or DB_PATH - _top_k = top_k or int(os.getenv("RAG_TOP_K", "15")) - - # --- 1. Embed the query --- - try: - from backend.embedding.embed import embed_chunks - embedded = embed_chunks([{"id": "query", "child_text": query}]) - query_vector = embedded[0]["vector"] - except Exception as e: - return {"answer": f"Embedding error: {e}", "citations": []} - - # --- 2. Search LanceDB --- - try: - import lancedb - db = lancedb.connect(LANCE_PATH) - if TABLE_NAME not in db.table_names(): - return {"answer": "No corpus indexed yet. Please run an analysis first.", "citations": []} - table = db.open_table(TABLE_NAME) - results_df = table.search(query_vector).limit(_top_k).to_pandas() - except Exception as e: - return {"answer": f"Vector search error: {e}", "citations": []} - - if results_df.empty: - return {"answer": "No corpus indexed yet. Please run an analysis first.", "citations": []} - - chunk_ids = results_df["chunk_id"].tolist() - - # --- 3. Fetch parent_text + article metadata from SQLite --- - conn = sqlite3.connect(_db_path) - conn.row_factory = sqlite3.Row - try: - placeholders = ",".join("?" for _ in chunk_ids) - rows = conn.execute( - f""" - SELECT c.id AS chunk_id, c.parent_text, c.article_id, - a.url, a.title, a.domain, a.publish_date - FROM chunks c - JOIN articles a ON a.id = c.article_id - WHERE c.id IN ({placeholders}) - """, - chunk_ids, - ).fetchall() - finally: - conn.close() - - # --- 4. Deduplicate by article (keep first occurrence per article_id) --- - seen_articles: set[str] = set() - deduped = [] - for row in rows: - if row["article_id"] not in seen_articles: - seen_articles.add(row["article_id"]) - deduped.append(dict(row)) - - if not deduped: - return {"answer": "Could not retrieve context from the corpus.", "citations": []} - - # --- 5. Assemble context and call Claude --- - from backend.synthesis.prompts import ( - SYSTEM_PROMPT, RAG_USER_TEMPLATE, CONTEXT_ITEM_TEMPLATE - ) - from backend.claude_client import get_client, get_model - - context_parts = [] - for i, row in enumerate(deduped, start=1): - text = (row.get("parent_text") or "")[:3000] - context_parts.append( - CONTEXT_ITEM_TEMPLATE.format( - index=i, - title=row.get("title") or "Untitled", - domain=row.get("domain") or "", - publish_date=row.get("publish_date") or "unknown date", - url=row.get("url") or "", - text=text, - ) - ) - context_str = "\n\n".join(context_parts) - - user_message = RAG_USER_TEMPLATE.format(context=context_str, query=query) - - response = get_client().messages.create( - model=get_model(), - max_tokens=1024, - system=SYSTEM_PROMPT, - messages=[{"role": "user", "content": user_message}], - ) - answer = response.content[0].text.strip() - - # --- 6. Build citations list --- - citations = [ - {"url": r["url"], "title": r["title"] or "Untitled", "domain": r["domain"] or ""} - for r in deduped - if r.get("url") - ] - - return {"answer": answer, "citations": citations} diff --git a/backend/topics.py b/backend/topics.py new file mode 100644 index 0000000..fee60cf --- /dev/null +++ b/backend/topics.py @@ -0,0 +1,271 @@ +""" +Topic modelling for one analysis: BERTopic's recipe without its PyTorch dependency. + +Articles (not chunks) are the unit: chunks of one article are so similar that +chunk-level clustering groups text by source instead of by argument. + + article vectors (mean of chunk embeddings, L2-normalised) + → UMAP to 5D → HDBSCAN (leaf selection: the finest stable clusters) + → merge topics that members can't tell apart (leaf selection over-splits) + → noise articles join their nearest topic only if they'd pass as a member + → topics merged down to MAX_TOPICS (the number of distinct map colours) + → c-TF-IDF over each topic's text for its key terms + +All functions here are pure (numpy in, numpy/py out) so they're unit-tested +without LanceDB, SQLite or the network. +""" + +from __future__ import annotations + +import re + +import numpy as np +from sklearn.cluster import HDBSCAN, AgglomerativeClustering +from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS, CountVectorizer + +NOISE = -1 +MAX_TOPICS = 8 +RANDOM_STATE = 42 + + +def normalize(vectors: np.ndarray) -> np.ndarray: + norms = np.linalg.norm(vectors, axis=1, keepdims=True) + return vectors / np.where(norms == 0, 1, norms) + + +def article_vectors( + chunk_vectors: np.ndarray, chunk_article_ids: list[str] +) -> tuple[list[str], np.ndarray]: + """Mean of each article's chunk vectors, normalised. Returns (ids, vectors).""" + order: dict[str, list[int]] = {} + for i, aid in enumerate(chunk_article_ids): + order.setdefault(aid, []).append(i) + ids = list(order) + X = np.stack([chunk_vectors[idx].mean(axis=0) for idx in order.values()]) + return ids, normalize(X.astype(np.float32)) + + +def reduce( + X: np.ndarray, dims: int, min_dist: float, neighbors: int = 10, spread: float = 1.0 +) -> np.ndarray: + """UMAP with cosine distance; neighbourhood size adapts to small corpora.""" + from umap import UMAP # slow import (numba); keep it out of module import + + n = len(X) + if n <= dims + 1: + # Too few points for UMAP's spectral init; pad with zeros (degenerate but valid). + return np.pad(X[:, :dims], ((0, 0), (0, max(0, dims - X.shape[1])))) + reducer = UMAP( + n_components=dims, + n_neighbors=max(2, min(neighbors, n - 1)), + min_dist=min_dist, + spread=spread, + metric="cosine", + random_state=RANDOM_STATE, + n_jobs=1, # implied by random_state; explicit to avoid a warning + ) + return reducer.fit_transform(X) + + +def find_topics(Z: np.ndarray) -> np.ndarray: + """ + HDBSCAN with leaf selection on the reduced space; label -1 is noise. + Minimum cluster size grows slowly with corpus size (3 for ~50 articles, + 8 for ~120). If density finds fewer than two topics (tiny or very uniform + corpora), fall back to Ward clustering into three. + """ + n = len(Z) + if n < 6: + return np.zeros(n, dtype=int) + min_size = max(3, round(n / 15)) + labels = HDBSCAN(min_cluster_size=min_size, cluster_selection_method="leaf").fit_predict(Z) + if len(set(labels) - {NOISE}) < 2: + labels = AgglomerativeClustering(n_clusters=min(3, n), linkage="ward").fit_predict(Z) + return relabel(labels) + + +def relabel(labels: np.ndarray) -> np.ndarray: + """Renumber topics 0..k-1 by descending size (noise stays -1).""" + ids = [t for t in set(labels.tolist()) if t != NOISE] + ids.sort(key=lambda t: (-int(np.sum(labels == t)), t)) + mapping = {old: new for new, old in enumerate(ids)} + return np.array([mapping.get(t, NOISE) for t in labels.tolist()], dtype=int) + + +def centroids(X: np.ndarray, labels: np.ndarray) -> dict[int, np.ndarray]: + return { + int(t): normalize(X[labels == t].mean(axis=0, keepdims=True))[0] + for t in sorted(set(labels.tolist()) - {NOISE}) + } + + +def merge_indistinct(X: np.ndarray, labels: np.ndarray, tolerance: float = 0.01) -> np.ndarray: + """ + Merge pairs of topics whose centroids are as similar to each other as the + topics' own members are to their centroids (median, less a small tolerance + for noise in that estimate): such a split carries no information. Leaf + selection otherwise cuts evenly spread groups into arbitrary sub-leaves. + """ + labels = labels.copy() + while True: + cents = centroids(X, labels) + ids = list(cents) + if len(ids) < 2: + return relabel(labels) + cohesion = {t: float(np.median(X[labels == t] @ cents[t])) for t in ids} + best, pair = -np.inf, None + for i, a in enumerate(ids): + for b in ids[i + 1 :]: + margin = float(cents[a] @ cents[b]) - min(cohesion[a], cohesion[b]) + if margin > best: + best, pair = margin, (a, b) + if best < -tolerance: + return relabel(labels) + labels[labels == pair[1]] = pair[0] + + +def assign_noise(X: np.ndarray, labels: np.ndarray) -> np.ndarray: + """ + A noise article joins its most similar topic if it's at least as similar + to that topic's centroid as the topic's least-typical member. Otherwise it + stays unclustered. + """ + labels = labels.copy() + cents = centroids(X, labels) + if not cents: + return labels + floor = {t: float((X[labels == t] @ c).min()) for t, c in cents.items()} + topic_ids = list(cents) + C = np.stack([cents[t] for t in topic_ids]) + for i in np.where(labels == NOISE)[0]: + sims = C @ X[i] + best = int(np.argmax(sims)) + if sims[best] >= floor[topic_ids[best]]: + labels[i] = topic_ids[best] + return labels + + +def cap_topics(X: np.ndarray, labels: np.ndarray, max_topics: int = MAX_TOPICS) -> np.ndarray: + """Merge the two most similar topics (by centroid) until at most max_topics remain.""" + labels = labels.copy() + while len(set(labels.tolist()) - {NOISE}) > max_topics: + cents = centroids(X, labels) + ids = list(cents) + C = np.stack([cents[t] for t in ids]) + sim = C @ C.T + np.fill_diagonal(sim, -np.inf) + a, b = np.unravel_index(int(np.argmax(sim)), sim.shape) + labels[labels == ids[b]] = ids[a] + return relabel(labels) + + +def distances(X: np.ndarray, labels: np.ndarray) -> np.ndarray: + """Cosine distance of each article to its topic centroid (nearest one for noise).""" + cents = centroids(X, labels) + if not cents: + return np.zeros(len(X)) + ids = list(cents) + C = np.stack([cents[t] for t in ids]) + sims = X @ C.T + out = np.empty(len(X)) + for i, t in enumerate(labels.tolist()): + out[i] = 1 - (sims[i, ids.index(t)] if t != NOISE else sims[i].max()) + return out + + +def representatives(X: np.ndarray, labels: np.ndarray, k: int = 3) -> dict[int, list[int]]: + """Indices of the k articles closest to each topic centroid.""" + out = {} + for t, c in centroids(X, labels).items(): + members = np.where(labels == t)[0] + out[t] = members[np.argsort(-(X[members] @ c))][:k].tolist() + return out + + +_URL = re.compile(r"\b(?:https?://|www\.)\S+") + +# Web-page and citation boilerplate that survives text extraction. +BOILERPLATE = frozenset( + """ + abstract accessed al archive archived article articles bibcode cite cited citation + click cookie cookies copyright doi download edit et fig figure figures html http + https isbn issn journal menu new opens original page pages pdf pmc pmid pp press + privacy published publisher read retrieved share site subscribe tab table vol + volume website wikipedia www + acad biol chem curr econ environ int lett med natl nat proc res rev sci soc + january february march april may june july august september october november december + """.split() +) + + +def topic_terms( + texts: list[str], labels: np.ndarray, top_n: int = 10 +) -> dict[int, list[tuple[str, float]]]: + """ + c-TF-IDF (BERTopic): treat each topic's text as one document. A term's + weight is its frequency in the topic times log(1 + A / f), where A is the + average word count per topic and f the term's frequency across all topics, + so words common everywhere sink and words particular to a topic rise. + + `texts` holds one string per article. A term must also appear in at least + two of a topic's articles, so one page's menus, references or author names + can't define a topic. + """ + topics_ = sorted(set(labels.tolist()) - {NOISE}) + if not topics_: + return {} + vectorizer = CountVectorizer( + stop_words=list(ENGLISH_STOP_WORDS | BOILERPLATE), + ngram_range=(1, 2), + token_pattern=r"(?u)\b[a-zA-Z][a-zA-Z-]+[a-zA-Z]\b", # no apostrophes: "it's" isn't a term + lowercase=True, + ) + per_article = vectorizer.fit_transform([_URL.sub(" ", t) for t in texts]).tocsr() + vocab = vectorizer.get_feature_names_out() + + counts = np.vstack([np.asarray(per_article[labels == t].sum(axis=0)) for t in topics_]) + spread = np.vstack([np.asarray((per_article[labels == t] > 0).sum(axis=0)) for t in topics_]) + + tf = counts / np.maximum(counts.sum(axis=1, keepdims=True), 1) + avg_words = counts.sum() / len(topics_) + idf = np.log(1 + avg_words / np.maximum(counts.sum(axis=0), 1)) + scores = tf * idf + if len(topics_) > 1: + # A term used by every topic can't tell them apart. + scores[:, (counts > 0).all(axis=0)] = 0 + for row, t in enumerate(topics_): + min_articles = min(2, int(np.sum(labels == t))) + scores[row, spread[row] < min_articles] = 0 + + out = {} + for row, t in enumerate(topics_): + picked: list[tuple[str, float]] = [] + for j in np.argsort(-scores[row])[: top_n * 3]: + if scores[row, j] <= 0: + break + term = str(vocab[j]) + # Skip a unigram already covered by a stronger bigram, and vice versa. + if any(term in p or p in term for p, _ in picked): + continue + picked.append((term, float(scores[row, j]))) + if len(picked) == top_n: + break + out[t] = picked + return out + + +def model_topics(X: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + The full topic model for normalised article vectors. + Returns (labels, 2D map coordinates, distance to topic centroid). + """ + Z5 = reduce(X, dims=5, min_dist=0.0) + labels = find_topics(Z5) + labels = merge_indistinct(X, labels) + labels = assign_noise(X, labels) + labels = cap_topics(X, labels) + # Map layout: a wider neighbourhood and looser packing than the clustering + # space, so topics get room on screen instead of crowding into a few islands + # (measured on real corpora: ~2.5x larger topic areas, still clearly separated). + coords = reduce(X, dims=2, min_dist=0.8, neighbors=20, spread=1.5) + return labels, coords, distances(X, labels) diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..41f1bdc --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,25 @@ +node_modules + +# Output +.output +.vercel +.netlify +.wrangler +/.svelte-kit +/build + +# OS +.DS_Store +Thumbs.db + +# Env +.env +.env.* +!.env.example +!.env.test + +# Vite +vite.config.js.timestamp-* +vite.config.ts.timestamp-* +# Playwright +test-results diff --git a/frontend/.npmrc b/frontend/.npmrc new file mode 100644 index 0000000..b6f27f1 --- /dev/null +++ b/frontend/.npmrc @@ -0,0 +1 @@ +engine-strict=true diff --git a/frontend/.prettierignore b/frontend/.prettierignore new file mode 100644 index 0000000..4eb1ed2 --- /dev/null +++ b/frontend/.prettierignore @@ -0,0 +1,13 @@ +# Package Managers +package-lock.json +pnpm-lock.yaml +yarn.lock +bun.lock +bun.lockb + +# Miscellaneous +/static/ + +# Generated (npm run gen:api) +/src/lib/api/gen/ +openapi.json diff --git a/frontend/.vscode/extensions.json b/frontend/.vscode/extensions.json new file mode 100644 index 0000000..175f389 --- /dev/null +++ b/frontend/.vscode/extensions.json @@ -0,0 +1,8 @@ +{ + "recommendations": [ + "svelte.svelte-vscode", + "esbenp.prettier-vscode", + "dbaeumer.vscode-eslint", + "bradlc.vscode-tailwindcss" + ] +} diff --git a/frontend/.vscode/settings.json b/frontend/.vscode/settings.json new file mode 100644 index 0000000..bc31e15 --- /dev/null +++ b/frontend/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "files.associations": { + "*.css": "tailwindcss" + } +} diff --git a/frontend/e2e/app.spec.ts b/frontend/e2e/app.spec.ts new file mode 100644 index 0000000..d9944e0 --- /dev/null +++ b/frontend/e2e/app.spec.ts @@ -0,0 +1,133 @@ +import { expect, test, type Page } from '@playwright/test'; + +// The synthetic map from scripts/seed_demo.py +const DEMO = '/analysis/demo-0000-0000-0000-000000000001'; +const TOPIC = 'Demo: urban heat and city design'; + +async function openDemo(page: Page, view = '') { + await page.goto(`${DEMO}${view}`); + await expect(page.getByRole('heading', { level: 1, name: TOPIC })).toBeVisible(); +} + +test('home lists the demo map and keeps the start button disabled until a topic is typed', async ({ + page +}) => { + await page.goto('/'); + await expect(page.getByRole('heading', { name: 'Map a conversation.' })).toBeVisible(); + await expect(page.getByRole('link', { name: TOPIC })).toBeVisible(); + await expect(page.getByText('12 articles')).toBeVisible(); + + const start = page.getByRole('button', { name: 'Map this topic' }); + await expect(start).toBeDisabled(); + await page.getByRole('button', { name: 'urban heat islands and city design' }).click(); + await expect(page.getByLabel('Topic')).toHaveValue('urban heat islands and city design'); + await expect(start).toBeEnabled(); +}); + +test('map shows every article, topic labels and the cluster key', async ({ page }) => { + await openDemo(page); + await expect(page.getByText('12 articles in 4 clusters')).toBeVisible(); + const map = page.getByRole('img', { name: /Map of 12 articles/ }); + await expect(map.locator('circle[stroke="var(--paper)"]')).toHaveCount(12); + for (const label of ['Reflective surfaces', 'Trees and green space', 'Heat and inequality']) { + await expect(map.getByText(label)).toBeVisible(); + } + await expect( + page.getByRole('complementary', { name: 'Clusters' }).getByText('Unclustered') + ).toBeVisible(); +}); + +test('selecting a topic focuses every view and shows its summary', async ({ page }) => { + await openDemo(page); + const key = page.getByRole('complementary', { name: 'Clusters' }); + await key.getByRole('button', { name: /Trees and green space/ }).click(); + await expect(page).toHaveURL(/\?cluster=1/); + await expect(key.getByText('canopy, parks and the upkeep', { exact: false })).toBeVisible(); + + // The selection travels with the view links. + await page.getByRole('link', { name: 'Outliers' }).click(); + await expect(page).toHaveURL(/\/outliers\?cluster=1/); + await expect(page.getByRole('main').getByRole('button')).toHaveCount(4); + await expect(page.getByRole('main').getByText('Watering trees in drought summers')).toBeVisible(); + + await key.getByRole('button', { name: 'Show all' }).click(); + await expect(page).not.toHaveURL(/cluster=/); +}); + +test('table view lists articles and opens the reader, which closes with Escape', async ({ + page +}) => { + await openDemo(page); + await page.getByRole('button', { name: 'Table' }).click(); + await page.getByRole('button', { name: 'Measuring albedo on city blocks' }).click(); + + const reader = page.getByRole('dialog'); + await expect( + reader.getByRole('heading', { name: 'Measuring albedo on city blocks' }) + ).toBeVisible(); + await expect(reader.getByText(/Abstract only/)).toBeVisible(); + await expect(page).toHaveURL(/article=demo-article-02/); + + await page.keyboard.press('Escape'); + await expect(reader).toBeHidden(); + await expect(page).not.toHaveURL(/article=/); +}); + +test('timeline draws a bar for each dated year', async ({ page }) => { + await openDemo(page, '/timeline'); + await expect(page.getByText('12 articles have a publish date')).toBeVisible(); + await expect(page.getByRole('img', { name: /per year/ })).toBeVisible(); +}); + +test('key terms show each topic with its summary and terms', async ({ page }) => { + await openDemo(page, '/terms'); + const main = page.getByRole('main'); + await expect(main.getByRole('heading', { name: /Heat and inequality/ })).toBeVisible(); + await expect(main.getByText('Who is most exposed', { exact: false })).toBeVisible(); + await expect(main.getByText('redlining')).toBeVisible(); +}); + +test('saved conversation renders with clickable citations', async ({ page }) => { + await openDemo(page, '/ask'); + await expect(page.getByText('Do cool roofs or trees cool cities more?')).toBeVisible(); + const cite = page.getByRole('button', { name: /Source 2:/ }); + await expect(cite).toBeVisible(); + await expect(page.getByText('Also searched, not cited (1)')).toBeVisible(); + + await cite.click(); + await expect( + page.getByRole('dialog').getByRole('heading', { name: 'Street trees and shade equity' }) + ).toBeVisible(); +}); + +test('theme toggle switches to dark and remembers it', async ({ page }) => { + await page.goto('/'); + await page.getByRole('button', { name: 'Switch to dark theme' }).click(); + await expect(page.locator('html')).toHaveClass(/dark/); + await page.reload(); + await expect(page.locator('html')).toHaveClass(/dark/); +}); + +test('an unknown map shows a helpful not-found page', async ({ page }) => { + await page.goto('/analysis/does-not-exist'); + await expect(page.getByRole('heading', { name: 'Not found' })).toBeVisible(); + await expect(page.getByRole('link', { name: 'Back to all maps' })).toBeVisible(); +}); + +test('claims show where articles agree and open the source', async ({ page }) => { + await openDemo(page, '/claims'); + const main = page.getByRole('main'); + const debated = main.getByRole('button', { name: /Cool roofs are the most effective/ }); + // Most debated first: the contested point leads. + await expect(main.getByRole('button', { expanded: false }).first()).toHaveAccessibleName( + /Cool roofs are the most effective/ + ); + await expect(debated).toContainText('3 articles: 1 agree, 1 qualify, 1 dispute'); + + await debated.click(); + await expect(main.getByText('Street trees cool pavements and people')).toBeVisible(); + await main.getByRole('button', { name: /Street trees and shade equity/ }).click(); + await expect( + page.getByRole('dialog').getByRole('heading', { name: 'Street trees and shade equity' }) + ).toBeVisible(); +}); diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000..5976349 --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,43 @@ +import prettier from 'eslint-config-prettier'; +import path from 'node:path'; +import js from '@eslint/js'; +import svelte from 'eslint-plugin-svelte'; +import { defineConfig, includeIgnoreFile } from 'eslint/config'; +import globals from 'globals'; +import ts from 'typescript-eslint'; + +const gitignorePath = path.resolve(import.meta.dirname, '.gitignore'); + +export default defineConfig( + includeIgnoreFile(gitignorePath), + // Generated from the backend's OpenAPI schema (npm run gen:api) + { ignores: ['src/lib/api/gen/'] }, + js.configs.recommended, + ts.configs.recommended, + svelte.configs.recommended, + prettier, + svelte.configs.prettier, + { + languageOptions: { globals: { ...globals.browser, ...globals.node } }, + rules: { + // typescript-eslint strongly recommend that you do not use the no-undef lint rule on TypeScript projects. + // see: https://typescript-eslint.io/troubleshooting/faqs/eslint/#i-get-errors-from-the-no-undef-rule-about-global-variables-not-being-defined-even-though-there-are-no-typescript-errors + 'no-undef': 'off' + } + }, + { + files: ['**/*.svelte', '**/*.svelte.ts', '**/*.svelte.js'], + languageOptions: { + parserOptions: { + projectService: true, + extraFileExtensions: ['.svelte'], + parser: ts.parser + } + } + }, + { + // Override or add rule settings here, such as: + // 'svelte/button-has-type': 'error' + rules: {} + } +); diff --git a/frontend/openapi-ts.config.ts b/frontend/openapi-ts.config.ts new file mode 100644 index 0000000..57b8329 --- /dev/null +++ b/frontend/openapi-ts.config.ts @@ -0,0 +1,14 @@ +import { defineConfig } from '@hey-api/openapi-ts'; + +// Regenerate after backend API changes: +// (repo root) uv run python -m scripts.export_openapi +// (frontend) npm run gen:api +export default defineConfig({ + input: './openapi.json', + output: { + path: 'src/lib/api/gen', + // tsconfig.json extends SvelteKit's $app/tsconfig, which the generator can't resolve + tsConfigPath: null + }, + plugins: ['@hey-api/client-fetch', '@hey-api/typescript', '@hey-api/sdk'] +}); diff --git a/frontend/openapi.json b/frontend/openapi.json new file mode 100644 index 0000000..8311fbf --- /dev/null +++ b/frontend/openapi.json @@ -0,0 +1,1604 @@ +{ + "openapi": "3.1.0", + "info": { + "title": "Discourse Lens API", + "version": "0.1.0" + }, + "paths": { + "/api/health": { + "get": { + "summary": "Health", + "operationId": "health", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/api/analyses": { + "get": { + "summary": "List Analyses", + "operationId": "list_analyses", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "items": { + "$ref": "#/components/schemas/Analysis" + }, + "type": "array", + "title": "Response List Analyses" + } + } + } + } + } + }, + "post": { + "summary": "Create Analysis", + "operationId": "create_analysis", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AnalysisCreate" + } + } + }, + "required": true + }, + "responses": { + "201": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Analysis" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}": { + "get": { + "summary": "Get Analysis", + "operationId": "get_analysis", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Analysis" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "summary": "Delete Analysis", + "operationId": "delete_analysis", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "204": { + "description": "Successful Response" + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/cancel": { + "post": { + "summary": "Cancel Analysis", + "description": "Ask a running analysis to stop; it reaches status 'cancelled' shortly after.", + "operationId": "cancel_analysis", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "202": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Analysis" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/plan": { + "get": { + "summary": "Get Plan", + "operationId": "get_plan", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Plan" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/plan/search": { + "post": { + "summary": "Search Plan", + "description": "Run edited queries again while the analysis waits for review.", + "operationId": "search_plan", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/PlanSearch" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Plan" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/plan/approve": { + "post": { + "summary": "Approve Plan", + "description": "Continue the paused analysis with the chosen pages (search results or your own URLs).", + "operationId": "approve_plan", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/PlanApprove" + } + } + } + }, + "responses": { + "202": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Analysis" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/events": { + "get": { + "summary": "Get Events", + "operationId": "get_events", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + }, + { + "name": "after", + "in": "query", + "required": false, + "schema": { + "type": "integer", + "default": 0, + "title": "After" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "array", + "items": { + "$ref": "#/components/schemas/AnalysisEvent" + }, + "title": "Response Get Events" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/stream": { + "get": { + "summary": "Stream Analysis", + "description": "Server-sent events until the analysis stops running:\n default `message` events \u2014 the Analysis row, whenever it changes\n `log` events (with `id:`) \u2014 new rows from the event log; EventSource\n resends Last-Event-ID on reconnect, so the log resumes where it left off.", + "operationId": "stream_analysis", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + }, + { + "name": "last-event-id", + "in": "header", + "required": false, + "schema": { + "type": "integer", + "default": 0, + "title": "Last-Event-Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/articles": { + "get": { + "summary": "List Articles", + "description": "Every article on the map, with its position, topic and outlier score.", + "operationId": "list_articles", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "array", + "items": { + "$ref": "#/components/schemas/MapArticle" + }, + "title": "Response List Articles" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/clusters": { + "get": { + "summary": "Get Clusters", + "description": "Topics, largest first, with their key terms. Unclustered articles have none.", + "operationId": "get_clusters", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "array", + "items": { + "$ref": "#/components/schemas/Cluster" + }, + "title": "Response Get Clusters" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/articles/{article_id}": { + "get": { + "summary": "Get Article", + "operationId": "get_article", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + }, + { + "name": "article_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Article Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Article" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/claims": { + "get": { + "summary": "Get Claims", + "operationId": "get_claims", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Claims" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "post": { + "summary": "Find Claims", + "description": "Find each article's claims and where they agree or disagree (Claude requests).", + "operationId": "find_claims", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "202": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Claims" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/chat": { + "get": { + "summary": "Get Chat", + "description": "The saved conversation for this analysis, oldest first.", + "operationId": "get_chat", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "array", + "items": { + "$ref": "#/components/schemas/ChatMessage" + }, + "title": "Response Get Chat" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "delete": { + "summary": "Clear Chat", + "operationId": "clear_chat", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "204": { + "description": "Successful Response" + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + }, + "post": { + "summary": "Ask", + "description": "Ask a question; the answer streams as server-sent events:\n sources \u2014 the numbered excerpts the answer may cite (ChatSource[])\n delta \u2014 {\"text\": ...}, a piece of the answer as it's written\n done \u2014 the saved answer (ChatMessage), with each source's `cited` set\n error \u2014 {\"detail\": ...}; nothing is saved\nThe question and answer are saved together only when the answer completes.", + "operationId": "ask", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ChatRequest" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "text/event-stream": {} + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/analyses/{analysis_id}/export": { + "get": { + "summary": "Export Obsidian", + "operationId": "export_obsidian", + "parameters": [ + { + "name": "analysis_id", + "in": "path", + "required": true, + "schema": { + "type": "string", + "title": "Analysis Id" + } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "Analysis": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "topic": { + "type": "string", + "title": "Topic" + }, + "status": { + "type": "string", + "enum": [ + "queued", + "crawling", + "reviewing", + "processing", + "embedding", + "projecting", + "done", + "error", + "cancelled" + ], + "title": "Status" + }, + "progress": { + "type": "integer", + "title": "Progress" + }, + "stage_detail": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Stage Detail" + }, + "article_count": { + "type": "integer", + "title": "Article Count" + }, + "error_msg": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Error Msg" + }, + "warning": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Warning" + }, + "created_ts": { + "type": "integer", + "title": "Created Ts" + }, + "updated_ts": { + "type": "integer", + "title": "Updated Ts" + } + }, + "type": "object", + "required": [ + "id", + "topic", + "status", + "progress", + "stage_detail", + "article_count", + "error_msg", + "warning", + "created_ts", + "updated_ts" + ], + "title": "Analysis" + }, + "AnalysisCreate": { + "properties": { + "topic": { + "type": "string", + "maxLength": 500, + "minLength": 1, + "title": "Topic" + }, + "review": { + "type": "boolean", + "title": "Review", + "default": false + } + }, + "type": "object", + "required": [ + "topic" + ], + "title": "AnalysisCreate" + }, + "AnalysisEvent": { + "properties": { + "id": { + "type": "integer", + "title": "Id" + }, + "ts": { + "type": "integer", + "title": "Ts" + }, + "kind": { + "type": "string", + "enum": [ + "stage", + "query", + "article", + "skip", + "cluster", + "warning", + "error" + ], + "title": "Kind" + }, + "message": { + "type": "string", + "title": "Message" + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Url" + } + }, + "type": "object", + "required": [ + "id", + "ts", + "kind", + "message", + "url" + ], + "title": "AnalysisEvent" + }, + "Article": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "analysis_id": { + "type": "string", + "title": "Analysis Id" + }, + "url": { + "type": "string", + "title": "Url" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Domain" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Title" + }, + "publish_date": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Publish Date" + }, + "crawl_ts": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Crawl Ts" + }, + "word_count": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Word Count" + }, + "language": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Language" + }, + "raw_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Raw Text" + }, + "status": { + "type": "string", + "title": "Status" + }, + "source_note": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Source Note" + } + }, + "type": "object", + "required": [ + "id", + "analysis_id", + "url", + "domain", + "title", + "publish_date", + "crawl_ts", + "word_count", + "language", + "raw_text", + "status" + ], + "title": "Article" + }, + "ChatMessage": { + "properties": { + "id": { + "type": "integer", + "title": "Id" + }, + "role": { + "type": "string", + "enum": [ + "user", + "assistant" + ], + "title": "Role" + }, + "text": { + "type": "string", + "title": "Text" + }, + "sources": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/ChatSource" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Sources" + }, + "created_ts": { + "type": "integer", + "title": "Created Ts" + } + }, + "type": "object", + "required": [ + "id", + "role", + "text", + "sources", + "created_ts" + ], + "title": "ChatMessage" + }, + "ChatRequest": { + "properties": { + "question": { + "type": "string", + "maxLength": 2000, + "minLength": 1, + "title": "Question" + } + }, + "type": "object", + "required": [ + "question" + ], + "title": "ChatRequest" + }, + "ChatSource": { + "properties": { + "n": { + "type": "integer", + "title": "N" + }, + "article_id": { + "type": "string", + "title": "Article Id" + }, + "url": { + "type": "string", + "title": "Url" + }, + "title": { + "type": "string", + "title": "Title" + }, + "domain": { + "type": "string", + "title": "Domain" + }, + "publish_date": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Publish Date" + }, + "cited": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "title": "Cited" + } + }, + "type": "object", + "required": [ + "n", + "article_id", + "url", + "title", + "domain", + "publish_date" + ], + "title": "ChatSource", + "description": "An excerpt the answer could cite as [n]." + }, + "Claims": { + "properties": { + "status": { + "anyOf": [ + { + "type": "string", + "enum": [ + "running", + "done", + "error" + ] + }, + { + "type": "null" + } + ], + "title": "Status" + }, + "detail": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Detail" + }, + "claim_count": { + "type": "integer", + "title": "Claim Count" + }, + "propositions": { + "items": { + "$ref": "#/components/schemas/Proposition" + }, + "type": "array", + "title": "Propositions" + } + }, + "type": "object", + "required": [ + "status", + "detail", + "claim_count", + "propositions" + ], + "title": "Claims", + "description": "Where the articles agree and disagree. status is null until claims are first found." + }, + "Cluster": { + "properties": { + "cluster_id": { + "type": "integer", + "title": "Cluster Id" + }, + "label": { + "type": "string", + "title": "Label" + }, + "description": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Description" + }, + "article_count": { + "type": "integer", + "title": "Article Count" + }, + "terms": { + "items": { + "$ref": "#/components/schemas/Term" + }, + "type": "array", + "title": "Terms" + } + }, + "type": "object", + "required": [ + "cluster_id", + "label", + "description", + "article_count", + "terms" + ], + "title": "Cluster" + }, + "HTTPValidationError": { + "properties": { + "detail": { + "items": { + "$ref": "#/components/schemas/ValidationError" + }, + "type": "array", + "title": "Detail" + } + }, + "type": "object", + "title": "HTTPValidationError" + }, + "MapArticle": { + "properties": { + "article_id": { + "type": "string", + "title": "Article Id" + }, + "url": { + "type": "string", + "title": "Url" + }, + "title": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Title" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Domain" + }, + "publish_date": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Publish Date" + }, + "word_count": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "title": "Word Count" + }, + "x": { + "type": "number", + "title": "X" + }, + "y": { + "type": "number", + "title": "Y" + }, + "cluster_id": { + "type": "integer", + "title": "Cluster Id" + }, + "distance": { + "type": "number", + "title": "Distance" + } + }, + "type": "object", + "required": [ + "article_id", + "url", + "title", + "domain", + "publish_date", + "word_count", + "x", + "y", + "cluster_id", + "distance" + ], + "title": "MapArticle", + "description": "An article as placed on the map." + }, + "Plan": { + "properties": { + "queries": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Queries" + }, + "results": { + "items": { + "$ref": "#/components/schemas/PlanResult" + }, + "type": "array", + "title": "Results" + } + }, + "type": "object", + "required": [ + "queries", + "results" + ], + "title": "Plan", + "description": "What a paused analysis found: its queries and the pages they returned." + }, + "PlanApprove": { + "properties": { + "urls": { + "items": { + "type": "string" + }, + "type": "array", + "minItems": 1, + "title": "Urls" + } + }, + "type": "object", + "required": [ + "urls" + ], + "title": "PlanApprove" + }, + "PlanResult": { + "properties": { + "url": { + "type": "string", + "title": "Url" + }, + "title": { + "type": "string", + "title": "Title" + }, + "snippet": { + "type": "string", + "title": "Snippet" + }, + "query": { + "type": "string", + "title": "Query" + } + }, + "type": "object", + "required": [ + "url", + "title", + "snippet", + "query" + ], + "title": "PlanResult" + }, + "PlanSearch": { + "properties": { + "queries": { + "items": { + "type": "string" + }, + "type": "array", + "maxItems": 12, + "minItems": 1, + "title": "Queries" + } + }, + "type": "object", + "required": [ + "queries" + ], + "title": "PlanSearch" + }, + "Proposition": { + "properties": { + "id": { + "type": "integer", + "title": "Id" + }, + "statement": { + "type": "string", + "title": "Statement" + }, + "claims": { + "items": { + "$ref": "#/components/schemas/PropositionClaim" + }, + "type": "array", + "title": "Claims" + } + }, + "type": "object", + "required": [ + "id", + "statement", + "claims" + ], + "title": "Proposition" + }, + "PropositionClaim": { + "properties": { + "claim_id": { + "type": "string", + "title": "Claim Id" + }, + "article_id": { + "type": "string", + "title": "Article Id" + }, + "text": { + "type": "string", + "title": "Text" + }, + "stance": { + "type": "string", + "enum": [ + "agrees", + "qualifies", + "disputes" + ], + "title": "Stance" + } + }, + "type": "object", + "required": [ + "claim_id", + "article_id", + "text", + "stance" + ], + "title": "PropositionClaim" + }, + "Term": { + "properties": { + "term": { + "type": "string", + "title": "Term" + }, + "weight": { + "type": "number", + "title": "Weight" + } + }, + "type": "object", + "required": [ + "term", + "weight" + ], + "title": "Term" + }, + "ValidationError": { + "properties": { + "loc": { + "items": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ] + }, + "type": "array", + "title": "Location" + }, + "msg": { + "type": "string", + "title": "Message" + }, + "type": { + "type": "string", + "title": "Error Type" + }, + "input": { + "title": "Input" + }, + "ctx": { + "type": "object", + "title": "Context" + } + }, + "type": "object", + "required": [ + "loc", + "msg", + "type" + ], + "title": "ValidationError" + } + } + } +} diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 0618da7..aaf49ab 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,3346 +1,5669 @@ { - "name": "discourse-lens-frontend", - "version": "0.0.1", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "discourse-lens-frontend", - "version": "0.0.1", - "dependencies": { - "d3": "^7.9.0", - "marked": "^18.0.2" - }, - "devDependencies": { - "@sveltejs/adapter-auto": "^3.0.0", - "@sveltejs/kit": "^2.57.1", - "@sveltejs/vite-plugin-svelte": "^5.1.1", 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diff --git a/frontend/package.json b/frontend/package.json index ee65354..f20123c 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,25 +1,60 @@ { - "name": "discourse-lens-frontend", - "version": "0.0.1", - "private": true, - "type": "module", - "scripts": { - "dev": "vite dev", - "build": "vite build", - "preview": "vite preview", - "lint": "eslint ." - }, - "devDependencies": { - "@sveltejs/adapter-auto": "^3.0.0", - "@sveltejs/kit": "^2.57.1", - "@sveltejs/vite-plugin-svelte": "^5.1.1", - "eslint": "^9.0.0", - "eslint-plugin-svelte": "^2.36.0", - "svelte": "^5.0.0", - "vite": "^6.0.0" - }, - "dependencies": { - "d3": "^7.9.0", - "marked": "^18.0.2" - } + "name": "frontend", + "private": true, + "version": "0.0.1", + "type": "module", + "scripts": { + "dev": "vite dev", + "build": "vite build", + "preview": "vite preview", + "prepare": "svelte-kit sync || echo ''", + "check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json", + "check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch", + "lint": "prettier --check . && eslint .", + "format": "prettier --write .", + "test:unit": "vitest", + "test": "vitest run && playwright test", + "gen:api": "openapi-ts", + "test:e2e": "playwright test" + }, + "devDependencies": { + "@eslint/js": "^10.0.1", + "@hey-api/openapi-ts": "^0.99.0", + "@playwright/test": "1.63", + "@sveltejs/adapter-static": "^4.0.0", + "@sveltejs/kit": "^3.0.0", + "@sveltejs/vite-plugin-svelte": "^7.2.0", + "@tailwindcss/typography": "^0.5.19", + "@tailwindcss/vite": "^4.3.0", + "@types/d3": "^7.4.3", + "@types/node": "^22", + "eslint": "^10.4.1", + "eslint-config-prettier": "^10.1.8", + "eslint-plugin-svelte": "^3.19.0", + "globals": "^17.6.0", + "prettier": "^3.8.3", + "prettier-plugin-svelte": "^4.1.0", + "prettier-plugin-tailwindcss": "^0.8.0", + "svelte": "^5.57.1", + "svelte-check": "^4.6.0", + "tailwindcss": "^4.3.0", + "typescript": "^6.0.3", + "typescript-eslint": "^8.60.1", + "vite": "^8.3.0", + "vitest": "^4.1.8" + }, + "imports": { + "#lib": "./src/lib/index.js", + "#lib/*": "./src/lib/*" + }, + "dependencies": { + "@fontsource-variable/mona-sans": "^5.3.0", + "@lucide/svelte": "^1.55.0", + "bits-ui": "^2.19.5", + "d3": "^7.9.0", + "dompurify": "^3.4.16", + "marked": "^18.1.0", + "mode-watcher": "^1.1.0", + "svelte-sonner": "^1.2.1" + } } diff --git a/frontend/playwright.config.ts b/frontend/playwright.config.ts new file mode 100644 index 0000000..532cd91 --- /dev/null +++ b/frontend/playwright.config.ts @@ -0,0 +1,45 @@ +import { defineConfig, devices } from '@playwright/test'; +import path from 'node:path'; + +/** + * End-to-end tests against the real backend serving the built app, seeded with + * the synthetic demo map (scripts/seed_demo.py) in a throwaway directory — + * no crawling, no API keys. Run `npm run build` first. + */ +const ROOT = path.resolve(import.meta.dirname, '..'); +const DATA = path.join(ROOT, '.e2e'); +const PYTHON = path.join( + ROOT, + '.venv', + process.platform === 'win32' ? 'Scripts/python.exe' : 'bin/python' +); +const PORT = 8011; + +export default defineConfig({ + testDir: 'e2e', + fullyParallel: false, // one shared database + forbidOnly: !!process.env.CI, + retries: process.env.CI ? 1 : 0, + reporter: process.env.CI ? [['github'], ['list']] : 'list', + use: { + baseURL: `http://localhost:${PORT}`, + trace: 'retain-on-failure' + }, + projects: [{ name: 'chromium', use: { ...devices['Desktop Chrome'] } }], + webServer: { + command: `"${PYTHON}" -m scripts.seed_demo --reset && "${PYTHON}" -m uvicorn backend.api.main:app --port ${PORT}`, + cwd: ROOT, + url: `http://localhost:${PORT}/api/health`, + reuseExistingServer: false, + timeout: 120_000, + env: { + DB_PATH: path.join(DATA, 'e2e.db'), + LANCE_PATH: path.join(DATA, 'vectors'), + // Nothing in these tests may reach the network. + RECOVER_ACADEMIC: 'false', + BRAVE_API_KEY: '', + ANTHROPIC_API_KEY: 'e2e-not-a-real-key', + OPENAI_API_KEY: 'e2e-not-a-real-key' + } + } +}); diff --git a/frontend/prettier.config.js b/frontend/prettier.config.js new file mode 100644 index 0000000..009f5ce --- /dev/null +++ b/frontend/prettier.config.js @@ -0,0 +1,12 @@ +/** @type {import("prettier").Config} */ +const config = { + useTabs: true, + singleQuote: true, + trailingComma: 'none', + printWidth: 100, + plugins: ['prettier-plugin-svelte', 'prettier-plugin-tailwindcss'], + overrides: [{ files: '*.svelte', options: { parser: 'svelte' } }], + tailwindStylesheet: './src/routes/layout.css' +}; + +export default config; diff --git a/frontend/src/app.d.ts b/frontend/src/app.d.ts new file mode 100644 index 0000000..da08e6d --- /dev/null +++ b/frontend/src/app.d.ts @@ -0,0 +1,13 @@ +// See https://svelte.dev/docs/kit/types#app.d.ts +// for information about these interfaces +declare global { + namespace App { + // interface Error {} + // interface Locals {} + // interface PageData {} + // interface PageState {} + // interface Platform {} + } +} + +export {}; diff --git a/frontend/src/app.html b/frontend/src/app.html index 84ffad1..6a2bb58 100644 --- a/frontend/src/app.html +++ b/frontend/src/app.html @@ -1,12 +1,12 @@ - - - - - %sveltekit.head% - - -
%sveltekit.body%
- + + + + + %sveltekit.head% + + +
%sveltekit.body%
+ diff --git a/frontend/src/components/ArticleSidebar.svelte b/frontend/src/components/ArticleSidebar.svelte deleted file mode 100644 index 722bc09..0000000 --- a/frontend/src/components/ArticleSidebar.svelte +++ /dev/null @@ -1,174 +0,0 @@ - - - - - - - diff --git a/frontend/src/components/ChatPanel.svelte b/frontend/src/components/ChatPanel.svelte deleted file mode 100644 index 0383f4b..0000000 --- a/frontend/src/components/ChatPanel.svelte +++ /dev/null @@ -1,324 +0,0 @@ - - -
-
- {#each PRESET_QUERIES as q} - - {/each} -
- -
- {#if messages.length === 0 && !loading} -
Ask a question about the indexed corpus.
- {/if} - {#each messages as msg} - {#if msg.role === 'user'} -
{msg.text}
- {:else} -
-
{@html renderMd(msg.text)}
- {#if msg.citations && msg.citations.length > 0} -
-
Sources
- {#each msg.citations as c} - - {c.title || c.domain} - {c.domain} - - {/each} -
- {/if} -
- {/if} - {/each} - {#if loading} -
Searching corpus…
- {/if} - {#if error} -
{error}
- - {/if} -
- -
- - -
-
- - diff --git a/frontend/src/components/ClusterMap.svelte b/frontend/src/components/ClusterMap.svelte deleted file mode 100644 index b74df54..0000000 --- a/frontend/src/components/ClusterMap.svelte +++ /dev/null @@ -1,217 +0,0 @@ - - -
- {#if articles.length === 0} -
No data — run an analysis first
- {/if} -
- -{#if tooltip.show && tooltip.data} -
-
{tooltip.data.title || 'Untitled'}
-
{tooltip.data.domain || ''}{tooltip.data.publish_date ? ' · ' + tooltip.data.publish_date : ''}
-
-{/if} - - diff --git a/frontend/src/components/ConsensusBars.svelte b/frontend/src/components/ConsensusBars.svelte deleted file mode 100644 index d0b0eaf..0000000 --- a/frontend/src/components/ConsensusBars.svelte +++ /dev/null @@ -1,177 +0,0 @@ - - -
- {#if clusters.length === 0} -
No data — run an analysis first
- {:else} -
- {#each clusters as cluster (cluster.id)} - {@const active = selectedCluster == null || selectedCluster === cluster.id} -
onSelectCluster(selectedCluster === cluster.id ? null : cluster.id)} - on:keydown={(e) => e.key === 'Enter' && onSelectCluster(selectedCluster === cluster.id ? null : cluster.id)} - > -
- {cluster.label.toUpperCase()} -
-
- {#each cluster.terms as t (t.term)} -
- {t.term} -
-
-
-
- {/each} -
-
- {/each} -
- {/if} -
- - diff --git a/frontend/src/components/OutlierFinder.svelte b/frontend/src/components/OutlierFinder.svelte deleted file mode 100644 index 524beac..0000000 --- a/frontend/src/components/OutlierFinder.svelte +++ /dev/null @@ -1,117 +0,0 @@ - - -
- {#if items.length === 0} -
No articles yet — run an analysis first
- {:else} - {#each items as item (item.chunk_id)} - - {/each} - {/if} -
- - diff --git a/frontend/src/components/TemporalDrift.svelte b/frontend/src/components/TemporalDrift.svelte deleted file mode 100644 index 6bd5448..0000000 --- a/frontend/src/components/TemporalDrift.svelte +++ /dev/null @@ -1,248 +0,0 @@ - - -
- {#if articles.length === 0} -
No data — run an analysis first
- {:else if months.length === 0} -
No date information in corpus
- {/if} -
- -{#if months.length > 0} -
- - - {formatMonth(activeMonth) || '—'} -
-{/if} - - diff --git a/frontend/src/lib/api.js b/frontend/src/lib/api.js deleted file mode 100644 index fa183c1..0000000 --- a/frontend/src/lib/api.js +++ /dev/null @@ -1,50 +0,0 @@ -const BASE = 'http://localhost:8001'; - -async function apiFetch(path, options = {}) { - const res = await fetch(`${BASE}${path}`, options); - if (!res.ok) { - let msg = `HTTP ${res.status}`; - try { const e = await res.json(); msg = e.error || msg; } catch {} - throw new Error(msg); - } - return res.json(); -} - -export const api = { - startJob: (topic) => apiFetch('/api/jobs', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ topic }), - }), - getJob: (id) => apiFetch(`/api/jobs/${id}`), - streamJob: (id, onUpdate, onError) => { - const source = new EventSource(`${BASE}/api/jobs/${id}/stream`); - source.onmessage = (e) => onUpdate(JSON.parse(e.data)); - source.addEventListener('error', (e) => { - if (e.data) { - try { onError?.(JSON.parse(e.data)); } catch { /* ignore parse errors */ } - } - }); - source.onerror = () => { /* connection dropped or stream closed by server */ }; - return source; - }, - getCorpus: () => apiFetch('/api/corpus'), - getClusters: () => apiFetch('/api/clusters'), - getOutliers: (clusterId = null) => apiFetch( - `/api/outliers${clusterId != null ? `?cluster_id=${clusterId}` : ''}` - ), - getArticle: (id) => apiFetch(`/api/articles/${id}`), - chat: (query) => apiFetch('/api/chat', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ query }), - }), - exportZip: () => fetch(`${BASE}/api/export`).then(async res => { - if (!res.ok) { - let msg = `HTTP ${res.status}`; - try { const e = await res.json(); msg = e.error || msg; } catch {} - throw new Error(msg); - } - return res.blob(); - }), -}; diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts new file mode 100644 index 0000000..c457099 --- /dev/null +++ b/frontend/src/lib/api.ts @@ -0,0 +1,155 @@ +/** + * Thin wrapper over the generated client (./gen, from the backend's OpenAPI + * schema): every call returns its data or throws an ApiError carrying the + * backend's `detail` message and HTTP status. + */ +import { client } from './api/gen/client.gen'; +import * as sdk from './api/gen/sdk.gen'; +import { readSse } from './api/sse'; +import type { Analysis, ChatMessage, ChatSource } from './api/gen/types.gen'; + +export type { + Analysis, + AnalysisEvent, + Article, + ChatMessage, + ChatSource, + Claims, + Cluster, + MapArticle, + Plan, + PlanResult, + Proposition, + PropositionClaim, + Term +} from './api/gen/types.gen'; + +// Same origin in production (FastAPI serves the app); Vite proxies /api in dev. +client.setConfig({ baseUrl: '' }); + +export type Fetch = typeof fetch; + +export class ApiError extends Error { + constructor( + message: string, + readonly status: number + ) { + super(message); + this.name = 'ApiError'; + } +} + +type Result = { data?: T; error?: unknown; response?: Response }; + +async function unwrap(call: Promise>): Promise { + let result: Result; + try { + result = await call; + } catch (e) { + throw new ApiError( + `Can't reach the Discourse Lens server. Is the backend running? (${(e as Error).message})`, + 0 + ); + } + const status = result.response?.status ?? 0; + if (result.error !== undefined || !result.response?.ok) { + throw new ApiError(detailOf(result.error) ?? `Request failed (HTTP ${status})`, status); + } + return result.data as T; +} + +function detailOf(error: unknown): string | undefined { + if (error && typeof error === 'object' && 'detail' in error) { + const detail = (error as { detail: unknown }).detail; + if (typeof detail === 'string') return detail; + // FastAPI validation errors: [{ msg, loc, ... }] + if (Array.isArray(detail) && detail[0]?.msg) return String(detail[0].msg); + } + return undefined; +} + +const path = (analysis_id: string) => ({ path: { analysis_id } }); + +export const api = { + listAnalyses: (fetch?: Fetch) => unwrap(sdk.listAnalyses({ fetch })), + createAnalysis: (topic: string, review = false) => + unwrap(sdk.createAnalysis({ body: { topic, review } })), + getAnalysis: (id: string, fetch?: Fetch) => unwrap(sdk.getAnalysis({ ...path(id), fetch })), + deleteAnalysis: (id: string) => unwrap(sdk.deleteAnalysis(path(id))), + cancelAnalysis: (id: string) => unwrap(sdk.cancelAnalysis(path(id))), + listArticles: (id: string, fetch?: Fetch) => unwrap(sdk.listArticles({ ...path(id), fetch })), + getClusters: (id: string, fetch?: Fetch) => unwrap(sdk.getClusters({ ...path(id), fetch })), + getArticle: (id: string, articleId: string) => + unwrap(sdk.getArticle({ path: { analysis_id: id, article_id: articleId } })), + getPlan: (id: string) => unwrap(sdk.getPlan(path(id))), + searchPlan: (id: string, queries: string[]) => + unwrap(sdk.searchPlan({ ...path(id), body: { queries } })), + approvePlan: (id: string, urls: string[]) => + unwrap(sdk.approvePlan({ ...path(id), body: { urls } })), + getClaims: (id: string) => unwrap(sdk.getClaims(path(id))), + findClaims: (id: string) => unwrap(sdk.findClaims(path(id))), + getChat: (id: string) => unwrap(sdk.getChat(path(id))), + clearChat: (id: string) => unwrap(sdk.clearChat(path(id))), + ask: streamAnswer +}; + +export interface AnswerHandlers { + onSources: (sources: ChatSource[]) => void; + onText: (piece: string) => void; +} + +/** + * Ask a question and stream the answer. Resolves with the saved answer once + * it's complete; rejects with ApiError on failure (nothing is saved then). + * Aborting the signal stops the answer (also unsaved). + */ +async function streamAnswer( + id: string, + question: string, + handlers: AnswerHandlers, + signal?: AbortSignal +): Promise { + let response: Response; + try { + response = await fetch(`/api/analyses/${encodeURIComponent(id)}/chat`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ question }), + signal + }); + } catch (e) { + if ((e as Error).name === 'AbortError') throw e; + throw new ApiError(`Can't reach the Discourse Lens server. (${(e as Error).message})`, 0); + } + if (!response.ok) { + let body: unknown; + try { + body = await response.json(); + } catch { + /* not JSON */ + } + throw new ApiError( + detailOf(body) ?? `Request failed (HTTP ${response.status})`, + response.status + ); + } + for await (const { event, data } of readSse(response)) { + const payload = JSON.parse(data); + if (event === 'sources') handlers.onSources(payload); + else if (event === 'delta') handlers.onText(payload.text); + else if (event === 'done') return payload as ChatMessage; + else if (event === 'error') throw new ApiError(payload.detail, 502); + } + throw new ApiError('The answer was cut off before it finished.', 0); +} + +export const RUNNING: ReadonlyArray = [ + 'queued', + 'crawling', + 'reviewing', + 'processing', + 'embedding', + 'projecting' +]; + +export const isRunning = (a: Pick) => RUNNING.includes(a.status); diff --git a/frontend/src/lib/api/gen/client.gen.ts b/frontend/src/lib/api/gen/client.gen.ts new file mode 100644 index 0000000..47828b7 --- /dev/null +++ b/frontend/src/lib/api/gen/client.gen.ts @@ -0,0 +1,16 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import { type Client, type ClientOptions, type Config, createClient, createConfig } from './client'; +import type { ClientOptions as ClientOptions2 } from './types.gen'; + +/** + * The `createClientConfig()` function will be called on client initialization + * and the returned object will become the client's initial configuration. + * + * You may want to initialize your client this way instead of calling + * `setConfig()`. This is useful for example if you're using Next.js + * to ensure your client always has the correct values. + */ +export type CreateClientConfig = (override?: Config) => Config & T>; + +export const client: Client = createClient(createConfig()); diff --git a/frontend/src/lib/api/gen/client/client.gen.ts b/frontend/src/lib/api/gen/client/client.gen.ts new file mode 100644 index 0000000..fc3f037 --- /dev/null +++ b/frontend/src/lib/api/gen/client/client.gen.ts @@ -0,0 +1,277 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import { createSseClient } from '../core/serverSentEvents.gen'; +import type { HttpMethod } from '../core/types.gen'; +import { getValidRequestBody } from '../core/utils.gen'; +import type { Client, Config, RequestOptions, ResolvedRequestOptions } from './types.gen'; +import { + buildUrl, + createConfig, + createInterceptors, + getParseAs, + mergeConfigs, + mergeHeaders, + setAuthParams, +} from './utils.gen'; + +type ReqInit = Omit & { + body?: any; + headers: ReturnType; +}; + +export const createClient = (config: Config = {}): Client => { + let _config = mergeConfigs(createConfig(), config); + + const getConfig = (): Config => ({ ..._config }); + + const setConfig = (config: Config): Config => { + _config = mergeConfigs(_config, config); + return getConfig(); + }; + + const interceptors = createInterceptors(); + + const beforeRequest = async < + TData = unknown, + TResponseStyle extends 'data' | 'fields' = 'fields', + ThrowOnError extends boolean = boolean, + Url extends string = string, + >( + options: RequestOptions, + ) => { + const opts = { + ..._config, + ...options, + fetch: options.fetch ?? _config.fetch ?? globalThis.fetch, + headers: mergeHeaders(_config.headers, options.headers), + serializedBody: undefined as string | undefined, + }; + + if (opts.security) { + await setAuthParams(opts); + } + + if (opts.requestValidator) { + await opts.requestValidator(opts); + } + + if (opts.body !== undefined && opts.bodySerializer) { + opts.serializedBody = opts.bodySerializer(opts.body) as string | undefined; + } + + // remove Content-Type header if body is empty to avoid sending invalid requests + if (opts.body === undefined || opts.serializedBody === '') { + opts.headers.delete('Content-Type'); + } + + const resolvedOpts = opts as typeof opts & + ResolvedRequestOptions; + const url = buildUrl(resolvedOpts); + + return { opts: resolvedOpts, url }; + }; + + const request: Client['request'] = async (options) => { + const throwOnError = options.throwOnError ?? _config.throwOnError; + const responseStyle = options.responseStyle ?? _config.responseStyle; + + let request: Request | undefined; + let response: Response | undefined; + + try { + const { opts, url } = await beforeRequest(options); + const requestInit: ReqInit = { + redirect: 'follow', + ...opts, + body: getValidRequestBody(opts), + }; + + request = new Request(url, requestInit); + + for (const fn of interceptors.request.fns) { + if (fn) { + request = await fn(request, opts); + } + } + + // fetch must be assigned here, otherwise it would throw the error: + // TypeError: Failed to execute 'fetch' on 'Window': Illegal invocation + const _fetch = opts.fetch!; + + response = await _fetch(request); + + for (const fn of interceptors.response.fns) { + if (fn) { + response = await fn(response, request, opts); + } + } + + const result = { + request, + response, + }; + + if (response.ok) { + const parseAs = + (opts.parseAs === 'auto' + ? getParseAs(response.headers.get('Content-Type')) + : opts.parseAs) ?? 'json'; + + if (response.status === 204 || response.headers.get('Content-Length') === '0') { + let emptyData: any; + switch (parseAs) { + case 'arrayBuffer': + case 'blob': + case 'text': + emptyData = await response[parseAs](); + break; + case 'formData': + emptyData = new FormData(); + break; + case 'stream': + emptyData = response.body; + break; + case 'json': + default: + emptyData = {}; + break; + } + return opts.responseStyle === 'data' + ? emptyData + : { + data: emptyData, + ...result, + }; + } + + let data: any; + switch (parseAs) { + case 'arrayBuffer': + case 'blob': + case 'formData': + case 'text': + data = await response[parseAs](); + break; + case 'json': { + // Some servers return 200 with no Content-Length and empty body. + // response.json() would throw; read as text and parse if non-empty. + const text = await response.text(); + data = text ? JSON.parse(text) : {}; + break; + } + case 'stream': + return opts.responseStyle === 'data' + ? response.body + : { + data: response.body, + ...result, + }; + } + + if (parseAs === 'json') { + if (opts.responseValidator) { + await opts.responseValidator(data); + } + + if (opts.responseTransformer) { + data = await opts.responseTransformer(data); + } + } + + return opts.responseStyle === 'data' + ? data + : { + data, + ...result, + }; + } + + const textError = await response.text(); + let jsonError: unknown; + + try { + jsonError = JSON.parse(textError); + } catch { + // noop + } + + throw jsonError ?? textError; + } catch (error) { + let finalError = error; + + for (const fn of interceptors.error.fns) { + if (fn) { + finalError = await fn(finalError, response, request, options as ResolvedRequestOptions); + } + } + + finalError = finalError || {}; + + if (throwOnError) { + throw finalError; + } + + // TODO: we probably want to return error and improve types + return responseStyle === 'data' + ? undefined + : { + error: finalError, + request, + response, + }; + } + }; + + const makeMethodFn = (method: Uppercase) => (options: RequestOptions) => + request({ ...options, method }); + + const makeSseFn = (method: Uppercase) => async (options: RequestOptions) => { + const { opts, url } = await beforeRequest(options); + return createSseClient({ + ...opts, + body: opts.body as BodyInit | null | undefined, + method, + onRequest: async (url, init) => { + let request = new Request(url, init); + for (const fn of interceptors.request.fns) { + if (fn) { + request = await fn(request, opts); + } + } + return request; + }, + serializedBody: getValidRequestBody(opts) as BodyInit | null | undefined, + url, + }); + }; + + const _buildUrl: Client['buildUrl'] = (options) => buildUrl({ ..._config, ...options }); + + return { + buildUrl: _buildUrl, + connect: makeMethodFn('CONNECT'), + delete: makeMethodFn('DELETE'), + get: makeMethodFn('GET'), + getConfig, + head: makeMethodFn('HEAD'), + interceptors, + options: makeMethodFn('OPTIONS'), + patch: makeMethodFn('PATCH'), + post: makeMethodFn('POST'), + put: makeMethodFn('PUT'), + request, + setConfig, + sse: { + connect: makeSseFn('CONNECT'), + delete: makeSseFn('DELETE'), + get: makeSseFn('GET'), + head: makeSseFn('HEAD'), + options: makeSseFn('OPTIONS'), + patch: makeSseFn('PATCH'), + post: makeSseFn('POST'), + put: makeSseFn('PUT'), + trace: makeSseFn('TRACE'), + }, + trace: makeMethodFn('TRACE'), + } as Client; +}; diff --git a/frontend/src/lib/api/gen/client/index.ts b/frontend/src/lib/api/gen/client/index.ts new file mode 100644 index 0000000..8c69331 --- /dev/null +++ b/frontend/src/lib/api/gen/client/index.ts @@ -0,0 +1,27 @@ +// This file is auto-generated by @hey-api/openapi-ts + +export type { Auth } from '../core/auth.gen'; +export type { QuerySerializerOptions } from '../core/bodySerializer.gen'; +export { + formDataBodySerializer, + jsonBodySerializer, + urlSearchParamsBodySerializer, +} from '../core/bodySerializer.gen'; +export { buildClientParams } from '../core/params.gen'; +export { serializeQueryKeyValue } from '../core/queryKeySerializer.gen'; +export type { ServerSentEventsResult } from '../core/serverSentEvents.gen'; +export type { ClientMeta } from '../core/types.gen'; +export { createClient } from './client.gen'; +export type { + Client, + ClientOptions, + Config, + CreateClientConfig, + Options, + RequestOptions, + RequestResult, + ResolvedRequestOptions, + ResponseStyle, + TDataShape, +} from './types.gen'; +export { createConfig, mergeHeaders } from './utils.gen'; diff --git a/frontend/src/lib/api/gen/client/types.gen.ts b/frontend/src/lib/api/gen/client/types.gen.ts new file mode 100644 index 0000000..193646c --- /dev/null +++ b/frontend/src/lib/api/gen/client/types.gen.ts @@ -0,0 +1,218 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { Auth } from '../core/auth.gen'; +import type { + ServerSentEventsOptions, + ServerSentEventsResult, +} from '../core/serverSentEvents.gen'; +import type { Client as CoreClient, Config as CoreConfig } from '../core/types.gen'; +import type { Middleware } from './utils.gen'; + +export type ResponseStyle = 'data' | 'fields'; + +export interface Config + extends Omit, CoreConfig { + /** + * Base URL for all requests made by this client. + */ + baseUrl?: T['baseUrl']; + /** + * Fetch API implementation. You can use this option to provide a custom + * fetch instance. + * + * @default globalThis.fetch + */ + fetch?: typeof fetch; + /** + * Please don't use the Fetch client for Next.js applications. The `next` + * options won't have any effect. + * + * Install {@link https://www.npmjs.com/package/@hey-api/client-next `@hey-api/client-next`} instead. + */ + next?: never; + /** + * Return the response data parsed in a specified format. By default, `auto` + * will infer the appropriate method from the `Content-Type` response header. + * You can override this behavior with any of the {@link Body} methods. + * Select `stream` if you don't want to parse response data at all. + * + * @default 'auto' + */ + parseAs?: 'arrayBuffer' | 'auto' | 'blob' | 'formData' | 'json' | 'stream' | 'text'; + /** + * Should we return only data or multiple fields (data, error, response, etc.)? + * + * @default 'fields' + */ + responseStyle?: ResponseStyle; + /** + * Throw an error instead of returning it in the response? + * + * @default false + */ + throwOnError?: T['throwOnError']; +} + +export interface RequestOptions< + TData = unknown, + TResponseStyle extends ResponseStyle = 'fields', + ThrowOnError extends boolean = boolean, + Url extends string = string, +> + extends + Config<{ + responseStyle: TResponseStyle; + throwOnError: ThrowOnError; + }>, + Pick< + ServerSentEventsOptions, + | 'onRequest' + | 'onSseError' + | 'onSseEvent' + | 'sseDefaultRetryDelay' + | 'sseMaxRetryAttempts' + | 'sseMaxRetryDelay' + > { + /** + * Any body that you want to add to your request. + * + * {@link https://developer.mozilla.org/docs/Web/API/fetch#body} + */ + body?: unknown; + path?: Record; + query?: Record; + /** + * Security mechanism(s) to use for the request. + */ + security?: ReadonlyArray; + url: Url; +} + +export interface ResolvedRequestOptions< + TResponseStyle extends ResponseStyle = 'fields', + ThrowOnError extends boolean = boolean, + Url extends string = string, +> extends RequestOptions { + headers: Headers; + serializedBody?: string; +} + +export type RequestResult< + TData = unknown, + TError = unknown, + ThrowOnError extends boolean = boolean, + TResponseStyle extends ResponseStyle = 'fields', +> = ThrowOnError extends true + ? Promise< + TResponseStyle extends 'data' + ? TData extends Record + ? TData[keyof TData] + : TData + : { + data: TData extends Record ? TData[keyof TData] : TData; + request: Request; + response: Response; + } + > + : Promise< + TResponseStyle extends 'data' + ? (TData extends Record ? TData[keyof TData] : TData) | undefined + : ( + | { + data: TData extends Record ? TData[keyof TData] : TData; + error: undefined; + } + | { + data: undefined; + error: TError extends Record ? TError[keyof TError] : TError; + } + ) & { + /** request may be undefined, because error may be from building the request object itself */ + request?: Request; + /** response may be undefined, because error may be from building the request object itself or from a network error */ + response?: Response; + } + >; + +export interface ClientOptions { + baseUrl?: string; + responseStyle?: ResponseStyle; + throwOnError?: boolean; +} + +type MethodFn = < + TData = unknown, + TError = unknown, + ThrowOnError extends boolean = false, + TResponseStyle extends ResponseStyle = 'fields', +>( + options: Omit, 'method'>, +) => RequestResult; + +type SseFn = < + TData = unknown, + // eslint-disable-next-line @typescript-eslint/no-unused-vars + _TError = unknown, + ThrowOnError extends boolean = false, + TResponseStyle extends ResponseStyle = 'fields', +>( + options: Omit, 'method'>, +) => Promise>; + +type RequestFn = < + TData = unknown, + TError = unknown, + ThrowOnError extends boolean = false, + TResponseStyle extends ResponseStyle = 'fields', +>( + options: Omit, 'method'> & + Pick>, 'method'>, +) => RequestResult; + +type BuildUrlFn = < + TData extends { + body?: unknown; + path?: Record; + query?: Record; + url: string; + }, +>( + options: TData & Options, +) => string; + +export type Client = CoreClient & { + interceptors: Middleware; +}; + +/** + * The `createClientConfig()` function will be called on client initialization + * and the returned object will become the client's initial configuration. + * + * You may want to initialize your client this way instead of calling + * `setConfig()`. This is useful for example if you're using Next.js + * to ensure your client always has the correct values. + */ +export type CreateClientConfig = ( + override?: Config, +) => Config & T>; + +export interface TDataShape { + body?: unknown; + headers?: unknown; + path?: unknown; + query?: unknown; + url: string; +} + +type OmitKeys = Pick>; + +export type Options< + TData extends TDataShape = TDataShape, + ThrowOnError extends boolean = boolean, + TResponse = unknown, + TResponseStyle extends ResponseStyle = 'fields', +> = OmitKeys< + RequestOptions, + 'body' | 'path' | 'query' | 'url' +> & + ([TData] extends [never] ? unknown : Omit); diff --git a/frontend/src/lib/api/gen/client/utils.gen.ts b/frontend/src/lib/api/gen/client/utils.gen.ts new file mode 100644 index 0000000..d4a7284 --- /dev/null +++ b/frontend/src/lib/api/gen/client/utils.gen.ts @@ -0,0 +1,316 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import { getAuthToken } from '../core/auth.gen'; +import type { QuerySerializerOptions } from '../core/bodySerializer.gen'; +import { jsonBodySerializer } from '../core/bodySerializer.gen'; +import { + serializeArrayParam, + serializeObjectParam, + serializePrimitiveParam, +} from '../core/pathSerializer.gen'; +import { getUrl } from '../core/utils.gen'; +import type { Client, ClientOptions, Config, RequestOptions } from './types.gen'; + +export const createQuerySerializer = ({ + parameters = {}, + ...args +}: QuerySerializerOptions = {}): ((queryParams: T) => string) => { + const querySerializer = (queryParams: T): string => { + const search: string[] = []; + if (queryParams && typeof queryParams === 'object') { + for (const name in queryParams) { + const value = queryParams[name]; + + if (value === undefined || value === null) { + continue; + } + + const options = parameters[name] || args; + + if (Array.isArray(value)) { + const serializedArray = serializeArrayParam({ + allowReserved: options.allowReserved, + explode: true, + name, + style: 'form', + value, + ...options.array, + }); + if (serializedArray) search.push(serializedArray); + } else if (typeof value === 'object') { + const serializedObject = serializeObjectParam({ + allowReserved: options.allowReserved, + explode: true, + name, + style: 'deepObject', + value: value as Record, + ...options.object, + }); + if (serializedObject) search.push(serializedObject); + } else { + const serializedPrimitive = serializePrimitiveParam({ + allowReserved: options.allowReserved, + name, + value: value as string, + }); + if (serializedPrimitive) search.push(serializedPrimitive); + } + } + } + return search.join('&'); + }; + return querySerializer; +}; + +/** + * Infers parseAs value from provided Content-Type header. + */ +export const getParseAs = (contentType: string | null): Exclude => { + if (!contentType) { + // If no Content-Type header is provided, the best we can do is return the raw response body, + // which is effectively the same as the 'stream' option. + return 'stream'; + } + + const cleanContent = contentType.split(';')[0]?.trim(); + + if (!cleanContent) { + return; + } + + if (cleanContent.startsWith('application/json') || cleanContent.endsWith('+json')) { + return 'json'; + } + + if (cleanContent === 'multipart/form-data') { + return 'formData'; + } + + if ( + ['application/', 'audio/', 'image/', 'video/'].some((type) => cleanContent.startsWith(type)) + ) { + return 'blob'; + } + + if (cleanContent.startsWith('text/')) { + return 'text'; + } + + return; +}; + +const checkForExistence = ( + options: Pick & { + headers: Headers; + }, + name?: string, +): boolean => { + if (!name) { + return false; + } + if ( + options.headers.has(name) || + options.query?.[name] || + options.headers.get('Cookie')?.includes(`${name}=`) + ) { + return true; + } + return false; +}; + +export async function setAuthParams( + options: Pick & { + headers: Headers; + }, +): Promise { + for (const auth of options.security ?? []) { + if (checkForExistence(options, auth.name)) { + continue; + } + + const token = await getAuthToken(auth, options.auth); + + if (!token) { + continue; + } + + const name = auth.name ?? 'Authorization'; + + switch (auth.in) { + case 'query': + if (!options.query) { + options.query = {}; + } + options.query[name] = token; + break; + case 'cookie': + options.headers.append('Cookie', `${name}=${token}`); + break; + case 'header': + default: + options.headers.set(name, token); + break; + } + } +} + +export const buildUrl: Client['buildUrl'] = (options) => + getUrl({ + baseUrl: options.baseUrl as string, + path: options.path, + query: options.query, + querySerializer: + typeof options.querySerializer === 'function' + ? options.querySerializer + : createQuerySerializer(options.querySerializer), + url: options.url, + }); + +export const mergeConfigs = (a: Config, b: Config): Config => { + const config = { ...a, ...b }; + if (config.baseUrl?.endsWith('/')) { + config.baseUrl = config.baseUrl.substring(0, config.baseUrl.length - 1); + } + config.headers = mergeHeaders(a.headers, b.headers); + return config; +}; + +const headersEntries = (headers: Headers): Array<[string, string]> => { + const entries: Array<[string, string]> = []; + headers.forEach((value, key) => { + entries.push([key, value]); + }); + return entries; +}; + +export const mergeHeaders = ( + ...headers: Array['headers'] | undefined> +): Headers => { + const mergedHeaders = new Headers(); + for (const header of headers) { + if (!header) { + continue; + } + + const iterator = header instanceof Headers ? headersEntries(header) : Object.entries(header); + + for (const [key, value] of iterator) { + if (value === null) { + mergedHeaders.delete(key); + } else if (Array.isArray(value)) { + for (const v of value) { + mergedHeaders.append(key, v as string); + } + } else if (value !== undefined) { + // assume object headers are meant to be JSON stringified, i.e., their + // content value in OpenAPI specification is 'application/json' + mergedHeaders.set( + key, + typeof value === 'object' ? JSON.stringify(value) : (value as string), + ); + } + } + } + return mergedHeaders; +}; + +type ErrInterceptor = ( + error: Err, + /** response may be undefined due to a network error where no response object is produced */ + response: Res | undefined, + /** request may be undefined, because error may be from building the request object itself */ + request: Req | undefined, + options: Options, +) => Err | Promise; + +type ReqInterceptor = (request: Req, options: Options) => Req | Promise; + +type ResInterceptor = ( + response: Res, + request: Req, + options: Options, +) => Res | Promise; + +class Interceptors { + fns: Array = []; + + clear(): void { + this.fns = []; + } + + eject(id: number | Interceptor): void { + const index = this.getInterceptorIndex(id); + if (this.fns[index]) { + this.fns[index] = null; + } + } + + exists(id: number | Interceptor): boolean { + const index = this.getInterceptorIndex(id); + return Boolean(this.fns[index]); + } + + getInterceptorIndex(id: number | Interceptor): number { + if (typeof id === 'number') { + return this.fns[id] ? id : -1; + } + return this.fns.indexOf(id); + } + + update(id: number | Interceptor, fn: Interceptor): number | Interceptor | false { + const index = this.getInterceptorIndex(id); + if (this.fns[index]) { + this.fns[index] = fn; + return id; + } + return false; + } + + use(fn: Interceptor): number { + this.fns.push(fn); + return this.fns.length - 1; + } +} + +export interface Middleware { + error: Interceptors>; + request: Interceptors>; + response: Interceptors>; +} + +export const createInterceptors = (): Middleware< + Req, + Res, + Err, + Options +> => ({ + error: new Interceptors>(), + request: new Interceptors>(), + response: new Interceptors>(), +}); + +const defaultQuerySerializer = createQuerySerializer({ + allowReserved: false, + array: { + explode: true, + style: 'form', + }, + object: { + explode: true, + style: 'deepObject', + }, +}); + +const defaultHeaders = { + 'Content-Type': 'application/json', +}; + +export const createConfig = ( + override: Config & T> = {}, +): Config & T> => ({ + ...jsonBodySerializer, + headers: defaultHeaders, + parseAs: 'auto', + querySerializer: defaultQuerySerializer, + ...override, +}); diff --git a/frontend/src/lib/api/gen/core/auth.gen.ts b/frontend/src/lib/api/gen/core/auth.gen.ts new file mode 100644 index 0000000..c663664 --- /dev/null +++ b/frontend/src/lib/api/gen/core/auth.gen.ts @@ -0,0 +1,48 @@ +// This file is auto-generated by @hey-api/openapi-ts + +export type AuthToken = string | undefined; + +export interface Auth { + /** + * Which part of the request do we use to send the auth? + * + * @default 'header' + */ + in?: 'header' | 'query' | 'cookie'; + /** + * A unique identifier for the security scheme. + * + * Defined only when there are multiple security schemes whose `Auth` + * shape would otherwise be identical. + */ + key?: string; + /** + * Header or query parameter name. + * + * @default 'Authorization' + */ + name?: string; + scheme?: 'basic' | 'bearer'; + type: 'apiKey' | 'http'; +} + +export const getAuthToken = async ( + auth: Auth, + callback: ((auth: Auth) => Promise | AuthToken) | AuthToken, +): Promise => { + const token = typeof callback === 'function' ? await callback(auth) : callback; + + if (!token) { + return; + } + + if (auth.scheme === 'bearer') { + return `Bearer ${token}`; + } + + if (auth.scheme === 'basic') { + return `Basic ${btoa(token)}`; + } + + return token; +}; diff --git a/frontend/src/lib/api/gen/core/bodySerializer.gen.ts b/frontend/src/lib/api/gen/core/bodySerializer.gen.ts new file mode 100644 index 0000000..67daca6 --- /dev/null +++ b/frontend/src/lib/api/gen/core/bodySerializer.gen.ts @@ -0,0 +1,82 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { ArrayStyle, ObjectStyle, SerializerOptions } from './pathSerializer.gen'; + +export type QuerySerializer = (query: Record) => string; + +export type BodySerializer = (body: unknown) => unknown; + +type QuerySerializerOptionsObject = { + allowReserved?: boolean; + array?: Partial>; + object?: Partial>; +}; + +export type QuerySerializerOptions = QuerySerializerOptionsObject & { + /** + * Per-parameter serialization overrides. When provided, these settings + * override the global array/object settings for specific parameter names. + */ + parameters?: Record; +}; + +const serializeFormDataPair = (data: FormData, key: string, value: unknown): void => { + if (typeof value === 'string' || value instanceof Blob) { + data.append(key, value); + } else if (value instanceof Date) { + data.append(key, value.toISOString()); + } else { + data.append(key, JSON.stringify(value)); + } +}; + +const serializeUrlSearchParamsPair = (data: URLSearchParams, key: string, value: unknown): void => { + if (typeof value === 'string') { + data.append(key, value); + } else { + data.append(key, JSON.stringify(value)); + } +}; + +export const formDataBodySerializer = { + bodySerializer: (body: unknown): FormData => { + const data = new FormData(); + + Object.entries(body as Record).forEach(([key, value]) => { + if (value === undefined || value === null) { + return; + } + if (Array.isArray(value)) { + value.forEach((v) => serializeFormDataPair(data, key, v)); + } else { + serializeFormDataPair(data, key, value); + } + }); + + return data; + }, +}; + +export const jsonBodySerializer = { + bodySerializer: (body: unknown): string => + JSON.stringify(body, (_key, value) => (typeof value === 'bigint' ? value.toString() : value)), +}; + +export const urlSearchParamsBodySerializer = { + bodySerializer: (body: unknown): string => { + const data = new URLSearchParams(); + + Object.entries(body as Record).forEach(([key, value]) => { + if (value === undefined || value === null) { + return; + } + if (Array.isArray(value)) { + value.forEach((v) => serializeUrlSearchParamsPair(data, key, v)); + } else { + serializeUrlSearchParamsPair(data, key, value); + } + }); + + return data.toString(); + }, +}; diff --git a/frontend/src/lib/api/gen/core/params.gen.ts b/frontend/src/lib/api/gen/core/params.gen.ts new file mode 100644 index 0000000..5e8908f --- /dev/null +++ b/frontend/src/lib/api/gen/core/params.gen.ts @@ -0,0 +1,178 @@ +// This file is auto-generated by @hey-api/openapi-ts + +type Slot = 'body' | 'headers' | 'path' | 'query'; + +export type Field = + | { + in: Exclude; + /** + * Field name. This is the name we want the user to see and use. + */ + key: string; + /** + * Field mapped name. This is the name we want to use in the request. + * If omitted, we use the same value as `key`. + */ + map?: string; + } + | { + in: Extract; + /** + * Key isn't required for bodies. + */ + key?: string; + map?: string; + } + | { + /** + * Field name. This is the name we want the user to see and use. + */ + key: string; + /** + * Field mapped name. This is the name we want to use in the request. + * If `in` is omitted, `map` aliases `key` to the transport layer. + */ + map: Slot; + }; + +export interface Fields { + allowExtra?: Partial>; + args?: ReadonlyArray; +} + +export type FieldsConfig = ReadonlyArray; + +const extraPrefixesMap: Record = { + $body_: 'body', + $headers_: 'headers', + $path_: 'path', + $query_: 'query', +}; +const extraPrefixes = Object.entries(extraPrefixesMap); + +type KeyMap = Map< + string, + | { + in: Slot; + map?: string; + } + | { + in?: never; + map: Slot; + } +>; + +function buildKeyMap(fields: FieldsConfig, map?: KeyMap): KeyMap { + if (!map) { + map = new Map(); + } + + for (const config of fields) { + if ('in' in config) { + if (config.key) { + map.set(config.key, { + in: config.in, + map: config.map, + }); + } + } else if ('key' in config) { + map.set(config.key, { + map: config.map, + }); + } else if (config.args) { + buildKeyMap(config.args, map); + } + } + + return map; +} + +interface Params { + body?: unknown; + headers: Record; + path: Record; + query: Record; +} + +function stripEmptySlots(params: Params): void { + for (const [slot, value] of Object.entries(params)) { + if (slot === 'body') continue; + if (value && typeof value === 'object' && !Array.isArray(value) && !Object.keys(value).length) { + delete params[slot as Slot]; + } + } +} + +export function buildClientParams(args: ReadonlyArray, fields: FieldsConfig): Params { + const params: Params = { + headers: Object.create(null), + path: Object.create(null), + query: Object.create(null), + }; + + const map = buildKeyMap(fields); + + function writeSlot(slot: Slot, key: string, value: unknown): void { + let record = params[slot] as Record | undefined; + if (record === undefined) { + record = Object.create(null) as Record; + params[slot] = record; + } + record[key] = value; + } + + let config: FieldsConfig[number] | undefined; + + for (const [index, arg] of args.entries()) { + if (fields[index]) { + config = fields[index]; + } + + if (!config) { + continue; + } + + if ('in' in config) { + if (config.key) { + const field = map.get(config.key)!; + const name = field.map || config.key; + if (field.in) { + writeSlot(field.in, name, arg); + } + } else { + params.body = arg; + } + } else { + for (const [key, value] of Object.entries(arg ?? {})) { + const field = map.get(key); + + if (field) { + if (field.in) { + const name = field.map || key; + writeSlot(field.in, name, value); + } else { + params[field.map] = value; + } + } else { + const extra = extraPrefixes.find(([prefix]) => key.startsWith(prefix)); + + if (extra) { + const [prefix, slot] = extra; + writeSlot(slot, key.slice(prefix.length), value); + } else if ('allowExtra' in config && config.allowExtra) { + for (const [slot, allowed] of Object.entries(config.allowExtra)) { + if (allowed) { + writeSlot(slot as Slot, key, value); + break; + } + } + } + } + } + } + } + + stripEmptySlots(params); + + return params; +} diff --git a/frontend/src/lib/api/gen/core/pathSerializer.gen.ts b/frontend/src/lib/api/gen/core/pathSerializer.gen.ts new file mode 100644 index 0000000..fab1ed4 --- /dev/null +++ b/frontend/src/lib/api/gen/core/pathSerializer.gen.ts @@ -0,0 +1,171 @@ +// This file is auto-generated by @hey-api/openapi-ts + +interface SerializeOptions extends SerializePrimitiveOptions, SerializerOptions {} + +interface SerializePrimitiveOptions { + allowReserved?: boolean; + name: string; +} + +export interface SerializerOptions { + /** + * @default true + */ + explode: boolean; + style: T; +} + +export type ArrayStyle = 'form' | 'spaceDelimited' | 'pipeDelimited'; +export type ArraySeparatorStyle = ArrayStyle | MatrixStyle; +type MatrixStyle = 'label' | 'matrix' | 'simple'; +export type ObjectStyle = 'form' | 'deepObject'; +type ObjectSeparatorStyle = ObjectStyle | MatrixStyle; + +interface SerializePrimitiveParam extends SerializePrimitiveOptions { + value: string; +} + +export const separatorArrayExplode = (style: ArraySeparatorStyle): '.' | ';' | ',' | '&' => { + switch (style) { + case 'label': + return '.'; + case 'matrix': + return ';'; + case 'simple': + return ','; + default: + return '&'; + } +}; + +export const separatorArrayNoExplode = (style: ArraySeparatorStyle): ',' | '|' | '%20' => { + switch (style) { + case 'form': + return ','; + case 'pipeDelimited': + return '|'; + case 'spaceDelimited': + return '%20'; + default: + return ','; + } +}; + +export const separatorObjectExplode = (style: ObjectSeparatorStyle): '.' | ';' | ',' | '&' => { + switch (style) { + case 'label': + return '.'; + case 'matrix': + return ';'; + case 'simple': + return ','; + default: + return '&'; + } +}; + +export const serializeArrayParam = ({ + allowReserved, + explode, + name, + style, + value, +}: SerializeOptions & { + value: unknown[]; +}): string => { + if (!explode) { + const joinedValues = ( + allowReserved ? value : value.map((v) => encodeURIComponent(v as string)) + ).join(separatorArrayNoExplode(style)); + switch (style) { + case 'label': + return `.${joinedValues}`; + case 'matrix': + return `;${name}=${joinedValues}`; + case 'simple': + return joinedValues; + default: + return `${name}=${joinedValues}`; + } + } + + const separator = separatorArrayExplode(style); + const joinedValues = value + .map((v) => { + if (style === 'label' || style === 'simple') { + return allowReserved ? v : encodeURIComponent(v as string); + } + + return serializePrimitiveParam({ + allowReserved, + name, + value: v as string, + }); + }) + .join(separator); + return style === 'label' || style === 'matrix' ? separator + joinedValues : joinedValues; +}; + +export const serializePrimitiveParam = ({ + allowReserved, + name, + value, +}: SerializePrimitiveParam): string => { + if (value === undefined || value === null) { + return ''; + } + + if (typeof value === 'object') { + throw new Error( + 'Deeply-nested arrays/objects aren’t supported. Provide your own `querySerializer()` to handle these.', + ); + } + + return `${name}=${allowReserved ? value : encodeURIComponent(value)}`; +}; + +export const serializeObjectParam = ({ + allowReserved, + explode, + name, + style, + value, + valueOnly, +}: SerializeOptions & { + value: Record | Date; + valueOnly?: boolean; +}): string => { + if (value instanceof Date) { + return valueOnly ? value.toISOString() : `${name}=${value.toISOString()}`; + } + + if (style !== 'deepObject' && !explode) { + let values: string[] = []; + Object.entries(value).forEach(([key, v]) => { + values = [...values, key, allowReserved ? (v as string) : encodeURIComponent(v as string)]; + }); + const joinedValues = values.join(','); + switch (style) { + case 'form': + return `${name}=${joinedValues}`; + case 'label': + return `.${joinedValues}`; + case 'matrix': + return `;${name}=${joinedValues}`; + default: + return joinedValues; + } + } + + const separator = separatorObjectExplode(style); + const joinedValues = Object.entries(value) + .map(([key, v]) => + serializePrimitiveParam({ + allowReserved, + name: style === 'deepObject' ? `${name}[${key}]` : key, + value: v as string, + }), + ) + .join(separator); + return style === 'label' || style === 'matrix' ? separator + joinedValues : joinedValues; +}; diff --git a/frontend/src/lib/api/gen/core/queryKeySerializer.gen.ts b/frontend/src/lib/api/gen/core/queryKeySerializer.gen.ts new file mode 100644 index 0000000..773b065 --- /dev/null +++ b/frontend/src/lib/api/gen/core/queryKeySerializer.gen.ts @@ -0,0 +1,117 @@ +// This file is auto-generated by @hey-api/openapi-ts + +/** + * JSON-friendly union that mirrors what Pinia Colada can hash. + */ +export type JsonValue = + | null + | string + | number + | boolean + | JsonValue[] + | { [key: string]: JsonValue }; + +/** + * Replacer that converts non-JSON values (bigint, Date, etc.) to safe substitutes. + */ +export const queryKeyJsonReplacer = (_key: string, value: unknown): unknown | undefined => { + if (value === undefined || typeof value === 'function' || typeof value === 'symbol') { + return undefined; + } + if (typeof value === 'bigint') { + return value.toString(); + } + if (value instanceof Date) { + return value.toISOString(); + } + return value; +}; + +/** + * Safely stringifies a value and parses it back into a JsonValue. + */ +export const stringifyToJsonValue = (input: unknown): JsonValue | undefined => { + try { + const json = JSON.stringify(input, queryKeyJsonReplacer); + if (json === undefined) { + return undefined; + } + return JSON.parse(json) as JsonValue; + } catch { + return undefined; + } +}; + +/** + * Detects plain objects (including objects with a null prototype). + */ +const isPlainObject = (value: unknown): value is Record => { + if (value === null || typeof value !== 'object') { + return false; + } + const prototype = Object.getPrototypeOf(value as object); + return prototype === Object.prototype || prototype === null; +}; + +/** + * Turns URLSearchParams into a sorted JSON object for deterministic keys. + */ +const serializeSearchParams = (params: URLSearchParams): JsonValue => { + const entries = Array.from(params.entries()).sort(([a], [b]) => a.localeCompare(b)); + const result: Record = {}; + + for (const [key, value] of entries) { + const existing = result[key]; + if (existing === undefined) { + result[key] = value; + continue; + } + + if (Array.isArray(existing)) { + (existing as string[]).push(value); + } else { + result[key] = [existing, value]; + } + } + + return result; +}; + +/** + * Normalizes any accepted value into a JSON-friendly shape for query keys. + */ +export const serializeQueryKeyValue = (value: unknown): JsonValue | undefined => { + if (value === null) { + return null; + } + + if (typeof value === 'string' || typeof value === 'number' || typeof value === 'boolean') { + return value; + } + + if (value === undefined || typeof value === 'function' || typeof value === 'symbol') { + return undefined; + } + + if (typeof value === 'bigint') { + return value.toString(); + } + + if (value instanceof Date) { + return value.toISOString(); + } + + if (Array.isArray(value)) { + return stringifyToJsonValue(value); + } + + if (typeof URLSearchParams !== 'undefined' && value instanceof URLSearchParams) { + return serializeSearchParams(value); + } + + if (isPlainObject(value)) { + return stringifyToJsonValue(value); + } + + return undefined; +}; diff --git a/frontend/src/lib/api/gen/core/serverSentEvents.gen.ts b/frontend/src/lib/api/gen/core/serverSentEvents.gen.ts new file mode 100644 index 0000000..ddf3c4d --- /dev/null +++ b/frontend/src/lib/api/gen/core/serverSentEvents.gen.ts @@ -0,0 +1,242 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { Config } from './types.gen'; + +export type ServerSentEventsOptions = Omit & + Pick & { + /** + * Fetch API implementation. You can use this option to provide a custom + * fetch instance. + * + * @default globalThis.fetch + */ + fetch?: typeof fetch; + /** + * Implementing clients can call request interceptors inside this hook. + */ + onRequest?: (url: string, init: RequestInit) => Promise; + /** + * Callback invoked when a network or parsing error occurs during streaming. + * + * This option applies only if the endpoint returns a stream of events. + * + * @param error The error that occurred. + */ + onSseError?: (error: unknown) => void; + /** + * Callback invoked when an event is streamed from the server. + * + * This option applies only if the endpoint returns a stream of events. + * + * @param event Event streamed from the server. + * @returns Nothing (void). + */ + onSseEvent?: (event: StreamEvent) => void; + serializedBody?: RequestInit['body']; + /** + * Default retry delay in milliseconds. + * + * This option applies only if the endpoint returns a stream of events. + * + * @default 3000 + */ + sseDefaultRetryDelay?: number; + /** + * Maximum number of retry attempts before giving up. + */ + sseMaxRetryAttempts?: number; + /** + * Maximum retry delay in milliseconds. + * + * Applies only when exponential backoff is used. + * + * This option applies only if the endpoint returns a stream of events. + * + * @default 30000 + */ + sseMaxRetryDelay?: number; + /** + * Optional sleep function for retry backoff. + * + * Defaults to using `setTimeout`. + */ + sseSleepFn?: (ms: number) => Promise; + url: string; + }; + +export interface StreamEvent { + data: TData; + event?: string; + id?: string; + retry?: number; +} + +export type ServerSentEventsResult = { + stream: AsyncGenerator< + TData extends Record ? TData[keyof TData] : TData, + TReturn, + TNext + >; +}; + +export function createSseClient({ + onRequest, + onSseError, + onSseEvent, + responseTransformer, + responseValidator, + sseDefaultRetryDelay, + sseMaxRetryAttempts, + sseMaxRetryDelay, + sseSleepFn, + url, + ...options +}: ServerSentEventsOptions): ServerSentEventsResult { + let lastEventId: string | undefined; + + const sleep = sseSleepFn ?? ((ms: number) => new Promise((resolve) => setTimeout(resolve, ms))); + + const createStream = async function* () { + let retryDelay: number = sseDefaultRetryDelay ?? 3000; + let attempt = 0; + const signal = options.signal ?? new AbortController().signal; + + while (true) { + if (signal.aborted) break; + + attempt++; + + const headers = + options.headers instanceof Headers + ? options.headers + : new Headers(options.headers as Record | undefined); + + if (lastEventId !== undefined) { + headers.set('Last-Event-ID', lastEventId); + } + + try { + const requestInit: RequestInit = { + redirect: 'follow', + ...options, + body: options.serializedBody, + headers, + signal, + }; + let request = new Request(url, requestInit); + if (onRequest) { + request = await onRequest(url, requestInit); + } + // fetch must be assigned here, otherwise it would throw the error: + // TypeError: Failed to execute 'fetch' on 'Window': Illegal invocation + const _fetch = options.fetch ?? globalThis.fetch; + const response = await _fetch(request); + + if (!response.ok) throw new Error(`SSE failed: ${response.status} ${response.statusText}`); + + if (!response.body) throw new Error('No body in SSE response'); + + const reader = response.body.pipeThrough(new TextDecoderStream()).getReader(); + + let buffer = ''; + + const abortHandler = () => { + try { + reader.cancel(); + } catch { + // noop + } + }; + + signal.addEventListener('abort', abortHandler); + + try { + while (true) { + const { done, value } = await reader.read(); + if (done) break; + buffer += value; + buffer = buffer.replace(/\r\n?/g, '\n'); // normalize line endings + + const chunks = buffer.split('\n\n'); + buffer = chunks.pop() ?? ''; + + for (const chunk of chunks) { + const lines = chunk.split('\n'); + const dataLines: Array = []; + let eventName: string | undefined; + + for (const line of lines) { + if (line.startsWith('data:')) { + dataLines.push(line.replace(/^data:\s*/, '')); + } else if (line.startsWith('event:')) { + eventName = line.replace(/^event:\s*/, ''); + } else if (line.startsWith('id:')) { + lastEventId = line.replace(/^id:\s*/, ''); + } else if (line.startsWith('retry:')) { + const parsed = Number.parseInt(line.replace(/^retry:\s*/, ''), 10); + if (!Number.isNaN(parsed)) { + retryDelay = parsed; + } + } + } + + let data: unknown; + let parsedJson = false; + + if (dataLines.length) { + const rawData = dataLines.join('\n'); + try { + data = JSON.parse(rawData); + parsedJson = true; + } catch { + data = rawData; + } + } + + if (parsedJson) { + if (responseValidator) { + await responseValidator(data); + } + + if (responseTransformer) { + data = await responseTransformer(data); + } + } + + onSseEvent?.({ + data, + event: eventName, + id: lastEventId, + retry: retryDelay, + }); + + if (dataLines.length) { + yield data as any; + } + } + } + } finally { + signal.removeEventListener('abort', abortHandler); + reader.releaseLock(); + } + + break; // exit loop on normal completion + } catch (error) { + // connection failed or aborted; retry after delay + onSseError?.(error); + + if (sseMaxRetryAttempts !== undefined && attempt >= sseMaxRetryAttempts) { + break; // stop after firing error + } + + // exponential backoff: double retry each attempt, cap at 30s + const backoff = Math.min(retryDelay * 2 ** (attempt - 1), sseMaxRetryDelay ?? 30000); + await sleep(backoff); + } + } + }; + + const stream = createStream(); + + return { stream }; +} diff --git a/frontend/src/lib/api/gen/core/types.gen.ts b/frontend/src/lib/api/gen/core/types.gen.ts new file mode 100644 index 0000000..c657c85 --- /dev/null +++ b/frontend/src/lib/api/gen/core/types.gen.ts @@ -0,0 +1,110 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { Auth, AuthToken } from './auth.gen'; +import type { BodySerializer, QuerySerializer, QuerySerializerOptions } from './bodySerializer.gen'; + +export type HttpMethod = + | 'connect' + | 'delete' + | 'get' + | 'head' + | 'options' + | 'patch' + | 'post' + | 'put' + | 'trace'; + +export type Client< + RequestFn = never, + Config = unknown, + MethodFn = never, + BuildUrlFn = never, + SseFn = never, +> = { + /** + * Returns the final request URL. + */ + buildUrl: BuildUrlFn; + getConfig: () => Config; + request: RequestFn; + setConfig: (config: Config) => Config; +} & { + [K in HttpMethod]: MethodFn; +} & ([SseFn] extends [never] ? { sse?: never } : { sse: { [K in HttpMethod]: SseFn } }); + +export interface Config { + /** + * Auth token or a function returning auth token. The resolved value will be + * added to the request payload as defined by its `security` array. + */ + auth?: ((auth: Auth) => Promise | AuthToken) | AuthToken; + /** + * A function for serializing request body parameter. By default, + * {@link JSON.stringify()} will be used. + */ + bodySerializer?: BodySerializer | null; + /** + * An object containing any HTTP headers that you want to pre-populate your + * `Headers` object with. + * + * {@link https://developer.mozilla.org/docs/Web/API/Headers/Headers#init See more} + */ + headers?: + | RequestInit['headers'] + | Record< + string, + string | number | boolean | (string | number | boolean)[] | null | undefined | unknown + >; + /** + * The request method. + * + * {@link https://developer.mozilla.org/docs/Web/API/fetch#method See more} + */ + method?: Uppercase; + /** + * A function for serializing request query parameters. By default, arrays + * will be exploded in form style, objects will be exploded in deepObject + * style, and reserved characters are percent-encoded. + * + * This method will have no effect if the native `paramsSerializer()` Axios + * API function is used. + * + * {@link https://swagger.io/docs/specification/serialization/#query View examples} + */ + querySerializer?: QuerySerializer | QuerySerializerOptions; + /** + * A function validating request data. This is useful if you want to ensure + * the request conforms to the desired shape, so it can be safely sent to + * the server. + */ + requestValidator?: (data: unknown) => Promise; + /** + * A function transforming response data before it's returned. This is useful + * for post-processing data, e.g., converting ISO strings into Date objects. + */ + responseTransformer?: (data: unknown) => Promise; + /** + * A function validating response data. This is useful if you want to ensure + * the response conforms to the desired shape, so it can be safely passed to + * the transformers and returned to the user. + */ + responseValidator?: (data: unknown) => Promise; +} + +/** + * Arbitrary metadata passed through the `meta` request option. + */ +// eslint-disable-next-line @typescript-eslint/no-empty-object-type +export interface ClientMeta {} + +type IsExactlyNeverOrNeverUndefined = [T] extends [never] + ? true + : [T] extends [never | undefined] + ? [undefined] extends [T] + ? false + : true + : false; + +export type OmitNever> = { + [K in keyof T as IsExactlyNeverOrNeverUndefined extends true ? never : K]: T[K]; +}; diff --git a/frontend/src/lib/api/gen/core/utils.gen.ts b/frontend/src/lib/api/gen/core/utils.gen.ts new file mode 100644 index 0000000..af56e07 --- /dev/null +++ b/frontend/src/lib/api/gen/core/utils.gen.ts @@ -0,0 +1,140 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { BodySerializer, QuerySerializer } from './bodySerializer.gen'; +import { + type ArraySeparatorStyle, + serializeArrayParam, + serializeObjectParam, + serializePrimitiveParam, +} from './pathSerializer.gen'; + +export interface PathSerializer { + path: Record; + url: string; +} + +export const PATH_PARAM_RE: RegExp = /\{[^{}]+\}/g; + +export const defaultPathSerializer = ({ path, url: _url }: PathSerializer): string => { + let url = _url; + const matches = _url.match(PATH_PARAM_RE); + if (matches) { + for (const match of matches) { + let explode = false; + let name = match.substring(1, match.length - 1); + let style: ArraySeparatorStyle = 'simple'; + + if (name.endsWith('*')) { + explode = true; + name = name.substring(0, name.length - 1); + } + + if (name.startsWith('.')) { + name = name.substring(1); + style = 'label'; + } else if (name.startsWith(';')) { + name = name.substring(1); + style = 'matrix'; + } + + const value = path[name]; + + if (value === undefined || value === null) { + continue; + } + + if (Array.isArray(value)) { + url = url.replace(match, serializeArrayParam({ explode, name, style, value })); + continue; + } + + if (typeof value === 'object') { + url = url.replace( + match, + serializeObjectParam({ + explode, + name, + style, + value: value as Record, + valueOnly: true, + }), + ); + continue; + } + + if (style === 'matrix') { + url = url.replace( + match, + `;${serializePrimitiveParam({ + name, + value: value as string, + })}`, + ); + continue; + } + + const replaceValue = encodeURIComponent( + style === 'label' ? `.${value as string}` : (value as string), + ); + url = url.replace(match, replaceValue); + } + } + return url; +}; + +export const getUrl = ({ + baseUrl, + path, + query, + querySerializer, + url: _url, +}: { + baseUrl?: string; + path?: Record; + query?: Record; + querySerializer: QuerySerializer; + url: string; +}): string => { + const pathUrl = _url.startsWith('/') ? _url : `/${_url}`; + let url = (baseUrl ?? '') + pathUrl; + if (path) { + url = defaultPathSerializer({ path, url }); + } + let search = query ? querySerializer(query) : ''; + if (search.startsWith('?')) { + search = search.substring(1); + } + if (search) { + url += `?${search}`; + } + return url; +}; + +export function getValidRequestBody(options: { + body?: unknown; + bodySerializer?: BodySerializer | null; + serializedBody?: unknown; +}): unknown { + const hasBody = options.body !== undefined; + const isSerializedBody = hasBody && options.bodySerializer; + + if (isSerializedBody) { + if ('serializedBody' in options) { + const hasSerializedBody = + options.serializedBody !== undefined && options.serializedBody !== ''; + + return hasSerializedBody ? options.serializedBody : null; + } + + // not all clients implement a serializedBody property (i.e., client-axios) + return options.body !== '' ? options.body : null; + } + + // plain/text body + if (hasBody) { + return options.body; + } + + // no body was provided + return undefined; +} diff --git a/frontend/src/lib/api/gen/index.ts b/frontend/src/lib/api/gen/index.ts new file mode 100644 index 0000000..1ba5c42 --- /dev/null +++ b/frontend/src/lib/api/gen/index.ts @@ -0,0 +1,4 @@ +// This file is auto-generated by @hey-api/openapi-ts + +export { approvePlan, ask, cancelAnalysis, clearChat, createAnalysis, deleteAnalysis, exportObsidian, findClaims, getAnalysis, getArticle, getChat, getClaims, getClusters, getEvents, getPlan, health, listAnalyses, listArticles, type Options, searchPlan, streamAnalysis } from './sdk.gen'; +export type { Analysis, AnalysisCreate, AnalysisEvent, ApprovePlanData, ApprovePlanError, ApprovePlanErrors, ApprovePlanResponse, ApprovePlanResponses, Article, AskData, AskError, AskErrors, AskResponses, CancelAnalysisData, CancelAnalysisError, CancelAnalysisErrors, CancelAnalysisResponse, CancelAnalysisResponses, ChatMessage, ChatRequest, ChatSource, Claims, ClearChatData, ClearChatError, ClearChatErrors, ClearChatResponse, ClearChatResponses, ClientOptions, Cluster, CreateAnalysisData, CreateAnalysisError, CreateAnalysisErrors, CreateAnalysisResponse, CreateAnalysisResponses, DeleteAnalysisData, DeleteAnalysisError, DeleteAnalysisErrors, DeleteAnalysisResponse, DeleteAnalysisResponses, ExportObsidianData, ExportObsidianError, ExportObsidianErrors, ExportObsidianResponses, FindClaimsData, FindClaimsError, FindClaimsErrors, FindClaimsResponse, FindClaimsResponses, GetAnalysisData, GetAnalysisError, GetAnalysisErrors, GetAnalysisResponse, GetAnalysisResponses, GetArticleData, GetArticleError, GetArticleErrors, GetArticleResponse, GetArticleResponses, GetChatData, GetChatError, GetChatErrors, GetChatResponse, GetChatResponses, GetClaimsData, GetClaimsError, GetClaimsErrors, GetClaimsResponse, GetClaimsResponses, GetClustersData, GetClustersError, GetClustersErrors, GetClustersResponse, GetClustersResponses, GetEventsData, GetEventsError, GetEventsErrors, GetEventsResponse, GetEventsResponses, GetPlanData, GetPlanError, GetPlanErrors, GetPlanResponse, GetPlanResponses, HealthData, HealthResponses, HttpValidationError, ListAnalysesData, ListAnalysesResponse, ListAnalysesResponses, ListArticlesData, ListArticlesError, ListArticlesErrors, ListArticlesResponse, ListArticlesResponses, MapArticle, Plan, PlanApprove, PlanResult, PlanSearch, Proposition, PropositionClaim, SearchPlanData, SearchPlanError, SearchPlanErrors, SearchPlanResponse, SearchPlanResponses, StreamAnalysisData, StreamAnalysisError, StreamAnalysisErrors, StreamAnalysisResponses, Term, ValidationError } from './types.gen'; diff --git a/frontend/src/lib/api/gen/sdk.gen.ts b/frontend/src/lib/api/gen/sdk.gen.ts new file mode 100644 index 0000000..c140ae4 --- /dev/null +++ b/frontend/src/lib/api/gen/sdk.gen.ts @@ -0,0 +1,173 @@ +// This file is auto-generated by @hey-api/openapi-ts + +import type { Client, ClientMeta, Options as Options2, RequestResult, ServerSentEventsResult, TDataShape } from './client'; +import { client } from './client.gen'; +import type { ApprovePlanData, ApprovePlanErrors, ApprovePlanResponses, AskData, AskErrors, AskResponses, CancelAnalysisData, CancelAnalysisErrors, CancelAnalysisResponses, ClearChatData, ClearChatErrors, ClearChatResponses, CreateAnalysisData, CreateAnalysisErrors, CreateAnalysisResponses, DeleteAnalysisData, DeleteAnalysisErrors, DeleteAnalysisResponses, ExportObsidianData, ExportObsidianErrors, ExportObsidianResponses, FindClaimsData, FindClaimsErrors, FindClaimsResponses, GetAnalysisData, GetAnalysisErrors, GetAnalysisResponses, GetArticleData, GetArticleErrors, GetArticleResponses, GetChatData, GetChatErrors, GetChatResponses, GetClaimsData, GetClaimsErrors, GetClaimsResponses, GetClustersData, GetClustersErrors, GetClustersResponses, GetEventsData, GetEventsErrors, GetEventsResponses, GetPlanData, GetPlanErrors, GetPlanResponses, HealthData, HealthResponses, ListAnalysesData, ListAnalysesResponses, ListArticlesData, ListArticlesErrors, ListArticlesResponses, SearchPlanData, SearchPlanErrors, SearchPlanResponses, StreamAnalysisData, StreamAnalysisErrors, StreamAnalysisResponses } from './types.gen'; + +export type Options = Options2 & { + /** + * You can provide a client instance returned by `createClient()` instead of + * individual options. This might be also useful if you want to implement a + * custom client. + */ + client?: Client; + /** + * You can pass arbitrary values through the `meta` object. This can be + * used to access values that aren't defined as part of the SDK function. + */ + meta?: keyof ClientMeta extends never ? Record : ClientMeta; +}; + +/** + * Health + */ +export const health = (options?: Options): RequestResult => (options?.client ?? client).get({ url: '/api/health', ...options }); + +/** + * List Analyses + */ +export const listAnalyses = (options?: Options): RequestResult => (options?.client ?? client).get({ url: '/api/analyses', ...options }); + +/** + * Create Analysis + */ +export const createAnalysis = (options: Options): RequestResult => (options.client ?? client).post({ + url: '/api/analyses', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options.headers + } +}); + +/** + * Delete Analysis + */ +export const deleteAnalysis = (options: Options): RequestResult => (options.client ?? client).delete({ url: '/api/analyses/{analysis_id}', ...options }); + +/** + * Get Analysis + */ +export const getAnalysis = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}', ...options }); + +/** + * Cancel Analysis + * + * Ask a running analysis to stop; it reaches status 'cancelled' shortly after. + */ +export const cancelAnalysis = (options: Options): RequestResult => (options.client ?? client).post({ url: '/api/analyses/{analysis_id}/cancel', ...options }); + +/** + * Get Plan + */ +export const getPlan = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/plan', ...options }); + +/** + * Search Plan + * + * Run edited queries again while the analysis waits for review. + */ +export const searchPlan = (options: Options): RequestResult => (options.client ?? client).post({ + url: '/api/analyses/{analysis_id}/plan/search', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options.headers + } +}); + +/** + * Approve Plan + * + * Continue the paused analysis with the chosen pages (search results or your own URLs). + */ +export const approvePlan = (options: Options): RequestResult => (options.client ?? client).post({ + url: '/api/analyses/{analysis_id}/plan/approve', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options.headers + } +}); + +/** + * Get Events + */ +export const getEvents = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/events', ...options }); + +/** + * Stream Analysis + * + * Server-sent events until the analysis stops running: + * default `message` events — the Analysis row, whenever it changes + * `log` events (with `id:`) — new rows from the event log; EventSource + * resends Last-Event-ID on reconnect, so the log resumes where it left off. + */ +export const streamAnalysis = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/stream', ...options }); + +/** + * List Articles + * + * Every article on the map, with its position, topic and outlier score. + */ +export const listArticles = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/articles', ...options }); + +/** + * Get Clusters + * + * Topics, largest first, with their key terms. Unclustered articles have none. + */ +export const getClusters = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/clusters', ...options }); + +/** + * Get Article + */ +export const getArticle = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/articles/{article_id}', ...options }); + +/** + * Get Claims + */ +export const getClaims = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/claims', ...options }); + +/** + * Find Claims + * + * Find each article's claims and where they agree or disagree (Claude requests). + */ +export const findClaims = (options: Options): RequestResult => (options.client ?? client).post({ url: '/api/analyses/{analysis_id}/claims', ...options }); + +/** + * Clear Chat + */ +export const clearChat = (options: Options): RequestResult => (options.client ?? client).delete({ url: '/api/analyses/{analysis_id}/chat', ...options }); + +/** + * Get Chat + * + * The saved conversation for this analysis, oldest first. + */ +export const getChat = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/chat', ...options }); + +/** + * Ask + * + * Ask a question; the answer streams as server-sent events: + * sources — the numbered excerpts the answer may cite (ChatSource[]) + * delta — {"text": ...}, a piece of the answer as it's written + * done — the saved answer (ChatMessage), with each source's `cited` set + * error — {"detail": ...}; nothing is saved + * The question and answer are saved together only when the answer completes. + */ +export const ask = (options: Options): Promise> => (options.client ?? client).sse.post({ + url: '/api/analyses/{analysis_id}/chat', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options.headers + } +}); + +/** + * Export Obsidian + */ +export const exportObsidian = (options: Options): RequestResult => (options.client ?? client).get({ url: '/api/analyses/{analysis_id}/export', ...options }); diff --git a/frontend/src/lib/api/gen/types.gen.ts b/frontend/src/lib/api/gen/types.gen.ts new file mode 100644 index 0000000..d74e276 --- /dev/null +++ b/frontend/src/lib/api/gen/types.gen.ts @@ -0,0 +1,1049 @@ +// This file is auto-generated by @hey-api/openapi-ts + +export type ClientOptions = { + baseUrl: `${string}://${string}` | (string & {}); +}; + +/** + * Analysis + */ +export type Analysis = { + /** + * Id + */ + id: string; + /** + * Topic + */ + topic: string; + /** + * Status + */ + status: 'queued' | 'crawling' | 'reviewing' | 'processing' | 'embedding' | 'projecting' | 'done' | 'error' | 'cancelled'; + /** + * Progress + */ + progress: number; + /** + * Stage Detail + */ + stage_detail: string | null; + /** + * Article Count + */ + article_count: number; + /** + * Error Msg + */ + error_msg: string | null; + /** + * Warning + */ + warning: string | null; + /** + * Created Ts + */ + created_ts: number; + /** + * Updated Ts + */ + updated_ts: number; +}; + +/** + * AnalysisCreate + */ +export type AnalysisCreate = { + /** + * Topic + */ + topic: string; + /** + * Review + */ + review?: boolean; +}; + +/** + * AnalysisEvent + */ +export type AnalysisEvent = { + /** + * Id + */ + id: number; + /** + * Ts + */ + ts: number; + /** + * Kind + */ + kind: 'stage' | 'query' | 'article' | 'skip' | 'cluster' | 'warning' | 'error'; + /** + * Message + */ + message: string; + /** + * Url + */ + url: string | null; +}; + +/** + * Article + */ +export type Article = { + /** + * Id + */ + id: string; + /** + * Analysis Id + */ + analysis_id: string; + /** + * Url + */ + url: string; + /** + * Domain + */ + domain: string | null; + /** + * Title + */ + title: string | null; + /** + * Publish Date + */ + publish_date: string | null; + /** + * Crawl Ts + */ + crawl_ts: number | null; + /** + * Word Count + */ + word_count: number | null; + /** + * Language + */ + language: string | null; + /** + * Raw Text + */ + raw_text: string | null; + /** + * Status + */ + status: string; + /** + * Source Note + */ + source_note?: string | null; +}; + +/** + * ChatMessage + */ +export type ChatMessage = { + /** + * Id + */ + id: number; + /** + * Role + */ + role: 'user' | 'assistant'; + /** + * Text + */ + text: string; + /** + * Sources + */ + sources: Array | null; + /** + * Created Ts + */ + created_ts: number; +}; + +/** + * ChatRequest + */ +export type ChatRequest = { + /** + * Question + */ + question: string; +}; + +/** + * ChatSource + * + * An excerpt the answer could cite as [n]. + */ +export type ChatSource = { + /** + * N + */ + n: number; + /** + * Article Id + */ + article_id: string; + /** + * Url + */ + url: string; + /** + * Title + */ + title: string; + /** + * Domain + */ + domain: string; + /** + * Publish Date + */ + publish_date: string | null; + /** + * Cited + */ + cited?: boolean | null; +}; + +/** + * Claims + * + * Where the articles agree and disagree. status is null until claims are first found. + */ +export type Claims = { + /** + * Status + */ + status: 'running' | 'done' | 'error' | null; + /** + * Detail + */ + detail: string | null; + /** + * Claim Count + */ + claim_count: number; + /** + * Propositions + */ + propositions: Array; +}; + +/** + * Cluster + */ +export type Cluster = { + /** + * Cluster Id + */ + cluster_id: number; + /** + * Label + */ + label: string; + /** + * Description + */ + description: string | null; + /** + * Article Count + */ + article_count: number; + /** + * Terms + */ + terms: Array; +}; + +/** + * HTTPValidationError + */ +export type HttpValidationError = { + /** + * Detail + */ + detail?: Array; +}; + +/** + * MapArticle + * + * An article as placed on the map. + */ +export type MapArticle = { + /** + * Article Id + */ + article_id: string; + /** + * Url + */ + url: string; + /** + * Title + */ + title: string | null; + /** + * Domain + */ + domain: string | null; + /** + * Publish Date + */ + publish_date: string | null; + /** + * Word Count + */ + word_count: number | null; + /** + * X + */ + x: number; + /** + * Y + */ + y: number; + /** + * Cluster Id + */ + cluster_id: number; + /** + * Distance + */ + distance: number; +}; + +/** + * Plan + * + * What a paused analysis found: its queries and the pages they returned. + */ +export type Plan = { + /** + * Queries + */ + queries: Array; + /** + * Results + */ + results: Array; +}; + +/** + * PlanApprove + */ +export type PlanApprove = { + /** + * Urls + */ + urls: Array; +}; + +/** + * PlanResult + */ +export type PlanResult = { + /** + * Url + */ + url: string; + /** + * Title + */ + title: string; + /** + * Snippet + */ + snippet: string; + /** + * Query + */ + query: string; +}; + +/** + * PlanSearch + */ +export type PlanSearch = { + /** + * Queries + */ + queries: Array; +}; + +/** + * Proposition + */ +export type Proposition = { + /** + * Id + */ + id: number; + /** + * Statement + */ + statement: string; + /** + * Claims + */ + claims: Array; +}; + +/** + * PropositionClaim + */ +export type PropositionClaim = { + /** + * Claim Id + */ + claim_id: string; + /** + * Article Id + */ + article_id: string; + /** + * Text + */ + text: string; + /** + * Stance + */ + stance: 'agrees' | 'qualifies' | 'disputes'; +}; + +/** + * Term + */ +export type Term = { + /** + * Term + */ + term: string; + /** + * Weight + */ + weight: number; +}; + +/** + * ValidationError + */ +export type ValidationError = { + /** + * Location + */ + loc: Array; + /** + * Message + */ + msg: string; + /** + * Error Type + */ + type: string; + /** + * Input + */ + input?: unknown; + /** + * Context + */ + ctx?: { + [key: string]: unknown; + }; +}; + +export type HealthData = { + body?: never; + path?: never; + query?: never; + url: '/api/health'; +}; + +export type HealthResponses = { + /** + * Successful Response + */ + 200: unknown; +}; + +export type ListAnalysesData = { + body?: never; + path?: never; + query?: never; + url: '/api/analyses'; +}; + +export type ListAnalysesResponses = { + /** + * Response List Analyses + * + * Successful Response + */ + 200: Array; +}; + +export type ListAnalysesResponse = ListAnalysesResponses[keyof ListAnalysesResponses]; + +export type CreateAnalysisData = { + body: AnalysisCreate; + path?: never; + query?: never; + url: '/api/analyses'; +}; + +export type CreateAnalysisErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type CreateAnalysisError = CreateAnalysisErrors[keyof CreateAnalysisErrors]; + +export type CreateAnalysisResponses = { + /** + * Successful Response + */ + 201: Analysis; +}; + +export type CreateAnalysisResponse = CreateAnalysisResponses[keyof CreateAnalysisResponses]; + +export type DeleteAnalysisData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}'; +}; + +export type DeleteAnalysisErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type DeleteAnalysisError = DeleteAnalysisErrors[keyof DeleteAnalysisErrors]; + +export type DeleteAnalysisResponses = { + /** + * Successful Response + */ + 204: void; +}; + +export type DeleteAnalysisResponse = DeleteAnalysisResponses[keyof DeleteAnalysisResponses]; + +export type GetAnalysisData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}'; +}; + +export type GetAnalysisErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetAnalysisError = GetAnalysisErrors[keyof GetAnalysisErrors]; + +export type GetAnalysisResponses = { + /** + * Successful Response + */ + 200: Analysis; +}; + +export type GetAnalysisResponse = GetAnalysisResponses[keyof GetAnalysisResponses]; + +export type CancelAnalysisData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/cancel'; +}; + +export type CancelAnalysisErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type CancelAnalysisError = CancelAnalysisErrors[keyof CancelAnalysisErrors]; + +export type CancelAnalysisResponses = { + /** + * Successful Response + */ + 202: Analysis; +}; + +export type CancelAnalysisResponse = CancelAnalysisResponses[keyof CancelAnalysisResponses]; + +export type GetPlanData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/plan'; +}; + +export type GetPlanErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetPlanError = GetPlanErrors[keyof GetPlanErrors]; + +export type GetPlanResponses = { + /** + * Successful Response + */ + 200: Plan; +}; + +export type GetPlanResponse = GetPlanResponses[keyof GetPlanResponses]; + +export type SearchPlanData = { + body: PlanSearch; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/plan/search'; +}; + +export type SearchPlanErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type SearchPlanError = SearchPlanErrors[keyof SearchPlanErrors]; + +export type SearchPlanResponses = { + /** + * Successful Response + */ + 200: Plan; +}; + +export type SearchPlanResponse = SearchPlanResponses[keyof SearchPlanResponses]; + +export type ApprovePlanData = { + body: PlanApprove; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/plan/approve'; +}; + +export type ApprovePlanErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ApprovePlanError = ApprovePlanErrors[keyof ApprovePlanErrors]; + +export type ApprovePlanResponses = { + /** + * Successful Response + */ + 202: Analysis; +}; + +export type ApprovePlanResponse = ApprovePlanResponses[keyof ApprovePlanResponses]; + +export type GetEventsData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: { + /** + * After + */ + after?: number; + }; + url: '/api/analyses/{analysis_id}/events'; +}; + +export type GetEventsErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetEventsError = GetEventsErrors[keyof GetEventsErrors]; + +export type GetEventsResponses = { + /** + * Response Get Events + * + * Successful Response + */ + 200: Array; +}; + +export type GetEventsResponse = GetEventsResponses[keyof GetEventsResponses]; + +export type StreamAnalysisData = { + body?: never; + headers?: { + /** + * Last-Event-Id + */ + 'last-event-id'?: number; + }; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/stream'; +}; + +export type StreamAnalysisErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type StreamAnalysisError = StreamAnalysisErrors[keyof StreamAnalysisErrors]; + +export type StreamAnalysisResponses = { + /** + * Successful Response + */ + 200: unknown; +}; + +export type ListArticlesData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/articles'; +}; + +export type ListArticlesErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ListArticlesError = ListArticlesErrors[keyof ListArticlesErrors]; + +export type ListArticlesResponses = { + /** + * Response List Articles + * + * Successful Response + */ + 200: Array; +}; + +export type ListArticlesResponse = ListArticlesResponses[keyof ListArticlesResponses]; + +export type GetClustersData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/clusters'; +}; + +export type GetClustersErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetClustersError = GetClustersErrors[keyof GetClustersErrors]; + +export type GetClustersResponses = { + /** + * Response Get Clusters + * + * Successful Response + */ + 200: Array; +}; + +export type GetClustersResponse = GetClustersResponses[keyof GetClustersResponses]; + +export type GetArticleData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + /** + * Article Id + */ + article_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/articles/{article_id}'; +}; + +export type GetArticleErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetArticleError = GetArticleErrors[keyof GetArticleErrors]; + +export type GetArticleResponses = { + /** + * Successful Response + */ + 200: Article; +}; + +export type GetArticleResponse = GetArticleResponses[keyof GetArticleResponses]; + +export type GetClaimsData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/claims'; +}; + +export type GetClaimsErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetClaimsError = GetClaimsErrors[keyof GetClaimsErrors]; + +export type GetClaimsResponses = { + /** + * Successful Response + */ + 200: Claims; +}; + +export type GetClaimsResponse = GetClaimsResponses[keyof GetClaimsResponses]; + +export type FindClaimsData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/claims'; +}; + +export type FindClaimsErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type FindClaimsError = FindClaimsErrors[keyof FindClaimsErrors]; + +export type FindClaimsResponses = { + /** + * Successful Response + */ + 202: Claims; +}; + +export type FindClaimsResponse = FindClaimsResponses[keyof FindClaimsResponses]; + +export type ClearChatData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/chat'; +}; + +export type ClearChatErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ClearChatError = ClearChatErrors[keyof ClearChatErrors]; + +export type ClearChatResponses = { + /** + * Successful Response + */ + 204: void; +}; + +export type ClearChatResponse = ClearChatResponses[keyof ClearChatResponses]; + +export type GetChatData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/chat'; +}; + +export type GetChatErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetChatError = GetChatErrors[keyof GetChatErrors]; + +export type GetChatResponses = { + /** + * Response Get Chat + * + * Successful Response + */ + 200: Array; +}; + +export type GetChatResponse = GetChatResponses[keyof GetChatResponses]; + +export type AskData = { + body: ChatRequest; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/chat'; +}; + +export type AskErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type AskError = AskErrors[keyof AskErrors]; + +export type AskResponses = { + /** + * Successful Response + */ + 200: unknown; +}; + +export type ExportObsidianData = { + body?: never; + path: { + /** + * Analysis Id + */ + analysis_id: string; + }; + query?: never; + url: '/api/analyses/{analysis_id}/export'; +}; + +export type ExportObsidianErrors = { + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ExportObsidianError = ExportObsidianErrors[keyof ExportObsidianErrors]; + +export type ExportObsidianResponses = { + /** + * Successful Response + */ + 200: unknown; +}; diff --git a/frontend/src/lib/api/sse.spec.ts b/frontend/src/lib/api/sse.spec.ts new file mode 100644 index 0000000..f3f8e63 --- /dev/null +++ b/frontend/src/lib/api/sse.spec.ts @@ -0,0 +1,29 @@ +import { describe, expect, it } from 'vitest'; +import { SseParser } from './sse'; + +describe('SseParser', () => { + it('parses named events with JSON data', () => { + const events = new SseParser().push( + 'event: delta\ndata: {"text":"Hi"}\n\nevent: done\ndata: {}\n\n' + ); + expect(events).toEqual([ + { event: 'delta', data: '{"text":"Hi"}' }, + { event: 'done', data: '{}' } + ]); + }); + + it('buffers events split across chunks', () => { + const p = new SseParser(); + expect(p.push('event: delta\nda')).toEqual([]); + expect(p.push('ta: {"text":"a"}\n')).toEqual([]); + expect(p.push('\nevent: done\ndata: 1\n\n')).toEqual([ + { event: 'delta', data: '{"text":"a"}' }, + { event: 'done', data: '1' } + ]); + }); + + it('handles CRLF, comments, default event names and multi-line data', () => { + const events = new SseParser().push(': keep-alive\r\n\r\ndata: one\r\ndata: two\r\n\r\n'); + expect(events).toEqual([{ event: 'message', data: 'one\ntwo' }]); + }); +}); diff --git a/frontend/src/lib/api/sse.ts b/frontend/src/lib/api/sse.ts new file mode 100644 index 0000000..6713e02 --- /dev/null +++ b/frontend/src/lib/api/sse.ts @@ -0,0 +1,55 @@ +/** + * Minimal server-sent-events parser for fetch() response bodies + * (EventSource only does GET; asking a question is a POST). + */ + +export interface SseEvent { + event: string; + data: string; +} + +/** Feed decoded text chunks in; complete events come out. Keeps partial events buffered. */ +export class SseParser { + #buffer = ''; + + push(chunk: string): SseEvent[] { + this.#buffer += chunk.replace(/\r\n?/g, '\n'); + const events: SseEvent[] = []; + let end: number; + while ((end = this.#buffer.indexOf('\n\n')) !== -1) { + const block = this.#buffer.slice(0, end); + this.#buffer = this.#buffer.slice(end + 2); + const parsed = parseBlock(block); + if (parsed) events.push(parsed); + } + return events; + } +} + +function parseBlock(block: string): SseEvent | null { + let event = 'message'; + const data: string[] = []; + for (const line of block.split('\n')) { + if (!line || line.startsWith(':')) continue; + const colon = line.indexOf(':'); + const field = colon === -1 ? line : line.slice(0, colon); + const value = colon === -1 ? '' : line.slice(colon + 1).replace(/^ /, ''); + if (field === 'event') event = value; + else if (field === 'data') data.push(value); + } + return data.length ? { event, data: data.join('\n') } : null; +} + +export async function* readSse(response: Response): AsyncGenerator { + const reader = response.body!.pipeThrough(new TextDecoderStream()).getReader(); + const parser = new SseParser(); + try { + while (true) { + const { done, value } = await reader.read(); + if (done) break; + yield* parser.push(value); + } + } finally { + reader.releaseLock(); + } +} diff --git a/frontend/src/lib/api/stream.ts b/frontend/src/lib/api/stream.ts new file mode 100644 index 0000000..091be8d --- /dev/null +++ b/frontend/src/lib/api/stream.ts @@ -0,0 +1,49 @@ +import type { Analysis, AnalysisEvent } from '#lib/api.ts'; + +export interface StreamHandlers { + onAnalysis: (analysis: Analysis) => void; + onEvent: (event: AnalysisEvent) => void; + onGone?: () => void; +} + +/** + * Follow an analysis over server-sent events. Analysis updates arrive as + * default messages, log rows as `log` events with ids (EventSource resends + * Last-Event-ID on reconnect, so nothing is duplicated or lost). + * The server closes the stream once the analysis stops running; we close the + * EventSource then too, instead of letting it reconnect forever. + * Returns a function that stops listening. + */ +export function followAnalysis(id: string, handlers: StreamHandlers): () => void { + const source = new EventSource(`/api/analyses/${encodeURIComponent(id)}/stream`); + let finished = false; + + source.onmessage = (e) => { + const analysis = JSON.parse(e.data) as Analysis; + handlers.onAnalysis(analysis); + if ( + !['queued', 'crawling', 'reviewing', 'processing', 'embedding', 'projecting'].includes( + analysis.status + ) + ) { + finished = true; + source.close(); + } + }; + source.addEventListener('log', (e) => { + handlers.onEvent(JSON.parse((e as MessageEvent).data) as AnalysisEvent); + }); + // Server-sent `event: error` carries a body (the analysis was deleted). + source.addEventListener('error', (e) => { + if ((e as MessageEvent).data) { + finished = true; + source.close(); + handlers.onGone?.(); + } else if (finished) { + source.close(); + } + // Otherwise: a dropped connection — EventSource retries on its own. + }); + + return () => source.close(); +} diff --git a/frontend/src/lib/assets/favicon.svg b/frontend/src/lib/assets/favicon.svg new file mode 100644 index 0000000..46bea1a --- /dev/null +++ b/frontend/src/lib/assets/favicon.svg @@ -0,0 +1 @@ +Discourse Lens diff --git a/frontend/src/lib/chat.svelte.ts b/frontend/src/lib/chat.svelte.ts new file mode 100644 index 0000000..f01afb4 --- /dev/null +++ b/frontend/src/lib/chat.svelte.ts @@ -0,0 +1,107 @@ +import { api, ApiError, type ChatMessage, type ChatSource } from '#lib/api.ts'; + +export interface Draft { + question: string; + text: string; + sources: ChatSource[]; +} + +interface Conversation { + loaded: boolean; + loadError: string | null; + /** Saved turns, oldest first */ + messages: ChatMessage[]; + /** The answer being streamed right now */ + draft: Draft | null; + error: string | null; + failedQuestion: string | null; + controller: AbortController | null; +} + +/** + * One conversation per analysis, kept at module level so switching views + * (which unmounts the Ask page) doesn't lose it — an answer keeps streaming + * in the background. Saved history comes from the server. + */ +// eslint-disable-next-line svelte/prefer-svelte-reactivity -- registry only grows; each conversation is reactive +const conversations = new Map(); + +export function conversation(analysisId: string): Conversation { + const existing = conversations.get(analysisId); + if (existing) return existing; + const fresh: Conversation = $state({ + loaded: false, + loadError: null, + messages: [], + draft: null, + error: null, + failedQuestion: null, + controller: null + }); + conversations.set(analysisId, fresh); + return fresh; +} + +export async function loadHistory(analysisId: string): Promise { + const c = conversation(analysisId); + if (c.loaded) return; + try { + c.messages = await api.getChat(analysisId); + c.loaded = true; + c.loadError = null; + } catch (e) { + c.loadError = (e as Error).message; + } +} + +export async function ask(analysisId: string, question: string): Promise { + const c = conversation(analysisId); + const q = question.trim(); + if (!q || c.draft) return; + c.error = null; + c.failedQuestion = null; + c.draft = { question: q, text: '', sources: [] }; + c.controller = new AbortController(); + try { + const saved = await api.ask( + analysisId, + q, + { + onSources: (sources) => c.draft && (c.draft.sources = sources), + onText: (piece) => c.draft && (c.draft.text += piece) + }, + c.controller.signal + ); + c.messages.push( + { id: -saved.id, role: 'user', text: q, sources: null, created_ts: saved.created_ts }, + saved + ); + } catch (e) { + if ((e as Error).name !== 'AbortError') { + c.error = e instanceof ApiError ? e.message : `Something went wrong: ${(e as Error).message}`; + c.failedQuestion = q; + } + } finally { + c.draft = null; + c.controller = null; + } +} + +/** Stop the answer being written; nothing from it is saved. */ +export function stop(analysisId: string): void { + conversation(analysisId).controller?.abort(); +} + +export async function retry(analysisId: string): Promise { + const c = conversation(analysisId); + if (c.failedQuestion) await ask(analysisId, c.failedQuestion); +} + +export async function clear(analysisId: string): Promise { + const c = conversation(analysisId); + stop(analysisId); + await api.clearChat(analysisId); + c.messages = []; + c.error = null; + c.failedQuestion = null; +} diff --git a/frontend/src/lib/citations.spec.ts b/frontend/src/lib/citations.spec.ts new file mode 100644 index 0000000..8fbde06 --- /dev/null +++ b/frontend/src/lib/citations.spec.ts @@ -0,0 +1,31 @@ +import { describe, expect, it } from 'vitest'; +import { linkCitations } from './citations'; + +const sources = [1, 2, 3, 4].map((n) => ({ n, title: `Title ${n}`, domain: 'x.test' })); +const ns = (html: string) => [...html.matchAll(/data-cite="(\d+)"/g)].map((m) => Number(m[1])); + +describe('linkCitations', () => { + it('turns single, adjacent, grouped and ranged citations into buttons', () => { + expect(ns(linkCitations('A [2]. B [1][3]. C [1, 4]. D [2-3].', sources))).toEqual([ + 2, 1, 3, 1, 4, 2, 3 + ]); + }); + + it('leaves markers that name no known source alone', () => { + const text = 'In [2024] a study [9] found it.'; + expect(linkCitations(text, sources)).toBe(text); + }); + + it('labels buttons with the source and escapes attribute text', () => { + const html = linkCitations('[1]', [{ n: 1, title: 'A "quoted" ', domain: 'x.test' }]); + expect(html).toContain('title="A "quoted" <b> (x.test)"'); + expect(html).toContain('aria-label="Source 1:'); + }); + + it('leaves markdown links untouched', () => { + const text = 'See [the report](https://x.test) [2].'; + expect(linkCitations(text, sources)).toMatch( + /^See \[the report\]\(https:\/\/x\.test\) `; + }) + .join(''); + }); +} diff --git a/frontend/src/lib/claims.spec.ts b/frontend/src/lib/claims.spec.ts new file mode 100644 index 0000000..65b2100 --- /dev/null +++ b/frontend/src/lib/claims.spec.ts @@ -0,0 +1,48 @@ +import { describe, expect, it } from 'vitest'; +import type { Proposition } from '#lib/api.ts'; +import { disagreement, estimate, ordered, tally, type Stance } from './claims.ts'; + +function prop(id: number, ...stances: Stance[]): Proposition { + return { + id, + statement: `P${id}`, + claims: stances.map((stance, i) => ({ + claim_id: `a${i}:0`, + article_id: `a${i}`, + text: 't', + stance + })) + }; +} + +describe('claims', () => { + it('tallies stances', () => { + expect(tally(prop(1, 'agrees', 'disputes', 'agrees'))).toEqual({ + agrees: 2, + qualifies: 0, + disputes: 1 + }); + }); + + it('scores disagreement, a qualification counting half', () => { + expect(disagreement(prop(1, 'agrees', 'agrees'))).toBe(0); + expect(disagreement(prop(1, 'agrees', 'qualifies'))).toBe(0.25); + expect(disagreement(prop(1, 'agrees', 'disputes'))).toBe(0.5); + }); + + it('orders by debate or by sources, keeping the given order on ties', () => { + const props = [ + prop(1, 'agrees', 'agrees', 'agrees', 'agrees'), + prop(2, 'agrees', 'disputes'), + prop(3, 'agrees', 'agrees'), + prop(4, 'agrees', 'agrees') + ]; + expect(ordered(props, 'debated').map((p) => p.id)).toEqual([2, 1, 3, 4]); + expect(ordered(props, 'sources').map((p) => p.id)).toEqual([1, 2, 3, 4]); + }); + + it('estimates cost in five-cent steps', () => { + expect(estimate(52)).toBe('about $0.30'); + expect(estimate(3)).toBe('about $0.05'); + }); +}); diff --git a/frontend/src/lib/claims.ts b/frontend/src/lib/claims.ts new file mode 100644 index 0000000..d2cca1f --- /dev/null +++ b/frontend/src/lib/claims.ts @@ -0,0 +1,43 @@ +import type { Proposition, PropositionClaim } from '#lib/api.ts'; + +export type Stance = PropositionClaim['stance']; +export type Order = 'debated' | 'sources'; + +export const STANCES: { stance: Stance; label: string; verb: string; color: string }[] = [ + { stance: 'agrees', label: 'Agree', verb: 'agree', color: 'var(--ink-2)' }, + { stance: 'qualifies', label: 'Qualify', verb: 'qualify', color: 'var(--series-other)' }, + { stance: 'disputes', label: 'Dispute', verb: 'dispute', color: 'var(--accent)' } +]; + +// Measured on a 52-article map: $0.27 with Claude Sonnet ($2 / $10 per million tokens). +const DOLLARS_PER_ARTICLE = 0.0055; + +export function tally(p: Proposition): Record { + const counts: Record = { agrees: 0, qualifies: 0, disputes: 0 }; + for (const c of p.claims) counts[c.stance] += 1; + return counts; +} + +/** 0 when every article agrees; higher the more of them qualify or dispute. */ +export function disagreement(p: Proposition): number { + const t = tally(p); + return (t.disputes + t.qualifies / 2) / p.claims.length; +} + +/** Most debated first, or most articles first; ties keep the order Claude gave. */ +export function ordered(props: Proposition[], order: Order): Proposition[] { + const key = + order === 'debated' + ? (p: Proposition) => [disagreement(p), p.claims.length] + : (p: Proposition) => [p.claims.length, disagreement(p)]; + return props + .map((p, i) => ({ p, i, k: key(p) })) + .toSorted((a, b) => b.k[0] - a.k[0] || b.k[1] - a.k[1] || a.i - b.i) + .map(({ p }) => p); +} + +/** "about $0.30" for a map of n articles */ +export function estimate(articles: number): string { + const dollars = Math.max(0.05, Math.ceil(articles * DOLLARS_PER_ARTICLE * 20) / 20); + return `about $${dollars.toFixed(2)}`; +} diff --git a/frontend/src/lib/components/Answer.svelte b/frontend/src/lib/components/Answer.svelte new file mode 100644 index 0000000..5b950df --- /dev/null +++ b/frontend/src/lib/components/Answer.svelte @@ -0,0 +1,156 @@ + + +{#snippet sourceRow(s: ChatSource)} +
  • + {s.n} + {#if ws.articles.some((a) => a.id === s.article_id)} + + {:else} + + + {s.title} + + + {/if} + {s.domain} +
    • +{/snippet} + +
    + + +
    + + {@html html} +
    + + {#if cited.length} +

    Sources

    +
      + {#each cited as s (s.n)}{@render sourceRow(s)}{/each} +
    + {/if} + {#if uncited.length} +
    + + Also searched, not cited ({uncited.length}) + +
      + {#each uncited as s (s.n)}{@render sourceRow(s)}{/each} +
    +
    + {/if} +
    + + diff --git a/frontend/src/lib/components/ArticleSheet.svelte b/frontend/src/lib/components/ArticleSheet.svelte new file mode 100644 index 0000000..f8839fd --- /dev/null +++ b/frontend/src/lib/components/ArticleSheet.svelte @@ -0,0 +1,125 @@ + + + !open && ws.openArticleById(null)}> + + + +
    +
    + {#if point} +

    + + {point.clusterLabel} +

    + {/if} +
    + + + +
    + +
    + + {article?.title ?? point?.title ?? 'Article'} + + + {#if article} + {article.domain} + {#if formatPublishDate(article.publish_date)}{formatPublishDate(article.publish_date)}{/if} + {#if article.word_count}{article.word_count.toLocaleString()} words{/if} + {/if} + + {#if article?.source_note} +

    +

    + {/if} + + {#if error} + + {:else if !article} +
    + {#each [92, 100, 84, 96, 60] as w, i (i)} +
    + {/each} +
    + {:else} +
    + {#each paragraphs.slice(0, PARAGRAPHS) as p, i (i)} +

    {p}

    + {/each} +
    + {#if paragraphs.length > PARAGRAPHS} +

    + Showing the opening of {paragraphs.length} paragraphs. +

    + {/if} + + + Open the original + + {/if} +
    +     +     +     diff --git a/frontend/src/lib/components/ArticleTable.svelte b/frontend/src/lib/components/ArticleTable.svelte new file mode 100644 index 0000000..33e5e21 --- /dev/null +++ b/frontend/src/lib/components/ArticleTable.svelte @@ -0,0 +1,58 @@ + + +
    + + + + + + + + + + + + {#each rows as a (a.id)} + + + + + + + {/each} + +
    Articles in this map, grouped by cluster
    ArticleCluster
    + + + + + {a.clusterLabel} + +
    +
    diff --git a/frontend/src/lib/components/ClusterKey.svelte b/frontend/src/lib/components/ClusterKey.svelte new file mode 100644 index 0000000..e4f167f --- /dev/null +++ b/frontend/src/lib/components/ClusterKey.svelte @@ -0,0 +1,69 @@ + + +
    +
    +

    Clusters

    + {#if selected != null} + + {/if} +
    +

    Pick one to focus every view on it.

    + +
      + {#each ws.clusters as c (c.id)} +
    • + +
      • + {/each} +
    + + {#if selectedInfo?.description} +

    + {selectedInfo.description} +

    + {/if} + + {#if folded} +

    + Only the {MAX_HUES} largest clusters get their own color. The rest share grey; their labels still + name them on the map. +

    + {/if} +
    diff --git a/frontend/src/lib/components/ConfirmDialog.svelte b/frontend/src/lib/components/ConfirmDialog.svelte new file mode 100644 index 0000000..2833828 --- /dev/null +++ b/frontend/src/lib/components/ConfirmDialog.svelte @@ -0,0 +1,45 @@ + + + + + + + {title} + {description} +
    + + Keep it + + { + open = false; + onconfirm(); + }} + > + {confirmLabel} + +
    +     +     +     diff --git a/frontend/src/lib/components/EndedView.svelte b/frontend/src/lib/components/EndedView.svelte new file mode 100644 index 0000000..21d36eb --- /dev/null +++ b/frontend/src/lib/components/EndedView.svelte @@ -0,0 +1,40 @@ + + +
    + {#if analysis.status === 'error'} +

    This map didn't finish

    +

    + {analysis.error_msg ?? 'The pipeline stopped with an unknown error.'} +

    + {:else} +

    This map was cancelled

    +

    Nothing it collected was kept.

    + {/if} + +
    diff --git a/frontend/src/lib/components/ProgressView.svelte b/frontend/src/lib/components/ProgressView.svelte new file mode 100644 index 0000000..28d65f3 --- /dev/null +++ b/frontend/src/lib/components/ProgressView.svelte @@ -0,0 +1,158 @@ + + +{#if analysis.status === 'reviewing'} + +{:else} +
    +
    +

    + {analysis.stage_detail ?? 'Starting'} +

    +
    +
    +
    +
    +
    +
    Done
    +
    {analysis.progress}%
    +
    +
    +
    Articles kept
    +
    {indexed}
    +
    +
    +
    Elapsed
    +
    {formatDuration(elapsed)}
    +
    +
    +

    + Most maps take 3 to 8 minutes. You can leave this page; the map keeps building and shows up + in your list. +

    + +
    + +
    +

    + Activity + + {plural(indexed, 'article')} kept, {plural(skipped, 'page')} skipped + +

    +
      + {#each newestFirst as e (e.id)} + {@const Icon = ICONS[e.kind]} +
    1. +
      2. + {:else} +
    2. Waiting for the first step…
      3. + {/each} +
    +
    +
    +{/if} diff --git a/frontend/src/lib/components/ReviewPanel.svelte b/frontend/src/lib/components/ReviewPanel.svelte new file mode 100644 index 0000000..efb4dab --- /dev/null +++ b/frontend/src/lib/components/ReviewPanel.svelte @@ -0,0 +1,242 @@ + + +
    + {#if loadError} + + {:else if !plan} +

    Loading the search results…

    + {:else} +
    +
    +

    Check the sources before reading

    +

    + Untick pages you don't want, add your own, or edit the searches and run them again. +

    + +

    Searches

    +
      + {#each queries as query, i (i)} +
    • + + + +
      • + {/each} +
    +
    + + +
    + +

    + +

    + + {#if invalidExtra.length} +

    Not a web address: {invalidExtra[0]}

    + {/if} + +
    + + +
    +
    + +
    + {#each groups as [query, results] (query)} + {@const allOn = results.every((r) => chosen.has(r.url))} +
    +
    +

    {query}

    + +
    +
      + {#each results as r (r.url)} +
    • + + e.currentTarget.checked ? chosen.add(r.url) : chosen.delete(r.url)} + /> +
      + +

      + + + {host(r.url)} + + +

      + {#if r.snippet}

      + {r.snippet} +

      {/if} +
      +
      • + {/each} +
    +
    + {:else} +

    The searches found nothing. Edit them and search again.

    + {/each} +
    +
    + {/if} +
    diff --git a/frontend/src/lib/components/StatusLabel.svelte b/frontend/src/lib/components/StatusLabel.svelte new file mode 100644 index 0000000..a0d29ba --- /dev/null +++ b/frontend/src/lib/components/StatusLabel.svelte @@ -0,0 +1,31 @@ + + + + {#if analysis.status === 'done'} + diff --git a/frontend/src/lib/components/SurveyMap.svelte b/frontend/src/lib/components/SurveyMap.svelte new file mode 100644 index 0000000..fdb027c --- /dev/null +++ b/frontend/src/lib/components/SurveyMap.svelte @@ -0,0 +1,269 @@ + + +
    + {#if width && height} + + + ws.selectCluster(null)} + role="presentation" + /> + + + {#each contours as c, i (i)} + + {/each} + + + {#each marks as m (m.a.id)} + (hover = { a: m.a, x: m.sx, y: m.sy })} + onmouseleave={() => (hover = null)} + onclick={(e) => { + e.stopPropagation(); + ws.openArticleById(m.a.id); + }} + role="presentation" + > + + + + {/each} + + {#each labels as l (l.id)} + { + e.stopPropagation(); + ws.selectCluster(l.id); + }} + role="presentation" + > + + + {l.text} + + + {/each} + + + {#if hover} +
    +

    {hover.a.title}

    +

    + {hover.a.domain} + {#if formatPublishDate(hover.a.date)}{formatPublishDate(hover.a.date)}{/if} +

    +

    + + {hover.a.clusterLabel} +

    +
    + {/if} + +
    + + + +
    + {/if} +
    + + diff --git a/frontend/src/lib/components/ThemeToggle.svelte b/frontend/src/lib/components/ThemeToggle.svelte new file mode 100644 index 0000000..af0d127 --- /dev/null +++ b/frontend/src/lib/components/ThemeToggle.svelte @@ -0,0 +1,17 @@ + + + diff --git a/frontend/src/lib/corpus.spec.ts b/frontend/src/lib/corpus.spec.ts new file mode 100644 index 0000000..2dd9f37 --- /dev/null +++ b/frontend/src/lib/corpus.spec.ts @@ -0,0 +1,139 @@ +import { describe, expect, it } from 'vitest'; +import type { Cluster, MapArticle } from '#lib/api.ts'; +import { + buildTimeline, + MAX_HUES, + parseYearMonth, + summarizeClusters, + toArticlePoints, + UNCLUSTERED, + type ArticlePoint +} from './corpus'; + +function mapArticle(over: Partial): MapArticle { + return { + article_id: 'a', + url: 'https://x.test/a', + title: 'A', + domain: 'x.test', + publish_date: null, + word_count: 500, + x: 0, + y: 0, + cluster_id: 0, + distance: 0.1, + ...over + }; +} + +function cluster(id: number, size: number, label = `Topic ${id}`): Cluster { + return { cluster_id: id, label, description: null, article_count: size, terms: [] }; +} + +function article(over: Partial): ArticlePoint { + return { + id: 'a', + title: 'A', + url: '', + domain: '', + date: null, + wordCount: 0, + clusterId: 0, + clusterLabel: 'Zero', + x: 0, + y: 0, + distance: 0, + ...over + }; +} + +describe('summarizeClusters', () => { + it('orders topics by size, gives each a hue, and puts unclustered last in grey', () => { + const clusters = summarizeClusters( + [cluster(1, 3), cluster(0, 9)], + [mapArticle({ cluster_id: 0 }), mapArticle({ article_id: 'b', cluster_id: UNCLUSTERED })] + ); + expect(clusters.map((c) => [c.id, c.slot])).toEqual([ + [0, 1], + [1, 2], + [UNCLUSTERED, null] + ]); + expect(clusters.at(-1)).toMatchObject({ label: 'Unclustered', articles: 1 }); + }); + + it('omits unclustered when every article has a topic', () => { + expect(summarizeClusters([cluster(0, 1)], [mapArticle({})]).map((c) => c.id)).toEqual([0]); + }); + + it('folds topics past the palette into grey', () => { + const many = Array.from({ length: MAX_HUES + 1 }, (_, i) => cluster(i, 20 - i)); + expect(summarizeClusters(many, []).at(-1)!.slot).toBeNull(); + }); +}); + +describe('toArticlePoints', () => { + it("maps API fields and names each article's topic", () => { + const clusters = summarizeClusters([cluster(0, 1, 'Cool roofs')], []); + const [a, b] = toArticlePoints( + [ + mapArticle({ title: null, domain: 'roofs.test', x: 2, y: 3, distance: 0.4 }), + mapArticle({ article_id: 'b', cluster_id: UNCLUSTERED }) + ], + clusters + ); + expect(a).toMatchObject({ + id: 'a', + title: 'roofs.test', + clusterLabel: 'Cool roofs', + x: 2, + y: 3, + distance: 0.4 + }); + expect(b.clusterLabel).toBe('Unclustered'); + }); +}); + +describe('parseYearMonth', () => { + it('accepts day and month precision', () => { + expect(parseYearMonth('2024-03-15')).toEqual([2024, 2]); + expect(parseYearMonth('2024-03')).toEqual([2024, 2]); + }); + it('rejects missing or malformed dates', () => { + expect(parseYearMonth(null)).toBeNull(); + expect(parseYearMonth('2024-13')).toBeNull(); + expect(parseYearMonth('March 2024')).toBeNull(); + }); +}); + +describe('buildTimeline', () => { + it('bins by month and fills gaps with zeros', () => { + const t = buildTimeline( + [ + article({ id: '1', date: '2024-01-05', clusterId: 0 }), + article({ id: '2', date: '2024-03-01', clusterId: 1 }), + article({ id: '3', date: null }) + ], + [0, 1] + ); + expect(t.unit).toBe('month'); + expect(t.bins.map((b) => b.key)).toEqual(['2024-01', '2024-02', '2024-03']); + expect(t.bins[1].total).toBe(0); + expect(t.bins[2].counts).toEqual({ 0: 0, 1: 1 }); + expect([t.dated, t.undated]).toEqual([2, 1]); + }); + + it('switches to years for long spans', () => { + const t = buildTimeline( + [article({ id: '1', date: '2010-06' }), article({ id: '2', date: '2024-02' })], + [0] + ); + expect(t.unit).toBe('year'); + expect(t.bins[0].key).toBe('2010'); + expect(t.bins.at(-1)!.key).toBe('2024'); + expect(t.bins).toHaveLength(15); + }); + + it('handles a corpus with no dates', () => { + expect(buildTimeline([article({})], [0])).toMatchObject({ bins: [], dated: 0, undated: 1 }); + }); +}); diff --git a/frontend/src/lib/corpus.ts b/frontend/src/lib/corpus.ts new file mode 100644 index 0000000..800c20a --- /dev/null +++ b/frontend/src/lib/corpus.ts @@ -0,0 +1,153 @@ +/** + * Shaping the API's map data for the views. + * Pure functions — no DOM, no Svelte — so they're unit-tested directly. + */ +import type { Cluster, MapArticle, Term } from '#lib/api.ts'; + +/** Articles the topic model couldn't place in any topic. */ +export const UNCLUSTERED = -1; + +export interface ArticlePoint { + id: string; + title: string; + url: string; + domain: string; + date: string | null; // YYYY-MM or YYYY-MM-DD + wordCount: number; + clusterId: number; + clusterLabel: string; + x: number; + y: number; + /** Cosine distance to its topic's centre: the outlier score */ + distance: number; +} + +export interface ClusterInfo { + id: number; + label: string; + description: string | null; + articles: number; + terms: Term[]; + /** 1-8: a categorical hue slot; null: the neutral ink (unclustered, or past 8) */ + slot: number | null; +} + +export const MAX_HUES = 8; + +/** + * Topics largest first, then "Unclustered" last if any article is. The + * largest MAX_HUES topics take the categorical slots in fixed order (the + * backend caps topics at 8, so normally every topic gets one). + */ +export function summarizeClusters(clusters: Cluster[], articles: MapArticle[]): ClusterInfo[] { + const ordered: ClusterInfo[] = [...clusters] + .sort((a, b) => b.article_count - a.article_count || a.cluster_id - b.cluster_id) + .map((c, i) => ({ + id: c.cluster_id, + label: c.label, + description: c.description, + articles: c.article_count, + terms: c.terms, + slot: i < MAX_HUES ? i + 1 : null + })); + const unclustered = articles.filter((a) => a.cluster_id === UNCLUSTERED).length; + if (unclustered > 0) { + ordered.push({ + id: UNCLUSTERED, + label: 'Unclustered', + description: "Articles that don't fit any topic well enough to join it.", + articles: unclustered, + terms: [], + slot: null + }); + } + return ordered; +} + +export function toArticlePoints(articles: MapArticle[], clusters: ClusterInfo[]): ArticlePoint[] { + const labels = new Map(clusters.map((c) => [c.id, c.label])); + return articles.map((a) => ({ + id: a.article_id, + title: a.title || a.domain || 'Untitled', + url: a.url, + domain: a.domain ?? '', + date: a.publish_date, + wordCount: a.word_count ?? 0, + clusterId: a.cluster_id, + clusterLabel: labels.get(a.cluster_id) ?? 'Unclustered', + x: a.x, + y: a.y, + distance: a.distance + })); +} + +export function clusterColor(slot: number | null): string { + return slot == null ? 'var(--series-other)' : `var(--series-${slot})`; +} + +// ── Timeline ──────────────────────────────────────────────────────────────── + +export interface TimelineBin { + key: string; // YYYY or YYYY-MM + start: Date; + counts: Record; + total: number; +} + +export interface Timeline { + unit: 'month' | 'year'; + bins: TimelineBin[]; + dated: number; + undated: number; +} + +/** Spans longer than this many months are binned by year instead. */ +export const MONTHLY_SPAN_LIMIT = 36; + +export function buildTimeline(articles: ArticlePoint[], clusterIds: number[]): Timeline { + const dated = articles + .map((a) => ({ a, ym: parseYearMonth(a.date) })) + .filter((d): d is { a: ArticlePoint; ym: [number, number] } => d.ym !== null); + const undated = articles.length - dated.length; + if (dated.length === 0) return { unit: 'month', bins: [], dated: 0, undated }; + + const index = (ym: [number, number]) => ym[0] * 12 + ym[1]; + const lo = Math.min(...dated.map((d) => index(d.ym))); + const hi = Math.max(...dated.map((d) => index(d.ym))); + const unit = hi - lo + 1 > MONTHLY_SPAN_LIMIT ? 'year' : 'month'; + + const keyOf = (ym: [number, number]) => + unit === 'year' ? String(ym[0]) : `${ym[0]}-${String(ym[1] + 1).padStart(2, '0')}`; + + // Every bin in the range, so gaps show as zero rather than vanishing. + const bins = new Map(); + const step = unit === 'year' ? 12 : 1; + const first = unit === 'year' ? Math.floor(lo / 12) * 12 : lo; + for (let i = first; i <= hi; i += step) { + const ym: [number, number] = [Math.floor(i / 12), i % 12]; + const counts: Record = {}; + for (const id of clusterIds) counts[id] = 0; + bins.set(keyOf(ym), { + key: keyOf(ym), + start: new Date(Date.UTC(ym[0], unit === 'year' ? 0 : ym[1], 1)), + counts, + total: 0 + }); + } + for (const { a, ym } of dated) { + const bin = bins.get(keyOf(ym))!; + bin.counts[a.clusterId] = (bin.counts[a.clusterId] ?? 0) + 1; + bin.total++; + } + return { unit, bins: [...bins.values()], dated: dated.length, undated }; +} + +/** "2024-03-15" | "2024-03" → [2024, 2] (zero-based month); anything else → null */ +export function parseYearMonth(date: string | null | undefined): [number, number] | null { + const m = date?.match(/^(\d{4})-(\d{2})/); + if (!m) return null; + const year = Number(m[1]); + const month = Number(m[2]) - 1; + if (month < 0 || month > 11 || year < 1900) return null; + return [year, month]; +} diff --git a/frontend/src/lib/d3helpers.js b/frontend/src/lib/d3helpers.js deleted file mode 100644 index da38d2f..0000000 --- a/frontend/src/lib/d3helpers.js +++ /dev/null @@ -1,24 +0,0 @@ -import * as d3 from 'd3'; - -// Distinct palette tuned for dark backgrounds -const PALETTE = [ - '#5a7fff', '#ff6b6b', '#ffd93d', '#6bcb77', '#ff9671', - '#a78bfa', '#38bdf8', '#fb923c', '#34d399', '#f472b6', -]; - -export function getClusterColor(clusterId) { - return PALETTE[((clusterId ?? 0) % PALETTE.length + PALETTE.length) % PALETTE.length]; -} - -export function getNodeRadius(wordCount) { - return d3.scaleSqrt().domain([0, 5000]).range([3, 11]).clamp(true)(wordCount || 500); -} - -export function formatMonth(dateStr) { - if (!dateStr) return null; - try { - const d = new Date((dateStr.length <= 7 ? dateStr + '-01' : dateStr)); - if (isNaN(d)) return null; - return d.toLocaleDateString('en-US', { year: 'numeric', month: 'short' }); - } catch { return null; } -} diff --git a/frontend/src/lib/format.ts b/frontend/src/lib/format.ts new file mode 100644 index 0000000..2fced14 --- /dev/null +++ b/frontend/src/lib/format.ts @@ -0,0 +1,38 @@ +const dateFmt = new Intl.DateTimeFormat(undefined, { + day: 'numeric', + month: 'short', + year: 'numeric' +}); +const monthFmt = new Intl.DateTimeFormat(undefined, { + month: 'short', + year: 'numeric', + timeZone: 'UTC' +}); +const yearFmt = new Intl.DateTimeFormat(undefined, { year: 'numeric', timeZone: 'UTC' }); + +/** Unix seconds → "11 Oct 2026" */ +export function formatTimestamp(ts: number): string { + return dateFmt.format(new Date(ts * 1000)); +} + +/** "2024-03-15" / "2024-03" → "Mar 2024"; null → null */ +export function formatPublishDate(date: string | null | undefined): string | null { + const m = date?.match(/^(\d{4})-(\d{2})/); + if (!m) return null; + return monthFmt.format(new Date(Date.UTC(Number(m[1]), Number(m[2]) - 1, 1))); +} + +export function formatBin(start: Date, unit: 'month' | 'year'): string { + return (unit === 'year' ? yearFmt : monthFmt).format(start); +} + +export function plural(n: number, one: string, many = `${one}s`): string { + return `${n.toLocaleString()} ${n === 1 ? one : many}`; +} + +/** Seconds → "3 min 20 s" / "45 s" */ +export function formatDuration(seconds: number): string { + const s = Math.max(0, Math.round(seconds)); + if (s < 60) return `${s} s`; + return `${Math.floor(s / 60)} min ${String(s % 60).padStart(2, '0')} s`; +} diff --git a/frontend/src/lib/index.ts b/frontend/src/lib/index.ts new file mode 100644 index 0000000..73c55ef --- /dev/null +++ b/frontend/src/lib/index.ts @@ -0,0 +1 @@ +// place files you want to import through the `#lib` alias in this folder. diff --git a/frontend/src/lib/presets.js b/frontend/src/lib/presets.js deleted file mode 100644 index 66ee356..0000000 --- a/frontend/src/lib/presets.js +++ /dev/null @@ -1,5 +0,0 @@ -export const PRESET_QUERIES = [ - 'What are the main camps or schools of thought in this discourse?', - 'What topics or angles appear underexplored in the current coverage?', - 'How has the conversation shifted or evolved over time?', -]; diff --git a/frontend/src/lib/workspace.svelte.ts b/frontend/src/lib/workspace.svelte.ts new file mode 100644 index 0000000..bfa05cf --- /dev/null +++ b/frontend/src/lib/workspace.svelte.ts @@ -0,0 +1,106 @@ +import { getContext, setContext } from 'svelte'; +import { goto } from '$app/navigation'; +import { resolve } from '$app/paths'; +import { page } from '$app/state'; +import type { Analysis, Cluster, MapArticle } from '#lib/api.ts'; +import { + clusterColor, + summarizeClusters, + toArticlePoints, + type ArticlePoint, + type ClusterInfo +} from '#lib/corpus.ts'; + +const VIEW_ROUTES = { + map: '/analysis/[id]', + timeline: '/analysis/[id]/timeline', + outliers: '/analysis/[id]/outliers', + terms: '/analysis/[id]/terms', + claims: '/analysis/[id]/claims', + ask: '/analysis/[id]/ask' +} as const; + +export type View = keyof typeof VIEW_ROUTES; + +/** + * Everything the result views share for one analysis: article-level points, + * clusters with their colors, and the selection — which lives in the URL + * (?cluster=, ?article=) so any view can be linked to and survives reloads. + */ +export class Workspace { + readonly articles: ArticlePoint[]; + readonly clusters: ClusterInfo[]; + readonly #byId: Map; + readonly #articleByUrl: Map; + + constructor( + readonly analysis: Analysis, + mapArticles: MapArticle[], + apiClusters: Cluster[] + ) { + this.clusters = summarizeClusters(apiClusters, mapArticles); + this.articles = toArticlePoints(mapArticles, this.clusters); + // eslint-disable-next-line svelte/prefer-svelte-reactivity -- built once per Workspace, never mutated + this.#byId = new Map(this.clusters.map((c) => [c.id, c])); + // eslint-disable-next-line svelte/prefer-svelte-reactivity -- built once per Workspace, never mutated + this.#articleByUrl = new Map(this.articles.map((a) => [a.url, a])); + } + + cluster(id: number): ClusterInfo | undefined { + return this.#byId.get(id); + } + + color(clusterId: number): string { + return clusterColor(this.#byId.get(clusterId)?.slot ?? null); + } + + articleByUrl(url: string): ArticlePoint | undefined { + return this.#articleByUrl.get(url); + } + + get selectedCluster(): number | null { + const v = page.url.searchParams.get('cluster'); + const id = v == null ? NaN : Number(v); + return Number.isInteger(id) && this.#byId.has(id) ? id : null; + } + + get openArticle(): string | null { + return page.url.searchParams.get('article'); + } + + selectCluster(id: number | null) { + this.#setParam('cluster', id == null || id === this.selectedCluster ? null : String(id)); + } + + openArticleById(id: string | null) { + this.#setParam('article', id); + } + + /** href for another view of this analysis, carrying the cluster selection */ + viewHref(view: View): string { + const href = resolve(VIEW_ROUTES[view], { id: this.analysis.id }); + const c = this.selectedCluster; + return c == null ? href : `${href}?cluster=${c}`; + } + + #setParam(key: string, value: string | null) { + // eslint-disable-next-line svelte/prefer-svelte-reactivity -- throwaway, only used to build the next URL + const url = new URL(page.url.href); + if (value == null) url.searchParams.delete(key); + else url.searchParams.set(key, value); + // Selection is view state: replace the history entry, keep scroll and focus. + // Same page, new query: page.url's path already includes any base path. + // eslint-disable-next-line svelte/no-navigation-without-resolve + goto(url, { replace: true, reset: false }); + } +} + +const KEY = Symbol('workspace'); + +export function setWorkspace(get: () => Workspace) { + setContext(KEY, get); +} + +export function useWorkspace(): Workspace { + return (getContext(KEY) as () => Workspace)(); +} diff --git a/frontend/src/routes/+error.svelte b/frontend/src/routes/+error.svelte new file mode 100644 index 0000000..d4b5533 --- /dev/null +++ b/frontend/src/routes/+error.svelte @@ -0,0 +1,12 @@ + + +
    +

    {page.status === 404 ? 'Not found' : 'Something went wrong'}

    +

    {page.error?.message ?? 'An unexpected error occurred.'}

    + Back to all maps +
    diff --git a/frontend/src/routes/+layout.svelte b/frontend/src/routes/+layout.svelte new file mode 100644 index 0000000..2e9bc2c --- /dev/null +++ b/frontend/src/routes/+layout.svelte @@ -0,0 +1,19 @@ + + + + + Discourse Lens + + + + + +{@render children()} diff --git a/frontend/src/routes/+layout.ts b/frontend/src/routes/+layout.ts new file mode 100644 index 0000000..77bbfd0 --- /dev/null +++ b/frontend/src/routes/+layout.ts @@ -0,0 +1,3 @@ +// Single-page app served by FastAPI: no server rendering, no prerendering. +export const ssr = false; +export const prerender = false; diff --git a/frontend/src/routes/+page.svelte b/frontend/src/routes/+page.svelte index 79e61b2..d36d55a 100644 --- a/frontend/src/routes/+page.svelte +++ b/frontend/src/routes/+page.svelte @@ -1,760 +1,181 @@ - - -{#if phase === 'input'} -
    - -
    -

    Discourse Lens

    -

    Map the shape of any conversation in under 10 minutes.

    -
    -
    -
    - - -
    -
    -
    - - -{:else if phase === 'loading'} -
    - -
    -
    {currentTopic}
    - {#if jobStatus === 'error'} -
    {jobError || 'Pipeline failed.'}
    - - {:else} -
    {STATUS_LABELS[jobStatus] || jobStatus}
    -
    -
    -
    -
    {jobProgress}%
    - {/if} -
    -
    - - -{:else if phase === 'results'} -
    - - {#if jobWarning} -
    {jobWarning}
    - {/if} - - -
    - {currentTopic} - {points.length} points · {clusterCount} clusters -
    - {#if exportError}{exportError}{/if} - - - -
    -
    - - - - - -
    - - -
    -
    - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - -
    - -
    - -
    -
    - - - -
    - - - -
    -{/if} - - +
    +
    + + + Discourse Lens + + +
    + +
    +
    +

    Map a conversation.

    +

    + Enter a topic. Discourse Lens reads around a hundred articles about it and charts the camps, + the outliers and how the conversation has moved. A map takes 3 to 8 minutes. +

    + +
    + + + +
    + + {#if formError} + + {/if} + +
    + Try + {#each SUGGESTIONS as s (s)} + + {/each} +
    +
    + +
    +

    Your maps

    + {#if data.analyses.length === 0} +

    + No maps yet. The first one shows up here as soon as it starts. +

    + {:else} +
      + {#each data.analyses as a (a.id)} +
    • + + {a.topic} + + + + +
      • + {/each} +
    + {/if} +
    +
    +
    + + diff --git a/frontend/src/routes/+page.ts b/frontend/src/routes/+page.ts new file mode 100644 index 0000000..d33ab1e --- /dev/null +++ b/frontend/src/routes/+page.ts @@ -0,0 +1,7 @@ +import { api } from '#lib/api.ts'; +import type { PageLoad } from './$types'; + +export const load: PageLoad = async ({ fetch, depends }) => { + depends('app:analyses'); + return { analyses: await api.listAnalyses(fetch) }; +}; diff --git a/frontend/src/routes/analysis/[id]/+layout.svelte b/frontend/src/routes/analysis/[id]/+layout.svelte new file mode 100644 index 0000000..02dd93d --- /dev/null +++ b/frontend/src/routes/analysis/[id]/+layout.svelte @@ -0,0 +1,125 @@ + + + + {data.analysis.topic} · Discourse Lens + + +
    +
    + + +
    + {#if data.analysis.status === 'done'} + + + + + {/if} + +
    +
    + +
    +

    + {data.analysis.topic} +

    + {#if data.analysis.status === 'done'} +

    + {plural(ws.articles.length, 'article')} in {plural(ws.clusters.length, 'cluster')}, mapped + {formatTimestamp(data.analysis.updated_ts)} +

    + {/if} +
    + + {#if isRunning(data.analysis)} + + {:else if data.analysis.status !== 'done'} + + {:else} + {#if data.analysis.warning} +

    +

    + {/if} + + + +
    +
    + {@render children()} +
    + +
    + + + {/if} +
    diff --git a/frontend/src/routes/analysis/[id]/+layout.ts b/frontend/src/routes/analysis/[id]/+layout.ts new file mode 100644 index 0000000..d70de73 --- /dev/null +++ b/frontend/src/routes/analysis/[id]/+layout.ts @@ -0,0 +1,22 @@ +import { error } from '@sveltejs/kit'; +import { api, ApiError } from '#lib/api.ts'; +import type { LayoutLoad } from './$types'; + +export const load: LayoutLoad = async ({ params, fetch, depends }) => { + depends('app:analysis'); + let analysis; + try { + analysis = await api.getAnalysis(params.id, fetch); + } catch (e) { + if (e instanceof ApiError && e.status === 404) { + error(404, 'There is no map with this address. It may have been deleted.'); + } + throw e; + } + if (analysis.status !== 'done') return { analysis, articles: [], clusters: [] }; + const [articles, clusters] = await Promise.all([ + api.listArticles(params.id, fetch), + api.getClusters(params.id, fetch) + ]); + return { analysis, articles, clusters }; +}; diff --git a/frontend/src/routes/analysis/[id]/+page.svelte b/frontend/src/routes/analysis/[id]/+page.svelte new file mode 100644 index 0000000..dc63b5e --- /dev/null +++ b/frontend/src/routes/analysis/[id]/+page.svelte @@ -0,0 +1,34 @@ + + +
    +
    + +
    + {#each [['map', 'Map'], ['table', 'Table']] as const as [v, label] (v)} + + {/each} +
    +
    +
    + {#if view === 'map'}{:else}{/if} +
    +
    diff --git a/frontend/src/routes/analysis/[id]/ask/+page.svelte b/frontend/src/routes/analysis/[id]/ask/+page.svelte new file mode 100644 index 0000000..9bd8190 --- /dev/null +++ b/frontend/src/routes/analysis/[id]/ask/+page.svelte @@ -0,0 +1,213 @@ + + +
    +
    +
    + {#if chat.loadError} + + {:else if chat.loaded && chat.messages.length === 0 && !chat.draft} +

    Ask the corpus

    +

    + Answers come only from the {plural(ws.articles.length, 'article')} in this map. Numbers like + 2 point to the article each claim rests on; click one to read + it. +

    +
      + {#each STARTERS as s (s)} +
    • + +
      • + {/each} +
    + {:else if chat.messages.length > 0} +
    + +
    + {/if} + +
    + {#each chat.messages as m, i (i)} + {#if m.role === 'user'} +

    {m.text}

    + {:else} + + {/if} + {/each} + + {#if chat.draft} +

    + {chat.draft.question} +

    + {#if chat.draft.text} + + {:else} +

    + + {chat.draft.sources.length + ? `Reading ${plural(chat.draft.sources.length, 'article')}…` + : 'Searching the corpus…'} +

    + {/if} + {/if} + + {#if chat.error} + + {/if} +
    +
    +
    + +
    { + e.preventDefault(); + send(question); + }} + > +
    + + + {#if chat.draft} + + {:else} + + {/if} +
    +
    +
    + + + + diff --git a/frontend/src/routes/analysis/[id]/claims/+page.svelte b/frontend/src/routes/analysis/[id]/claims/+page.svelte new file mode 100644 index 0000000..47d10f9 --- /dev/null +++ b/frontend/src/routes/analysis/[id]/claims/+page.svelte @@ -0,0 +1,262 @@ + + +
    + {#if loadError} + + {:else if !claims} +

    Loading claims…

    + {:else if running} +
    +

    +

    +

    {claims.detail ?? 'Starting'}

    +

    + Claude reads the opening of each article, then compares what they say. This takes about a + minute. You can use the other views meanwhile. +

    +
    + {:else if claims.status == null || claims.status === 'error'} +
    +

    Where do the sources agree?

    +

    + Claude reads the opening of each article and notes its main claims, then finds the points + that two or more articles take a position on, and whether each agrees, qualifies or disputes + them. +

    + {#if claims.status === 'error'} + + {/if} + +

    + Uses Claude: {estimate(ws.articles.length)} for {plural(ws.articles.length, 'article')}. +

    +
    + {:else} +
    +

    + Points two or more articles take a position on{ws.selectedCluster != null + ? `; faded ones don't involve ${ws.cluster(ws.selectedCluster)?.label}` + : ''}. +

    +
    +
      + {#each STANCES as s (s.stance)} +
    • + + {s.label} +
      • + {/each} +
    + +
    +
    + +
      + {#each rows as p (p.id)} + {@const t = tally(p)} + {@const expanded = open.has(p.id)} +
    1. + + + {#if expanded} +
      + {#each STANCES as s (s.stance)} + {@const members = p.claims.filter((c) => c.stance === s.stance)} + {#if members.length} +
      +

      + + {s.label} + {members.length} +

      +
        + {#each members as c (c.claim_id)} + {@const a = articleById.get(c.article_id)} +
      • +

        {c.text}

        + {#if a} + + {/if} +
        • + {/each} +
      +
      + {/if} + {/each} +
      + {/if} +
      2. + {:else} +
    2. + No point came up in two or more articles. The sources may be too varied to compare. +
      3. + {/each} +
    + +

    + From {plural(claims.claim_count, 'claim')} in the articles' openings. + + ({estimate(ws.articles.length)}) +

    + {/if} +
    diff --git a/frontend/src/routes/analysis/[id]/outliers/+page.svelte b/frontend/src/routes/analysis/[id]/outliers/+page.svelte new file mode 100644 index 0000000..46c7fb4 --- /dev/null +++ b/frontend/src/routes/analysis/[id]/outliers/+page.svelte @@ -0,0 +1,59 @@ + + +
    +

    + Articles furthest from the middle of their cluster{ws.selectedCluster != null + ? ` (${ws.cluster(ws.selectedCluster)?.label})` + : ''}. They often argue something the rest of their camp doesn't, or don't fit any camp well. +

    + +
      + {#each rows as r (r.id)} +
    1. + +
      2. + {:else} +
    2. No articles in this cluster.
      3. + {/each} +
    +
    diff --git a/frontend/src/routes/analysis/[id]/terms/+page.svelte b/frontend/src/routes/analysis/[id]/terms/+page.svelte new file mode 100644 index 0000000..1c785e2 --- /dev/null +++ b/frontend/src/routes/analysis/[id]/terms/+page.svelte @@ -0,0 +1,72 @@ + + +
    +

    + Words each topic uses far more than the others do. Bars are relative to the topic's strongest + word. +

    +
    + {#each ordered as c (c.id)} + {@const top = c.terms[0]?.weight || 1} +
    +

    + +

    + {#if c.description} +

    {c.description}

    + {/if} +
      + {#each c.terms as t (t.term)} +
    • + {t.term} + + + +
      • + {:else} +
    • + No key terms yet. Re-cluster this map to compute them. +
      • + {/each} +
    +
    + {/each} +
    +
    diff --git a/frontend/src/routes/analysis/[id]/timeline/+page.svelte b/frontend/src/routes/analysis/[id]/timeline/+page.svelte new file mode 100644 index 0000000..fadca9c --- /dev/null +++ b/frontend/src/routes/analysis/[id]/timeline/+page.svelte @@ -0,0 +1,149 @@ + + +
    +

    + {#if timeline.dated === 0} + None of these articles carry a publish date, so there's no timeline to draw. + {:else} + Articles per {timeline.unit} by cluster. {plural(timeline.dated, 'article')} have a publish date{timeline.undated + ? `; ${timeline.undated} don't and are left out` + : ''}. + {/if} +

    + + {#if timeline.dated > 0} +
    + {#if width && height} + + {#each yTicks as t (t)} + + {t} + {/each} + + + {#each segments as s (s.bin.key + ':' + s.cluster)} + + + {/each} + + {#each timeline.bins as b, i (b.key)} + {#if i % tickEvery === 0} + + {formatBin(b.start, timeline.unit)} + + {/if} + + (hover = b)} + onmouseleave={() => (hover = null)} + role="presentation" + /> + {/each} + + + {#if hover && hover.total > 0} + {@const left = x(hover.key)! + x.bandwidth() + 10} +
    +

    {formatBin(hover.start, timeline.unit)}

    +
      + {#each ws.clusters.filter((c) => hover!.counts[c.id]) as c (c.id)} +
    • + + {c.label} + {hover.counts[c.id]} +
      • + {/each} +
    +
    + {/if} + {/if} +
    + {/if} +
    + + diff --git a/frontend/src/routes/layout.css b/frontend/src/routes/layout.css new file mode 100644 index 0000000..771fea3 --- /dev/null +++ b/frontend/src/routes/layout.css @@ -0,0 +1,116 @@ +@import 'tailwindcss'; +@plugin '@tailwindcss/typography'; +@import '@fontsource-variable/mona-sans/wdth.css'; + +/* mode-watcher toggles a `dark` class on */ +@custom-variant dark (&:where(.dark, .dark *)); + +/* + * Survey-chart palette. Cool chart paper and slate-teal ink; one accent + * (survey-marker magenta) for the primary action and focus. Cluster hues are + * the only other saturated colors — see #lib/palette. + */ +:root { + color-scheme: light; + --paper: #f3f6f7; + --raised: #fbfcfc; + --ink: #1b2a33; + --ink-2: #3e4f59; + --muted: #5b6b74; + --rule: #c9d4d9; + --rule-soft: #dde5e8; + --accent: #b0306a; + --accent-ink: #ffffff; + --danger: #b42318; + + /* Categorical cluster hues, light steps (validated on --paper) */ + --series-1: #2a78d6; + --series-2: #eb6834; + --series-3: #1baf7a; + --series-4: #eda100; + --series-5: #e87ba4; + --series-6: #008300; + --series-7: #4a3aa7; + --series-8: #e34948; + --series-other: #8b9aa2; +} + +:root.dark { + color-scheme: dark; + --paper: #121d25; + --raised: #172530; + --ink: #dce6ea; + --ink-2: #b5c3c9; + --muted: #8a9ca5; + --rule: #2a3b45; + --rule-soft: #1f2e38; + --accent: #e0609a; + --accent-ink: #121d25; + --danger: #f0776b; + + /* Same hues stepped for the night surface (validated on --paper) */ + --series-1: #3987e5; + --series-2: #d95926; + --series-3: #199e70; + --series-4: #c98500; + --series-5: #d55181; + --series-6: #008300; + --series-7: #9085e9; + --series-8: #e66767; + --series-other: #5e707a; +} + +@theme inline { + --color-paper: var(--paper); + --color-raised: var(--raised); + --color-ink: var(--ink); + --color-ink-2: var(--ink-2); + --color-muted: var(--muted); + --color-rule: var(--rule); + --color-rule-soft: var(--rule-soft); + --color-accent: var(--accent); + --color-accent-ink: var(--accent-ink); + --color-danger: var(--danger); + --font-sans: 'Mona Sans Variable', ui-sans-serif, system-ui, sans-serif; +} + +@layer base { + html { + background: var(--paper); + color: var(--ink); + font-family: var(--font-sans); + font-size: 15px; + line-height: 1.5; + -webkit-font-smoothing: antialiased; + } + body { + min-height: 100dvh; + } + :focus-visible { + outline: 2px solid var(--accent); + outline-offset: 2px; + } + ::selection { + background: color-mix(in oklab, var(--accent) 25%, transparent); + } +} + +@utility tabular { + font-variant-numeric: tabular-nums; +} + +/* Expanded width is the display voice: titles and map labels. */ +@utility display { + font-stretch: 125%; + font-weight: 600; + letter-spacing: -0.01em; +} + +@media (prefers-reduced-motion: reduce) { + *, + *::before, + *::after { + animation-duration: 0.01ms !important; + transition-duration: 0.01ms !important; + } +} diff --git a/frontend/static/robots.txt b/frontend/static/robots.txt new file mode 100644 index 0000000..b6dd667 --- /dev/null +++ b/frontend/static/robots.txt @@ -0,0 +1,3 @@ +# allow crawling everything by default +User-agent: * +Disallow: diff --git a/frontend/svelte.config.js b/frontend/svelte.config.js deleted file mode 100644 index b42b17b..0000000 --- a/frontend/svelte.config.js +++ /dev/null @@ -1,10 +0,0 @@ -import adapter from '@sveltejs/adapter-auto'; - -/** @type {import('@sveltejs/kit').Config} */ -const config = { - kit: { - adapter: adapter() - } -}; - -export default config; diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000..2c1e028 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,8 @@ +{ + "extends": "$app/tsconfig", + "compilerOptions": { + "strict": true, + "types": ["$app/types", "node"] + }, + "include": ["src", "vite.config.ts", "playwright.config.ts", "e2e"] +} diff --git a/frontend/vite.config.js b/frontend/vite.config.js deleted file mode 100644 index 3406f32..0000000 --- a/frontend/vite.config.js +++ /dev/null @@ -1,6 +0,0 @@ -import { sveltekit } from '@sveltejs/kit/vite'; -import { defineConfig } from 'vite'; - -export default defineConfig({ - plugins: [sveltekit()] -}); diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 0000000..5d41fde --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,40 @@ +import tailwindcss from '@tailwindcss/vite'; +import { defineConfig } from 'vitest/config'; +import adapter from '@sveltejs/adapter-static'; +import { sveltekit } from '@sveltejs/kit/vite'; + +// The backend serves the built app and the API from one origin, so the client +// always uses relative /api URLs. In dev, Vite forwards them to uvicorn. +const API_ORIGIN = process.env.API_ORIGIN ?? 'http://localhost:8001'; + +export default defineConfig({ + plugins: [ + tailwindcss(), + sveltekit({ + compilerOptions: { + // Force runes mode for the project, except for libraries. Can be removed in svelte 6. + runes: ({ filename }) => + filename.split(/[/\\]/).includes('node_modules') ? undefined : true + }, + // Single-page app: every unknown path falls back to index.html. + adapter: adapter({ fallback: 'index.html' }) + }) + ], + server: { + proxy: { '/api': API_ORIGIN } + }, + test: { + expect: { requireAssertions: true }, + projects: [ + { + extends: './vite.config.ts', + test: { + name: 'server', + environment: 'node', + include: ['src/**/*.{test,spec}.{js,ts}'], + exclude: ['src/**/*.svelte.{test,spec}.{js,ts}'] + } + } + ] + } +}); diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..8ca9c81 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,52 @@ +[project] +name = "discourse-lens" +version = "0.2.0" +description = "Map the shape of any conversation: crawl, embed, cluster and query ~100 articles on a topic." +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "anthropic>=1,<2", + "datasketch>=1.6", + "fastapi>=0.115", + "fastembed>=0.5", + "httpx>=0.27", + "lancedb>=0.20", + "langdetect>=1.0.9", + "numpy>=1.26", + "openai>=1.40", + "pandas>=2.2", + "playwright>=1.45", + "pyarrow>=15", + "pydantic-settings>=2.4", + "pypdf>=5", + "scikit-learn>=1.5", + "tiktoken>=0.7", + "trafilatura>=2.0", + "umap-learn>=0.5.6", + "uvicorn[standard]>=0.30", +] + +[project.optional-dependencies] +vertex = ["anthropic[vertex]>=1,<2"] + +[dependency-groups] +dev = [ + "pytest>=8", + "ruff>=0.6", +] + +[tool.uv] +package = false + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] + +[tool.ruff] +line-length = 100 +target-version = "py312" +extend-exclude = ["venv", "frontend"] + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B"] +ignore = ["E501"] # long prompt strings; formatting handles the rest diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index eec6a6f..0000000 --- a/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -fastapi==0.115.0 -uvicorn[standard]==0.30.0 -playwright==1.45.0 -trafilatura==1.12.0 -tiktoken==0.7.0 -datasketch==1.6.5 -langdetect==1.0.9 -lancedb==0.9.0 -umap-learn==0.5.6 -scikit-learn==1.5.0 -anthropic==0.34.0 -# For Vertex AI support: pip install anthropic[vertex] (adds google-auth) -# google-auth>=2.0.0 -openai==1.40.0 -httpx==0.27.0 -python-dotenv==1.0.1 diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/export_openapi.py b/scripts/export_openapi.py new file mode 100644 index 0000000..2ba3702 --- /dev/null +++ b/scripts/export_openapi.py @@ -0,0 +1,10 @@ +"""Write the FastAPI OpenAPI schema to frontend/openapi.json (input for the typed client).""" + +import json +from pathlib import Path + +from backend.api.main import app + +out = Path(__file__).resolve().parent.parent / "frontend" / "openapi.json" +out.write_text(json.dumps(app.openapi(), indent=2) + "\n", encoding="utf-8") +print(f"wrote {out}") diff --git a/scripts/recluster.py b/scripts/recluster.py new file mode 100644 index 0000000..ed0b257 --- /dev/null +++ b/scripts/recluster.py @@ -0,0 +1,53 @@ +""" +Rebuild the map and topics of finished analyses from their stored embeddings, +without crawling again. Use after changing the topic model, or to upgrade maps +made by older versions. + + uv run python -m scripts.recluster [ ...] + uv run python -m scripts.recluster --all + +Each run makes one Claude request per analysis (to name its topics). +""" + +from __future__ import annotations + +import argparse +import sys + +from backend.db import connect, init_db +from backend.embedding.project import run_projection + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("ids", nargs="*", help="analysis ids (prefixes are fine)") + parser.add_argument("--all", action="store_true", help="every finished analysis") + args = parser.parse_args(argv) + if not args.ids and not args.all: + parser.error("give analysis ids or --all") + + init_db() + conn = connect() + done = conn.execute( + "SELECT id, topic FROM analyses WHERE status = 'done' ORDER BY created_ts" + ).fetchall() + conn.close() + targets = done if args.all else [ + a for a in done if any(a["id"].startswith(prefix) for prefix in args.ids) + ] + if not targets: + print("No matching finished analyses.", file=sys.stderr) + return 1 + + for a in targets: + print(f"\n{a['topic']} ({a['id'][:8]})") + run_projection( + a["id"], + on_step=lambda _f, detail: print(f" {detail}"), + on_label=lambda label, n: print(f" {label} ({n} articles)"), + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/seed_demo.py b/scripts/seed_demo.py new file mode 100644 index 0000000..adc70c1 --- /dev/null +++ b/scripts/seed_demo.py @@ -0,0 +1,181 @@ +""" +Create a small, fully synthetic demo map — no crawling, no API keys. + +Used by the end-to-end tests, and handy for trying the interface before +setting up search, embedding and Claude keys. + + uv run python -m scripts.seed_demo # into the configured database + DB_PATH=/tmp/e2e.db LANCE_PATH=/tmp/e2e-vectors uv run python -m scripts.seed_demo --reset +""" + +from __future__ import annotations + +import argparse +import json +import shutil +import time +from pathlib import Path + +from backend.config import settings +from backend.db import connect, init_db +from backend.embedding import store +from backend.synthesis import claims + +DEMO_ID = "demo-0000-0000-0000-000000000001" +DEMO_TOPIC = "Demo: urban heat and city design" + +# (cluster, title, domain, publish date, x, y, distance) +ARTICLES = [ + (0, "Cool roofs lower rooftop temperatures", "roofs.example", "2023-06", 1.0, 1.2, 0.08), + (0, "White paint versus green roofs", "roofs.example", "2024-02", 1.3, 0.9, 0.10), + (0, "Measuring albedo on city blocks", "albedo.example", "2024-07", 0.8, 1.5, 0.12), + (0, "Reflective pavements in practice", "streets.example", "2025-01", 1.6, 1.4, 0.31), + (1, "Street trees and shade equity", "canopy.example", "2022-08", -1.2, 0.4, 0.09), + (1, "Canopy cover maps for planners", "canopy.example", "2023-03", -1.0, 0.1, 0.11), + (1, "Parks as cool islands", "parks.example", "2024-05", -1.5, 0.7, 0.14), + (1, "Watering trees in drought summers", "parks.example", "2025-04", -0.7, -0.1, 0.27), + (2, "Who lives in the hottest neighbourhoods", "justice.example", "2021-07", 0.2, -1.4, 0.07), + (2, "Heat, redlining and health", "justice.example", "2023-09", 0.5, -1.1, 0.12), + (2, "Cooling centres and outreach", "health.example", "2024-08", -0.1, -1.7, 0.18), + (-1, "A history of urban climatology", "history.example", "2019-05", 2.4, -0.6, 0.45), +] + +CLUSTERS = [ + (0, "Reflective surfaces", "Articles on cool roofs, albedo and reflective pavements as a fast way to cut surface heat.", + [["cool roofs", 0.31], ["albedo", 0.24], ["reflective", 0.19], ["pavement", 0.12]]), + (1, "Trees and green space", "Articles on canopy, parks and the upkeep that makes trees an effective cooling strategy.", + [["canopy", 0.33], ["shade", 0.22], ["parks", 0.18], ["drought", 0.09]]), + (2, "Heat and inequality", "Articles on who is most exposed to urban heat and how cities respond.", + [["redlining", 0.29], ["exposure", 0.21], ["health", 0.17], ["outreach", 0.08]]), +] + + +def body(title: str, n: int) -> str: + """Placeholder article text (synthetic; long enough to read as an article).""" + lines = [f"{title}. This is demo text for Discourse Lens, not a real article."] + lines += [f"Paragraph {i + 1} discusses {title.lower()} in a fictional city, " + "with figures invented for the demo." for i in range(n)] + return "\n\n".join(lines) + + +def seed(reset: bool = False) -> str: + if reset: + Path(settings.db_path).unlink(missing_ok=True) + for suffix in ("-wal", "-shm"): + Path(settings.db_path + suffix).unlink(missing_ok=True) + shutil.rmtree(settings.lance_path, ignore_errors=True) + init_db() + now = int(time.time()) + conn = connect() + try: + conn.execute("DELETE FROM analyses WHERE id = ?", (DEMO_ID,)) + conn.execute( + "INSERT INTO analyses (id, topic, status, progress, stage_detail, article_count, " + "created_ts, updated_ts) VALUES (?,?,?,?,?,?,?,?)", + (DEMO_ID, DEMO_TOPIC, "done", 100, f"Indexed {len(ARTICLES)} articles", + len(ARTICLES), now, now), + ) + vectors = [] + for i, (cluster, title, domain, date, x, y, dist) in enumerate(ARTICLES): + aid = f"demo-article-{i:02d}" + text = body(title, 6) + conn.execute( + "INSERT INTO articles (id, analysis_id, url, domain, title, publish_date, crawl_ts, " + "word_count, language, raw_text, status, source_note) VALUES (?,?,?,?,?,?,?,?,?,?,?,?)", + (aid, DEMO_ID, f"https://{domain}/{aid}", domain, title, date, now, + len(text.split()), "en", text, "ok", + "Abstract only: the full text isn't openly available" if i == 2 else None), + ) + conn.execute( + "INSERT INTO chunks (id, analysis_id, article_id, parent_id, parent_text, child_text, " + "chunk_index, token_count, is_duplicate) VALUES (?,?,?,?,?,?,?,?,0)", + (f"{aid}#p0c0", DEMO_ID, aid, f"{aid}#p0", text, text, 0, len(text.split())), + ) + conn.execute( + "INSERT INTO placements (article_id, analysis_id, x, y, cluster_id, distance) " + "VALUES (?,?,?,?,?,?)", + (aid, DEMO_ID, x, y, cluster, dist), + ) + vectors.append({"chunk_id": f"{aid}#p0c0", "article_id": aid, "child_text": text, + "domain": domain, "publish_date": date, + "vector": [float(cluster == k) for k in (0, 1, 2)] + [x, y]}) + for cid, label, description, terms in CLUSTERS: + size = sum(1 for a in ARTICLES if a[0] == cid) + conn.execute( + "INSERT INTO clusters (analysis_id, cluster_id, label, description, article_count, terms) " + "VALUES (?,?,?,?,?,?)", + (DEMO_ID, cid, label, description, size, json.dumps(terms)), + ) + for kind, message in [("stage", "Crawling 14 pages"), ("article", ARTICLES[0][1]), + ("skip", "Too short (40 words)"), ("stage", "Done")]: + conn.execute( + "INSERT INTO analysis_events (analysis_id, ts, kind, message) VALUES (?,?,?,?)", + (DEMO_ID, now, kind, message), + ) + sources = [ + {"n": 1, "chunk_id": "demo-article-00#p0c0", "article_id": "demo-article-00", + "url": "https://roofs.example/demo-article-00", "title": ARTICLES[0][1], + "domain": "roofs.example", "publish_date": "2023-06", "cited": True}, + {"n": 2, "chunk_id": "demo-article-04#p0c0", "article_id": "demo-article-04", + "url": "https://canopy.example/demo-article-04", "title": ARTICLES[4][1], + "domain": "canopy.example", "publish_date": "2022-08", "cited": True}, + {"n": 3, "chunk_id": "demo-article-08#p0c0", "article_id": "demo-article-08", + "url": "https://justice.example/demo-article-08", "title": ARTICLES[8][1], + "domain": "justice.example", "publish_date": "2021-07", "cited": False}, + ] + conn.execute( + "INSERT INTO chat_messages (analysis_id, role, text, sources, created_ts) " + "VALUES (?, 'user', ?, NULL, ?)", + (DEMO_ID, "Do cool roofs or trees cool cities more?", now), + ) + conn.execute( + "INSERT INTO chat_messages (analysis_id, role, text, sources, created_ts) " + "VALUES (?, 'assistant', ?, ?, ?)", + (DEMO_ID, + "The demo articles disagree. Cool roofs act fastest on surface heat [1], while " + "trees also bring shade along streets [2].", json.dumps(sources), now), + ) + conn.execute( + "UPDATE analyses SET claims_status='done', claims_detail=? WHERE id=?", + ("2 shared claims from 5", DEMO_ID), + ) + conn.commit() + finally: + conn.close() + found = [ + {"id": f"demo-article-{i:02d}:0", "article_id": f"demo-article-{i:02d}", "position": 0, + "text": text} + for i, text in [ + (0, "Cool roofs cut rooftop temperatures more than any other single measure."), + (4, "Street trees cool pavements and people more than reflective roofs do."), + (1, "Cool roofs help on sunny days but do little for street-level heat."), + (8, "The hottest neighbourhoods get the fewest cooling investments."), + (9, "Formerly redlined districts still have the least tree cover and shade."), + ] + ] + claims._save(DEMO_ID, found, [ + {"statement": "Cool roofs are the most effective way to cool a city.", "claims": [ + {"claim_id": "demo-article-00:0", "stance": "agrees"}, + {"claim_id": "demo-article-01:0", "stance": "qualifies"}, + {"claim_id": "demo-article-04:0", "stance": "disputes"}, + ]}, + {"statement": "Cooling investment is unevenly spread across neighbourhoods.", "claims": [ + {"claim_id": "demo-article-08:0", "stance": "agrees"}, + {"claim_id": "demo-article-09:0", "stance": "agrees"}, + ]}, + ]) + store.drop_embeddings(DEMO_ID) + store.write_embeddings(DEMO_ID, vectors) + return DEMO_ID + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[1]) + parser.add_argument("--reset", action="store_true", + help="delete the database and vectors first (only for throwaway paths)") + args = parser.parse_args() + print(f"Seeded demo map {seed(args.reset)} into {settings.db_path}") + + +if __name__ == "__main__": + main() diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..9723a7d --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,74 @@ +import json + +import pytest + +from backend.config import settings +from backend.db import connect, init_db + + +@pytest.fixture +def isolated(tmp_path, monkeypatch): + """Point the app at a fresh SQLite file and LanceDB directory.""" + monkeypatch.setattr(settings, "db_path", str(tmp_path / "test.db")) + monkeypatch.setattr(settings, "lance_path", str(tmp_path / "vectors")) + init_db() + return tmp_path + + +def add_analysis(analysis_id, topic="topic", status="done"): + conn = connect() + conn.execute( + "INSERT INTO analyses (id, topic, status, progress, created_ts, updated_ts) " + "VALUES (?,?,?,?,?,?)", + (analysis_id, topic, status, 100, 1, 1), + ) + conn.commit() + conn.close() + + +def add_article(analysis_id, article_id, cluster_id=0, distance=0.1, x=0.0, y=0.0, chunks=1): + """Insert an article, `chunks` chunks of it, and its map placement.""" + conn = connect() + conn.execute( + "INSERT INTO articles (id, analysis_id, url, title, status) VALUES (?,?,?,?,?)", + (article_id, analysis_id, f"https://example.com/{article_id}", article_id.upper(), "ok"), + ) + conn.executemany( + "INSERT INTO chunks (id, analysis_id, article_id, child_text) VALUES (?,?,?,?)", + [(f"{article_id}#{i}", analysis_id, article_id, f"text {i}") for i in range(chunks)], + ) + conn.execute( + "INSERT INTO placements (article_id, analysis_id, x, y, cluster_id, distance) " + "VALUES (?,?,?,?,?,?)", + (article_id, analysis_id, x, y, cluster_id, distance), + ) + conn.commit() + conn.close() + + +def add_cluster(analysis_id, cluster_id, label, article_count=1, terms=(), description=None): + conn = connect() + conn.execute( + "INSERT INTO clusters (analysis_id, cluster_id, label, description, article_count, terms) " + "VALUES (?,?,?,?,?,?)", + (analysis_id, cluster_id, label, description, article_count, json.dumps(list(terms))), + ) + conn.commit() + conn.close() + + +_VOCAB = ( + "energy efficiency demand rebound policy price carbon household city heat roof tree " + "climate study report evidence cost growth consumption supply market tax standard data " + "model risk exposure equity income region urban rural building water power grid fuel " + "coal oil gas solar wind storage transport vehicle industry economy research analysis" +).split() + + +def prose(seed: int, words: int = 400) -> str: + """Varied, article-length filler text (repeating one sentence is unrealistic: + it has only a handful of distinct word sequences).""" + import random + + rng = random.Random(seed) + return " ".join(rng.choice(_VOCAB) for _ in range(words)) + "." diff --git a/tests/test_analyses_api.py b/tests/test_analyses_api.py new file mode 100644 index 0000000..d08afc1 --- /dev/null +++ b/tests/test_analyses_api.py @@ -0,0 +1,171 @@ +import pytest +from conftest import add_analysis, add_article, add_cluster +from fastapi.testclient import TestClient + +from backend.api import main +from backend.db import add_event, connect +from backend.embedding import store + + +@pytest.fixture +def client(isolated, monkeypatch): + started = [] + monkeypatch.setattr( + main.pipeline, "start", + lambda analysis_id, topic, review=False: started.append((analysis_id, topic)) + ) + with TestClient(main.app) as c: + c.started = started + yield c + + +def test_create_and_list(client): + r = client.post("/api/analyses", json={"topic": " urban heat islands "}) + assert r.status_code == 201 + created = r.json() + assert created["topic"] == "urban heat islands" + assert created["status"] == "queued" + assert client.started == [(created["id"], "urban heat islands")] + + listed = client.get("/api/analyses").json() + assert [a["id"] for a in listed] == [created["id"]] + + +def test_blank_topic_rejected(client): + assert client.post("/api/analyses", json={"topic": ""}).status_code == 422 + + +def test_unknown_analysis_is_404_with_string_detail(client): + r = client.get("/api/analyses/nope/articles") + assert r.status_code == 404 + assert r.json() == {"detail": "Analysis not found"} + + +def test_articles_are_scoped_to_analysis(client): + add_analysis("an1") + add_analysis("an2") + add_article("an1", "a1", x=1.5, y=-2.0, distance=0.3) + add_article("an2", "b1") + + articles = client.get("/api/analyses/an1/articles").json() + assert [a["article_id"] for a in articles] == ["a1"] + assert articles[0] | {"url": None} == { + "article_id": "a1", "url": None, "title": "A1", "domain": None, + "publish_date": None, "word_count": None, "x": 1.5, "y": -2.0, + "cluster_id": 0, "distance": 0.3, + } + + +def test_clusters_carry_terms_and_description(client): + add_analysis("an1") + add_cluster("an1", 1, "Heat equity", 4, [["gentrification", 0.12]], "Who bears the heat.") + add_cluster("an1", 0, "Cool roofs", 9) + + clusters = client.get("/api/analyses/an1/clusters").json() + assert [c["label"] for c in clusters] == ["Cool roofs", "Heat equity"] + assert clusters[1] == { + "cluster_id": 1, "label": "Heat equity", "description": "Who bears the heat.", + "article_count": 4, "terms": [{"term": "gentrification", "weight": 0.12}], + } + + +def test_article_must_belong_to_analysis(client): + add_analysis("an1") + add_analysis("an2") + add_article("an1", "a1") + assert client.get("/api/analyses/an1/articles/a1").status_code == 200 + assert client.get("/api/analyses/an2/articles/a1").status_code == 404 + + +def test_delete_cascades_rows_and_vectors(client): + add_analysis("an1") + add_analysis("an2") + add_article("an1", "a1") + add_cluster("an1", 0, "Zero") + add_article("an2", "b1") + store.write_embeddings("an1", [{"chunk_id": "a1#0", "article_id": "a1", "vector": [0.1, 0.2]}]) + + assert client.delete("/api/analyses/an1").status_code == 204 + + conn = connect() + counts = { + t: conn.execute(f"SELECT COUNT(*) FROM {t} WHERE analysis_id='an1'").fetchone()[0] + for t in ("articles", "chunks", "placements", "clusters") + } + other = conn.execute("SELECT COUNT(*) FROM chunks WHERE analysis_id='an2'").fetchone()[0] + conn.close() + assert counts == {"articles": 0, "chunks": 0, "placements": 0, "clusters": 0} + assert other == 1 + assert store.open_table("an1") is None + assert client.get("/api/analyses/an1").status_code == 404 + + +def test_cannot_delete_running_analysis(client): + add_analysis("an1", status="embedding") + assert client.delete("/api/analyses/an1").status_code == 409 + + +def test_restart_fails_interrupted_analyses(isolated): + from backend.db import init_db + + add_analysis("an1", status="crawling") + init_db() + conn = connect() + row = conn.execute("SELECT status, error_msg FROM analyses WHERE id='an1'").fetchone() + conn.close() + assert row["status"] == "error" + assert "restart" in row["error_msg"] + + +def test_cancel_requires_running_analysis(client): + add_analysis("an1", status="done") + assert client.post("/api/analyses/an1/cancel").status_code == 409 + + +def test_cancel_running_analysis(client, monkeypatch): + add_analysis("an1", status="crawling") + cancelled = [] + monkeypatch.setattr(main.pipeline, "cancel", lambda aid: cancelled.append(aid) or True) + r = client.post("/api/analyses/an1/cancel") + assert r.status_code == 202 + assert cancelled == ["an1"] + + +def test_events_endpoint_pages_with_after(client): + add_analysis("an1") + add_event("an1", "stage", "Crawling") + add_event("an1", "article", "Title", "https://x") + events = client.get("/api/analyses/an1/events").json() + assert [e["kind"] for e in events] == ["stage", "article"] + later = client.get(f"/api/analyses/an1/events?after={events[0]['id']}").json() + assert [e["message"] for e in later] == ["Title"] + + +def _sse(text): + """Parse an SSE body into a list of {event, id, data} dicts.""" + out = [] + for block in text.strip().split("\n\n"): + msg = {"event": "message"} + for line in block.splitlines(): + key, _, value = line.partition(": ") + msg[key] = value + out.append(msg) + return out + + +def test_stream_sends_log_events_and_resumes(client): + add_analysis("an1", status="done") + for i in range(3): + add_event("an1", "stage", f"step {i}") + + msgs = _sse(client.get("/api/analyses/an1/stream").text) + logs = [m for m in msgs if m["event"] == "log"] + assert len(logs) == 3 + assert msgs[-1]["event"] == "message" and '"status":"done"' in msgs[-1]["data"] + + resumed = _sse( + client.get("/api/analyses/an1/stream", headers={"Last-Event-ID": logs[0]["id"]}).text + ) + assert [m["data"] for m in resumed if m["event"] == "log"] == [ + logs[1]["data"], logs[2]["data"] + ] diff --git a/tests/test_chat.py b/tests/test_chat.py new file mode 100644 index 0000000..4ec0cb0 --- /dev/null +++ b/tests/test_chat.py @@ -0,0 +1,225 @@ +import json + +import pytest +from conftest import add_analysis +from fastapi.testclient import TestClient + +from backend.api import main +from backend.db import connect +from backend.embedding import store +from backend.synthesis import chat +from backend.synthesis.chat import Source + +# ── citations and prompt ───────────────────────────────────────────────────── + + +@pytest.mark.parametrize( + "text, expected", + [ + ("Cool roofs win [2]. Trees add humidity [1][3].", [2, 1, 3]), + ("Grouped [1, 4] and ranged [2-3] and dashed [5–6].", [1, 4, 2, 3, 5, 6]), + ("Repeats [2] keep first order [1] [2].", [2, 1]), + ("Out of range [9] and [0] are ignored; years like [2024] too.", []), + ("No citations at all.", []), + ], +) +def test_cited_numbers(text, expected): + assert chat.cited_numbers(text, available={1, 2, 3, 4, 5, 6}) == expected + + +def _source(n, article="a"): + return Source(n=n, chunk_id=f"{article}{n}#0", article_id=f"{article}{n}", + url=f"https://x.test/{n}", title=f"T{n}", domain="x.test", + publish_date="2024-05", excerpt=f"excerpt {n}") + + +def test_messages_number_the_excerpts_and_keep_recent_history(): + history = [] + for i in range(10): + history += [{"role": "user", "text": f"q{i}"}, {"role": "assistant", "text": f"a{i}"}] + messages = chat.build_messages(history, "new question", [_source(1), _source(2)]) + + assert len(messages) == 2 * chat.HISTORY_TURNS + 1 + assert messages[0] == {"role": "user", "content": f"q{10 - chat.HISTORY_TURNS}"} + last = messages[-1]["content"] + assert "[1] T1 (x.test, 2024-05)\nexcerpt 1" in last + assert "[2] T2" in last + assert last.endswith("Question: new question") + + +# ── retrieval (real LanceDB, stubbed embeddings) ───────────────────────────── + + +def _seed_corpus(analysis_id="an1"): + """Three articles; the query vector points at article b, the keywords at article c.""" + add_analysis(analysis_id) + conn = connect() + texts = { + "a": ["general background on city climate", "more background"], + "b": ["semantic match chunk one", "semantic match chunk two"], + "c": ["mentions NDVI and Landsat imagery explicitly", "unrelated filler"], + } + rows = [] + for art, chunks in texts.items(): + conn.execute( + "INSERT INTO articles (id, analysis_id, url, title, domain, status) VALUES (?,?,?,?,?,?)", + (art, analysis_id, f"https://x.test/{art}", f"Article {art}", "x.test", "ok"), + ) + for i, text in enumerate(chunks): + cid = f"{art}#{i}" + conn.execute( + "INSERT INTO chunks (id, analysis_id, article_id, parent_text, child_text) " + "VALUES (?,?,?,?,?)", + (cid, analysis_id, art, f"parent of {cid}", text), + ) + vec = [1.0, 0.0, 0.0] if art == "b" else [0.0, 1.0, float(i)] + rows.append({"chunk_id": cid, "article_id": art, "vector": vec, "child_text": text}) + conn.commit() + conn.close() + store.write_embeddings(analysis_id, rows) + + +@pytest.fixture +def corpus(isolated, monkeypatch): + _seed_corpus() + seen = [] + + def fake_embed(chunks, kind="document", on_batch=None): + seen.append((chunks[0]["child_text"], kind)) + return [{"chunk_id": c["id"], "vector": [1.0, 0.0, 0.0]} for c in chunks] + + monkeypatch.setattr(chat, "embed_chunks", fake_embed) + return seen + + +def test_hybrid_retrieval_finds_semantic_and_keyword_matches(corpus): + sources = chat.retrieve("an1", "NDVI Landsat") + articles = [s.article_id for s in sources] + assert "b" in articles # nearest by vector + assert "c" in articles # only matched by keyword + assert len(articles) == len(set(articles)) # one excerpt per article + assert [s.n for s in sources] == list(range(1, len(sources) + 1)) + assert sources[0].excerpt.startswith("parent of ") + + +def test_followups_embed_the_previous_question_too(corpus): + chat.retrieve("an1", "and the second one?", previous_question="compare cool roofs and trees") + assert corpus[-1] == ("compare cool roofs and trees\nand the second one?", "query") + + +def test_retrieval_skips_excluded_articles_and_numbers_from_first_n(corpus): + sources = chat.retrieve("an1", "NDVI Landsat", exclude_articles=frozenset({"b"}), first_n=7) + assert "b" not in [s.article_id for s in sources] + assert sources[0].n == 7 + + +def test_followup_carries_cited_sources_and_continues_numbering(corpus): + history = [ + {"role": "user", "text": "first question"}, + {"role": "assistant", "text": "Answer [2].", "sources": [ + {"n": 1, "chunk_id": "a#0", "article_id": "a", "cited": False}, + {"n": 2, "chunk_id": "b#1", "article_id": "b", "cited": True}, + ]}, + ] + carried = chat.carried_sources(history) + assert [(s.n, s.article_id, s.excerpt) for s in carried] == [(2, "b", "parent of b#1")] + assert chat.next_source_number(history) == 3 + + sources, _text = chat.answer_stream("an1", "NDVI Landsat", history) + numbers = [s.n for s in sources] + assert numbers[0] == 2 # the carried source keeps its number + assert numbers[1:] == list(range(3, 3 + len(numbers) - 1)) # new ones continue the count + assert [s.article_id for s in sources].count("b") == 1 # not retrieved twice + + +def test_retrieval_without_vectors_is_no_corpus(isolated): + add_analysis("empty") + with pytest.raises(chat.NoCorpusError): + chat.retrieve("empty", "anything") + + +# ── API: streaming, saving, clearing ───────────────────────────────────────── + + +def _parse_sse(body: str) -> list[tuple[str, object]]: + events = [] + for block in body.strip().split("\n\n"): + fields = dict(line.split(": ", 1) for line in block.splitlines()) + events.append((fields["event"], json.loads(fields["data"]))) + return events + + +@pytest.fixture +def client(isolated, monkeypatch): + add_analysis("an1") + calls = [] + + def fake_answer_stream(analysis_id, question, history): + calls.append((question, [m["text"] for m in history])) + return [_source(1), _source(2)], iter(["Roofs win ", "[2]."]) + + monkeypatch.setattr("backend.synthesis.chat.answer_stream", fake_answer_stream) + with TestClient(main.app) as c: + c.calls = calls + yield c + + +def test_answer_streams_then_saves_the_turn(client): + r = client.post("/api/analyses/an1/chat", json={"question": " Who wins? "}) + assert r.headers["content-type"].startswith("text/event-stream") + events = _parse_sse(r.text) + + assert [e for e, _ in events] == ["sources", "delta", "delta", "done"] + assert [s["n"] for s in events[0][1]] == [1, 2] + assert "excerpt" not in events[0][1][0] + done = events[-1][1] + assert done["text"] == "Roofs win [2]." + assert [s["cited"] for s in done["sources"]] == [False, True] + + history = client.get("/api/analyses/an1/chat").json() + assert [(m["role"], m["text"]) for m in history] == [ + ("user", "Who wins?"), ("assistant", "Roofs win [2].") + ] + + client.post("/api/analyses/an1/chat", json={"question": "Why?"}) + assert client.calls[-1] == ("Why?", ["Who wins?", "Roofs win [2]."]) + + +def test_failed_answer_reports_error_and_saves_nothing(client, monkeypatch): + def broken(analysis_id, question, history): + def text(): + yield "Partial" + raise RuntimeError("connection reset") + return [_source(1)], text() + + monkeypatch.setattr("backend.synthesis.chat.answer_stream", broken) + events = _parse_sse(client.post("/api/analyses/an1/chat", json={"question": "Q"}).text) + assert events[-1][0] == "error" + assert "connection reset" in events[-1][1]["detail"] + assert client.get("/api/analyses/an1/chat").json() == [] + + +def test_no_corpus_is_409_before_streaming(client, monkeypatch): + def none(*a): + raise chat.NoCorpusError("This analysis has no indexed corpus yet.") + + monkeypatch.setattr("backend.synthesis.chat.answer_stream", none) + r = client.post("/api/analyses/an1/chat", json={"question": "Q"}) + assert r.status_code == 409 + + +def test_clear_chat(client): + client.post("/api/analyses/an1/chat", json={"question": "Q"}) + assert client.delete("/api/analyses/an1/chat").status_code == 204 + assert client.get("/api/analyses/an1/chat").json() == [] + + +def test_blank_question_rejected(client): + assert client.post("/api/analyses/an1/chat", json={"question": ""}).status_code == 422 + + +def test_query_from_a_different_embedding_model_is_explained(corpus, monkeypatch): + monkeypatch.setattr(chat, "embed_chunks", lambda chunks, kind="document": [ + {"chunk_id": c["id"], "vector": [0.1] * 768} for c in chunks]) + with pytest.raises(chat.EmbeddingMismatchError, match="different model"): + chat.retrieve("an1", "anything") diff --git a/tests/test_claims.py b/tests/test_claims.py new file mode 100644 index 0000000..f4b718e --- /dev/null +++ b/tests/test_claims.py @@ -0,0 +1,174 @@ +import json +import re + +import pytest +from conftest import add_analysis, add_article +from fastapi.testclient import TestClient + +from backend.api import main +from backend.db import connect, init_db +from backend.synthesis import claims + +# ── validation ─────────────────────────────────────────────────────────────── + + +def _labels(*pairs): + """1, 2, ... -> claim dicts, from (article_id, text) pairs.""" + return {i + 1: {"id": f"{a}:{i}", "article_id": a, "text": t} + for i, (a, t) in enumerate(pairs)} + + +def test_groups_drop_unknown_reused_and_single_article_claims(): + labels = _labels(("a", "x"), ("b", "y"), ("a", "z"), ("c", "w")) + raw = [ + {"statement": "P1", "claims": [{"claim": 1, "stance": "agrees"}, + {"claim": 2, "stance": "disputes"}, + {"claim": 99, "stance": "agrees"}]}, + # claim 1 is used already, which leaves one article + {"statement": "P2", "claims": [{"claim": 1, "stance": "agrees"}, + {"claim": 3, "stance": "agrees"}]}, + # two claims from article a: only the first counts, so one article: dropped + {"statement": "P3", "claims": [{"claim": 3, "stance": "agrees"}, + {"claim": 3, "stance": "qualifies"}]}, + {"statement": " P4 ", "claims": [{"claim": 3, "stance": "qualifies"}, + {"claim": 4, "stance": "agrees"}]}, + ] + out = claims.validate_groups(raw, labels) + assert [p["statement"] for p in out] == ["P1", "P4"] + assert [(m["claim_id"], m["stance"]) for m in out[0]["claims"]] == [ + ("a:0", "agrees"), ("b:1", "disputes")] + + +def test_one_stance_per_article_within_a_proposition(): + labels = _labels(("a", "x"), ("a", "y"), ("b", "z")) + raw = [{"statement": "P", "claims": [{"claim": 1, "stance": "agrees"}, + {"claim": 2, "stance": "disputes"}, + {"claim": 3, "stance": "agrees"}]}] + out = claims.validate_groups(raw, labels) + assert [m["claim_id"] for m in out[0]["claims"]] == ["a:0", "b:2"] + + +def test_opening_keeps_the_first_words(): + assert claims.opening("one two three", words=2) == "one two …" + assert claims.opening("one two", words=2) == "one two" + + +# ── extraction and grouping with a stubbed Claude ──────────────────────────── + + +def fake_claude(prompt, *, json_schema, **_): + """Two claims per article; then one proposition from the first claim of each article.""" + if json_schema is claims.EXTRACT_SCHEMA: + labels = re.findall(r'
    ', prompt) + return json.dumps({"articles": [ + {"article": label, "claims": [f"{label} claim one", f"{label} claim two", " "]} + for label in labels] + [{"article": "A99", "claims": ["ghost"]}]}) + firsts = [int(n) for n in re.findall(r"^(\d+)\. \[A\d+\] A\d+ claim one$", prompt, re.M)] + return json.dumps({"propositions": [{ + "statement": "Cool roofs lower temperatures", + "claims": [{"claim": c, "stance": "disputes" if i == 1 else "agrees"} + for i, c in enumerate(firsts)], + }]}) + + +def test_extract_maps_labels_back_to_articles(monkeypatch): + monkeypatch.setattr(claims, "complete", fake_claude) + articles = [{"id": f"art{i}", "title": "T", "domain": "x.test", "raw_text": "words " * 50} + for i in range(12)] + progress = [] + found = claims.extract_claims(articles, on_batch=progress.append) + assert len(found) == 24 # blank claims and unknown labels dropped + assert found[0] == {"id": "art0:0", "article_id": "art0", "position": 0, + "text": "A1 claim one"} + assert found[-1]["article_id"] == "art11" # second batch labels restart at A1 + assert progress == [10, 12] + + +# ── API: find, poll, read ──────────────────────────────────────────────────── + + +@pytest.fixture +def client(isolated, monkeypatch): + monkeypatch.setattr(claims, "complete", fake_claude) + monkeypatch.setattr(claims, "_launch", claims._run) # inline, so tests don't race a thread + return TestClient(main.app) + + +def _seed(analysis_id="an1", n=3): + add_analysis(analysis_id) + for i in range(n): + add_article(analysis_id, f"art{i}") + conn = connect() + conn.execute("UPDATE articles SET raw_text = 'Some article text.', domain = 'x.test'") + conn.commit() + conn.close() + + +def test_find_claims_then_read_them(client): + _seed() + assert client.get("/api/analyses/an1/claims").json() == { + "status": None, "detail": None, "claim_count": 0, "propositions": []} + + r = client.post("/api/analyses/an1/claims") + assert r.status_code == 202 + + body = client.get("/api/analyses/an1/claims").json() + assert body["status"] == "done" + assert body["claim_count"] == 6 + [p] = body["propositions"] + assert p["statement"] == "Cool roofs lower temperatures" + assert [(c["article_id"], c["stance"], c["text"]) for c in p["claims"]] == [ + ("art0", "agrees", "A1 claim one"), + ("art1", "disputes", "A2 claim one"), + ("art2", "agrees", "A3 claim one"), + ] + + +def test_running_again_replaces_the_previous_claims(client): + _seed() + client.post("/api/analyses/an1/claims") + client.post("/api/analyses/an1/claims") + body = client.get("/api/analyses/an1/claims").json() + assert body["claim_count"] == 6 and len(body["propositions"]) == 1 + + +def test_too_few_articles_is_an_error_not_a_crash(client): + _seed(n=1) + client.post("/api/analyses/an1/claims") + body = client.get("/api/analyses/an1/claims").json() + assert body["status"] == "error" + assert "at least two" in body["detail"] + + +def test_unfinished_map_cannot_find_claims(client): + add_analysis("an2", status="crawling") + assert client.post("/api/analyses/an2/claims").status_code == 409 + + +def test_already_running_is_a_conflict(isolated, monkeypatch): + _seed() + monkeypatch.setattr(claims, "_running", {"an1"}) + assert TestClient(main.app).post("/api/analyses/an1/claims").status_code == 409 + + +def test_restart_marks_a_running_job_interrupted(isolated): + _seed() + conn = connect() + conn.execute("UPDATE analyses SET claims_status = 'running'") + conn.commit() + conn.close() + init_db() + body = TestClient(main.app).get("/api/analyses/an1/claims").json() + assert body["status"] == "error" + assert body["detail"] == "Interrupted by a server restart" + + +def test_deleting_the_analysis_removes_its_claims(client): + _seed() + client.post("/api/analyses/an1/claims") + assert client.delete("/api/analyses/an1").status_code == 204 + conn = connect() + counts = [conn.execute(f"SELECT COUNT(*) FROM {t}").fetchone()[0] + for t in ("claims", "propositions", "proposition_claims")] + conn.close() + assert counts == [0, 0, 0] diff --git a/tests/test_export.py b/tests/test_export.py index 16ad692..078a0d7 100644 --- a/tests/test_export.py +++ b/tests/test_export.py @@ -1,6 +1,6 @@ import pytest -from backend.export import article_slug, build_canvas, build_vault_files +from backend.export import article_slug, build_canvas, build_vault_files # ── article_slug ──────────────────────────────────────────────────────────── diff --git a/tests/test_migration.py b/tests/test_migration.py new file mode 100644 index 0000000..d8d58d6 --- /dev/null +++ b/tests/test_migration.py @@ -0,0 +1,158 @@ +"""Schema migrations: v1 (one corpus + jobs) → v2 (analyses) → … → v4 (placements, clusters).""" + +import sqlite3 + +import lancedb + +from backend.config import settings +from backend.db import connect, init_db +from backend.embedding import store + +V1_SCHEMA = """ +CREATE TABLE articles (id TEXT PRIMARY KEY, url TEXT NOT NULL UNIQUE, domain TEXT, title TEXT, + publish_date TEXT, crawl_ts INTEGER, word_count INTEGER, language TEXT DEFAULT 'en', + raw_text TEXT, status TEXT DEFAULT 'ok'); +CREATE TABLE chunks (id TEXT PRIMARY KEY, article_id TEXT REFERENCES articles(id), + parent_id TEXT, parent_text TEXT, child_text TEXT, chunk_index INTEGER, + token_count INTEGER, is_duplicate INTEGER DEFAULT 0); +CREATE TABLE projections (chunk_id TEXT PRIMARY KEY REFERENCES chunks(id), x REAL, y REAL, + cluster_id INTEGER, cluster_label TEXT, distance_from_centroid REAL); +CREATE TABLE jobs (id TEXT PRIMARY KEY, topic TEXT, status TEXT, progress INTEGER DEFAULT 0, + article_count INTEGER DEFAULT 0, error_msg TEXT, warning TEXT, created_ts INTEGER, + updated_ts INTEGER); +""" + + +def _make_v1(tmp_path, monkeypatch, with_corpus=True, warning_column=True): + monkeypatch.setattr(settings, "db_path", str(tmp_path / "old.db")) + monkeypatch.setattr(settings, "lance_path", str(tmp_path / "vectors")) + conn = sqlite3.connect(settings.db_path) + schema = V1_SCHEMA if warning_column else V1_SCHEMA.replace("warning TEXT, ", "") + conn.executescript(schema) + jobs = [ + ("old-job", "older topic", "done", 100, 10, None, None, 1, 2), + ("job-1", "jevons paradox", "done", 100, 52, None, "sparse", 3, 4), + ("job-err", "broken", "error", 20, 0, "boom", None, 5, 6), + ] + if warning_column: + conn.executemany("INSERT INTO jobs VALUES (?,?,?,?,?,?,?,?,?)", jobs) + else: + conn.executemany( + "INSERT INTO jobs VALUES (?,?,?,?,?,?,?,?)", [j[:6] + j[7:] for j in jobs] + ) + if with_corpus: + conn.execute("INSERT INTO articles (id, url, title) VALUES ('a1', 'https://x/1', 'T')") + conn.execute("INSERT INTO chunks (id, article_id, child_text) VALUES ('a1#p0c0', 'a1', 't')") + conn.execute( + "INSERT INTO projections VALUES ('a1#p0c0', 1.0, 2.0, 0, 'Label', 0.5)" + ) + lancedb.connect(settings.lance_path).create_table( + store.LEGACY_TABLE_NAME, + data=[{"chunk_id": "a1#p0c0", "article_id": "a1", "vector": [0.1, 0.2], + "child_text": "t", "domain": "x", "publish_date": "", "cluster_id": 0}], + ) + conn.commit() + conn.close() + + +def test_corpus_becomes_latest_finished_job(tmp_path, monkeypatch): + _make_v1(tmp_path, monkeypatch) + init_db() + + conn = connect() + analyses = [dict(r) for r in conn.execute("SELECT * FROM analyses")] + assert len(analyses) == 1 + assert analyses[0]["id"] == "job-1" + assert analyses[0]["topic"] == "jevons paradox" + assert analyses[0]["warning"] == "sparse" + for table in ("articles", "chunks", "placements", "clusters"): + ids = {r[0] for r in conn.execute(f"SELECT analysis_id FROM {table}")} + assert ids == {"job-1"}, table + placement = dict(conn.execute("SELECT * FROM placements").fetchone()) + assert placement | {"analysis_id": None} == { + "article_id": "a1", "analysis_id": None, "x": 1.0, "y": 2.0, "cluster_id": 0, "distance": 0.5, + } + assert conn.execute("SELECT label FROM clusters").fetchone()[0] == "Label" + assert conn.execute("PRAGMA user_version").fetchone()[0] == 8 + tables = {r[0] for r in conn.execute("SELECT name FROM sqlite_master WHERE type='table'")} + conn.close() + assert not tables & {"jobs", "articles_v1", "projections"} + + rows = store.read_all_embeddings("job-1") + assert [r["chunk_id"] for r in rows] == ["a1#p0c0"] + assert "cluster_id" not in rows[0] + db = lancedb.connect(settings.lance_path) + assert not store.table_exists(db, store.LEGACY_TABLE_NAME) + assert (tmp_path / "old.db.v1.bak").exists() + assert (tmp_path / "vectors.v1.bak").is_dir() + + +def test_empty_v1_database_migrates_to_no_analyses(tmp_path, monkeypatch): + _make_v1(tmp_path, monkeypatch, with_corpus=False) + init_db() + conn = connect() + assert conn.execute("SELECT COUNT(*) FROM analyses").fetchone()[0] == 0 + conn.close() + + +def test_migration_is_idempotent(tmp_path, monkeypatch): + _make_v1(tmp_path, monkeypatch) + init_db() + init_db() + conn = connect() + assert conn.execute("SELECT COUNT(*) FROM articles").fetchone()[0] == 1 + conn.close() + + +def test_v1_without_warning_column(tmp_path, monkeypatch): + # The `warning` column was added to v1 later; early databases lack it. + _make_v1(tmp_path, monkeypatch, warning_column=False) + init_db() + conn = connect() + row = conn.execute("SELECT id, warning FROM analyses").fetchone() + conn.close() + assert (row["id"], row["warning"]) == ("job-1", None) + + +def test_v3_projections_become_article_placements(tmp_path, monkeypatch): + """Each article takes its majority cluster and the mean position of those chunks.""" + monkeypatch.setattr(settings, "db_path", str(tmp_path / "v3.db")) + monkeypatch.setattr(settings, "lance_path", str(tmp_path / "vectors")) + init_db() + conn = connect() + conn.execute( + "INSERT INTO analyses (id, topic, status, created_ts, updated_ts) " + "VALUES ('an', 't', 'done', 1, 1)" + ) + conn.execute("INSERT INTO articles (id, analysis_id, url) VALUES ('a', 'an', 'u')") + conn.executemany( + "INSERT INTO chunks (id, analysis_id, article_id) VALUES (?, 'an', 'a')", + [("a#0",), ("a#1",), ("a#2",)], + ) + conn.execute( + "CREATE TABLE projections (chunk_id TEXT PRIMARY KEY, analysis_id TEXT, x REAL, y REAL, " + "cluster_id INTEGER, cluster_label TEXT, distance_from_centroid REAL)" + ) + conn.executemany( + "INSERT INTO projections VALUES (?, 'an', ?, ?, ?, ?, ?)", + [ + ("a#0", 2.0, 2.0, 1, "One", 0.4), + ("a#1", 4.0, 6.0, 1, "One", 0.7), + ("a#2", 99.0, 99.0, 0, "Zero", 9.0), + ], + ) + conn.execute("PRAGMA user_version = 3") + conn.commit() + conn.close() + + init_db() + conn = connect() + p = dict(conn.execute("SELECT x, y, cluster_id, distance FROM placements").fetchone()) + clusters = [tuple(r) for r in conn.execute("SELECT cluster_id, label, article_count FROM clusters")] + has_projections = conn.execute( + "SELECT 1 FROM sqlite_master WHERE name='projections'" + ).fetchone() + conn.close() + assert p == {"x": 3.0, "y": 4.0, "cluster_id": 1, "distance": 0.7} + assert clusters == [(1, "One", 1)] + assert has_projections is None diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..f99468c --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,260 @@ +""" +Pipeline orchestration with the network-bound stages stubbed out: search, +crawl, extraction, embeddings and projection are fakes; chunking, dedup, +SQLite and LanceDB are real. +""" + +import asyncio +import time + +import pytest +from conftest import add_analysis, prose + +from backend import pipeline +from backend.db import connect +from backend.embedding import store +from backend.ingestion.extractor import SkipArticle + +TEXTS = {"1": prose(1), "2": prose(2)} +TEXTS["copy"] = "Syndicated from the wire. " + TEXTS["1"] # near-copy of article 1 + + +@pytest.fixture +def fakes(isolated, monkeypatch): + """Stub every external stage; tests override individual pieces.""" + state = {"embed_hook": None, "crawl_hook": None} + + def collect_urls(topic, on_query=None): + if on_query: + on_query(1, 2, "query one", 3) + on_query(2, 2, "query two", None) + return ["https://a.test/1", "https://a.test/2", "https://a.test/copy", "https://a.test/short"] + + def crawl_urls(urls, on_page=None, should_stop=None): + if state["crawl_hook"]: + state["crawl_hook"](should_stop) + out = [] + for u in urls: + if should_stop and should_stop(): + break + if on_page: + on_page(u, True) + out.append({"url": u, "html": ""}) + return out + + def extract_article(analysis_id, url, html): + if url.endswith("short"): + raise SkipArticle("too short (12 words)") + n = url.rsplit("/", 1)[1] + return { + "id": f"{analysis_id[:4]}-{n}", "url": url, "domain": "a.test", + "title": f"Article {n}", "raw_text": TEXTS[n], + "word_count": 840, "language": "en", "crawl_ts": 0, "status": "ok", + } + + def embed_chunks(chunks, kind="document", on_batch=None): + if state["embed_hook"]: + state["embed_hook"]() + if on_batch: + on_batch(len(chunks), len(chunks)) + return [{"chunk_id": c["id"], "vector": [float(i % 7), 1.0, 0.5]} + for i, c in enumerate(chunks)] + + def run_projection(analysis_id, on_step=None, on_label=None): + on_step(0.0, "Projecting") + conn = connect() + articles = [r[0] for r in conn.execute( + "SELECT DISTINCT article_id FROM chunks WHERE analysis_id=? AND is_duplicate=0", + (analysis_id,), + )] + conn.executemany( + "INSERT INTO placements (article_id, analysis_id, x, y, cluster_id, distance) " + "VALUES (?,?,0,0,0,0.1)", + [(a, analysis_id) for a in articles], + ) + conn.execute( + "INSERT INTO clusters (analysis_id, cluster_id, label, article_count) VALUES (?,0,'Rebound',?)", + (analysis_id, len(articles)), + ) + conn.commit() + conn.close() + on_label("Rebound", len(articles)) + + monkeypatch.setattr("backend.ingestion.search.collect_urls", collect_urls) + monkeypatch.setattr("backend.ingestion.crawler.crawl_urls", crawl_urls) + monkeypatch.setattr("backend.ingestion.extractor.extract_article", extract_article) + monkeypatch.setattr("backend.embedding.embed.embed_chunks", embed_chunks) + monkeypatch.setattr("backend.embedding.project.run_projection", run_projection) + return state + + +def _analysis(aid): + conn = connect() + row = dict(conn.execute("SELECT * FROM analyses WHERE id=?", (aid,)).fetchone()) + conn.close() + return row + + +def _events(aid): + conn = connect() + rows = [dict(r) for r in conn.execute( + "SELECT kind, message, url FROM analysis_events WHERE analysis_id=? ORDER BY id", (aid,))] + conn.close() + return rows + + +def _count(table, aid): + conn = connect() + n = conn.execute(f"SELECT COUNT(*) FROM {table} WHERE analysis_id=?", (aid,)).fetchone()[0] + conn.close() + return n + + +def _run(aid, topic="jevons"): + asyncio.run(pipeline.run_pipeline(pipeline.RunContext(aid), topic)) + + +def test_happy_path_records_progress_and_events(fakes): + add_analysis("an1", status="queued") + _run("an1") + + a = _analysis("an1") + assert (a["status"], a["progress"], a["article_count"]) == ("done", 100, 2) + assert a["warning"].startswith("Only 2 articles") + assert a["stage_detail"] == "Indexed 2 articles" + + events = _events("an1") + kinds = {e["kind"] for e in events} + assert {"stage", "query", "article", "skip", "cluster", "warning"} <= kinds + assert {"kind": "skip", "message": "Too short (12 words)", + "url": "https://a.test/short"} in events + assert {"kind": "skip", "message": "Near-copy of “Article 1”", + "url": "https://a.test/copy"} in events + conn = connect() + status = conn.execute("SELECT status FROM articles WHERE url='https://a.test/copy'").fetchone()[0] + conn.close() + assert status == "duplicate" + assert any(e["kind"] == "warning" and "query two" in e["message"] for e in events) + assert store.open_table("an1") is not None + + +def test_cancel_during_crawl_discards_data(fakes): + add_analysis("an1", status="queued") + + def wait_for_cancel(should_stop): + deadline = time.monotonic() + 5 + while not should_stop() and time.monotonic() < deadline: + time.sleep(0.01) + + fakes["crawl_hook"] = wait_for_cancel + + async def scenario(): + pipeline.start("an1", "jevons") + task, _ctx = pipeline._runs["an1"] + while _analysis("an1")["stage_detail"] != "Crawling 4 pages": + await asyncio.sleep(0.01) + assert pipeline.cancel("an1") is True + await task + + asyncio.run(scenario()) + a = _analysis("an1") + assert a["status"] == "cancelled" + assert _events("an1")[-1]["message"] == "Cancelled" + assert _count("articles", "an1") == 0 + assert "an1" not in pipeline._runs + + +def test_cancel_inside_worker_thread_propagates(fakes): + """Cancelled is raised in a to_thread worker (via on_batch) and must still land.""" + add_analysis("an1", status="queued") + ctx = pipeline.RunContext("an1") + fakes["embed_hook"] = ctx.cancel + + asyncio.run(pipeline.run_pipeline(ctx, "jevons")) + + assert _analysis("an1")["status"] == "cancelled" + for table in ("articles", "chunks", "placements", "clusters"): + assert _count(table, "an1") == 0, table + assert store.open_table("an1") is None + + +def test_no_search_results_is_an_error(fakes, monkeypatch): + monkeypatch.setattr("backend.ingestion.search.collect_urls", lambda topic, on_query=None: []) + add_analysis("an1", status="queued") + _run("an1") + a = _analysis("an1") + assert a["status"] == "error" + assert "no results" in a["error_msg"] + assert _events("an1")[-1]["kind"] == "error" + + +def test_everything_filtered_is_an_error(fakes, monkeypatch): + def skip_all(analysis_id, url, html): + raise SkipArticle("not English (de)") + + monkeypatch.setattr("backend.ingestion.extractor.extract_article", skip_all) + add_analysis("an1", status="queued") + _run("an1") + assert "quality filters" in _analysis("an1")["error_msg"] + + +def test_progress_maps_into_band_and_throttles(isolated): + add_analysis("an1", status="crawling") + ctx = pipeline.RunContext("an1") + ctx.progress((10, 50), 0.5, "half") + assert _analysis("an1")["progress"] == 30 + ctx.progress((10, 50), 0.6, "throttled") # within the write interval + assert _analysis("an1")["stage_detail"] == "half" + ctx.progress((10, 50), 1.0, "finished") # completion always writes + assert (_analysis("an1")["progress"], _analysis("an1")["stage_detail"]) == (50, "finished") + + +def test_cancel_unknown_run_returns_false(): + assert pipeline.cancel("not-running") is False + + +def test_blocked_academic_page_is_recovered(fakes, monkeypatch): + """A page that extracts too short but carries a DOI gets its open-access text.""" + from backend.ingestion import scholarly + + monkeypatch.setattr( + "backend.ingestion.search.collect_urls", + lambda topic, on_query=None: ["https://a.test/1", "https://a.test/2", + "https://doi.org/10.1234/blocked.short"], + ) + calls = [] + + def fake_recover(url, html, client): + calls.append(url) + return scholarly.Recovered("A recovered paper", prose(9), "2024-03-01", + "Open-access PDF from repository.test") + + monkeypatch.setattr(scholarly, "recover", fake_recover) + add_analysis("an1", status="queued") + _run("an1") + + assert calls == ["https://doi.org/10.1234/blocked.short"] + conn = connect() + row = dict(conn.execute( + "SELECT title, status, source_note, publish_date FROM articles WHERE url LIKE '%blocked%'" + ).fetchone()) + conn.close() + assert row == {"title": "A recovered paper", "status": "ok", + "source_note": "Open-access PDF from repository.test", "publish_date": "2024-03-01"} + assert _analysis("an1")["article_count"] == 3 + assert any(e["message"] == "Recovered 1 academic articles from open sources" for e in _events("an1")) + + +def test_recovery_can_be_turned_off(fakes, monkeypatch): + from backend.config import settings + from backend.ingestion import scholarly + + monkeypatch.setattr(settings, "recover_academic", False) + monkeypatch.setattr(scholarly, "recover", lambda *a: pytest.fail("recover called")) + monkeypatch.setattr( + "backend.ingestion.search.collect_urls", + lambda topic, on_query=None: ["https://a.test/1", "https://doi.org/10.1234/x.short"], + ) + add_analysis("an1", status="queued") + _run("an1") + assert _analysis("an1")["status"] == "done" diff --git a/tests/test_projection.py b/tests/test_projection.py new file mode 100644 index 0000000..8b6aa1f --- /dev/null +++ b/tests/test_projection.py @@ -0,0 +1,101 @@ +"""run_projection end to end on synthetic embeddings, with the Claude call stubbed.""" + +import json + +from conftest import add_analysis, prose +from test_topics import blobs + +from backend.db import connect +from backend.embedding import project, store + +MARKERS = ["alphaword", "betaword", "gammaword"] # letters only: numbers never become terms + + +def _seed(analysis_id="an1", centers=3, n_per=8): + """Articles in `centers` well-separated groups, two chunks each, embedded.""" + X, truth = blobs(n_per=n_per, centers=centers, dim=16) + add_analysis(analysis_id) + conn = connect() + rows = [] + for i, (vec, group) in enumerate(zip(X, truth, strict=True)): + aid = f"a{i}" + conn.execute( + "INSERT INTO articles (id, analysis_id, url, title, raw_text, status) VALUES (?,?,?,?,?,?)", + (aid, analysis_id, f"https://x.test/{i}", f"Article {i}", + f"{MARKERS[group]} " * 60 + prose(int(group)), "ok"), + ) + for c in range(2): + rows.append({"chunk_id": f"{aid}#{c}", "article_id": aid, "vector": vec.tolist()}) + conn.commit() + conn.close() + store.write_embeddings(analysis_id, rows) + return truth + + +def _fake_complete(prompt, **kwargs): + ids = sorted({int(line.split()[1]) for line in prompt.splitlines() if line.startswith("Topic ")}) + return json.dumps({"topics": [ + {"id": t, "label": f"Camp {t}", "description": f"About camp {t}."} for t in ids + ]}) + + +def test_projection_writes_placements_and_named_clusters(isolated, monkeypatch): + truth = _seed() + monkeypatch.setattr("backend.claude_client.complete", _fake_complete) + labelled = [] + + project.run_projection("an1", on_label=lambda label, n: labelled.append((label, n))) + + conn = connect() + placements = conn.execute("SELECT article_id, cluster_id FROM placements").fetchall() + clusters = [dict(r) for r in conn.execute("SELECT * FROM clusters ORDER BY cluster_id")] + conn.close() + + assert len(placements) == len(truth) + assert [c["label"] for c in clusters] == ["Camp 0", "Camp 1", "Camp 2"] + assert all(c["description"].startswith("About camp") for c in clusters) + assert sum(c["article_count"] for c in clusters) == len(truth) + # each topic's strongest key term is its marker word (possibly as a bigram) + tops = [json.loads(c["terms"])[0][0] for c in clusters] + assert sorted(next(m for m in MARKERS if m in t) for t in tops) == MARKERS + assert labelled == [(c["label"], c["article_count"]) for c in clusters] + + +def test_naming_failure_falls_back_to_key_terms(isolated, monkeypatch): + _seed() + + def boom(*a, **k): + raise RuntimeError("API down") + + monkeypatch.setattr("backend.claude_client.complete", boom) + project.run_projection("an1") + + conn = connect() + labels = [r[0] for r in conn.execute("SELECT label FROM clusters")] + conn.close() + assert len(labels) == 3 + assert all("word" in label.lower() for label in labels) + + +def test_rerun_replaces_previous_results(isolated, monkeypatch): + _seed() + monkeypatch.setattr("backend.claude_client.complete", _fake_complete) + project.run_projection("an1") + project.run_projection("an1") + conn = connect() + n_clusters = conn.execute("SELECT COUNT(*) FROM clusters").fetchone()[0] + n_placements = conn.execute("SELECT COUNT(*) FROM placements").fetchone()[0] + conn.close() + assert (n_clusters, n_placements) == (3, 24) + + +def test_name_topics_fills_gaps_from_terms(monkeypatch): + monkeypatch.setattr( + "backend.claude_client.complete", + lambda *a, **k: json.dumps({"topics": [{"id": 0, "label": "Named", "description": "D."}]}), + ) + named = project.name_topics({ + 0: {"terms": ["a"], "examples": [], "size": 3}, + 1: {"terms": ["roofs", "albedo"], "examples": [], "size": 2}, + }) + assert named == {0: ("Named", "D."), 1: ("Roofs, albedo", None)} diff --git a/tests/test_quality_fixes.py b/tests/test_quality_fixes.py new file mode 100644 index 0000000..14da31b --- /dev/null +++ b/tests/test_quality_fixes.py @@ -0,0 +1,123 @@ +import pytest +from conftest import prose + +from backend.config import settings +from backend.embedding import embed +from backend.processing.dedup import chunk_and_dedup +from backend.synthesis.chat import order_and_dedupe_by_article + +# ── cross-article dedup ────────────────────────────────────────────────────── + +SYNDICATED = ( + "The central bank raised interest rates by a quarter point on Wednesday, " + "citing persistent inflation in services and a resilient labour market. " +) * 20 + + +def test_dedup_catches_duplicates_across_articles(): + articles = [ + {"id": "a1", "raw_text": SYNDICATED}, + {"id": "a2", "raw_text": SYNDICATED}, + ] + chunks = chunk_and_dedup(articles) + a1 = [c for c in chunks if c["article_id"] == "a1"] + a2 = [c for c in chunks if c["article_id"] == "a2"] + assert a1 and a2 + assert all(c["is_duplicate"] == 1 for c in a2) + # the first copy keeps at least one chunk + assert any(c["is_duplicate"] == 0 for c in a1) + + +def test_dedup_keeps_distinct_articles(): + articles = [ + {"id": "a1", "raw_text": SYNDICATED}, + {"id": "a2", "raw_text": "Coral reefs bleach when ocean temperatures stay high. " * 40}, + ] + chunks = chunk_and_dedup(articles) + assert any(c["is_duplicate"] == 0 for c in chunks if c["article_id"] == "a2") + + +def test_dedup_skips_empty_text(): + assert chunk_and_dedup([{"id": "a1", "raw_text": ""}]) == [] + + +# ── nomic task prefixes ────────────────────────────────────────────────────── + +@pytest.mark.parametrize("mode", ["fastembed", "ollama", "local"]) +def test_nomic_document_prefix(monkeypatch, mode): + monkeypatch.setattr(settings, "embedding_mode", mode) + assert embed.prepare_texts(["hi"], "document") == ["search_document: hi"] + + +def test_nomic_query_prefix(monkeypatch): + monkeypatch.setattr(settings, "embedding_mode", "local") + assert embed.prepare_texts(["hi"], "query") == ["search_query: hi"] + + +def test_openai_has_no_prefix(monkeypatch): + monkeypatch.setattr(settings, "embedding_mode", "openai") + assert embed.prepare_texts(["hi"], "query") == ["hi"] + + +def test_fastembed_vectors_are_unit_length_like_ollamas(monkeypatch): + class FakeModel: + def embed(self, texts, batch_size): + return iter([[3.0, 4.0]] * len(texts)) + + monkeypatch.setattr(settings, "embedding_mode", "fastembed") + monkeypatch.setattr(embed, "_fastembed_model", lambda: FakeModel()) + out = embed.embed_chunks([{"id": "c1", "child_text": "hi"}, {"id": "c2", "child_text": "yo"}]) + assert out == [{"chunk_id": "c1", "vector": [0.6, 0.8]}, {"chunk_id": "c2", "vector": [0.6, 0.8]}] + + +def test_unknown_kind_rejected(): + with pytest.raises(ValueError): + embed.prepare_texts(["hi"], "passage") + + +# ── RAG ranking order ──────────────────────────────────────────────────────── + +def test_rag_keeps_best_ranked_chunk_per_article(): + ranked_ids = ["a2#p0c1", "a1#p0c0", "a2#p0c0"] + # SQLite returns rows in arbitrary order + rows = [ + {"chunk_id": "a2#p0c0", "article_id": "a2", "parent_text": "worse"}, + {"chunk_id": "a1#p0c0", "article_id": "a1", "parent_text": "second"}, + {"chunk_id": "a2#p0c1", "article_id": "a2", "parent_text": "best"}, + ] + out = order_and_dedupe_by_article(rows, ranked_ids) + assert [r["chunk_id"] for r in out] == ["a2#p0c1", "a1#p0c0"] + + +# ── article-level dedup ────────────────────────────────────────────────────── + +def test_syndicated_copy_with_different_intro_is_a_duplicate(): + from backend.processing.dedup import find_duplicate_articles + + story = prose(1) + articles = [ + {"id": "orig", "raw_text": story}, + {"id": "copy", "raw_text": "Reprinted with permission from the Wire Service. " + story}, + {"id": "other", "raw_text": prose(2)}, + ] + assert find_duplicate_articles(articles) == {"copy": "orig"} + + +def test_edited_reprint_is_a_duplicate_but_related_article_is_not(): + """An edited reprint keeps most of the text; a different article on the + same subject shares only scattered phrases.""" + from backend.processing.dedup import find_duplicate_articles + + original = prose(1, words=600) + words = original.split() + # Reprint: new intro, a trimmed middle, a new footer. + reprint = " ".join(["This story first appeared elsewhere."] + words[:250] + words[350:] + + ["Republished under a Creative Commons licence."]) + related = " ".join(words[:60]) + " " + prose(3, words=540) # quotes the original's opening + + found = find_duplicate_articles([ + {"id": "orig", "raw_text": original}, + {"id": "reprint", "raw_text": reprint}, + {"id": "related", "raw_text": related}, + ]) + assert found == {"reprint": "orig"} diff --git a/tests/test_review.py b/tests/test_review.py new file mode 100644 index 0000000..90c9445 --- /dev/null +++ b/tests/test_review.py @@ -0,0 +1,123 @@ +"""The optional review step: pause after search, edit queries, approve pages.""" + +import asyncio + +import pytest +from conftest import add_analysis +from fastapi.testclient import TestClient + +from backend import pipeline +from backend.api import main +from backend.db import connect +from backend.ingestion.search import SearchResult + + +def _results(*urls, query="q1"): + return [SearchResult(u, f"Title of {u}", "snippet", query) for u in urls] + + +def _status(aid): + conn = connect() + row = conn.execute("SELECT status, stage_detail FROM analyses WHERE id=?", (aid,)).fetchone() + conn.close() + return dict(row) + + +@pytest.fixture +def paused(isolated, monkeypatch): + """A real pipeline run (search stubbed) that pauses for review, plus a client.""" + monkeypatch.setattr( + "backend.ingestion.search.collect_results", + lambda topic, on_query=None: (["q1"], _results("https://a.test/1", "https://a.test/2")), + ) + crawled_with = [] + + def fake_crawl(ctx, urls): + crawled_with.append(urls) + ctx.check() + raise RuntimeError("stop after crawl") # the rest of the pipeline is tested elsewhere + + monkeypatch.setattr(pipeline, "_crawl", fake_crawl) + add_analysis("an1", status="queued") + return crawled_with + + +def _run_with(client_actions): + """Start the pipeline in the test client's loop; run actions while it waits.""" + with TestClient(main.app) as client: + loop_runner = client.portal + + async def start(): + pipeline.start("an1", "topic", review=True) + + loop_runner.call(start) + task = pipeline._runs["an1"][0] + for _ in range(200): + if _status("an1")["status"] == "reviewing": + break + loop_runner.call(asyncio.sleep, 0.02) + client_actions(client) + + async def finish(): + await asyncio.wait_for(task, 5) + + loop_runner.call(finish) + + +def test_pauses_with_plan_then_crawls_only_approved_pages(paused): + def actions(client): + assert _status("an1") == {"status": "reviewing", + "stage_detail": "Waiting for you to review 2 pages"} + plan = client.get("/api/analyses/an1/plan").json() + assert plan["queries"] == ["q1"] + assert [r["url"] for r in plan["results"]] == ["https://a.test/1", "https://a.test/2"] + + r = client.post("/api/analyses/an1/plan/approve", + json={"urls": ["https://a.test/2", "https://mine.test/added", "https://a.test/2"]}) + assert r.status_code == 202 + + _run_with(actions) + assert paused == [["https://a.test/2", "https://mine.test/added"]] + + +def test_search_again_replaces_the_plan(paused, monkeypatch): + monkeypatch.setattr("backend.ingestion.search.search_all", + lambda queries, on_query=None: _results("https://b.test/1", query=queries[0])) + + def actions(client): + plan = client.post("/api/analyses/an1/plan/search", json={"queries": [" better query "]}).json() + assert plan["queries"] == ["better query"] + assert [r["url"] for r in plan["results"]] == ["https://b.test/1"] + assert client.get("/api/analyses/an1/plan").json() == plan + client.post("/api/analyses/an1/plan/approve", json={"urls": ["https://b.test/1"]}) + + _run_with(actions) + + +def test_invalid_urls_are_rejected(paused): + def actions(client): + r = client.post("/api/analyses/an1/plan/approve", json={"urls": ["javascript:alert(1)"]}) + assert r.status_code == 422 + assert "Not a web address" in r.json()["detail"] + client.post("/api/analyses/an1/plan/approve", json={"urls": ["https://a.test/1"]}) + + _run_with(actions) + + +def test_cancel_while_paused(paused): + def actions(client): + assert client.post("/api/analyses/an1/cancel").status_code == 202 + + _run_with(actions) + assert _status("an1")["status"] == "cancelled" + assert paused == [] # never crawled + + +def test_review_endpoints_need_a_paused_analysis(isolated): + add_analysis("done1", status="done") + with TestClient(main.app) as client: + assert client.post("/api/analyses/done1/plan/approve", + json={"urls": ["https://a.test"]}).status_code == 409 + assert client.post("/api/analyses/done1/plan/search", + json={"queries": ["x"]}).status_code == 409 + assert client.get("/api/analyses/done1/plan").status_code == 404 diff --git a/tests/test_scholarly.py b/tests/test_scholarly.py new file mode 100644 index 0000000..e7487c2 --- /dev/null +++ b/tests/test_scholarly.py @@ -0,0 +1,173 @@ +import httpx +import pytest + +from backend.ingestion import scholarly +from backend.ingestion.scholarly import Identifier, identify + +# ── identify ───────────────────────────────────────────────────────────────── + + +@pytest.mark.parametrize( + "url, expected", + [ + ("https://doi.org/10.1093/jsh/shaf109", ("doi", "10.1093/jsh/shaf109")), + ("https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL109634/full", + ("doi", "10.1029/2024GL109634")), + ("https://www.frontiersin.org/journals/x/articles/10.3389/fenvs.2026.1770260/xml", + ("doi", "10.3389/fenvs.2026.1770260")), + ("https://pmc.ncbi.nlm.nih.gov/articles/PMC6458494/", ("pmcid", "PMC6458494")), + ("https://pubmed.ncbi.nlm.nih.gov/31234567/", ("pmid", "31234567")), + ("https://www.sciencedirect.com/science/article/pii/S0360132322001234", + ("pii", "S0360132322001234")), + ("https://arxiv.org/abs/2404.14082", ("doi", "10.48550/arXiv.2404.14082")), + ("https://www.mdpi.com/2073-4433/16/7/791", ("mdpi", "2073-4433/16/7/791")), + ], +) +def test_identify_from_url(url, expected): + found = identify(url) + assert (found.kind, found.value) == expected + + +def test_identify_from_citation_meta_tags(): + html = '' + assert identify("https://example.org/article/123", html) == Identifier("doi", "10.1016/j.uclim.2023.101234") + html = '' + assert identify("https://example.org/a", html) == Identifier("pmid", "31234567") + + +def test_ordinary_pages_have_no_identifier(): + assert identify("https://www.nytimes.com/2024/07/01/climate/heat.html", "

    hi

    ") is None + + +# ── helpers ────────────────────────────────────────────────────────────────── + + +def test_abstract_rebuilt_from_inverted_index(): + inv = {"Cities": [0], "trap": [1], "heat": [2, 5], "and": [3], "release": [4]} + assert scholarly.abstract_text(inv) == "Cities trap heat and release heat" + assert scholarly.abstract_text(None) == "" + + +def test_pdf_text_cleanup_joins_lines_and_drops_references(): + body = "Urban heat is meas-\nured by satellites.\nIt varies by block.\n\n" + "Text. " * 50 + text = scholarly.clean_pdf_text(body + "\nReferences\n1. Smith J. Heat. 2020.") + assert "measured by satellites. It varies by block." in text + assert "Smith" not in text + + +# ── recovery paths (stubbed web) ───────────────────────────────────────────── + +ARTICLE = " ".join(["cool roofs reduce urban heat"] * 150) # 750 words + + +def client_for(routes): + """A client whose requests are answered by `routes[(host, path-prefix)]`.""" + + def handler(request: httpx.Request) -> httpx.Response: + for (host, prefix), make in routes.items(): + if request.url.host == host and request.url.path.startswith(prefix): + return make(request) + return httpx.Response(404) + + return httpx.Client(transport=httpx.MockTransport(handler)) + + +def work(**over): + return { + "title": "Cool roofs in hot cities", + "publication_date": "2024-05-02", + "ids": {}, + "best_oa_location": {"is_oa": True, "pdf_url": "https://journal.test/paper.pdf"}, + "locations": [], + "abstract_inverted_index": {w: [i] for i, w in enumerate("word" + str(i) for i in range(80))}, + **over, + } + + +def test_doi_with_open_access_pdf(monkeypatch): + monkeypatch.setattr(scholarly, "pdf_text", lambda url, client: ARTICLE) + client = client_for({("api.openalex.org", "/works/doi:"): lambda r: httpx.Response(200, json=work())}) + got = scholarly.recover("https://doi.org/10.1234/abc", client=client) + assert got.title == "Cool roofs in hot cities" + assert got.publish_date == "2024-05-02" + assert got.note == "Open-access PDF from journal.test" + assert len(got.text.split()) == 750 + + +def test_blocked_pdf_falls_back_to_abstract(monkeypatch): + monkeypatch.setattr(scholarly, "pdf_text", lambda url, client: None) + client = client_for({("api.openalex.org", "/works/doi:"): lambda r: httpx.Response(200, json=work())}) + got = scholarly.recover("https://doi.org/10.1234/abc", client=client) + assert got.note.startswith("Abstract only") + assert got.text.split()[:2] == ["word0", "word1"] + + +def test_tries_other_open_access_locations(monkeypatch): + tried = [] + + def fake_pdf(url, client): + tried.append(url) + return ARTICLE if "repository" in url else None + + monkeypatch.setattr(scholarly, "pdf_text", fake_pdf) + w = work(locations=[{"is_oa": True, "pdf_url": "https://repository.test/copy.pdf"}]) + client = client_for({("api.openalex.org", "/works/doi:"): lambda r: httpx.Response(200, json=w)}) + got = scholarly.recover("https://doi.org/10.1234/abc", client=client) + assert tried == ["https://journal.test/paper.pdf", "https://repository.test/copy.pdf"] + assert got.note == "Open-access PDF from repository.test" + + +PMC_XML = """
    +Heat and health +Introduction

    Heat kills more people than floods.

    +

    Cities are hotter.

    Smith 2020
    """ + + +def test_pmc_page_uses_europe_pmc_full_text(): + client = client_for({ + ("www.ebi.ac.uk", "/europepmc/webservices/rest/PMC1234567/fullTextXML"): + lambda r: httpx.Response(200, text=PMC_XML), + }) + got = scholarly.recover("https://pmc.ncbi.nlm.nih.gov/articles/PMC1234567/", client=client) + assert got.title == "Heat and health" + assert got.text == "Introduction\n\nHeat kills more people than floods.\n\nCities are hotter." + assert got.note == "Full text from Europe PMC" + + +def test_elsevier_pii_resolved_through_crossref(monkeypatch): + monkeypatch.setattr(scholarly, "pdf_text", lambda url, client: ARTICLE) + seen = [] + + def openalex(request): + seen.append(request.url.path) + return httpx.Response(200, json=work()) + + client = client_for({ + ("api.crossref.org", "/works"): lambda r: httpx.Response( + 200, json={"message": {"items": [{"DOI": "10.1016/j.x.2024.1"}]}}), + ("api.openalex.org", "/works/doi:"): openalex, + }) + got = scholarly.recover("https://www.sciencedirect.com/science/article/pii/S0360132322001234", + client=client) + assert seen == ["/works/doi:10.1016/j.x.2024.1"] + assert got is not None + + +def test_unknown_work_and_unidentifiable_pages_recover_nothing(): + client = client_for({}) + assert scholarly.recover("https://doi.org/10.1234/missing", client=client) is None + assert scholarly.recover("https://blog.test/post", client=client) is None + + +def test_network_errors_are_contained(): + def boom(request): + raise httpx.ConnectError("down") + + client = httpx.Client(transport=httpx.MockTransport(boom)) + assert scholarly.recover("https://doi.org/10.1234/abc", client=client) is None + + +def test_plain_pdf_links_are_read_directly(monkeypatch): + monkeypatch.setattr(scholarly, "pdf_text", lambda url, client: ARTICLE) + got = scholarly.recover("https://city.gov/reports/heat-plan.pdf", client=client_for({})) + assert (got.note, got.title) == ("Read from the PDF", None) diff --git a/tests/test_search.py b/tests/test_search.py new file mode 100644 index 0000000..8edae7e --- /dev/null +++ b/tests/test_search.py @@ -0,0 +1,58 @@ +import httpx +import pytest + +from backend.config import settings +from backend.ingestion import search + + +def _fake_get(payload, seen): + def get(url, *, params, headers, max_retries=3): + seen.append((url, params)) + return httpx.Response(200, json=payload, request=httpx.Request("GET", url)) + return get + + +def test_searxng_results_parsed(monkeypatch): + seen = [] + monkeypatch.setattr(settings, "search_provider", "searxng") + monkeypatch.setattr(settings, "searxng_url", "http://localhost:8888/") + monkeypatch.setattr(search, "_get_with_retries", _fake_get( + {"results": [{"url": "https://a.test", "title": "A", "content": "about A"}, {"title": "no url"}]}, + seen, + )) + results = search.web_search("cool roofs") + assert results == [search.SearchResult("https://a.test", "A", "about A", "cool roofs")] + assert seen == [("http://localhost:8888/search", + {"q": "cool roofs", "format": "json", "language": "en"})] + + +def test_searxng_needs_a_url(monkeypatch): + monkeypatch.setattr(settings, "search_provider", "searxng") + monkeypatch.setattr(settings, "searxng_url", "") + with pytest.raises(RuntimeError, match="SEARXNG_URL"): + search.web_search("x") + + +def test_brave_is_the_default(monkeypatch): + seen = [] + monkeypatch.setattr(settings, "search_provider", "brave") + monkeypatch.setattr(search, "_get_with_retries", _fake_get( + {"web": {"results": [{"url": "https://b.test", "title": "B", "description": "about B"}]}}, seen, + )) + assert [r.url for r in search.web_search("q")] == ["https://b.test"] + assert "brave.com" in seen[0][0] + + +def test_search_all_dedupes_and_survives_failed_queries(monkeypatch): + def fake(query): + if query == "broken": + raise httpx.ConnectError("down") + return [search.SearchResult(u, "", "", query) for u in ("https://x.test/1", "https://x.test/2")] + + monkeypatch.setattr(search, "web_search", fake) + progress = [] + results = search.search_all(["one", "broken", "two"], + on_query=lambda d, t, q, n: progress.append((q, n))) + assert [r.url for r in results] == ["https://x.test/1", "https://x.test/2"] + assert [r.query for r in results] == ["one", "one"] + assert progress == [("one", 2), ("broken", None), ("two", 0)] diff --git a/tests/test_spa.py b/tests/test_spa.py new file mode 100644 index 0000000..b550add --- /dev/null +++ b/tests/test_spa.py @@ -0,0 +1,46 @@ +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient + +from backend.api.spa import spa_router + + +@pytest.fixture +def client(tmp_path): + build = tmp_path / "build" + (build / "_app").mkdir(parents=True) + (build / "index.html").write_text("app") + (build / "_app" / "main.js").write_text("console.log(1)") + (tmp_path / "secret.txt").write_text("outside the build") + + app = FastAPI() + + @app.get("/api/health") + def health(): + return {"status": "ok"} + + app.include_router(spa_router(build)) + return TestClient(app) + + +def test_static_file_is_served(client): + r = client.get("/_app/main.js") + assert r.status_code == 200 + assert r.text == "console.log(1)" + + +def test_client_routes_fall_back_to_index(client): + for path in ("/", "/analysis/abc", "/analysis/abc/timeline"): + r = client.get(path) + assert r.status_code == 200, path + assert r.text == "app" + + +def test_api_routes_win_and_unknown_api_paths_404(client): + assert client.get("/api/health").json() == {"status": "ok"} + assert client.get("/api/nope").status_code == 404 + + +def test_cannot_escape_build_dir(client): + r = client.get("/..%2Fsecret.txt") + assert "outside the build" not in r.text diff --git a/tests/test_topics.py b/tests/test_topics.py new file mode 100644 index 0000000..f0fca6f --- /dev/null +++ b/tests/test_topics.py @@ -0,0 +1,120 @@ +import numpy as np +import pytest + +from backend import topics +from backend.topics import NOISE + + +def blobs(n_per=12, centers=4, dim=32, spread=0.05, seed=0): + """Well-separated groups of unit vectors.""" + rng = np.random.default_rng(seed) + C = topics.normalize(rng.normal(size=(centers, dim))) + X = np.concatenate([C[c] + rng.normal(scale=spread, size=(n_per, dim)) for c in range(centers)]) + truth = np.repeat(np.arange(centers), n_per) + return topics.normalize(X), truth + + +def same_partition(a, b): + """True if two labelings group the items identically (ids may differ).""" + pairs = {(x, y) for x, y in zip(a.tolist(), b.tolist(), strict=True)} + return len({x for x, _ in pairs}) == len(pairs) == len({y for _, y in pairs}) + + +def test_article_vectors_average_and_normalise(): + v = np.array([[2.0, 0], [0, 2.0], [3.0, 0]]) + ids, X = topics.article_vectors(v, ["a", "a", "b"]) + assert ids == ["a", "b"] + np.testing.assert_allclose(X[0], [np.sqrt(0.5), np.sqrt(0.5)]) + np.testing.assert_allclose(np.linalg.norm(X, axis=1), 1) + + +def test_model_recovers_separated_topics(): + X, truth = blobs() + labels, coords, dist = topics.model_topics(X) + # UMAP's floating point differs across platforms, so an edge point or two + # may stay unclustered (by design); the topics themselves must be exact. + clustered = labels != NOISE + assert set(labels[clustered].tolist()) == {0, 1, 2, 3} + assert clustered.mean() >= 0.9 + assert same_partition(labels[clustered], truth[clustered]) + assert coords.shape == (len(X), 2) + assert np.all(dist >= -1e-6) and np.all(dist < 0.2) + + +def test_relabel_orders_by_size_and_keeps_noise(): + out = topics.relabel(np.array([5, 5, 2, 2, 2, NOISE, 9])) + assert out.tolist() == [1, 1, 0, 0, 0, NOISE, 2] + + +def test_noise_joins_topic_only_when_typical(): + X, truth = blobs(centers=2) + labels = truth.copy() + # The most typical member of topic 0, wrongly marked noise. + typical = int(np.argmax(X[truth == 0] @ topics.centroids(X, truth)[0])) + labels[typical] = NOISE + stray = topics.normalize(np.random.default_rng(1).normal(size=(1, X.shape[1]))) + X = np.vstack([X, stray]) + labels = np.append(labels, NOISE) + + out = topics.assign_noise(X, labels) + assert out[typical] == 0 # rejoined + assert out[-1] == NOISE # unlike anything: stays unclustered + + +def test_cap_merges_most_similar_topics(): + X, truth = blobs(centers=5, n_per=6) + # Make topics 3 and 4 nearly identical so they're the pair to merge. + X[truth == 4] = X[truth == 3][: (truth == 4).sum()] + 1e-3 + X = topics.normalize(X) + out = topics.cap_topics(X, truth, max_topics=4) + assert len(set(out.tolist())) == 4 + assert len(set(out[(truth == 3) | (truth == 4)].tolist())) == 1 + + +def test_tiny_corpus_is_one_topic(): + X, _ = blobs(n_per=1, centers=4) + assert topics.find_topics(X).tolist() == [0, 0, 0, 0] + + +def test_topic_terms_prefer_distinctive_words(): + texts = [ + "cool roofs reflect sunlight; white roofs lower city heat", + "green roofs and cool roofs cut rooftop heat in the city", + "heat equity: redlining and gentrification shape city heat exposure", + "gentrification and redlining concentrate heat in poorer city blocks", + ] + labels = np.array([0, 0, 1, 1]) + terms = topics.topic_terms(texts, labels, top_n=4) + zero = [t for t, _ in terms[0]] + one = [t for t, _ in terms[1]] + assert any("roofs" in t for t in zero) + assert any("gentrification" in t or "redlining" in t for t in one) + assert "city" not in zero + one # in every topic: no signal + assert all("http" not in t for t in zero + one) + + +def test_topic_terms_ignore_noise_and_urls(): + terms = topics.topic_terms( + ["see https://example.com/page about tariffs and tariffs", "unrelated words here"], + np.array([0, NOISE]), + ) + assert list(terms) == [0] + assert all("example" not in t for t, _ in terms[0]) + + +@pytest.mark.parametrize("k", [1, 3]) +def test_representatives_are_closest_members(k): + X, truth = blobs(centers=2) + reps = topics.representatives(X, truth, k=k) + assert set(reps) == {0, 1} + for t, idx in reps.items(): + assert len(idx) == k + 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