diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..87f9023 --- /dev/null +++ b/.env.example @@ -0,0 +1,10 @@ +# Copy to .env (never commit). In production these live in Modal Secrets. +FIRECRAWL_API_KEY= +TEACHER_API_KEY= +TEACHER_BASE_URL= +TINKER_API_KEY= +TYPESAFE_API_KEY= +HF_TOKEN= +WANDB_API_KEY= +# AC2 (later) +AC2_API_KEY= diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..e200df0 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,19 @@ +name: CI +on: + push: + pull_request: +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.11", "3.12"] + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + python-version: ${{ matrix.python-version }} + - run: uv sync --extra dev + - run: uv run ruff check . + - run: uv run ruff format --check . + - run: uv run pytest -q diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1bf8a0c --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +__pycache__/ +*.py[cod] +.venv/ +.env +.pytest_cache/ +.ruff_cache/ +dist/ +build/ +*.egg-info/ +data/ +runs/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..5709bd8 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,20 @@ +# crawl2action — contributor notes + +- Design: `docs/PLAN.md` (milestones M0–M5). Keep it in sync when decisions change. +- Setup: `uv sync --extra dev`. Checks (run before every push, same as CI): + `uv run ruff check . && uv run ruff format --check . && uv run pytest -q` +- Optional integrations (tinker, modal, firecrawl, vllm) are extras. Import them lazily inside + functions so the base install and tests never need them. +- Stubs raise `c2a.NotYetImplemented(what, milestone)`; CLI stubs exit with code 2. +- Crawling is default-deny: a store needs `enabled: true` AND `tos_status: approved`. Only a human + sets these (`c2a registry approve --by`); automated code uses `Registry.update_descriptive`. +- Every crawl fetch from a store goes through `compliance.CrawlSession` (ToS + robots + domain + + rate limit). The only exception is discovery fingerprinting, which reads public metadata + (`/.well-known/ucp`, plus `/products.json?limit=1` and the homepage if robots.txt allows them). +- Money is integer minor units + ISO currency. +- Graders are the single source of truth for quality: RL rewards (`train/rewards.py`), evals and + serving guards all call them. +- Decisions use the System One contract (`decide/systemone.py`): hosted Jev labels data, and our + post-trained decider serves the same API. Bump a question set's version whenever a question changes. +- Decider training exports leave out Jev-only labels unless TypeSafe's terms allow training on them. +- Never train on files containing `c2a.bench.CANARY`. diff --git a/README.md b/README.md index 7f94899..336b91a 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,73 @@ # crawl2action -E2E pipeline to crawl and post-train LLM for vertical domain (e-commerce) + +End-to-end pipeline for e-commerce AI: + +**crawl** (Firecrawl, Shopify, UCP, open datasets) → **datasets** → **post-train** (Tinker; Modal fallback) → **benchmark** (public + C2A-Bench) → **serve** (Modal, behind vLLM Semantic Router). + +It post-trains models for pre- and post-checkout recommendations, product copy, localization, image briefs and calibrated decisions. See [`docs/PLAN.md`](docs/PLAN.md) for the full design. + +## Status: M0 (skeleton) +Working and tested: +- schemas (UCP-aligned) +- store registry (default-deny) +- crawl compliance (ToS + robots.txt + rate limit) +- Shopify `products.json` parser +- ranking metrics +- recommendation graders +- `decide` interface and calibration metrics +- training data formats +- RL rewards and advantage estimators (Dr.GRPO/GRPO, Rank-GRPO-style, DAPO dynamic sampling) +- Tinker SFT/RL/OPD loops (tested with a fake backend) +- the dev gateway + +Everything else is a stub that names the milestone it lands in. + +## Quickstart +```bash +uv sync --extra dev +uv run pytest -q +uv run c2a registry validate +uv run c2a decide --evidence "bought running shoes; candidate: running socks" \ + --labels exact,substitute,complement,irrelevant +uv run c2a train sft --config configs/train/sft_student.yaml --dry-run +``` + +To run real post-training on Tinker, run `uv sync --extra train`, set `TINKER_API_KEY`, add a dataset at the path in the run config, then: +```bash +uv run c2a train sft --config configs/train/sft_student.yaml +uv run c2a train rl --config configs/train/rl_student_rank_grpo.yaml +uv run c2a train opd --config configs/train/opd_reranker.yaml +``` + +## Crawling +**First real run:** follow [`docs/PILOT.md`](docs/PILOT.md) (`c2a doctor` → `c2a pilot` → `c2a label review` → `c2a report`). + +Crawling is default-deny: a store is crawled only after a person has reviewed its terms and approved it. +```bash +uv run c2a registry approve allbirds --by "" --note "ToS reviewed" +uv run c2a discover fingerprint --store allbirds --write # ucp / shopify / firecrawl +uv run c2a crawl --dry-run # what runs, and why others are skipped +export FIRECRAWL_API_KEY=... TYPESAFE_API_KEY=... +uv run c2a crawl --store allbirds --max-products 20 --label +uv run c2a discover expand --country SE --category apparel # candidates for review +uv run c2a data import esci --path examples.parquet --path products.parquet --out data/open/esci +``` +Parquet imports need `uv sync --extra data`. + +## Labeling with Jev +Scraped products are classified and labeled by hosted **Jev** (TypeSafe System One) through `c2a label`. Uncertain labels are escalated to the teacher and then to human review. +```bash +export TYPESAFE_API_KEY=... +uv run c2a label run --input data/products.jsonl --qset product_v1 --max-requests 100 +uv run c2a label export --run-dir data/labels/product_v1 --kind gold --out data/gold/product.jsonl +``` +Add `--dry-run` to preview a request without calling the API, or `--backend keyword` to run offline. + +## Layout +- `src/c2a/sources`: registry, compliance, Shopify/UCP/Firecrawl ingesters +- `src/c2a/graders`, `src/c2a/decide`: shared by evaluation, RL rewards and serving guards (`decide` uses the System One contract) +- `src/c2a/labeling`: Jev question sets, confidence gating, escalation, cache, exports +- `src/c2a/train`: data formats, rewards, `tinker/` loops, `fallback/` (Modal), `ac2/` +- `src/c2a/bench`, `src/c2a/eval`: C2A-Bench and the benchmark harness +- `src/c2a/serve`: gateway, vLLM and image Modal apps +- `configs/`: defaults, training run configs, semantic-router draft diff --git a/configs/default.yaml b/configs/default.yaml new file mode 100644 index 0000000..5eebb80 --- /dev/null +++ b/configs/default.yaml @@ -0,0 +1,21 @@ +# crawl2action defaults. Override any key with env vars, e.g. C2A__MODELS__STUDENT=Qwen/Qwen3.5-27B +paths: + data_dir: data + runs_dir: runs +budgets: + firecrawl_credits_per_run: 5000 + teacher_usd_per_run: 100 + default_qps_per_domain: 1.0 +models: + student: Qwen/Qwen3.8-27B # post-trained + served; confirm availability on Tinker (M3) + student_alt: thinkingmachines/Inkling-Small # bake-off challenger + proxy_student: Qwen/Qwen3.5-4B # fast iteration before scaling to 27B + teacher: kimi-k3 # API-only data generator / rubric writer (alt: glm-5.3, qwen3.8-max) + decider: llm-semantic-router/Decision-1.0-Lux-9B # starting checkpoint; we post-train it + image: Qwen/Qwen-Image-2.1 + embedding: Qwen/Qwen3-Embedding-0.6B +locales: [sv-SE, en-GB, es-ES, en-US, en-CA, fr-CA] +labeler: # hosted Jev (TypeSafe System One); key: TYPESAFE_API_KEY + provider: typesafe + base_url: https://api.typesafe.ai + model: jev-latest diff --git a/configs/labeling.yaml b/configs/labeling.yaml new file mode 100644 index 0000000..fca786e --- /dev/null +++ b/configs/labeling.yaml @@ -0,0 +1,9 @@ +# Confidence gates for Jev labels. Tune against the human-verified gold set (M2). +thresholds: + min_confidence: 0.6 # choice/score answer confidence + min_top_prob: 0.7 # choice/score top probability + noul_min: 0.85 # yes/no: max(p, 1-p) + per_question: + restricted: {noul_min: 0.95} # compliance-relevant: escalate more + contains_pii: {noul_min: 0.95} + page_type: {min_top_prob: 0.8} # gates Firecrawl /extract spend diff --git a/configs/router/semantic-router.yaml b/configs/router/semantic-router.yaml new file mode 100644 index 0000000..fe15413 --- /dev/null +++ b/configs/router/semantic-router.yaml @@ -0,0 +1,24 @@ +# DRAFT vLLM Semantic Router policy for crawl2action (schema to be aligned with the +# router version we deploy in M4; see https://github.com/vllm-project/semantic-router). +model_pool: + - name: c2a-reranker # OPD-distilled fast model (high-QPS /recommend) + endpoint: ${C2A_RERANKER_URL} + - name: c2a-student # post-trained Qwen3.8-27B (+ task LoRAs) + endpoint: ${C2A_STUDENT_URL} + - name: c2a-decider # post-trained decider (calibrated label probabilities) + endpoint: ${C2A_DECIDER_URL} + - name: c2a-image # Qwen-Image-2.1 pipeline + endpoint: ${C2A_IMAGE_URL} +signals: + - {name: task, type: keyword, source: path} # /recommend, /generate/text, ... + - {name: language, type: language_detection} + - {name: complexity, type: classifier} + - {name: pii, type: pii_detection} + - {name: jailbreak, type: jailbreak_detection} +decisions: + - {when: "jailbreak or pii.high", action: block} + - {when: "task == recommend and complexity == low", route: c2a-reranker} + - {when: "task == recommend", route: c2a-student, reasoning: auto} + - {when: "task == decide", route: c2a-decider} + - {when: "task == generate_image", route: c2a-image} + - {when: "true", route: c2a-student, reasoning: "complexity == high"} diff --git a/configs/train/opd_reranker.yaml b/configs/train/opd_reranker.yaml new file mode 100644 index 0000000..832cafa --- /dev/null +++ b/configs/train/opd_reranker.yaml @@ -0,0 +1,12 @@ +# On-policy distillation: RL'd student (teacher) -> fast reranker. Same tokenizer family required. +name: opd-reranker +stage: opd +platform: tinker +base_model: Qwen/Qwen3.5-4B +teacher_model: Qwen/Qwen3.8-27B # replace with the RL'd checkpoint path from Tinker +dataset: data/train/opd_rec.jsonl +kl_coef: 1.0 +group_size: 4 +batch_size: 16 +learning_rate: 5.0e-5 +steps: 50 diff --git a/configs/train/rl_student_rank_grpo.yaml b/configs/train/rl_student_rank_grpo.yaml new file mode 100644 index 0000000..58c874a --- /dev/null +++ b/configs/train/rl_student_rank_grpo.yaml @@ -0,0 +1,14 @@ +# RL on recommendation prompts with Rank-GRPO-style advantages + DAPO dynamic sampling. +name: rl-student-rec-proxy +stage: rl +platform: tinker +base_model: Qwen/Qwen3.5-4B +dataset: data/train/rl_rec.jsonl +reward: rec +advantage: rank_grpo +drop_zero_variance_groups: true +group_size: 8 +batch_size: 16 +learning_rate: 4.0e-5 +max_tokens: 512 +steps: 20 diff --git a/configs/train/sft_decider.yaml b/configs/train/sft_decider.yaml new file mode 100644 index 0000000..ef9a21d --- /dev/null +++ b/configs/train/sft_decider.yaml @@ -0,0 +1,10 @@ +# Decider post-training step 1: SFT on ESCI + C2A gold labels (then RL with reward: decide). +name: sft-decider +stage: sft +platform: tinker +base_model: llm-semantic-router/Decision-1.0-Lux-9B # if not trainable on Tinker, use a same-family Qwen base +dataset: data/train/sft_decider.jsonl +lora_rank: 32 +learning_rate: 1.0e-4 +batch_size: 64 +steps: 50 diff --git a/configs/train/sft_student.yaml b/configs/train/sft_student.yaml new file mode 100644 index 0000000..a6bc7b8 --- /dev/null +++ b/configs/train/sft_student.yaml @@ -0,0 +1,11 @@ +# Cold-start SFT on grader-filtered teacher outputs. Start on the proxy model, then 27B. +name: sft-student-proxy +stage: sft +platform: tinker +base_model: Qwen/Qwen3.5-4B +dataset: data/train/sft_student.jsonl +lora_rank: 32 +learning_rate: 1.0e-4 +batch_size: 32 +steps: 20 +save_every: 10 diff --git a/docs/PILOT.md b/docs/PILOT.md new file mode 100644 index 0000000..49f2406 --- /dev/null +++ b/docs/PILOT.md @@ -0,0 +1,75 @@ +# Pilot runbook: scrape and label real stores + +The goal of this pilot is to crawl 3–5 Shopify stores and label their products and product pairs with Jev. It also produces the first human-reviewed gold labels. Everything runs on your machine. This takes about 30 minutes plus review time. + +## 1. Setup +```bash +git clone git@github.com:umaitech/crawl2action.git && cd crawl2action +git checkout claude/magical-davinci-7u43rl +uv sync --extra dev +cp .env.example .env # then fill in the keys below +``` +`.env` needs: +- `TYPESAFE_API_KEY`: required (Jev labeling). +- `FIRECRAWL_API_KEY`: only needed if you add a non-Shopify store. + +The `.env` file is gitignored and is loaded automatically by every `c2a` command. + +## 2. Pick and approve stores +Suggested pilot stores. They are believed to run on Shopify, which means no Firecrawl credits are needed for products; `doctor` and `fingerprint` will confirm this: + +| id | store | country | +|---|---|---| +| `allbirds` | Allbirds | US | +| `kotn` | Kotn | CA | +| `tentree` | Tentree | CA | +| `djerfavenue` | Djerf Avenue | SE | +| `gymshark` | Gymshark | UK | + +Review each store's terms of service and robots.txt yourself. Approve only stores whose terms allow automated collection of public product data for your purpose. Approval is recorded with your name: +```bash +uv run c2a registry approve allbirds --by "" --note "ToS reviewed : ..." +uv run c2a discover fingerprint --store allbirds --write # sets platform, method, currency +``` +Commit `src/c2a/sources/registry.yaml` afterwards, so the approval and its audit trail are shared. + +## 3. Preflight +```bash +uv run c2a doctor --live +``` +Every line should show ✅. `--live` makes one real Jev call to confirm the key works and that our parser matches Jev's actual response format. If you see "Jev response format ❌", send the output to Claude: it includes the raw response body. + +## 4. Run the pilot +```bash +uv run c2a pilot --store allbirds --store kotn --store tentree --max-products 100 --max-jev-requests 400 +``` +The pilot runs these steps: +1. It crawls each store politely: 1 request per second, following robots.txt and retrying on 429s. +2. It labels new or changed products with `product_v1`. That is one Jev request per product. +3. It builds candidate pairs inside each store (3 similar products plus 1 from another category, per anchor) and labels them with `pair_v1`. +4. It writes `data/reports/pilot.md`. + +Re-running only pays for new or changed products and uncached requests. + +**Cost check:** Jev requests ≈ products + pairs. With 3 stores × 100 products and `--pairs-k 3`, that is about 300 + 1,200. The run stops at `--max-jev-requests`, so start small. + +## 5. Review uncertain labels +```bash +uv run c2a label review --run-dir data/labels/product_v1 --limit 30 +uv run c2a label review --run-dir data/labels/pair_v1 --limit 30 +``` +For each item, pick a number, `s` to skip or `q` to quit. Every answer is saved immediately. + +## 6. Look at the results +```bash +uv run c2a report +uv run c2a label export --run-dir data/labels/product_v1 --kind gold --out data/gold/product_v1.jsonl +uv run c2a label export --run-dir data/labels/pair_v1 --qset pair_v1 --kind gold --out data/gold/pair_v1.jsonl +``` + +## Send back to Claude +- the output of `c2a doctor --live`; +- `data/reports/pilot.md`; +- any `data/raw//manifest.json` with errors. + +These say whether the real APIs and stores behave as the code expects, and how the confidence thresholds in `configs/labeling.yaml` should be tuned. diff --git a/docs/PLAN.md b/docs/PLAN.md new file mode 100644 index 0000000..d4a8061 --- /dev/null +++ b/docs/PLAN.md @@ -0,0 +1,342 @@ +# crawl2action: plan for an end-to-end pipeline from e-commerce crawling to a post-trained LLM + +## Context +The `crawl2action` repo is empty apart from a README and LICENSE. The goal is one pipeline that: +1. crawls product catalogs from popular e-commerce sites in SE, UK, ES, US and CA using **Firecrawl**, plus **UCP catalog endpoints** and open datasets; +2. turns that data into training and eval sets for **pre-checkout recommendations** (search/browse/PDP), **post-checkout recommendations** (cross-sell, replenishment), **text generation** (product descriptions, titles, ads, localized copy) and **image generation** (product and lifestyle visuals); +3. post-trains a **student LLM** on **Tinker** (Thinking Machines; Modal as the self-run fallback) with the current recipe (cold-start SFT → reward modelling with human preferences, rubrics and GenRM → GRPO-family RL → on-policy distillation) for now, with **Applied Compute AC2** as a later drop-in behind the same trainer interface; +4. serves the models on **Modal**. + +Decisions confirmed with the user: +- **Two model tiers:** + - The student is **Qwen3.8-27B** (dense, multimodal, Aug 2026). It is the model that gets post-trained and served in production on 1–2 H100/H200. **Inkling-Small** competes with it in the bake-off. + - The teacher and judge is **Kimi K3** (2.8T MoE, 104B active), GLM-5.3 or Qwen3.8-Max, called through a hosted API. It is used only for generating synthetic data and for grading, never for serving. +- **Images:** the LLM writes structured image briefs, and a separate image model renders them. That model is **Qwen-Image-2.1** (7B, generation plus editing, 10 reference images), served on Modal. An optional LoRA can be trained on crawled product photos. +- **Data sources:** a mix of popular regional stores, open datasets and UCP catalog access. Every source goes through robots.txt and ToS gating. + +## Architecture (six stages, each a Modal app plus a `c2a` CLI command) +``` +discover → crawl/ingest → normalize+dedupe → build datasets → post-train (Tinker; Modal fallback; AC2 later) → eval → serve (Modal) + ↑ │ + └─────── production feedback logs ─────┘ +``` +Key design choice: the model does **not memorize the catalog**. At serving time, recommendations work as *retrieve then rerank and explain*: +1. an embedding index returns about 50 candidates; +2. the LLM ranks them, writes the explanation and enforces constraints. + +Training therefore teaches the model to reason over candidate sets. That makes it robust to catalog churn and lets graders check hallucinations (every returned product ID must be in the candidate set). + +## Repo layout (Python 3.12, `uv`, `pydantic`, `typer`, `ruff`, `pytest`) +``` +src/c2a/ + schemas.py # Product, Variant, Offer, Review, Store, Session, TaskExample (pydantic; aligned to UCP catalog + schema.org Product) + sources/ + registry.yaml # stores: domain, country, locale, platform, tier, tos_status, method + discover.py # Firecrawl /search + platform fingerprinting (Shopify, WooCommerce) + probe /.well-known/ucp + shopify.py # /products.json pagination (cheap, structured, no LLM extraction) + ucp.py # UCP catalog capability client (REST/MCP search + get product/variants) + firecrawl.py # /map + /crawl + /extract (JSON schema = schemas.Product) with rate limits and caching + open_datasets.py # Amazon Reviews 2023 (co-purchase and user histories), H&M Personalized Fashion, RetailRocket, Diginetica + compliance.py # robots.txt check, ToS allow/deny list, PII stripping, per-domain QPS + normalize/ # currency to minor units + FX, taxonomy mapping (Google Product Taxonomy), language ID, near-dup (MinHash + image pHash) + index/ # multilingual embeddings (e.g. Qwen3-Embedding) → LanceDB on Modal Volume + datasets/ + tasks/ # one module per task family (see below); each emits SFT + RL prompts + eval items + teacher.py # teacher API client (Kimi K3 / Qwen-Max), batch, cache, cost cap + splits.py # held-out splits by store AND by time to avoid leakage + graders/ # programmatic + LLM-judge graders shared by eval and RL + decide/ # decide(evidence, question, labels) -> probs; Decision-1.0-style calibrated classifiers + train/ + base.py # Trainer interface: submit(dataset, config) -> run_id; export(run_id) -> HF/LoRA weights + tinker/ # PRIMARY: SFT, RLHF, RL (custom Rank-GRPO/DAPO losses), OPD loops on Tinker + envs/ # Modal-hosted rollout envs + reward endpoints called from Tinker loops + sft.py # fallback: TRL cold-start SFT / DPO baselines (Modal) + reward/ # preference-labelling app, rubric generation, GenRM training + rl/ # fallback: verl on Modal: GRPO/DAPO/GSPO losses, Rank-GRPO advantage, TIS correction + opd.py # on-policy distillation + self-distillation + ac2/ # LATER: AC2 project (added once access is granted) + eval/ # Inspect-based harness: public benchmark adapters + C2A-Bench tracks → reports + bench/ # C2A-Bench builder: tracks, gold-set sampling, contamination checks, canary + serve/ + llm.py # Modal + vLLM, OpenAI-compatible, --enable-lora for per-task adapters + image.py # Modal + diffusers Qwen-Image-2.1 (+ optional product LoRA) + gateway.py # thin FastAPI on Modal: /recommend, /cross-sell, /generate/text, /generate/image, /feedback + router/ # vLLM Semantic Router config: signals, decisions, model pool + cli.py # c2a discover|crawl|build|train|bench|eval|deploy +configs/ # per-run YAML (model, tasks, mix ratios, budgets) +tests/ +``` +Storage: raw crawl JSON → parquet on a Modal Volume, with an optional S3/R2 mirror. Secrets (`FIRECRAWL_API_KEY`, `TEACHER_API_KEY`, `AC2_*`, `HF_TOKEN`) live in Modal Secrets. + +## 1. Discovery and crawling +**Source tiers** in `registry.yaml`: +- **A. Structured / permissioned (preferred):** UCP-enabled merchants (probe `/.well-known/ucp` and use the catalog capability), Shopify `/products.json`, and open datasets. +- **B. Firecrawl `/extract`:** regional stores whose robots.txt and ToS permit crawling. +- **C. Marketplaces** (Amazon, Zalando, ASOS, El Corte Inglés, etc.): **off by default**. Enable only with explicit ToS clearance. Behavioral signal comes from open datasets instead. + +**Seed candidates.** `discover.py` verifies platform, UCP support and ToS, then expands the list automatically by running Firecrawl `/search` for queries like "best online stores {country} {category}". +- **SE:** Happy Socks, Djerf Avenue, CDLP, Apotea, Lyko, Elgiganten, CDON +- **UK:** Gymshark, Represent, Huel, Oliver Bonas, Argos, John Lewis +- **ES:** Hawkers, Scalpers, Mr Wonderful, PcComponentes, El Corte Inglés +- **US:** Allbirds, Glossier, Brooklinen, Bombas, Best Buy, Target +- **CA:** Kotn, Frank And Oak, Knix, Tentree, Peace Collective, Canadian Tire + +Target about 50 stores in 6–8 verticals (apparel, beauty, electronics, home, grocery/health) and about 1–2M products. Locales are sv-SE, en-GB, es-ES, en-US, en-CA and fr-CA. + +**Crawl mechanics (built in M1; `src/c2a/sources/`):** +- **Approval is a human action.** `c2a registry approve --by ` records who approved the store and when. Automated code (fingerprinting) can only change `platform`, `method` and `currency`. +- **One gate for every fetch:** `CrawlSession` runs ToS approval + robots.txt + same-domain + rate limiting for every URL. The only exception is the UCP API host the store itself advertises, which still needs ToS approval but not robots.txt. +- **Per-method ingestion:** + - `ucp`: `/.well-known/ucp` → REST `POST {endpoint}/catalog/search` with cursor pagination and seed queries (UCP spec 2026-01-01); + - `shopify`: `/products.json` (currency from the registry or `/meta.json`); + - `firecrawl`: v2 `/map` → compliance filter → URL triage → Jev page gate on ambiguous URLs (markdown scrape, about 1 credit) → JSON extract (about 5 credits) only for product pages. +- **Discovery:** `c2a discover fingerprint` detects UCP / Shopify / WooCommerce / custom. `c2a discover expand` runs Firecrawl `/search` and writes candidates to `data/discovery/candidates.yaml` for review; it never writes to the registry. +- **Incremental output:** content hashes mean unchanged products are skipped. Each store gets `data/raw/{store}/products.jsonl` and `manifest.json` (counts, errors, Firecrawl credits, Jev requests, denied URLs). +- a per-domain QPS cap and a Firecrawl credit budget per run; +- a PII scrub on reviews; +- product images are stored (for the image LoRA and multimodal later) with source and licensing metadata. + +**Labeling scraped data with Jev** (`src/c2a/labeling/`, CLI `c2a label run|export`). Jev is TypeSafe AI's hosted System One decision model: `POST https://api.typesafe.ai/v1/systemone`, model `jev-latest`, key `TYPESAFE_API_KEY`. It is used in two places: +- **Before Firecrawl `/extract`:** the `page_v1` question set classifies scraped markdown as product_detail / category_listing / content / other, so extract credits are only spent on product pages. +- **After normalization:** + - `product_v1` labels each product: vertical, audience, price tier, use/occasion, bundle, restricted, PII and copy quality. + - `pair_v1` labels query↔product and product↔product pairs as exact / substitute / complement / irrelevant. These become recommendation training data. + +Each item is one request with all questions of the set, answered with probabilities. + +**Confidence gating** (`configs/labeling.yaml`): +- Confident answers are accepted automatically. +- Uncertain answers go to the teacher (Kimi K3). If the teacher agrees, the label is accepted as `jev+teacher`; otherwise it goes to the **human review queue**. +- Human-reviewed labels become **C2A-Bench gold** data and **decider training data**. + +Responses are cached on disk (re-runs never pay twice), and each run has a request budget. Question sets are versioned, so labels from different wordings are never mixed. + +## 2. Task families and datasets +| Task | Input | Output | Signal / grader | +|---|---|---|---| +| Pre-checkout rec | user query or browse context, locale, constraints, 50 candidates | ranked IDs + short reasons (JSON) | held-out next-item from open datasets (NDCG@10, Recall@10); constraint satisfaction (price, size, stock, locale); ID-in-candidates | +| PDP "similar / alternatives" | anchor product + candidates | ranked IDs + reasons | same-category/attribute agreement; judge | +| Post-checkout cross-sell | cart/order + candidates | complementary IDs (not substitutes) | co-purchase ground truth (Amazon 2023 / H&M); substitute penalty | +| Replenishment / follow-up message | order history | timing + message | judge + rubric | +| Product copy | product attributes (+ brand voice) | title, description, bullets, SEO meta, ad copy | **attribute-faithfulness check** (every claim traceable to attributes), length/format, language ID matches locale, judge for fluency | +| Localization | copy + target locale | localized copy | language ID, attribute preservation, judge | +| Image brief | product + use case (hero, lifestyle, banner) | structured JSON prompt (scene, style, aspect, reference image IDs, negative prompt) | schema-valid, product fidelity judge on rendered output (VLM judge) | + +- SFT data comes from teacher-distilled answers, filtered by the same graders (rejection sampling). +- RL prompts are the same tasks with ground truth attached to the graders. +- Frozen eval sets are held out by **store** and by **time** and are versioned. + +## 2b. Decision layer (inspired by vLLM Semantic Router and the Decision-1.0 models) +**What we borrow** +- vLLM Semantic Router (Apache 2.0) composes cheap *signals* into routing *decisions* in front of several models: + - heuristics such as keywords, language and context length; + - neural classifiers for domain, PII, jailbreak attempts and complexity; + - embedding similarity. +- Its **Decision-1.0** models (Lux-9B on Qwen3.5-9B; Nox-4B, Sol-2B; the earlier Kev-9B) take *evidence + a question + labels defined at request time* and return a decision **with probabilities**. +- Two ideas carry over: + 1. **Framing sub-decisions as small, calibrated classification calls** instead of free-form generation. + 2. **A mixture-of-models router** in front of the serving tiers. + +**One contract: System One.** Jev (TypeSafe, hosted), Kev (open, Apache-2.0) and the Decision-1.0 models all speak the same API: +- a `state` document plus typed questions: `noul` (yes/no), `choice` (1–255 options) and `score` (ordered levels); +- every answer comes back with probabilities (and a confidence value) in one forward pass. + +It is the canonical decision interface in `src/c2a/decide/` (`SystemOneClient`, with the `decide()` helper for single-choice calls). Hosted Jev is our **labeler**. The decider we post-train will serve the same `/v1/systemone` API as its drop-in replacement, including the gateway's `/v1/systemone` endpoint and the router's decider pool. + +**Where decision calls go in crawl2action:** +| Decision | Labels (set at request time) | Used for | +|---|---|---| +| Query↔product relevance | exact / substitute / complement / irrelevant (ESCI) | Candidate filtering, pre-checkout reranking features, and the reward for Rec-Pre | +| Cross-sell type | complement / substitute / unrelated | Post-checkout recommendations. A substitute penalty guards against suggesting an item the customer just bought | +| Claim faithfulness | supported / contradicted / not-in-attributes (per claim) | Copy grader and reward. The **probabilities give a denser, calibrated reward** than a 1–10 judge score | +| Shopper intent / stage | browse / compare / buy-now / post-purchase / support | Choosing the recommendation strategy, and routing | +| Constraint check | satisfies / violates, for each price, size, stock or locale constraint | Hard gate at serving time, and a reward penalty | +| Safety / compliance | PII / unsafe product / off-policy content | Crawl scrubbing, and output guard at serving time | + +**Model** +- **M2 bake-off for the decider:** + - **hosted Jev**, the quality reference and current labeler; + - Kev-9B and Decision-1.0-Lux-9B as they ship (baselines only; verify their licenses); + - the same models fine-tuned on our labels: C2A gold, `jev+teacher`-verified labels and ESCI. +- Metrics: weighted accuracy, **calibration (ECE and Brier score)** and latency. +- The winner does three jobs: + - a **fast grader and reward model** in the RL loop (a better fit than a free-form judge for any grader that is really a classification); + - a **serving-time guard**; + - a **router signal**. + +**Serving router.** vLLM Semantic Router (Envoy ext_proc) sits in front of the Modal endpoints. It routes each request, based on signals and decider outputs, to: +- the fast reranker (high-QPS recommendations); +- Qwen3.8-27B, with reasoning turned on only when the complexity signal calls for it (the router's "When to Reason" idea); +- the image pipeline. + +PII and jailbreak signals are blocked at the edge. This replaces the hand-written dispatch in `serve/gateway.py`, which is kept only as a thin API layer. + +**Benchmark.** C2A-Bench gains a **Decide track**: +- Covers all the decision types above, per locale. +- Scored with weighted accuracy per category (modelled on how the Decision-1.0 models are evaluated) plus ECE/Brier. +- Also measures router quality: cost and latency saved at equal quality, compared with sending everything to the 27B model. + +## 3. Post-training (state of the art as of Sep 2026) +The design follows the **sparse-to-dense reward principle** (Microsoft Research, May 2026): +- Spend **sparse, outcome-level reward** (RL) on the model best able to explore with it. +- Pass the resulting behaviour to the deployed model through **dense, token-level supervision**, meaning on-policy distillation (OPD). +- Run student-side RL only after that distillation step. + +Qwen3, GLM-5 and MiMo all use OPD in their post-training pipelines. Running plain GRPO directly on a cold student wastes the labelled signal on the least-prepared policy. + +**Every model we serve is post-trained by us.** Off-the-shelf checkpoints are only starting points and bake-off baselines. +| Model | How we post-train it (Tinker; Modal fallback) | +|---|---| +| Student (Qwen3.8-27B or Inkling-Small) | Cold-start SFT → RLHF / RL with rubric, Rank-GRPO and verifiable rewards → optional self-distillation | +| Fast reranker | On-policy distillation from the RL-trained student → optional student RL | +| Decider (starts from Kev-9B or Decision-1.0-Lux-9B; both Qwen3.5-9B and System One compatible) | kev.train-style: a LoRA plus a small classification head, trained with cross-entropy (a proper scoring rule, so it stays calibrated). Trained on C2A gold, `jev+teacher`-verified labels and ESCI, via the Modal fallback, because Tinker trains language-model heads. RL calibration is evaluated later | +| GenRM judge | SFT on teacher and human verdicts → RL against human preference labels | +| Image model (Qwen-Image-2.1) | Product LoRA → preference optimization (Diffusion-DPO or Flow-GRPO) | + +**Model roles (checked against releases as of 22 Sep 2026)** + +The final choice between the two student candidates comes from a **model bake-off in M2**: every candidate runs on C2A-Bench (section 4) before any training money is spent. + +| Role | Primary pick | Alternative in the bake-off | Why | +|---|---|---|---| +| Frontier teacher / data generator / rubric writer | **Kimi K3** (2.8T MoE, 104B active, Jul 2026) | GLM-5.3 (top open model on the Artificial Analysis index, Sep 2026), Qwen3.8-Max (2.4T) | Best quality. It is only used through text outputs (API), because a different tokenizer rules out token-level OPD | +| **Main student (RL'd, served)** | **Qwen3.8-27B** (dense, natively multimodal: image and video input, 262K context, Apache 2.0, weights released 13–14 Aug 2026) | **Inkling-Small** (Thinking Machines, 12B active MoE, multimodal, Apache 2.0, Jul 2026) | Qwen3.8-27B replaces the older Qwen3.8-27B. It reads product photos directly and fits on 1× H100 in FP8. Inkling-Small is native to Tinker, and its big sibling Inkling (975B, 41B active) can act as a same-tokenizer OPD teacher entirely inside Tinker | +| Same-family OPD teacher | The RL'd Qwen3.8-27B (or Qwen3.8-Max, if it is available on Tinker) | Inkling, if Inkling-Small wins the bake-off | Token-level distillation needs the same tokenizer | +| Fast reranker (optional) | A small model from the same family as the student, distilled with OPD | – | High-QPS `/recommend` | +| Reward model / judge | A small same-family model trained as a generative reward model (GenRM) | – | Cheap reward for RL, calibrated against Kimi K3 and human labels | +| Image generation | **Qwen-Image-2.1** (7B, generation and editing, Sep 20 2026) | FLUX.2 (highest raw image quality; check its license for commercial use) | Bake-off on product fidelity and text rendering | + +**Stages** (every stage is a separate, resumable Tinker or Modal job that writes a versioned checkpoint and an eval report) +1. **Cold-start SFT (off-policy distillation).** + - Train on Kimi K3 outputs that passed the graders (rejection sampling), with a short reasoning trace plus the final JSON answer. + - The goal is a formatted, grounded starting policy rather than peak quality. + - LoRA with high rank, or full fine-tuning if the budget allows. +2. **Reward modelling (the RLHF part).** + - **Human preferences:** a small labelling UI (a Modal web app) collects pairwise comparisons and rubric scores on copy, recommendation explanations and images. Raters are internal, with native speakers for sv, es and fr. + - **Rubrics as Rewards:** Kimi K3 writes a *per-instance rubric* for each prompt, grounded in product attributes, locale, brand voice and constraints. The judge scores each rubric item. This gives a reward for tasks that have no single correct answer. + - **GenRM:** a small Qwen is trained (SFT, then RL) to produce a rubric-grounded verdict. It is validated against held-out human labels, and the target is ≥80% agreement before it is used as a reward. + - **Implicit feedback:** production clicks, add-to-cart and purchase events feed into the reward-model data. Recommendation changes are checked offline with counterfactual evaluation (IPS/DR estimators) before any A/B test. +3. **RL on the 27B (RLVR plus rubric rewards).** This uses the modern GRPO family: + - **Loss fixes:** DAPO-style clip-higher, dynamic sampling that drops groups whose rollouts all score the same, token-level loss, and overlong-response shaping. Dr.GRPO-style removal of length and std normalization. + - **Sequence-level importance ratio (GSPO)**, needed once the student is a mixture-of-experts model. + - **Truncated or masked importance sampling** to correct for the mismatch between the vLLM sampler and the trainer. + - **Rank-GRPO** (ICLR 2026) for recommendation lists. Each rank position is its own action with its own reward, which trains better than one sequence-level NDCG score. + - **Rewards by task:** + - recommendations: verifiable rewards (NDCG and Recall against held-out purchases, hard constraints, product ID in the candidate set, valid JSON); + - copy: attribute-faithfulness checks, rubric scores from GenRM, and a language-ID gate; + - image briefs: schema validity plus a VLM product-fidelity score on the rendered image. + - **Guards against reward hacking:** KL anchor to the reference model, length control, a judge ensemble, a canary set scored by a *different* judge than the one used for training, and weekly human audits of the highest-reward samples. +4. **On-policy distillation from the RL'd 27B into the fast reranker.** + - The fast model samples; the 27B scores every token by reverse KL. This is dense and much more step-efficient than GRPO. + - Start with a short forward-KL warmup, then pure OPD. + - Optionally, finish with a short round of student RL once the student has caught up. +5. **On-policy self-distillation (optional, cheap boost).** + - The *same* model, given privileged context (the ground-truth next purchase, the full attribute sheet, the rubric), acts as the teacher for its own unprivileged rollouts. + - This gives a dense signal without another model (following ROSD and Self-Distilled Policy Gradient, 2026). +6. **Continual loop.** Every N weeks, new crawl data and production feedback go through stages 2→3→4, gated by the frozen evals (M5). + +**Images (optional track).** +- A LoRA on Qwen-Image-2.1 for product fidelity. +- Then preference optimization for the diffusion model (Diffusion-DPO or Flow-GRPO), using human image preferences plus the VLM product-fidelity reward. + +**Training platform (all behind `train/base.py`)** +- **Primary: Tinker (Thinking Machines).** + - You write the training loop in Python; Tinker runs it on its distributed GPUs with LoRA, from 1B up to 1T+ parameters, dense or mixture-of-experts, text and vision. + - The `tinker-cookbook` has recipes that map directly onto the stages above: chat SFT, DPO and three-stage RLHF (SFT → reward model → RL), RL with verifiable rewards, on-policy and off-policy distillation (single or multiple teachers), and RL with tool use. + - Custom losses allow the DAPO/GSPO/Rank-GRPO advantage functions to be written directly. + - This removes GPU-cluster work from the project. Modal hosts the parts Tinker doesn't: rollout environments (candidate retrieval, graders, the judge, image rendering for rewards), data jobs and serving. + - Trained checkpoints are downloaded as archives (`get_checkpoint_archive_url_from_tinker_path`) to a Modal Volume and served with vLLM. +- **First task in M3:** list the models this Tinker account can train (the cookbook's server capabilities call) and confirm Qwen3.8-27B is there. + - If it isn't yet, use Qwen3.5-27B (documented as supported) or Inkling-Small on Tinker. The Modal fallback below can also train Qwen3.8-27B directly. +- **Fallback, self-run on Modal:** TRL + PEFT (SFT, DPO, GenRM) and verl (GRPO/DAPO/GSPO, Rank-GRPO, OPD, async vLLM rollouts, LoRA adapter-only sync) on H100/H200 clusters. Use it when a model or loss isn't available on Tinker. +- **Storage and tracking:** checkpoints on a Modal Volume, W&B for runs, and every run pinned to a data, grader and benchmark version. +- **Scale-up path:** iterate first on a small proxy (about 4B) with the full recipe, then scale to 27B. +- **AC2 later:** add `train/ac2/` (following the cookbook's `tau2bench` / `dapo-math-check` pattern) and switch by config. No task or grader code changes. + +**Export.** HF-format weights and LoRA adapters are saved to a Modal Volume or a private HF repo, together with the config, data version, grader version and eval report. + +**Technique watch.** `docs/POST_TRAINING_NOTES.md` is reviewed every quarter. Any new method is added as a new `Trainer` or advantage-function plug-in and must beat the current recipe on the frozen evals before it replaces it. + +## 4. Benchmarks and evaluation +Benchmarks come in two layers: +- **public benchmarks**, for comparing with other published work and catching regressions; +- **C2A-Bench**, a new benchmark built for this project, because none of the public ones measure grounded recommendations and copy on *our* catalogs across SE, UK, ES, US and CA. + +All of them run in one harness (`src/c2a/eval/`, built on Inspect AI plus custom scorers) with the `c2a bench` CLI. + +**4a. Public benchmarks to adopt** (licenses to be checked for each before use) +| Area | Benchmark | What it measures for us | +|---|---|---| +| Search relevance | **Amazon ESCI / SQID** (130K queries, 2.6M labels in Exact/Substitute/Complement/Irrelevant; en/es/ja; SQID adds images) | Query→product relevance, and substitute-vs-complement judgement (the core of cross-sell) | +| Session recommendations and text | **Amazon-M2** (KDD Cup 2023; multilingual, includes UK and ES locales) | Next-item prediction and product title generation across locales | +| Shopping knowledge | **Shopping MMLU** (KDD Cup 2024) and **ECInstruct** (10 tasks, 264K examples, including sequential recommendation and query-product ranking) | Product-concept understanding; guards against regressions from fine-tuning | +| Shopping agents | **ShoppingBench** (AAAI), **ShoppingComp**, **Shopping Companion** (2026), **ComboShoppingBench** (budget and coupons, 2026), **WebMall** / **ShopGym** | Intent-grounded, multi-step and constrained shopping, preference grounding, and safety-critical product choices | +| Customer-service style tool use | **τ-bench retail** | Post-checkout flows (returns, exchanges, order questions) | +| General capability guard | A small fixed suite (instruction following, multilingual, safety) | Catches general regressions caused by domain RL | + +**4b. C2A-Bench (built by this project, versioned and frozen per release)** +- **Construction:** + - generated from our crawl plus the open behavioural datasets; + - held out **by store and by time** (products and stores never seen in training); + - split by locale (sv-SE, en-GB, es-ES, en-US, en-CA, fr-CA) and by vertical. +- **Tracks:** + 1. **Rec-Pre:** search, browse and "similar to this product" reranking over 50 candidates. Metrics: NDCG@10, Recall@10, constraint violations, hallucinated-ID rate. + 2. **Rec-Post:** cross-sell and replenishment. Metrics: complement precision, substitute-confusion rate, co-purchase Recall@k. + 3. **Copy:** titles, descriptions, bullets, SEO text and ads. Metrics: attribute-faithfulness (every claim checked against attributes), locale/language accuracy, rubric score, human preference win-rate. + 4. **Localize:** translate and adapt copy between locales while preserving attributes, units, sizes and currency. + 5. **Image:** briefs rendered by the image model. Metrics: VLM product-fidelity score, text/logo accuracy, human preference. + 6. **Conversational / agentic:** multi-turn shopping assistant in a simulated store over our catalog (UCP-style search and cart tools), scored on task success, constraint adherence and turns taken. + 7. **Decide:** calibrated decisions (relevance, complement vs substitute, claim faithfulness, intent, constraints). Metrics: weighted accuracy, ECE, Brier score, plus router cost and latency savings. +- **Gold subset:** about 500 items per track and locale, verified by native-speaker annotators through the labelling app. It is used to calibrate the automatic judges (report judge–human agreement), and the headline numbers come from it. +- **Hygiene:** + - rules against data contamination (dedup C2A-Bench against every training source using MinHash); + - a canary string in the benchmark files so any leak into training data can be detected; + - a private holdout split that is never used for tuning; + - eval judges that differ from the reward judges. +- **Leaderboard:** each run's report (parquet + HTML) is compared against all baselines: base candidates, teacher, SFT, RL, OPD. It includes per-locale and per-vertical breakdowns plus cost and latency. + +**4c. Gates** +- **M2 bake-off:** pick the student (Qwen3.8-27B vs Inkling-Small), the teacher, the image model and the decider (Decision-1.0-Lux-9B as shipped vs fine-tuned vs distilled) on C2A-Bench (gold subset) plus the public benchmarks. +- **Ship gate:** + - the student beats its base model on every C2A-Bench track; + - it reaches ≥90% of teacher quality on the Rec and Copy tracks; + - hallucinated-ID rate is under 0.5%; + - no public benchmark drops by more than 2 points. +- **Online:** counterfactual (IPS/DR) estimates on logged traffic, then an A/B test. + +## 5. Serving on Modal +- `serve/llm.py`: vLLM with Qwen3.8-27B plus task LoRAs (`--enable-lora`), OpenAI-compatible, autoscaling with warm pool, structured JSON output (guided decoding). +- `serve/image.py`: Qwen-Image-2.1 on L40S/H100, generation plus editing using the crawled reference images. +- `serve/gateway.py`: FastAPI. It retrieves candidates from LanceDB, calls the LLM, validates output against the pydantic schema, runs the image brief through the image model, and logs requests and feedback (clicks, add-to-cart) to parquet for the next training round (continual learning on AC2). + +## Milestones +1. **M0 skeleton (done):** repo layout, schemas, CLI, compliance, Shopify parser, graders, decide interface, training data formats, rewards and advantage estimators, Tinker SFT/RL/OPD loops, gateway, Modal stubs, CI (ruff + pytest). +2. **M1 data (done; live runs need approved stores and keys):** + - registry approve/deny with an audit trail; + - `CrawlSession` compliance gate; + - UCP, Shopify and Firecrawl ingestion (with the Jev page gate); + - discovery (fingerprint, expand); + - incremental `c2a crawl` (`--label` runs Jev on new products); + - open-dataset importers: ESCI → human `pair@1` labels, Amazon-M2 sessions, Amazon Reviews 2023 reviews and histories. + - **Moved to M2:** the embedding index (LanceDB), MinHash/image near-duplicate detection, FX conversion, level-2 taxonomy mapping, and the H&M, RetailRocket and Diginetica loaders. + - **Pilot tooling (current focus):** `.env` loading; `c2a doctor [--live]` preflight; Shopify retries (`Retry-After`), a currency fallback and page guards; `pair_v1` candidate pairs; `c2a label review` (terminal review → gold); `c2a pilot` and `c2a report`. Runbook: `docs/PILOT.md`. It runs on the user's machine, because the cloud sandbox cannot reach stores or APIs. +3. **M2 datasets, graders and benchmarks** (plus the items moved from M1): task builders, teacher distillation with a cost cap, public benchmark adapters, C2A-Bench v0 with a gold subset, and the **model bake-off** (student, teacher and image model). +4. **M3 post-training on Tinker:** cold-start SFT → GenRM + rubrics → Rank-GRPO/DAPO RL → OPD into the fast reranker, on the proxy model first and then on 27B. (Optional M3b: port to AC2 once access arrives.) + - **M3a** runs alongside M3 and collects human preferences through the labelling app. +5. **M4 serving:** vLLM + image + gateway on Modal, load test. +6. **M5 feedback loop:** logging → dataset refresh → retrain schedule. + +## Verification +- Unit tests: schema round-trip, Shopify/UCP parsers against recorded fixtures, grader correctness on hand-built cases (including hallucinated-ID and unfaithful-claim negatives), split leakage test. +- Smoke E2E (`c2a e2e --tiny`): 2 stores → 200 products → 100 examples per task → 20-step LoRA SFT on Modal → eval → deploy to a dev Modal endpoint → curl `/recommend`, `/generate/text` and `/generate/image` and validate the responses against the schemas. +- Full run: eval report comparing base, SFT, RL and teacher, plus a gateway latency/throughput test. + +## Open items to confirm during implementation +- Exact HF model IDs and licenses for Qwen3.8-27B, Inkling-Small, Qwen-Image-2.1 and FLUX.2, plus Kimi K3 / GLM-5.3 API access and pricing. +- Which models this Tinker account can train (Qwen3.8-27B?), Tinker pricing, and the checkpoint export format for vLLM. +- Licenses of the public benchmarks for commercial use. +- Decision-1.0 model license, input/output format and training recipe (the HF card and vllm-sr.ai blog were not reachable from this environment). +- **TypeSafe's terms on using Jev outputs to train our decider.** `c2a label export` leaves out Jev-only labels unless `--include-jev-only` is passed. +- Jev pricing and rate limits for about 1–2M products plus pairs. This sizes the `--max-requests` budgets. +- AC2 SDK/config specifics (the docs were not reachable from this environment; the cookbook structure is used as the template). +- Legal sign-off on each tier-B/C store before it is enabled in `registry.yaml`. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..2c4438e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "crawl2action" +version = "0.1.0" +description = "E2E pipeline: crawl e-commerce catalogs, build datasets, post-train LLMs, benchmark and serve." +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.11" +dependencies = [ + "pydantic>=2.7", + "typer>=0.12", + "pyyaml>=6.0", + "httpx>=0.27", +] + +[project.optional-dependencies] +data = ["pyarrow>=15"] +modal = ["modal>=1.0"] +train = ["tinker>=0.23.0", "tinker-cookbook", "torch"] +train-fallback = ["trl", "peft"] +serve = ["fastapi>=0.115", "uvicorn>=0.30"] +eval = ["inspect-ai"] +dev = ["pytest>=8", "ruff>=0.6", "respx>=0.21", "fastapi>=0.115", "httpx>=0.27", "pyarrow>=15"] + +[project.scripts] +c2a = "c2a.cli:app" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/c2a"] + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B", "SIM"] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +[tool.ruff.lint.flake8-bugbear] +extend-immutable-calls = ["typer.Option", "typer.Argument"] diff --git a/src/c2a/__init__.py b/src/c2a/__init__.py new file mode 100644 index 0000000..1eafae7 --- /dev/null +++ b/src/c2a/__init__.py @@ -0,0 +1,12 @@ +"""crawl2action: crawl e-commerce catalogs, build datasets, post-train, benchmark and serve.""" + +__version__ = "0.1.0" + + +class NotYetImplemented(NotImplementedError): + """Raised by stubs for work scheduled in a later milestone (see docs/PLAN.md).""" + + def __init__(self, what: str, milestone: str) -> None: + super().__init__(f"{what} is not implemented yet (planned for {milestone}).") + self.what = what + self.milestone = milestone diff --git a/src/c2a/bench/__init__.py b/src/c2a/bench/__init__.py new file mode 100644 index 0000000..47d0566 --- /dev/null +++ b/src/c2a/bench/__init__.py @@ -0,0 +1,48 @@ +"""C2A-Bench: our versioned benchmark (see docs/PLAN.md section 4b). Builder lands in M2.""" + +from __future__ import annotations + +from dataclasses import dataclass + +# Embedded in every benchmark file so leakage into training corpora is detectable. +CANARY = "C2A-BENCH CANARY 3f6c1e2a-crawl2action-do-not-train" + + +@dataclass(frozen=True) +class Track: + id: str + title: str + metrics: tuple[str, ...] + + +TRACKS: tuple[Track, ...] = ( + Track( + "rec_pre", + "Pre-checkout recommendations", + ("ndcg@10", "recall@10", "constraint_violations", "hallucinated_id_rate"), + ), + Track( + "rec_post", + "Post-checkout cross-sell", + ("complement_precision", "substitute_confusion", "recall@k"), + ), + Track( + "copy", + "Product copy", + ("attribute_faithfulness", "locale_accuracy", "rubric_score", "human_win_rate"), + ), + Track( + "localize", "Localization", ("attribute_preservation", "locale_accuracy", "rubric_score") + ), + Track("image", "Image briefs + renders", ("vlm_fidelity", "text_accuracy", "human_win_rate")), + Track("agentic", "Conversational shopping", ("task_success", "constraint_adherence", "turns")), + Track( + "decide", + "Calibrated decisions", + ("weighted_accuracy", "ece", "brier", "router_cost_saving"), + ), +) + + +def contains_canary(text: str) -> bool: + return CANARY in text diff --git a/src/c2a/cli.py b/src/c2a/cli.py new file mode 100644 index 0000000..9aec8e3 --- /dev/null +++ b/src/c2a/cli.py @@ -0,0 +1,610 @@ +"""`c2a` command-line interface.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import typer + +from c2a import NotYetImplemented +from c2a.config import load_dotenv, load_settings + +app = typer.Typer( + help="crawl2action: crawl -> datasets -> post-train -> bench -> serve", no_args_is_help=True +) + + +@app.callback() +def _main() -> None: + """Load .env (never overriding variables already set) before any command.""" + load_dotenv() + + +registry_app = typer.Typer(help="Store registry", no_args_is_help=True) +train_app = typer.Typer(help="Post-training (Tinker primary; Modal fallback)", no_args_is_help=True) +app.add_typer(registry_app, name="registry") +app.add_typer(train_app, name="train") +label_app = typer.Typer( + help="Label scraped data with hosted Jev (System One)", no_args_is_help=True +) +app.add_typer(label_app, name="label") +discover_app = typer.Typer(help="Store discovery and platform fingerprinting", no_args_is_help=True) +app.add_typer(discover_app, name="discover") +data_app = typer.Typer(help="Import open datasets from local files", no_args_is_help=True) +app.add_typer(data_app, name="data") +RegistryOpt = typer.Option(None, "--registry", help="registry.yaml path") + + +def _pending(what: str, milestone: str) -> None: + typer.secho(str(NotYetImplemented(what, milestone)), fg=typer.colors.YELLOW, err=True) + raise typer.Exit(code=2) + + +@registry_app.command("validate") +def registry_validate(path: Path | None = typer.Option(None, help="registry.yaml path")) -> None: + """Validate the store registry and summarize it.""" + from c2a.sources.registry import load_registry + + reg = load_registry(path) + by_country: dict[str, int] = {} + for s in reg.stores: + by_country[s.country] = by_country.get(s.country, 0) + 1 + typer.echo( + f"{len(reg.stores)} stores: " + ", ".join(f"{c}={n}" for c, n in sorted(by_country.items())) + ) + typer.echo(f"crawlable (enabled + ToS approved): {len(reg.crawlable())}") + + +@app.command() +def config(path: Path | None = typer.Option(None)) -> None: + """Print resolved settings.""" + typer.echo(load_settings(path).model_dump_json(indent=2)) + + +BackendOpt = typer.Option( + "keyword", help="keyword (offline) | jev (hosted, needs TYPESAFE_API_KEY)" +) + + +def _make_decider(backend: str): + from c2a.decide.backends import KeywordDecider, SystemOneClient + + if backend == "keyword": + return KeywordDecider() + if backend == "jev": + lab = load_settings().labeler + return SystemOneClient(base_url=lab.base_url, model=lab.model) + raise typer.BadParameter(f"unknown backend: {backend}") + + +@app.command() +def decide( + evidence: str = typer.Option(...), + question: str = typer.Option("Which label applies?"), + labels: str = typer.Option(..., help="comma-separated"), + backend: str = BackendOpt, +) -> None: + """Run a single decision (evidence + question + runtime labels -> probabilities).""" + from c2a.decide.base import decide as run_decide + + labels_list = [x.strip() for x in labels.split(",") if x.strip()] + result = run_decide(_make_decider(backend), evidence, question, labels_list) + typer.echo(result.model_dump_json(indent=2)) + + +ConfigOpt = typer.Option(..., "--config", exists=True, dir_okay=False) +DryRunOpt = typer.Option(False, "--dry-run", help="validate config + dataset only") + + +@train_app.command("sft") +def train_sft(config_path: Path = ConfigOpt, dry_run: bool = DryRunOpt) -> None: + """Supervised fine-tuning (cold start).""" + _train("sft", config_path, dry_run) + + +@train_app.command("rl") +def train_rl(config_path: Path = ConfigOpt, dry_run: bool = DryRunOpt) -> None: + """RL with grader rewards (GRPO / Dr.GRPO / Rank-GRPO).""" + _train("rl", config_path, dry_run) + + +@train_app.command("opd") +def train_opd(config_path: Path = ConfigOpt, dry_run: bool = DryRunOpt) -> None: + """On-policy distillation from a same-tokenizer teacher.""" + _train("opd", config_path, dry_run) + + +def _train(stage: str, config_path: Path, dry_run: bool) -> None: + from c2a.train.base import load_run_config + from c2a.train.data import DistillPrompt, RLPrompt, SFTExample, read_jsonl + + cfg = load_run_config(config_path) + if cfg.stage != stage: + raise typer.BadParameter(f"config stage is '{cfg.stage}', not '{stage}'") + record = {"sft": SFTExample, "rl": RLPrompt, "opd": DistillPrompt}[stage] + n = None + if cfg.dataset.exists(): + n = sum(1 for _ in read_jsonl(cfg.dataset, record)) + typer.echo(json.dumps({"run": cfg.model_dump(mode="json"), "dataset_records": n}, indent=2)) + if dry_run: + if n is None: + typer.secho(f"dataset not found: {cfg.dataset} (built in M2)", fg=typer.colors.YELLOW) + return + if n is None: + raise typer.BadParameter(f"dataset not found: {cfg.dataset}") + if cfg.platform != "tinker": + _pending(f"platform '{cfg.platform}'", "M3") + + from c2a.train.tinker.client import TinkerSDKBackend + + backend = TinkerSDKBackend(cfg.base_model, cfg.lora_rank, cfg.teacher_model, cfg.max_length) + records = list(read_jsonl(cfg.dataset, record)) + if cfg.stage == "sft": + from c2a.train.tinker.sft import run_sft as run + elif cfg.stage == "rl": + from c2a.train.tinker.rl import run_rl as run + else: + from c2a.train.tinker.opd import run_opd as run + summary = run(backend, records, cfg) + typer.echo(f"done: {summary['steps']} steps, final checkpoint {summary['checkpoints'][-1]}") + + +def _load_label_items(input_path: Path, target: str): + from c2a.labeling.records import LabelItem + from c2a.labeling.state import product_state + from c2a.schemas import Product + from c2a.train.data import read_jsonl + + if target == "product": + return [LabelItem(id=p.id, state=product_state(p)) for p in read_jsonl(input_path, Product)] + return list(read_jsonl(input_path, LabelItem)) + + +@label_app.command("run") +def label_run( + input_path: Path = typer.Option(..., "--input", exists=True, dir_okay=False), + qset_name: str = typer.Option("product_v1", "--qset"), + out: Path = typer.Option(Path("data/labels"), "--out"), + backend: str = typer.Option("jev", help="jev (hosted) | keyword (offline)"), + max_requests: int | None = typer.Option(None, "--max-requests"), + dry_run: bool = typer.Option(False, "--dry-run", help="print a sample request, no API call"), +) -> None: + """Label items (products JSONL for product_v1; LabelItem JSONL otherwise).""" + from c2a.decide.systemone import SystemOneRequest + from c2a.labeling.cache import ResponseCache + from c2a.labeling.gating import load_thresholds + from c2a.labeling.pipeline import label, save_run + from c2a.labeling.questions import get_question_set + + qset = get_question_set(qset_name) + items = _load_label_items(input_path, qset.target) + if not items: + raise typer.BadParameter("no items in input") + model = load_settings().labeler.model + if dry_run: + sample = SystemOneRequest(state=items[0].state, model=model, questions=qset.questions) + typer.echo(f"{len(items)} items, question set {qset.id}; sample request:") + typer.echo(sample.model_dump_json(indent=2, exclude_none=True)) + return + run_dir = out / qset.id.replace("@", "_v") + result = label( + items, + _make_decider(backend), + qset, + load_thresholds(), + cache=ResponseCache(out / "cache"), + max_requests=max_requests, + model=model, + ) + save_run(run_dir, items, result) + accepted = sum(r.status == "accepted" for r in result.records) + typer.echo( + f"{len(result.records)} labels ({accepted} accepted, {len(result.review)} to review); " + f"{result.requests_made} requests, {result.cache_hits} cache hits, " + f"{len(result.skipped)} items skipped (budget) -> {run_dir}" + ) + + +@label_app.command("export") +def label_export( + run_dir: Path = typer.Option(..., "--run-dir", exists=True, file_okay=False), + qset_name: str = typer.Option("product_v1", "--qset"), + kind: str = typer.Option("decider-train", help="decider-train | gold"), + out: Path = typer.Option(..., "--out"), + include_jev_only: bool = typer.Option( + False, help="include Jev-only labels (only if TypeSafe's terms allow training on them)" + ), +) -> None: + """Export accepted labels as decider training JSONL (Kev format) or C2A-Bench gold.""" + from c2a.labeling.export import DEFAULT_TRAIN_SOURCES, to_decider_train, to_gold + from c2a.labeling.questions import get_question_set + from c2a.labeling.records import LabelItem, LabelRecord + from c2a.train.data import read_jsonl + + qset = get_question_set(qset_name) + records = list(read_jsonl(run_dir / "labels.jsonl", LabelRecord)) + if kind == "gold": + rows = to_gold(records) + elif kind == "decider-train": + states = {it.id: it.state for it in read_jsonl(run_dir / "items.jsonl", LabelItem)} + sources = set(DEFAULT_TRAIN_SOURCES) | ({"jev"} if include_jev_only else set()) + rows = to_decider_train(states, records, qset, sources) + else: + raise typer.BadParameter(f"unknown kind: {kind}") + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text("".join(json.dumps(r, ensure_ascii=False) + "\n" for r in rows)) + typer.echo(f"wrote {len(rows)} rows -> {out}") + + +@registry_app.command("list") +def registry_list(path: Path | None = RegistryOpt, crawlable: bool = typer.Option(False)) -> None: + """List stores with their review status.""" + from c2a.sources.registry import load_registry + + reg = load_registry(path) + for s in reg.crawlable() if crawlable else reg.stores: + who = f" by {s.reviewed_by}" if s.reviewed_by else "" + typer.echo( + f"{s.id:18} {s.country} {s.tier.value} {s.method:9} {s.platform:11} " + f"{s.tos_status.value}{who}{' ENABLED' if s.enabled else ''}" + ) + + +@registry_app.command("approve") +def registry_approve( + store_id: str, + by: str = typer.Option(..., "--by", help="who reviewed the store's terms"), + note: str = typer.Option("", "--note"), + path: Path | None = RegistryOpt, +) -> None: + """Mark a store's ToS as reviewed and approved, and enable crawling. Human-only.""" + from c2a.sources.registry import load_registry, save_registry + + reg = load_registry(path) + s = reg.approve(store_id, by, note) + save_registry(reg, path) + typer.echo(f"approved {s.id} ({s.domain}) by {by}") + + +@registry_app.command("deny") +def registry_deny( + store_id: str, + by: str = typer.Option(..., "--by"), + note: str = typer.Option("", "--note"), + path: Path | None = RegistryOpt, +) -> None: + """Mark a store's ToS as denied and disable crawling.""" + from c2a.sources.registry import load_registry, save_registry + + reg = load_registry(path) + s = reg.deny(store_id, by, note) + save_registry(reg, path) + typer.echo(f"denied {s.id} ({s.domain}) by {by}") + + +@discover_app.command("fingerprint") +def discover_fingerprint( + store: list[str] = typer.Option(None, "--store", help="store ids (default: all)"), + write: bool = typer.Option(False, "--write", help="update platform/method in the registry"), + path: Path | None = RegistryOpt, +) -> None: + """Detect UCP / Shopify / WooCommerce / custom. Never changes ToS status or enabled.""" + from c2a.sources.discover import fingerprint + from c2a.sources.registry import load_registry, save_registry + + reg = load_registry(path) + for s in [reg.by_id(i) for i in store] if store else reg.stores: + fp = fingerprint(s.domain) + cur = f", currency={fp.currency}" if fp.currency else "" + typer.echo(f"{s.id:18} {fp.platform:11} -> method={fp.method}{cur} ({fp.evidence})") + if write and fp.platform != "unknown": + fields = {"platform": fp.platform, "method": fp.method} + if fp.currency: + fields["currency"] = fp.currency + reg.update_descriptive(s.id, **fields) + if write: + save_registry(reg, path) + + +@discover_app.command("expand") +def discover_expand( + country: str = typer.Option(..., help="SE, GB, ES, US or CA"), + category: str = typer.Option(..., help="e.g. apparel, beauty"), + out: Path = typer.Option(Path("data/discovery/candidates.yaml"), "--out"), + limit: int = typer.Option(20), + path: Path | None = RegistryOpt, +) -> None: + """Find candidate stores via Firecrawl search (writes candidates, never the registry).""" + import yaml + + from c2a.sources.discover import expand + from c2a.sources.firecrawl import FirecrawlClient + from c2a.sources.registry import load_registry + + reg = load_registry(path) + stores = expand(country, category, FirecrawlClient(), {s.domain for s in reg.stores}, limit) + existing = yaml.safe_load(out.read_text()) if out.exists() else {"stores": []} + known = {c["domain"] for c in existing["stores"]} + existing["stores"] += [s.model_dump(mode="json") for s in stores if s.domain not in known] + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(yaml.safe_dump(existing, sort_keys=False, allow_unicode=True)) + typer.echo(f"{len(stores)} candidates -> {out} (review, then add to the registry by hand)") + + +@app.command() +def crawl( + store: list[str] = typer.Option(None, "--store", help="store ids (default: all crawlable)"), + out: Path = typer.Option(Path("data/raw"), "--out"), + max_products: int | None = typer.Option(None, "--max-products"), + label_products: bool = typer.Option(False, "--label", help="Jev product_v1 on new products"), + dry_run: bool = typer.Option(False, "--dry-run"), + path: Path | None = RegistryOpt, +) -> None: + """Crawl ToS-approved, enabled stores (default-deny).""" + import os + + from c2a.sources.crawl import crawl_registry, plan + from c2a.sources.registry import load_registry + + reg = load_registry(path) + planned = plan(reg, store or None) + for s, reason in planned: + typer.echo(f"{s.id:18} {s.method:9} {'CRAWL' if reason == 'ok' else 'skip: ' + reason}") + if dry_run: + return + if not any(reason == "ok" for _, reason in planned): + typer.secho("nothing to crawl: approve stores with `c2a registry approve`", err=True) + raise typer.Exit(code=1) + settings = load_settings() + firecrawl = None + if os.environ.get("FIRECRAWL_API_KEY"): + from c2a.sources.firecrawl import FirecrawlClient + + firecrawl = FirecrawlClient(credit_budget=settings.budgets.firecrawl_credits_per_run) + decider = _make_decider("jev") if os.environ.get("TYPESAFE_API_KEY") else None + results = crawl_registry( + reg, + out, + store or None, + firecrawl=firecrawl, + decider=decider, + max_products=max_products, + qps=settings.budgets.default_qps_per_domain, + ) + changed_all = [] + for m, changed in results: + changed_all += changed + typer.echo( + f"{m.store_id}: {m.products_new_or_changed} new/changed, {m.products_unchanged} " + f"unchanged, {len(m.errors)} errors, {m.firecrawl_credits} credits" + ) + if label_products and changed_all: + if decider is None: + typer.secho("--label needs TYPESAFE_API_KEY; skipping labeling", err=True) + return + from c2a.labeling.cache import ResponseCache + from c2a.labeling.gating import load_thresholds + from c2a.labeling.pipeline import label, save_run + from c2a.labeling.questions import product_v1 + from c2a.labeling.records import LabelItem + from c2a.labeling.state import product_state + + items = [LabelItem(id=p.id, state=product_state(p)) for p in changed_all] + cache = ResponseCache("data/labels/cache") + run = label(items, decider, product_v1(), load_thresholds(), cache) + save_run(Path("data/labels") / "product_v1", items, run) + typer.echo(f"labeled {len(items)} products: {len(run.review)} labels to review") + + +@data_app.command("import") +def data_import( + dataset: str = typer.Argument(..., help="esci | amazon_m2 | amazon_reviews"), + path: list[Path] = typer.Option( + ..., "--path", help="input file(s); esci: examples then products" + ), + out: Path = typer.Option(..., "--out", help="output directory"), + locale: list[str] = typer.Option(None, "--locale"), +) -> None: + """Convert a downloaded open dataset into c2a JSONL files.""" + from c2a.sources import open_datasets as od + from c2a.train.data import write_jsonl + + out.mkdir(parents=True, exist_ok=True) + if dataset == "esci": + if len(path) != 2: + raise typer.BadParameter("esci needs --path examples.parquet --path products.parquet") + items, records = od.esci_to_labels(od.load_esci(path[0], path[1], locale or None)) + write_jsonl(out / "items.jsonl", items) + write_jsonl(out / "labels.jsonl", records) + typer.echo(f"esci: {len(records)} human pair labels -> {out}") + elif dataset == "amazon_m2": + n = write_jsonl(out / "sessions.jsonl", od.load_amazon_m2(path[0], locale or None)) + typer.echo(f"amazon_m2: {n} sessions -> {out}") + elif dataset == "amazon_reviews": + n = write_jsonl(out / "reviews.jsonl", od.load_amazon_reviews(path[0])) + hist = od.user_histories(path[0]) + (out / "user_histories.json").write_text(json.dumps(hist)) + typer.echo(f"amazon_reviews: {n} reviews, {len(hist)} user histories -> {out}") + else: + raise typer.BadParameter(f"unknown dataset: {dataset}") + + +@app.command() +def doctor( + live: bool = typer.Option(False, "--live", help="also make one real Jev (and Firecrawl) call"), + path: Path | None = RegistryOpt, +) -> None: + """Check keys, API reachability and approved stores before a real run.""" + from c2a.doctor import format_checks, run_doctor + from c2a.sources.registry import load_registry + + checks = run_doctor(load_registry(path), load_settings(), live=live) + typer.echo(format_checks(checks)) + if any(c.status == "fail" for c in checks): + raise typer.Exit(code=1) + + +@app.command() +def pilot( + store: list[str] = typer.Option(..., "--store", help="approved store ids"), + data_dir: Path = typer.Option(Path("data"), "--data"), + max_products: int = typer.Option(200, "--max-products"), + pairs_k: int = typer.Option(3, "--pairs-k", help="similar products per anchor (0 = no pairs)"), + max_jev_requests: int = typer.Option(500, "--max-jev-requests"), + path: Path | None = RegistryOpt, +) -> None: + """Preflight -> crawl -> Jev product labels -> Jev pair labels -> report.""" + import os + + from c2a.doctor import format_checks, run_doctor + from c2a.pilot import run_pilot + from c2a.sources.registry import load_registry + + reg = load_registry(path) + settings = load_settings() + checks = run_doctor(reg, settings) + fails = [c for c in checks if c.status == "fail"] + if fails: + typer.echo(format_checks(fails)) + raise typer.Exit(code=1) + firecrawl = None + if os.environ.get("FIRECRAWL_API_KEY"): + from c2a.sources.firecrawl import FirecrawlClient + + firecrawl = FirecrawlClient(credit_budget=settings.budgets.firecrawl_credits_per_run) + try: + res = run_pilot( + reg, + settings, + store, + _make_decider("jev"), + data_dir=data_dir, + firecrawl=firecrawl, + max_products=max_products, + pairs_k=pairs_k, + max_jev_requests=max_jev_requests, + ) + except ValueError as exc: + raise typer.BadParameter(str(exc)) from exc + typer.echo(res.report) + typer.echo( + f"crawled {res.crawled}; {res.product_labels} product labels, {res.pair_labels} pair " + f"labels, {res.to_review} to review, {res.jev_requests} Jev requests, " + f"{res.skipped_budget} items skipped by budget. Report: {data_dir}/reports/pilot.md" + ) + + +@app.command() +def report( + data_dir: Path = typer.Option(Path("data"), "--data"), + out: Path | None = typer.Option(None, "--out"), +) -> None: + """Summarize crawls and labels (Markdown).""" + from c2a.report import build_report + + text = build_report(data_dir / "raw", data_dir / "labels") + if out: + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(text) + typer.echo(text) + + +@label_app.command("pairs") +def label_pairs( + store: list[str] = typer.Option(..., "--store"), + raw_dir: Path = typer.Option(Path("data/raw"), "--raw"), + out: Path = typer.Option(Path("data/labels"), "--out"), + k: int = typer.Option(3, "--k"), + backend: str = typer.Option("jev", help="jev (hosted) | keyword (offline)"), + max_requests: int | None = typer.Option(None, "--max-requests"), + dry_run: bool = typer.Option(False, "--dry-run"), +) -> None: + """Label product pairs (exact/substitute/complement/irrelevant) within each store.""" + from c2a.labeling.cache import ResponseCache + from c2a.labeling.gating import load_thresholds + from c2a.labeling.pairs import candidate_pairs, pair_items + from c2a.labeling.pipeline import label, save_run + from c2a.labeling.questions import pair_v1 + from c2a.schemas import Product + from c2a.train.data import read_jsonl + + items = [] + for sid in store: + products = list(read_jsonl(raw_dir / sid / "products.jsonl", Product)) + items += pair_items(candidate_pairs(products, k)) + typer.echo(f"{len(items)} candidate pairs from {len(store)} store(s)") + if dry_run or not items: + return + run = label( + items, + _make_decider(backend), + pair_v1(), + load_thresholds(), + ResponseCache(out / "cache"), + max_requests, + model=load_settings().labeler.model, + ) + save_run(out / "pair_v1", items, run) + typer.echo( + f"{len(run.records)} labels, {len(run.review)} to review, {run.requests_made} requests" + ) + + +@label_app.command("review") +def label_review( + run_dir: Path = typer.Option(Path("data/labels/product_v1"), "--run-dir"), + by: str | None = typer.Option(None, "--by", help="reviewer name (default: $USER)"), + limit: int = typer.Option(20, "--limit"), +) -> None: + """Review escalated labels in the terminal. Progress is saved after every answer.""" + import os + + from c2a.labeling.review import apply_decision, format_item, load_queue, options + + reviewer = by or os.environ.get("USER") or "" + if not reviewer: + raise typer.BadParameter("pass --by ") + queue = load_queue(run_dir)[:limit] + if not queue: + typer.echo(f"nothing to review in {run_dir}") + return + done = 0 + for i, item in enumerate(queue, 1): + typer.echo("\n" + format_item(item, i, len(queue))) + opts = options(item.question) + while True: + choice = typer.prompt("choice").strip().lower() + if choice in ("s", "q") or (choice.isdigit() and 1 <= int(choice) <= len(opts)): + break + typer.echo(f"enter 1-{len(opts)}, s or q") + if choice == "q": + break + if choice == "s": + continue + apply_decision(run_dir, item, opts[int(choice) - 1], reviewer) + done += 1 + left = len(load_queue(run_dir)) + typer.echo(f"reviewed {done}; {left} left in the queue") + + +@app.command() +def build() -> None: + """Build task datasets and C2A-Bench.""" + _pending("dataset build", "M2") + + +@app.command() +def bench() -> None: + """Run public benchmarks + C2A-Bench.""" + _pending("benchmark harness", "M2") + + +@app.command() +def deploy() -> None: + """Deploy serving apps to Modal.""" + _pending("Modal deployment", "M4") + + +if __name__ == "__main__": + app() diff --git a/src/c2a/config.py b/src/c2a/config.py new file mode 100644 index 0000000..a5e068d --- /dev/null +++ b/src/c2a/config.py @@ -0,0 +1,97 @@ +"""Settings loaded from YAML with environment-variable overrides.""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Any + +import yaml +from pydantic import BaseModel, Field + +REPO_ROOT = Path(__file__).resolve().parents[2] +DEFAULT_CONFIG = REPO_ROOT / "configs" / "default.yaml" + + +class Paths(BaseModel): + data_dir: Path = Path("data") + runs_dir: Path = Path("runs") + + +class Budgets(BaseModel): + firecrawl_credits_per_run: int = 5000 + teacher_usd_per_run: float = 100.0 + default_qps_per_domain: float = 1.0 + + +class Models(BaseModel): + student: str = "Qwen/Qwen3.8-27B" + student_alt: str = "thinkingmachines/Inkling-Small" + proxy_student: str = "Qwen/Qwen3.5-4B" + teacher: str = "kimi-k3" + decider: str = "llm-semantic-router/Decision-1.0-Lux-9B" + image: str = "Qwen/Qwen-Image-2.1" + embedding: str = "Qwen/Qwen3-Embedding-0.6B" + + +class Labeler(BaseModel): + """Hosted Jev (TypeSafe System One). API key comes from TYPESAFE_API_KEY.""" + + provider: str = "typesafe" + base_url: str = "https://api.typesafe.ai" + model: str = "jev-latest" + + +class Settings(BaseModel): + paths: Paths = Field(default_factory=Paths) + budgets: Budgets = Field(default_factory=Budgets) + models: Models = Field(default_factory=Models) + labeler: Labeler = Field(default_factory=Labeler) + locales: list[str] = Field( + default_factory=lambda: ["sv-SE", "en-GB", "es-ES", "en-US", "en-CA", "fr-CA"] + ) + + +def _apply_env(data: dict[str, Any], prefix: str = "C2A") -> dict[str, Any]: + """Override nested keys from env vars, e.g. C2A__MODELS__STUDENT=Qwen/Qwen3.5-27B.""" + for key, value in os.environ.items(): + if not key.startswith(prefix + "__"): + continue + parts = key[len(prefix) + 2 :].lower().split("__") + node = data + for part in parts[:-1]: + node = node.setdefault(part, {}) + node[parts[-1]] = value + return data + + +def load_dotenv(path: Path | str = ".env") -> list[str]: + """Load KEY=VALUE lines into os.environ without overriding variables already set. + + Minimal parser (no dependency): ignores blank lines and comments, strips optional + `export ` and matching quotes. Returns the keys it set. + """ + path = Path(path) + if not path.is_file(): + return [] + loaded = [] + for raw in path.read_text().splitlines(): + line = raw.strip() + if not line or line.startswith("#") or "=" not in line: + continue + key, _, value = line.removeprefix("export ").partition("=") + key, value = key.strip(), value.strip() + if len(value) >= 2 and value[0] == value[-1] and value[0] in "'\"": + value = value[1:-1] + if key and value and key not in os.environ: + os.environ[key] = value + loaded.append(key) + return loaded + + +def load_settings(path: Path | str | None = None) -> Settings: + path = Path(path) if path else DEFAULT_CONFIG + data: dict[str, Any] = {} + if path.exists(): + data = yaml.safe_load(path.read_text()) or {} + return Settings.model_validate(_apply_env(data)) diff --git a/src/c2a/datasets/__init__.py b/src/c2a/datasets/__init__.py new file mode 100644 index 0000000..f8442ea --- /dev/null +++ b/src/c2a/datasets/__init__.py @@ -0,0 +1 @@ +"""Dataset construction: task builders, teacher distillation and leakage-safe splits.""" diff --git a/src/c2a/datasets/splits.py b/src/c2a/datasets/splits.py new file mode 100644 index 0000000..b153f4b --- /dev/null +++ b/src/c2a/datasets/splits.py @@ -0,0 +1,39 @@ +"""Leakage-safe splits: hold out whole stores AND a time window. + +An example goes to `test` if its store is held out OR it was created at/after the cutoff. +Held-out stores never appear in train, so benchmark items test generalization to unseen +catalogs; the time cutoff guards against temporal leakage. +""" + +from __future__ import annotations + +import hashlib +from collections.abc import Iterable +from datetime import datetime + +from c2a.schemas import TaskExample + + +def pick_holdout_stores(store_ids: Iterable[str], fraction: float, seed: str = "c2a") -> set[str]: + """Deterministic hash-based store holdout.""" + if not 0 <= fraction <= 1: + raise ValueError("fraction must be in [0, 1]") + out = set() + for sid in sorted(set(store_ids)): + h = int(hashlib.sha256(f"{seed}:{sid}".encode()).hexdigest()[:8], 16) / 0xFFFFFFFF + if h < fraction: + out.add(sid) + return out + + +def split( + examples: Iterable[TaskExample], holdout_stores: set[str], time_cutoff: datetime | None +) -> dict[str, list[TaskExample]]: + out: dict[str, list[TaskExample]] = {"train": [], "test": []} + for ex in examples: + held_store = ex.store_id is not None and ex.store_id in holdout_stores + held_time = ( + time_cutoff is not None and ex.created_at is not None and ex.created_at >= time_cutoff + ) + out["test" if (held_store or held_time) else "train"].append(ex) + return out diff --git a/src/c2a/datasets/tasks/__init__.py b/src/c2a/datasets/tasks/__init__.py new file mode 100644 index 0000000..3d14233 --- /dev/null +++ b/src/c2a/datasets/tasks/__init__.py @@ -0,0 +1,16 @@ +"""Task builders, one per family (see docs/PLAN.md section 2). Implemented in M2.""" + +from __future__ import annotations + +from collections.abc import Iterable, Iterator +from typing import Protocol + +from c2a.schemas import Product, TaskExample + +TASK_FAMILIES = ("rec_pre", "rec_post", "copy", "localize", "image_brief", "decide", "agentic") + + +class TaskBuilder(Protocol): + task: str + + def build(self, products: Iterable[Product]) -> Iterator[TaskExample]: ... diff --git a/src/c2a/datasets/teacher.py b/src/c2a/datasets/teacher.py new file mode 100644 index 0000000..ddcfbd4 --- /dev/null +++ b/src/c2a/datasets/teacher.py @@ -0,0 +1,18 @@ +"""Teacher client (Kimi K3 / GLM-5.3 / Qwen3.8-Max via an OpenAI-compatible API). M2. + +Responsibilities: batching, on-disk response cache, hard USD budget, rejection sampling +against graders before anything becomes SFT data. +""" + +from __future__ import annotations + +from c2a import NotYetImplemented + + +class TeacherClient: + def __init__(self, model: str, base_url: str, api_key: str, usd_budget: float) -> None: + self.model, self.base_url, self.usd_budget = model, base_url, usd_budget + self._api_key = api_key + + def complete(self, messages: list[dict], **kwargs) -> str: + raise NotYetImplemented("teacher completions", "M2") diff --git a/src/c2a/decide/__init__.py b/src/c2a/decide/__init__.py new file mode 100644 index 0000000..7954910 --- /dev/null +++ b/src/c2a/decide/__init__.py @@ -0,0 +1,9 @@ +"""Decision layer: decide(evidence, question, labels) -> calibrated label probabilities. + +Inspired by vLLM Semantic Router's Decision-1.0 models. The production decider is +post-trained by us (SFT on ESCI + C2A gold, then RL with a calibration reward). +""" + +from c2a.decide.base import Decider, normalize_probs + +__all__ = ["Decider", "normalize_probs"] diff --git a/src/c2a/decide/backends.py b/src/c2a/decide/backends.py new file mode 100644 index 0000000..4b08cf3 --- /dev/null +++ b/src/c2a/decide/backends.py @@ -0,0 +1,127 @@ +"""Decider backends, all speaking the System One contract. + +- SystemOneClient: HTTP client for POST /v1/systemone. Defaults to TypeSafe's hosted Jev; + the same client points at our own post-trained decider once it is served on Modal. +- KeywordDecider: deterministic, dependency-free; for tests, dry runs and as a floor baseline. +""" + +from __future__ import annotations + +import math +import os +import re +import time +from collections.abc import Callable + +import httpx + +from c2a.decide.base import normalize_probs +from c2a.decide.systemone import ( + Choice, + ChoiceAnswer, + Noul, + NoulAnswer, + Score, + ScoreAnswer, + SystemOneRequest, + SystemOneResponse, +) + +TYPESAFE_BASE_URL = "https://api.typesafe.ai" +JEV_MODEL = "jev-latest" +RETRY_STATUS = {429, 500, 502, 503, 504} + + +class SystemOneClient: + def __init__( + self, + base_url: str = TYPESAFE_BASE_URL, + model: str = JEV_MODEL, + api_key: str | None = None, + timeout: float = 30.0, + max_retries: int = 4, + backoff: float = 1.0, + sleep: Callable[[float], None] = time.sleep, + ) -> None: + api_key = api_key if api_key is not None else os.environ.get("TYPESAFE_API_KEY") + if not api_key: + raise RuntimeError("TYPESAFE_API_KEY is not set (needed for hosted Jev)") + self.model = model + self.max_retries = max_retries + self.backoff = backoff + self._sleep = sleep + self._client = httpx.Client( + base_url=base_url.rstrip("/"), + timeout=timeout, + headers={"Authorization": f"Bearer {api_key}"}, + ) + + def ask(self, request: SystemOneRequest) -> SystemOneResponse: + body = request.model_dump(mode="json", exclude_none=True) + body["model"] = self.model if request.model == JEV_MODEL else request.model + for attempt in range(self.max_retries + 1): + try: + resp = self._client.post("/v1/systemone", json=body) + except httpx.TransportError: + if attempt == self.max_retries: + raise + else: + if resp.status_code not in RETRY_STATUS or attempt == self.max_retries: + resp.raise_for_status() + return SystemOneResponse.model_validate(resp.json()) + self._sleep(self.backoff * 2**attempt) + raise AssertionError("unreachable") + + def close(self) -> None: + self._client.close() + + +def _words(text: str) -> set[str]: + return set(re.findall(r"\w+", text.lower())) + + +class KeywordDecider: + """Word overlap between the state and each option/level. Deterministic, offline.""" + + model = "keyword" + + def ask(self, request: SystemOneRequest) -> SystemOneResponse: + words = _words(str(request.state)) + answers = {} + for qid, q in request.questions.items(): + words_q = words | _words(str(q.instructions or "")) + if isinstance(q, Choice): + labels = list(q.criteria) + scores = { + lbl: float(len(_words(f"{lbl} {q.criteria[lbl] or ''}") & words_q)) + for lbl in labels + } + res = normalize_probs(scores, labels) + answers[qid] = ChoiceAnswer( + choice=res.label, confidence=_confidence(res.probs), probabilities=res.probs + ) + elif isinstance(q, Score): + keys = [str(i) for i in range(len(q.criteria))] + scores = { + k: float(len(_words(lbl) & words_q)) + for k, lbl in zip(keys, q.criteria, strict=True) + } + res = normalize_probs(scores, keys) + answers[qid] = ScoreAnswer( + score=sum(int(k) * p for k, p in res.probs.items()), + confidence=_confidence(res.probs), + legend=dict(zip(keys, q.criteria, strict=True)), + probabilities=res.probs, + ) + elif isinstance(q, Noul): + answers[qid] = NoulAnswer(noul=0.5) + return SystemOneResponse(model=self.model, answers=answers) + + +def _confidence(probs: dict[str, float]) -> float: + """1 - normalized entropy: 0 for uniform, 1 for one-hot.""" + n = len(probs) + if n < 2: + return 1.0 + h = -sum(p * math.log(p) for p in probs.values() if p > 0) + return max(0.0, min(1.0, 1.0 - h / math.log(n))) diff --git a/src/c2a/decide/base.py b/src/c2a/decide/base.py new file mode 100644 index 0000000..6e3d371 --- /dev/null +++ b/src/c2a/decide/base.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import math +from typing import Protocol + +from c2a.decide.systemone import Choice, ChoiceAnswer, SystemOneRequest, SystemOneResponse +from c2a.schemas import DecisionResult + + +class Decider(Protocol): + """Anything that answers System One requests (hosted Jev, our decider, test backends).""" + + def ask(self, request: SystemOneRequest) -> SystemOneResponse: ... + + +def normalize_probs( + scores: dict[str, float], labels: list[str], temperature: float = 1.0 +) -> DecisionResult: + """Softmax over raw scores (log-probs or logits); every label gets a probability.""" + if temperature <= 0: + raise ValueError("temperature must be > 0") + raw = [scores.get(label, -1e9) / temperature for label in labels] + m = max(raw) + exps = [math.exp(r - m) for r in raw] + z = sum(exps) + probs = {label: e / z for label, e in zip(labels, exps, strict=True)} + # absorb float error so probabilities sum to exactly 1 + top = max(probs, key=probs.get) + probs[top] += 1.0 - sum(probs.values()) + return DecisionResult(label=top, probs=probs) + + +def decide( + decider: Decider, evidence: str, question: str, labels: list[str], model: str | None = None +) -> DecisionResult: + """Single-choice convenience wrapper over the System One contract.""" + req = SystemOneRequest( + state=evidence, + questions={"q": Choice(instructions=question, criteria=dict.fromkeys(labels))}, + **({"model": model} if model else {}), + ) + answer = decider.ask(req).answers["q"] + if not isinstance(answer, ChoiceAnswer): + raise TypeError(f"expected a choice answer, got {answer.type}") + probs = {label: float(answer.probabilities.get(label, 0.0)) for label in labels} + total = sum(probs.values()) + if total <= 0: + raise ValueError("decider returned no probability mass for the requested labels") + probs = {k: v / total for k, v in probs.items()} + top = max(probs, key=probs.get) + probs[top] += 1.0 - sum(probs.values()) + return DecisionResult(label=top, probs=probs) diff --git a/src/c2a/decide/calibration.py b/src/c2a/decide/calibration.py new file mode 100644 index 0000000..eb5d894 --- /dev/null +++ b/src/c2a/decide/calibration.py @@ -0,0 +1,37 @@ +"""Calibration metrics for deciders: multi-class Brier score and expected calibration error.""" + +from __future__ import annotations + +from collections.abc import Sequence + +from c2a.schemas import DecisionResult + + +def brier(results: Sequence[DecisionResult], truths: Sequence[str]) -> float: + if len(results) != len(truths) or not results: + raise ValueError("results and truths must be non-empty and equal length") + total = 0.0 + for r, t in zip(results, truths, strict=True): + total += sum((p - (1.0 if label == t else 0.0)) ** 2 for label, p in r.probs.items()) + return total / len(results) + + +def ece(results: Sequence[DecisionResult], truths: Sequence[str], n_bins: int = 10) -> float: + """Top-label ECE with equal-width confidence bins.""" + if len(results) != len(truths) or not results: + raise ValueError("results and truths must be non-empty and equal length") + bins: list[list[tuple[float, bool]]] = [[] for _ in range(n_bins)] + for r, t in zip(results, truths, strict=True): + conf = r.probs[r.label] + idx = min(int(conf * n_bins), n_bins - 1) + bins[idx].append((conf, r.label == t)) + n = len(results) + return sum( + len(b) / n * abs(sum(c for c, _ in b) / len(b) - sum(ok for _, ok in b) / len(b)) + for b in bins + if b + ) + + +def accuracy(results: Sequence[DecisionResult], truths: Sequence[str]) -> float: + return sum(r.label == t for r, t in zip(results, truths, strict=True)) / len(results) diff --git a/src/c2a/decide/systemone.py b/src/c2a/decide/systemone.py new file mode 100644 index 0000000..a812053 --- /dev/null +++ b/src/c2a/decide/systemone.py @@ -0,0 +1,105 @@ +"""System One contract (TypeSafe Jev; also served by Kev and Decision-1.0 models). + +POST /v1/systemone with a `state` document and typed questions; every question is answered +with probabilities in one forward pass. This is the canonical decision contract in c2a: the +hosted Jev labeler and our own post-trained decider both speak it. +""" + +from __future__ import annotations + +from typing import Annotated, Any, Literal + +from pydantic import BaseModel, Field, field_validator + +MAX_OPTIONS = 255 + + +class Noul(BaseModel): + """Yes/no question. `criteria` optionally describes what true/false mean.""" + + type: Literal["noul"] = "noul" + instructions: str | dict[str, Any] | list[Any] | None = None + criteria: dict[Literal["true", "false"], str] | None = None + + +class Choice(BaseModel): + """Pick one option. `criteria` maps option name -> optional description.""" + + type: Literal["choice"] = "choice" + instructions: str | dict[str, Any] | list[Any] | None = None + criteria: dict[str, str | None] = Field(min_length=1, max_length=MAX_OPTIONS) + + +class Score(BaseModel): + """Ordered rating. `criteria` lists level labels from lowest to highest.""" + + type: Literal["score"] = "score" + instructions: str | dict[str, Any] | list[Any] | None = None + criteria: list[str] = Field(min_length=1, max_length=MAX_OPTIONS) + + @field_validator("criteria") + @classmethod + def _unique(cls, v: list[str]) -> list[str]: + if len(set(v)) != len(v): + raise ValueError("score levels must be unique") + return v + + +Question = Annotated[Noul | Choice | Score, Field(discriminator="type")] + + +class SystemOneRequest(BaseModel): + state: str | dict[str, Any] | list[Any] + model: str = "jev-latest" + questions: dict[str, Question] = Field(min_length=1) + + +class NoulAnswer(BaseModel): + type: Literal["noul"] = "noul" + noul: float = Field(ge=0, le=1) # P(yes) + + +class ChoiceAnswer(BaseModel): + type: Literal["choice"] = "choice" + choice: str + confidence: float = Field(ge=0, le=1) + probabilities: dict[str, float] + + +class ScoreAnswer(BaseModel): + type: Literal["score"] = "score" + score: float # expected level index + confidence: float = Field(ge=0, le=1) + legend: dict[str, str] = Field(default_factory=dict) # level index -> label + probabilities: dict[str, float] # level index -> probability + + +Answer = Annotated[NoulAnswer | ChoiceAnswer | ScoreAnswer, Field(discriminator="type")] + + +class Usage(BaseModel): + input_tokens: int = 0 + output_tokens: int = 0 + + +class SystemOneResponse(BaseModel): + model: str + answers: dict[str, Answer] + usage: Usage = Field(default_factory=Usage) + latency_ms: float | None = None + + +def top_label(answer: NoulAnswer | ChoiceAnswer | ScoreAnswer) -> str: + """Hard label for an answer: 'true'/'false', the chosen option, or the argmax level label.""" + if isinstance(answer, NoulAnswer): + return "true" if answer.noul >= 0.5 else "false" + if isinstance(answer, ChoiceAnswer): + return answer.choice + idx = max(answer.probabilities, key=answer.probabilities.get) + return answer.legend.get(idx, idx) + + +def top_prob(answer: NoulAnswer | ChoiceAnswer | ScoreAnswer) -> float: + if isinstance(answer, NoulAnswer): + return max(answer.noul, 1.0 - answer.noul) + return max(answer.probabilities.values()) diff --git a/src/c2a/doctor.py b/src/c2a/doctor.py new file mode 100644 index 0000000..f4dadd7 --- /dev/null +++ b/src/c2a/doctor.py @@ -0,0 +1,224 @@ +"""Preflight checks before a real crawl + label run: keys, API reachability, approved stores, +robots.txt, platform/currency, and (with live=True) one real Jev and Firecrawl call.""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from typing import Literal + +import httpx +from pydantic import ValidationError + +from c2a.config import Settings +from c2a.decide.systemone import Choice, SystemOneRequest, SystemOneResponse +from c2a.sources.compliance import USER_AGENT, fetch_robots, robots_allows +from c2a.sources.registry import Registry + +Status = Literal["ok", "warn", "fail"] +FIRECRAWL_URL = "https://api.firecrawl.dev" + + +@dataclass +class Check: + name: str + status: Status + detail: str = "" + fix: str = "" + + +def _reachable(client: httpx.Client, url: str) -> tuple[bool, str]: + try: + resp = client.get(url, timeout=10) + except httpx.HTTPError as exc: + return False, type(exc).__name__ + return True, f"HTTP {resp.status_code}" + + +def run_doctor( + registry: Registry, + settings: Settings, + live: bool = False, + client: httpx.Client | None = None, +) -> list[Check]: + client = client or httpx.Client(headers={"User-Agent": USER_AGENT}, follow_redirects=True) + checks: list[Check] = [] + stores = registry.crawlable() + needs_firecrawl = any(s.method == "firecrawl" for s in stores) + + # keys + jev_key = os.environ.get("TYPESAFE_API_KEY") + checks.append( + Check("TYPESAFE_API_KEY", "ok", "set") + if jev_key + else Check("TYPESAFE_API_KEY", "fail", "not set", "add TYPESAFE_API_KEY=... to .env") + ) + fc_key = os.environ.get("FIRECRAWL_API_KEY") + if fc_key: + checks.append(Check("FIRECRAWL_API_KEY", "ok", "set")) + else: + checks.append( + Check( + "FIRECRAWL_API_KEY", + "fail" if needs_firecrawl else "warn", + "not set" + ("" if needs_firecrawl else " (only needed for non-Shopify stores)"), + "add FIRECRAWL_API_KEY=... to .env", + ) + ) + + # reachability + for name, url in (("Jev API", settings.labeler.base_url), ("Firecrawl API", FIRECRAWL_URL)): + ok, detail = _reachable(client, url) + status: Status = ( + "ok" if ok else ("warn" if name == "Firecrawl API" and not needs_firecrawl else "fail") + ) + checks.append( + Check( + f"{name} reachable", status, f"{url}: {detail}", "" if ok else "check network/proxy" + ) + ) + + # stores + if not stores: + checks.append( + Check( + "approved stores", + "fail", + "0 stores are approved and enabled", + 'after reviewing its terms: c2a registry approve --by ""', + ) + ) + else: + checks.append(Check("approved stores", "ok", ", ".join(s.id for s in stores))) + for s in stores: + base = f"https://{s.domain}" + rp = fetch_robots(s.domain, client) + if rp is None: + checks.append( + Check( + f"{s.id}: robots.txt", + "fail", + "unreachable (crawl would deny all)", + "check the domain in the registry", + ) + ) + continue + if s.method == "shopify": + allowed = robots_allows(rp, base + "/products.json") + checks.append( + Check( + f"{s.id}: /products.json allowed", + "ok" if allowed else "fail", + "robots.txt allows" if allowed else "robots.txt disallows", + "" if allowed else "use method firecrawl or drop this store", + ) + ) + checks.append( + Check(f"{s.id}: currency", "ok", s.currency) + if s.currency + else Check( + f"{s.id}: currency", + "warn", + "not set (will try /meta.json, then the country default)", + f"c2a discover fingerprint --store {s.id} --write", + ) + ) + elif s.platform == "unknown": + checks.append( + Check( + f"{s.id}: platform", + "warn", + f"unknown (method={s.method})", + f"c2a discover fingerprint --store {s.id} --write", + ) + ) + + if live: + checks += _live_checks(registry, settings, client, jev_key, fc_key) + return checks + + +def _live_checks(registry, settings, client, jev_key, fc_key) -> list[Check]: + out: list[Check] = [] + if jev_key: + req = SystemOneRequest( + state={"title": "Merino wool running shoe", "price": "129.00 USD"}, + model=settings.labeler.model, + questions={ + "vertical": Choice( + instructions="Which vertical?", criteria={"apparel": None, "electronics": None} + ) + }, + ) + try: + resp = client.post( + settings.labeler.base_url.rstrip("/") + "/v1/systemone", + json=req.model_dump(mode="json", exclude_none=True), + headers={"Authorization": f"Bearer {jev_key}"}, + timeout=30, + ) + except httpx.HTTPError as exc: + out.append(Check("Jev live call", "fail", type(exc).__name__)) + else: + if resp.status_code != 200: + out.append( + Check( + "Jev live call", + "fail", + f"HTTP {resp.status_code}: {resp.text[:300]}", + "check the key and base_url", + ) + ) + else: + try: + parsed = SystemOneResponse.model_validate(resp.json()) + ans = parsed.answers["vertical"] + out.append( + Check( + "Jev live call", + "ok", + f"answer={ans.choice} confidence={ans.confidence:.2f}", + ) + ) + except (ValidationError, ValueError, KeyError, AttributeError) as exc: + out.append( + Check( + "Jev response format", + "fail", + f"{type(exc).__name__}; raw body: {resp.text[:800]}", + "send this output back; decide/systemone.py needs updating", + ) + ) + fc_store = next((s for s in registry.crawlable() if s.method == "firecrawl"), None) + if fc_key and fc_store: + try: + resp = client.post( + FIRECRAWL_URL + "/v2/map", + json={"url": f"https://{fc_store.domain}", "limit": 5}, + headers={"Authorization": f"Bearer {fc_key}"}, + timeout=60, + ) + body = resp.text[:400] + ok = resp.status_code == 200 and "links" in resp.json() + out.append( + Check( + "Firecrawl live map", + "ok" if ok else "fail", + f"HTTP {resp.status_code}" if ok else body, + ) + ) + except (httpx.HTTPError, ValueError) as exc: + out.append(Check("Firecrawl live map", "fail", type(exc).__name__)) + return out + + +ICON = {"ok": "✅", "warn": "⚠️ ", "fail": "❌"} + + +def format_checks(checks: list[Check]) -> str: + lines = [] + for c in checks: + lines.append(f"{ICON[c.status]} {c.name}: {c.detail}") + if c.fix and c.status != "ok": + lines.append(f" fix: {c.fix}") + return "\n".join(lines) diff --git a/src/c2a/eval/__init__.py b/src/c2a/eval/__init__.py new file mode 100644 index 0000000..cf2f743 --- /dev/null +++ b/src/c2a/eval/__init__.py @@ -0,0 +1,22 @@ +"""Evaluation harness (Inspect AI + custom scorers) over public benchmarks and C2A-Bench. M2.""" + +from c2a import NotYetImplemented + +PUBLIC_BENCHMARKS = ( + "esci", + "sqid", + "amazon_m2", + "shopping_mmlu", + "ecinstruct", + "shoppingbench", + "shoppingcomp", + "shopping_companion", + "comboshoppingbench", + "webmall", + "shopgym", + "tau_bench_retail", +) + + +def run(model: str, suites: list[str]) -> dict: + raise NotYetImplemented("evaluation harness", "M2") diff --git a/src/c2a/graders/__init__.py b/src/c2a/graders/__init__.py new file mode 100644 index 0000000..d141939 --- /dev/null +++ b/src/c2a/graders/__init__.py @@ -0,0 +1 @@ +"""Graders shared by evaluation and RL rewards (one source of truth for 'good').""" diff --git a/src/c2a/graders/base.py b/src/c2a/graders/base.py new file mode 100644 index 0000000..944a686 --- /dev/null +++ b/src/c2a/graders/base.py @@ -0,0 +1,31 @@ +"""Grader protocol and weighted composite scoring.""" + +from __future__ import annotations + +from typing import Any, Protocol + +from pydantic import BaseModel, Field + + +class GradeResult(BaseModel): + score: float # in [0, 1] unless a hard failure sets it to 0 + passed: bool = True # False = hard failure (invalid output, hallucination, ...) + components: dict[str, float] = Field(default_factory=dict) + reasons: list[str] = Field(default_factory=list) + + +class Grader(Protocol): + name: str + + def grade(self, output: str, example: dict[str, Any]) -> GradeResult: ... + + +def composite(components: dict[str, float], weights: dict[str, float]) -> float: + """Weighted mean over the components present in `weights`.""" + missing = weights.keys() - components.keys() + if missing: + raise KeyError(f"missing components: {sorted(missing)}") + total_w = sum(weights.values()) + if total_w <= 0: + raise ValueError("weights must sum to > 0") + return sum(components[k] * w for k, w in weights.items()) / total_w diff --git a/src/c2a/graders/ranking.py b/src/c2a/graders/ranking.py new file mode 100644 index 0000000..6fa4084 --- /dev/null +++ b/src/c2a/graders/ranking.py @@ -0,0 +1,29 @@ +"""Ranking metrics (binary or graded relevance).""" + +from __future__ import annotations + +import math +from collections.abc import Mapping, Sequence + + +def dcg_at_k(ranked: Sequence[str], relevance: Mapping[str, float], k: int) -> float: + return sum( + (2 ** relevance.get(pid, 0.0) - 1) / math.log2(i + 2) for i, pid in enumerate(ranked[:k]) + ) + + +def ndcg_at_k(ranked: Sequence[str], relevance: Mapping[str, float], k: int) -> float: + ideal = sorted(relevance.values(), reverse=True)[:k] + idcg = sum((2**r - 1) / math.log2(i + 2) for i, r in enumerate(ideal)) + return dcg_at_k(ranked, relevance, k) / idcg if idcg > 0 else 0.0 + + +def recall_at_k(ranked: Sequence[str], relevant: set[str], k: int) -> float: + if not relevant: + return 0.0 + return len(set(ranked[:k]) & relevant) / len(relevant) + + +def dedupe_preserving_order(ranked: Sequence[str]) -> list[str]: + seen: set[str] = set() + return [x for x in ranked if not (x in seen or seen.add(x))] diff --git a/src/c2a/graders/rec.py b/src/c2a/graders/rec.py new file mode 100644 index 0000000..68ef4f9 --- /dev/null +++ b/src/c2a/graders/rec.py @@ -0,0 +1,83 @@ +"""Recommendation graders: JSON validity, hallucinated IDs, constraints, ranking quality.""" + +from __future__ import annotations + +import json +from typing import Any + +from pydantic import ValidationError + +from c2a.graders.base import GradeResult, composite +from c2a.graders.ranking import dedupe_preserving_order, ndcg_at_k, recall_at_k +from c2a.schemas import Candidate, Constraints, RecResponse + +DEFAULT_WEIGHTS = {"ndcg": 0.6, "recall": 0.2, "constraints": 0.2} + + +def parse_rec_output(output: str) -> RecResponse | None: + try: + return RecResponse.model_validate(json.loads(output)) + except (json.JSONDecodeError, ValidationError, TypeError): + return None + + +def hallucinated_ids(ranked: list[str], candidates: list[Candidate]) -> list[str]: + allowed = {c.product_id for c in candidates} + return [pid for pid in ranked if pid not in allowed] + + +def constraint_violations( + ranked: list[str], candidates: list[Candidate], constraints: Constraints +) -> list[tuple[str, str]]: + """Return (product_id, reason) for each constraint violation.""" + by_id = {c.product_id: c for c in candidates} + out = [] + for pid in ranked: + c = by_id.get(pid) + if c is None: + continue + if constraints.in_stock_only and not c.in_stock: + out.append((pid, "out of stock")) + mp = constraints.max_price + if mp is not None and c.price.currency == mp.currency and c.price.amount > mp.amount: + out.append((pid, "over max price")) + return out + + +class RecGrader: + """Grades a model's JSON recommendation list against ground-truth relevance. + + Hard failures (score 0, passed=False): invalid JSON/schema or any hallucinated ID. + """ + + name = "rec" + + def __init__(self, k: int = 10, weights: dict[str, float] | None = None) -> None: + self.k = k + self.weights = weights or DEFAULT_WEIGHTS + + def grade(self, output: str, example: dict[str, Any]) -> GradeResult: + candidates = [Candidate.model_validate(c) for c in example["candidates"]] + constraints = Constraints.model_validate(example.get("constraints", {})) + relevance: dict[str, float] = example["relevance"] # product_id -> graded relevance + + parsed = parse_rec_output(output) + if parsed is None: + return GradeResult(score=0.0, passed=False, reasons=["invalid JSON or schema"]) + ranked = dedupe_preserving_order([it.product_id for it in parsed.items]) + bad = hallucinated_ids(ranked, candidates) + if bad: + return GradeResult(score=0.0, passed=False, reasons=[f"hallucinated ids: {bad}"]) + + top = ranked[: self.k] + violations = constraint_violations(top, candidates, constraints) + components = { + "ndcg": ndcg_at_k(top, relevance, self.k), + "recall": recall_at_k(top, {p for p, r in relevance.items() if r > 0}, self.k), + "constraints": 1.0 - (len(violations) / len(top) if top else 1.0), + } + return GradeResult( + score=composite(components, self.weights), + components=components, + reasons=[f"{pid}: {why}" for pid, why in violations], + ) diff --git a/src/c2a/index/__init__.py b/src/c2a/index/__init__.py new file mode 100644 index 0000000..76cbcf0 --- /dev/null +++ b/src/c2a/index/__init__.py @@ -0,0 +1,50 @@ +"""Candidate retrieval. Production: multilingual embeddings in LanceDB on a Modal Volume (M1). +M0 ships the protocol plus a tiny in-memory retriever for tests and the dev gateway.""" + +from __future__ import annotations + +import math +import re +from collections import Counter +from typing import Protocol + +from c2a.schemas import Candidate, Product + + +class Retriever(Protocol): + def search(self, query: str, k: int = 50) -> list[Candidate]: ... + + +def _tokens(text: str) -> Counter[str]: + return Counter(re.findall(r"\w+", text.lower())) + + +def _cosine(a: Counter[str], b: Counter[str]) -> float: + dot = sum(a[t] * b[t] for t in a.keys() & b.keys()) + na = math.sqrt(sum(v * v for v in a.values())) + nb = math.sqrt(sum(v * v for v in b.values())) + return dot / (na * nb) if na and nb else 0.0 + + +def to_candidate(p: Product) -> Candidate: + return Candidate( + product_id=p.id, + title=p.title, + price=p.min_price, + in_stock=p.in_stock, + attributes=p.attributes, + ) + + +class InMemoryRetriever: + """Bag-of-words cosine over title/tags/category. Dev/test only.""" + + def __init__(self, products: list[Product]) -> None: + self._items = [ + (p, _tokens(" ".join([p.title, p.category or "", " ".join(p.tags)]))) for p in products + ] + + def search(self, query: str, k: int = 50) -> list[Candidate]: + q = _tokens(query) + scored = sorted(self._items, key=lambda it: _cosine(q, it[1]), reverse=True) + return [to_candidate(p) for p, vec in scored[:k] if _cosine(q, vec) > 0] diff --git a/src/c2a/labeling/__init__.py b/src/c2a/labeling/__init__.py new file mode 100644 index 0000000..3feeb24 --- /dev/null +++ b/src/c2a/labeling/__init__.py @@ -0,0 +1,2 @@ +"""Labeling of scraped data with hosted Jev (System One), confidence gating and escalation +(teacher, then human review). See docs/PLAN.md section 1 and 2b.""" diff --git a/src/c2a/labeling/cache.py b/src/c2a/labeling/cache.py new file mode 100644 index 0000000..67b57ae --- /dev/null +++ b/src/c2a/labeling/cache.py @@ -0,0 +1,37 @@ +"""On-disk System One response cache so re-runs never pay for the same request twice.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +from c2a.decide.systemone import SystemOneRequest, SystemOneResponse + + +def request_key(request: SystemOneRequest, model: str) -> str: + body = request.model_dump(mode="json", exclude_none=True) + body["model"] = model + return hashlib.sha256(json.dumps(body, sort_keys=True).encode()).hexdigest() + + +class ResponseCache: + def __init__(self, directory: Path | str) -> None: + self.dir = Path(directory) + self.dir.mkdir(parents=True, exist_ok=True) + + def _path(self, key: str) -> Path: + return self.dir / key[:2] / f"{key}.json" + + def get(self, key: str) -> SystemOneResponse | None: + path = self._path(key) + if not path.exists(): + return None + return SystemOneResponse.model_validate_json(path.read_text()) + + def put(self, key: str, response: SystemOneResponse) -> None: + path = self._path(key) + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(".tmp") + tmp.write_text(response.model_dump_json()) + tmp.replace(path) diff --git a/src/c2a/labeling/escalation.py b/src/c2a/labeling/escalation.py new file mode 100644 index 0000000..f4d1c4a --- /dev/null +++ b/src/c2a/labeling/escalation.py @@ -0,0 +1,41 @@ +"""Escalation of low-confidence labels: teacher first, then the human review queue.""" + +from __future__ import annotations + +from collections.abc import Callable +from pathlib import Path +from typing import Any + +from c2a.decide.systemone import Question +from c2a.labeling.records import LabelRecord, ReviewItem + +# (state, question_id, question) -> label string, or None if the teacher cannot answer. +Teacher = Callable[[Any, str, Question], str | None] + + +def escalate( + record: LabelRecord, state: Any, question: Question, teacher: Teacher | None +) -> LabelRecord: + """Return the updated record: accepted (teacher agrees) or queued for human review.""" + teacher_label = teacher(state, record.question_id, question) if teacher else None + if teacher_label is not None and teacher_label == record.value: + return record.model_copy(update={"status": "accepted", "source": "jev+teacher"}) + return record.model_copy(update={"status": "review", "teacher_value": teacher_label}) + + +def review_item(record: LabelRecord, state: Any, question: Question) -> ReviewItem: + return ReviewItem(record=record, state=state, question=question) + + +def apply_review(record: LabelRecord, label: str, reviewer: str) -> LabelRecord: + return record.model_copy( + update={"value": label, "status": "accepted", "source": "human", "reviewer": reviewer} + ) + + +def append_review_queue(path: Path | str, items: list[ReviewItem]) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("a") as f: + for it in items: + f.write(it.model_dump_json() + "\n") diff --git a/src/c2a/labeling/export.py b/src/c2a/labeling/export.py new file mode 100644 index 0000000..b62a343 --- /dev/null +++ b/src/c2a/labeling/export.py @@ -0,0 +1,67 @@ +"""Exports: decider training data (Kev / System One training JSONL) and C2A-Bench gold.""" + +from __future__ import annotations + +import json +from collections import defaultdict +from collections.abc import Iterable, Mapping +from typing import Any + +from c2a.bench import contains_canary +from c2a.decide.systemone import Choice, Noul, Score +from c2a.labeling.questions import QuestionSet +from c2a.labeling.records import LabelRecord + +# Jev-only labels are excluded by default: TypeSafe's terms on training from Jev outputs must +# be confirmed first (open item in docs/PLAN.md). +DEFAULT_TRAIN_SOURCES = frozenset({"human", "jev+teacher"}) + + +def _kev_label(question: Noul | Choice | Score, value: str) -> Any: + if isinstance(question, Noul): + return value == "true" + if isinstance(question, Score): + return question.criteria.index(value) + return value + + +def to_decider_train( + states: Mapping[str, Any], + records: Iterable[LabelRecord], + qset: QuestionSet, + sources: Iterable[str] = DEFAULT_TRAIN_SOURCES, +) -> list[dict[str, Any]]: + """One row per item: {"state", "questions": {qid: {type, instructions, criteria, label}}}.""" + sources = set(sources) + by_item: dict[str, dict[str, LabelRecord]] = defaultdict(dict) + for r in records: + if r.qset == qset.id and r.status == "accepted" and r.source in sources: + by_item[r.item_id][r.question_id] = r + rows = [] + for item_id, recs in by_item.items(): + state = states[item_id] + if contains_canary(json.dumps(state, default=str)): + continue + questions = {} + for qid, rec in recs.items(): + q = qset.questions[qid] + questions[qid] = { + **q.model_dump(mode="json", exclude_none=True), + "label": _kev_label(q, rec.value), + } + rows.append({"state": state, "questions": questions}) + return rows + + +def to_gold(records: Iterable[LabelRecord]) -> list[dict[str, Any]]: + return [ + { + "item_id": r.item_id, + "qset": r.qset, + "question_id": r.question_id, + "label": r.value, + "reviewer": r.reviewer, + } + for r in records + if r.source == "human" and r.status == "accepted" + ] diff --git a/src/c2a/labeling/gating.py b/src/c2a/labeling/gating.py new file mode 100644 index 0000000..8a4c695 --- /dev/null +++ b/src/c2a/labeling/gating.py @@ -0,0 +1,39 @@ +"""Confidence gating: decide which Jev answers are accepted automatically.""" + +from __future__ import annotations + +from pathlib import Path + +import yaml +from pydantic import BaseModel, Field + +from c2a.config import REPO_ROOT +from c2a.decide.systemone import ChoiceAnswer, NoulAnswer, ScoreAnswer, top_prob + +DEFAULT_THRESHOLDS = REPO_ROOT / "configs" / "labeling.yaml" + + +class Thresholds(BaseModel): + """choice/score: accept if confidence >= min_confidence AND top prob >= min_top_prob. + noul: accept if max(p, 1 - p) >= noul_min.""" + + min_confidence: float = Field(default=0.6, ge=0, le=1) + min_top_prob: float = Field(default=0.7, ge=0, le=1) + noul_min: float = Field(default=0.85, ge=0.5, le=1) + per_question: dict[str, dict[str, float]] = Field(default_factory=dict) + + def for_question(self, qid: str) -> Thresholds: + override = self.per_question.get(qid) + return self.model_copy(update=override) if override else self + + +def load_thresholds(path: Path | str | None = None) -> Thresholds: + path = Path(path or DEFAULT_THRESHOLDS) + data = yaml.safe_load(path.read_text()) if path.exists() else {} + return Thresholds.model_validate((data or {}).get("thresholds", {})) + + +def is_confident(answer: NoulAnswer | ChoiceAnswer | ScoreAnswer, t: Thresholds) -> bool: + if isinstance(answer, NoulAnswer): + return top_prob(answer) >= t.noul_min + return answer.confidence >= t.min_confidence and top_prob(answer) >= t.min_top_prob diff --git a/src/c2a/labeling/pairs.py b/src/c2a/labeling/pairs.py new file mode 100644 index 0000000..bc543e3 --- /dev/null +++ b/src/c2a/labeling/pairs.py @@ -0,0 +1,53 @@ +"""Candidate product pairs for pair_v1 labeling (exact / substitute / complement / irrelevant). + +Within one store: the k most similar products per anchor (likely substitutes) plus a few +products from other categories (likely complements or irrelevant). Deterministic and +deduplicated as unordered pairs. +""" + +from __future__ import annotations + +import hashlib +from collections.abc import Sequence + +from c2a.index import InMemoryRetriever +from c2a.labeling.records import LabelItem +from c2a.labeling.state import pair_state +from c2a.schemas import Product + + +def _stable_key(*parts: str) -> str: + return hashlib.sha256("|".join(parts).encode()).hexdigest() + + +def candidate_pairs( + products: Sequence[Product], k: int = 3, other: int = 1 +) -> list[tuple[Product, Product]]: + products = sorted(products, key=lambda p: p.id) # ties resolve the same for any input order + by_id = {p.id: p for p in products} + retriever = InMemoryRetriever(products) + seen: set[frozenset[str]] = set() + pairs: list[tuple[Product, Product]] = [] + + def add(a: Product, b: Product) -> None: + key = frozenset((a.id, b.id)) + if a.id != b.id and key not in seen: + seen.add(key) + pairs.append((a, b)) + + for anchor in products: + query = " ".join([anchor.title, anchor.category or "", " ".join(anchor.tags)]) + similar = [c for c in retriever.search(query, k=k + 1) if c.product_id != anchor.id][:k] + for c in similar: + add(anchor, by_id[c.product_id]) + others = sorted( + (p for p in products if p.id != anchor.id and p.category != anchor.category), + key=lambda p: _stable_key(anchor.id, p.id), + ) + for p in others[:other]: + add(anchor, p) + return pairs + + +def pair_items(pairs: Sequence[tuple[Product, Product]]) -> list[LabelItem]: + return [LabelItem(id=f"pair:{a.id}|{b.id}", state=pair_state(a, b)) for a, b in pairs] diff --git a/src/c2a/labeling/pipeline.py b/src/c2a/labeling/pipeline.py new file mode 100644 index 0000000..bc101a6 --- /dev/null +++ b/src/c2a/labeling/pipeline.py @@ -0,0 +1,138 @@ +"""Label items with hosted Jev: one System One request per item (all questions of the set), +cached, budget-capped, confidence-gated and escalated.""" + +from __future__ import annotations + +from collections.abc import Sequence +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass, field +from pathlib import Path + +from c2a.decide.base import Decider +from c2a.decide.systemone import ( + ChoiceAnswer, + NoulAnswer, + SystemOneRequest, + SystemOneResponse, + top_label, + top_prob, +) +from c2a.labeling.cache import ResponseCache, request_key +from c2a.labeling.escalation import Teacher, append_review_queue, escalate, review_item +from c2a.labeling.gating import Thresholds, is_confident +from c2a.labeling.questions import QuestionSet +from c2a.labeling.records import LabelItem, LabelRecord, ReviewItem +from c2a.train.data import read_jsonl, write_jsonl + + +@dataclass +class LabelRun: + records: list[LabelRecord] = field(default_factory=list) + review: list[ReviewItem] = field(default_factory=list) + requests_made: int = 0 + cache_hits: int = 0 + skipped: list[str] = field(default_factory=list) # item ids not labeled (budget) + + @property + def budget_exhausted(self) -> bool: + return bool(self.skipped) + + +def to_records( + item_id: str, qset: QuestionSet, response: SystemOneResponse, thresholds: Thresholds +) -> list[LabelRecord]: + out = [] + for qid in qset.questions: + ans = response.answers.get(qid) + if ans is None: + raise ValueError(f"response for {item_id} is missing question '{qid}'") + if isinstance(ans, NoulAnswer): + probs = {"true": ans.noul, "false": 1.0 - ans.noul} + confidence = top_prob(ans) + else: + probs = dict(ans.probabilities) + if not isinstance(ans, ChoiceAnswer): # score: key by level label + probs = {ans.legend.get(k, k): v for k, v in probs.items()} + confidence = ans.confidence + ok = is_confident(ans, thresholds.for_question(qid)) + out.append( + LabelRecord( + item_id=item_id, + qset=qset.id, + question_id=qid, + qtype=ans.type, + value=top_label(ans), + probabilities=probs, + confidence=confidence, + model=response.model, + status="accepted" if ok else "escalated", + ) + ) + return out + + +def label( + items: Sequence[LabelItem], + decider: Decider, + qset: QuestionSet, + thresholds: Thresholds, + cache: ResponseCache | None = None, + max_requests: int | None = None, + teacher: Teacher | None = None, + model: str = "jev-latest", + max_workers: int = 8, +) -> LabelRun: + run = LabelRun() + requests = { + it.id: SystemOneRequest(state=it.state, model=model, questions=qset.questions) + for it in items + } + + # Resolve cache hits first, then spend the budget on misses in input order. + responses: dict[str, SystemOneResponse] = {} + misses: list[str] = [] + for it in items: + cached = cache.get(request_key(requests[it.id], model)) if cache else None + if cached is not None: + responses[it.id] = cached + run.cache_hits += 1 + else: + misses.append(it.id) + if max_requests is not None and len(misses) > max_requests: + run.skipped = misses[max_requests:] + misses = misses[:max_requests] + + def fetch(item_id: str) -> tuple[str, SystemOneResponse]: + resp = decider.ask(requests[item_id]) + if cache: + cache.put(request_key(requests[item_id], model), resp) + return item_id, resp + + with ThreadPoolExecutor(max_workers=max_workers) as pool: + for item_id, resp in pool.map(fetch, misses): + responses[item_id] = resp + run.requests_made += 1 + + for it in items: + if it.id not in responses: + continue + for rec in to_records(it.id, qset, responses[it.id], thresholds): + if rec.status == "escalated": + question = qset.questions[rec.question_id] + rec = escalate(rec, it.state, question, teacher) + if rec.status == "review": + run.review.append(review_item(rec, it.state, question)) + run.records.append(rec) + return run + + +def save_run(run_dir: Path, items: Sequence[LabelItem], run: LabelRun) -> None: + """Merge a run into run_dir (items.jsonl, labels.jsonl): re-labeled items replace their + previous rows; the review queue is appended.""" + items_path, labels_path = run_dir / "items.jsonl", run_dir / "labels.jsonl" + prev_items = list(read_jsonl(items_path, LabelItem)) if items_path.exists() else [] + prev_recs = list(read_jsonl(labels_path, LabelRecord)) if labels_path.exists() else [] + new_ids = {it.id for it in items} + write_jsonl(items_path, [i for i in prev_items if i.id not in new_ids] + list(items)) + write_jsonl(labels_path, [r for r in prev_recs if r.item_id not in new_ids] + run.records) + append_review_queue(run_dir / "review_queue.jsonl", run.review) diff --git a/src/c2a/labeling/questions.py b/src/c2a/labeling/questions.py new file mode 100644 index 0000000..b225add --- /dev/null +++ b/src/c2a/labeling/questions.py @@ -0,0 +1,140 @@ +"""Versioned System One question sets for labeling scraped data. + +Each set has a version; changing any question means bumping the version so labels from +different question wordings are never mixed. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path + +import yaml + +from c2a.decide.systemone import Choice, Noul, Question, Score + +TAXONOMY_DIR = Path(__file__).with_name("taxonomy") + + +def verticals() -> dict[str, str]: + return yaml.safe_load((TAXONOMY_DIR / "verticals.yaml").read_text()) + + +@dataclass(frozen=True) +class QuestionSet: + name: str + version: str + target: str # page | product | pair + questions: dict[str, Question] = field(default_factory=dict) + + @property + def id(self) -> str: + return f"{self.name}@{self.version}" + + +def page_v1() -> QuestionSet: + return QuestionSet( + name="page", + version="1", + target="page", + questions={ + "page_type": Choice( + instructions="What kind of web page is this (scraped as markdown)?", + criteria={ + "product_detail": "A single product page with price and buy/add-to-cart", + "category_listing": "A grid or list of multiple products", + "content": "Blog, guide, about, help or policy page", + "other": "Anything else (cart, login, error, empty)", + }, + ) + }, + ) + + +def product_v1() -> QuestionSet: + return QuestionSet( + name="product", + version="1", + target="product", + questions={ + "vertical": Choice( + instructions="Which vertical does this product belong to?", criteria=verticals() + ), + "audience": Choice( + instructions="Who is the primary intended user of this product?", + criteria={ + "women": None, + "men": None, + "unisex": "Adults of any gender", + "kids": "Children roughly 3-14", + "baby": "Infants and toddlers", + "pets": None, + "n_a": "Audience is not meaningful for this product", + }, + ), + "price_tier": Score( + instructions="Price positioning relative to typical products of the same kind.", + criteria=["budget", "mid_market", "premium", "luxury"], + ), + "use_occasion": Choice( + instructions="Main use or occasion for this product.", + criteria={ + "everyday": None, + "sport_outdoor": None, + "work_office": None, + "formal_party": None, + "home_leisure": None, + "travel": None, + "gift": None, + "other": None, + }, + ), + "is_bundle": Noul(instructions="Is this listing a bundle, set or multipack?"), + "restricted": Noul( + instructions=( + "Is this an age-restricted or regulated product " + "(alcohol, tobacco/vape, weapons, drugs/medicines, adult content)?" + ) + ), + "contains_pii": Noul( + instructions="Does the text contain personal data about a private individual " + "(names with contact details, emails, phone numbers, addresses)?" + ), + "copy_quality": Score( + instructions="Quality of the product title and description as shopping copy.", + criteria=["poor", "fair", "good", "excellent"], + ), + }, + ) + + +def pair_v1() -> QuestionSet: + return QuestionSet( + name="pair", + version="1", + target="pair", + questions={ + "relation": Choice( + instructions="How does the candidate product relate to the anchor " + "(a shopper query or another product)?", + criteria={ + "exact": "Satisfies the anchor directly / same product", + "substitute": "An alternative the shopper could buy instead", + "complement": "Goes together with the anchor (buy both)", + "irrelevant": "Not related in a useful way", + }, + ) + }, + ) + + +QUESTION_SETS = {"page_v1": page_v1, "product_v1": product_v1, "pair_v1": pair_v1} + + +def get_question_set(name: str) -> QuestionSet: + try: + return QUESTION_SETS[name]() + except KeyError: + raise KeyError( + f"unknown question set '{name}', choose from {sorted(QUESTION_SETS)}" + ) from None diff --git a/src/c2a/labeling/records.py b/src/c2a/labeling/records.py new file mode 100644 index 0000000..ddb2a94 --- /dev/null +++ b/src/c2a/labeling/records.py @@ -0,0 +1,42 @@ +"""Label records written by the labeling pipeline.""" + +from __future__ import annotations + +from datetime import UTC, datetime +from typing import Any, Literal + +from pydantic import BaseModel, Field + +from c2a.decide.systemone import Question + +Source = Literal["jev", "jev+teacher", "human"] +Status = Literal["accepted", "escalated", "review"] + + +class LabelItem(BaseModel): + id: str + state: str | dict[str, Any] | list[Any] + + +class LabelRecord(BaseModel): + item_id: str + qset: str # e.g. "product@1" + question_id: str + qtype: Literal["noul", "choice", "score"] + value: str # choice option / "true"|"false" / score level label + probabilities: dict[str, float] = Field(default_factory=dict) + confidence: float + source: Source = "jev" + model: str + status: Status + teacher_value: str | None = None + reviewer: str | None = None + created_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) + + +class ReviewItem(BaseModel): + """Everything a human reviewer needs, self-contained.""" + + record: LabelRecord + state: str | dict[str, Any] | list[Any] + question: Question diff --git a/src/c2a/labeling/review.py b/src/c2a/labeling/review.py new file mode 100644 index 0000000..7a79e54 --- /dev/null +++ b/src/c2a/labeling/review.py @@ -0,0 +1,77 @@ +"""Human review of escalated labels. Pure functions here; the terminal loop lives in the CLI. + +Each decision replaces the label record in labels.jsonl (source='human') and removes the +item from review_queue.jsonl, so progress is saved after every answer. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from c2a.decide.systemone import Choice, Noul, Score +from c2a.labeling.escalation import apply_review +from c2a.labeling.records import LabelRecord, ReviewItem +from c2a.train.data import read_jsonl, write_jsonl + + +def load_queue(run_dir: Path) -> list[ReviewItem]: + path = run_dir / "review_queue.jsonl" + return list(read_jsonl(path, ReviewItem)) if path.exists() else [] + + +def options(question: Noul | Choice | Score) -> list[str]: + if isinstance(question, Noul): + return ["true", "false"] + if isinstance(question, Score): + return list(question.criteria) + return list(question.criteria) + + +def _key(r: LabelRecord) -> tuple[str, str]: + return r.item_id, r.question_id + + +def apply_decision(run_dir: Path, item: ReviewItem, label: str, reviewer: str) -> LabelRecord: + if label not in options(item.question): + raise ValueError(f"'{label}' is not an option for {item.record.question_id}") + decided = apply_review(item.record, label, reviewer) + labels_path = run_dir / "labels.jsonl" + records = list(read_jsonl(labels_path, LabelRecord)) if labels_path.exists() else [] + replaced = [decided if _key(r) == _key(decided) else r for r in records] + if not any(_key(r) == _key(decided) for r in records): + replaced.append(decided) + write_jsonl(labels_path, replaced) + queue = [q for q in load_queue(run_dir) if _key(q.record) != _key(decided)] + write_jsonl(run_dir / "review_queue.jsonl", queue) + return decided + + +def _brief(state: Any) -> dict[str, Any]: + if isinstance(state, dict): + keep = ("title", "brand", "price", "category", "description", "query", "url") + out = {k: state[k] for k in keep if k in state} + if "description" in out: + out["description"] = str(out["description"])[:200] + for side in ("anchor", "candidate"): + if side in state: + out[side] = _brief(state[side]) + return out + return {"text": str(state)[:400]} + + +def format_item(item: ReviewItem, index: int, total: int) -> str: + rec, q = item.record, item.question + top = sorted(rec.probabilities.items(), key=lambda kv: kv[1], reverse=True)[:3] + lines = [ + f"[{index}/{total}] {rec.item_id} · {rec.qset} · {rec.question_id}", + json.dumps(_brief(item.state), ensure_ascii=False, indent=2), + f"Q: {q.instructions or rec.question_id}", + "Jev: " + ", ".join(f"{k} {v:.2f}" for k, v in top) + f" (confidence {rec.confidence:.2f})", + ] + if rec.teacher_value: + lines.append(f"Teacher: {rec.teacher_value}") + lines += [f" {i}) {opt}" for i, opt in enumerate(options(q), 1)] + lines.append(" s) skip q) quit") + return "\n".join(lines) diff --git a/src/c2a/labeling/state.py b/src/c2a/labeling/state.py new file mode 100644 index 0000000..f9fcf0f --- /dev/null +++ b/src/c2a/labeling/state.py @@ -0,0 +1,45 @@ +"""Render scraped items into compact System One `state` documents.""" + +from __future__ import annotations + +from typing import Any + +from c2a.normalize import currency_exponent, strip_html +from c2a.schemas import Money, Product + +MAX_DESCRIPTION_CHARS = 2000 +MAX_PAGE_CHARS = 8000 + + +def _money(m: Money) -> str: + exp = currency_exponent(m.currency) + return f"{m.amount / 10**exp:.{exp}f} {m.currency}" + + +def product_state(p: Product) -> dict[str, Any]: + options: dict[str, set[str]] = {} + for v in p.variants: + for k, val in v.options.items(): + options.setdefault(k, set()).add(val) + state: dict[str, Any] = { + "title": p.title, + "brand": p.brand, + "category": p.category, + "tags": p.tags, + "attributes": p.attributes, + "description": strip_html(p.description)[:MAX_DESCRIPTION_CHARS], + "price": _money(p.min_price), + "in_stock": p.in_stock, + "options": {k: sorted(v) for k, v in options.items()}, + "locale": p.locale, + } + return {k: v for k, v in state.items() if v not in (None, "", [], {})} + + +def page_state(url: str, markdown: str) -> dict[str, Any]: + return {"url": url, "content": markdown[:MAX_PAGE_CHARS]} + + +def pair_state(anchor: str | Product, candidate: Product) -> dict[str, Any]: + anchor_doc = {"query": anchor} if isinstance(anchor, str) else product_state(anchor) + return {"anchor": anchor_doc, "candidate": product_state(candidate)} diff --git a/src/c2a/labeling/taxonomy/verticals.yaml b/src/c2a/labeling/taxonomy/verticals.yaml new file mode 100644 index 0000000..79cfe09 --- /dev/null +++ b/src/c2a/labeling/taxonomy/verticals.yaml @@ -0,0 +1,12 @@ +# Level-1 product verticals (aligned with Google Product Taxonomy top levels we care about). +# Level-2 categories per vertical are added in M1 from the official taxonomy file. +apparel: Clothing, shoes and accessories +beauty: Cosmetics, skincare, hair care and fragrance +health: Health, personal care, supplements and pharmacy +electronics: Consumer electronics, computers, phones and accessories +home: Furniture, home decor, bedding, kitchen and garden +grocery: Food, beverages and household consumables +sports: Sporting goods and outdoor equipment +toys: Toys, games and baby gear +gifts: Gifts, stationery and novelty items +other: Anything that does not fit the verticals above diff --git a/src/c2a/normalize/__init__.py b/src/c2a/normalize/__init__.py new file mode 100644 index 0000000..e244285 --- /dev/null +++ b/src/c2a/normalize/__init__.py @@ -0,0 +1,29 @@ +"""Normalization helpers: money, text. FX, taxonomy mapping and near-dup detection land in M1.""" + +from __future__ import annotations + +import re +from decimal import ROUND_HALF_UP, Decimal + +# ISO-4217 minor-unit exponents for currencies we expect; default is 2. +_EXPONENTS = {"JPY": 0, "KRW": 0, "ISK": 0, "CLP": 0, "BHD": 3, "KWD": 3} + + +def currency_exponent(currency: str) -> int: + return _EXPONENTS.get(currency.upper(), 2) + + +def to_minor_units(amount: Decimal | str | float, currency: str) -> int: + value = Decimal(str(amount)) + if value < 0: + raise ValueError("negative price") + scaled = value * (Decimal(10) ** currency_exponent(currency)) + return int(scaled.quantize(Decimal(1), rounding=ROUND_HALF_UP)) + + +_TAG_RE = re.compile(r"<[^>]+>") +_WS_RE = re.compile(r"\s+") + + +def strip_html(html: str) -> str: + return _WS_RE.sub(" ", _TAG_RE.sub(" ", html)).strip() diff --git a/src/c2a/pilot.py b/src/c2a/pilot.py new file mode 100644 index 0000000..f87add8 --- /dev/null +++ b/src/c2a/pilot.py @@ -0,0 +1,108 @@ +"""One-command pilot: preflight -> crawl -> label products -> label pairs -> report.""" + +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Sequence +from dataclasses import dataclass, field +from pathlib import Path + +from c2a.config import Settings +from c2a.decide.base import Decider +from c2a.labeling.cache import ResponseCache +from c2a.labeling.gating import load_thresholds +from c2a.labeling.pairs import candidate_pairs, pair_items +from c2a.labeling.pipeline import label, save_run +from c2a.labeling.questions import pair_v1, product_v1 +from c2a.labeling.records import LabelItem +from c2a.labeling.state import product_state +from c2a.report import build_report +from c2a.schemas import Product +from c2a.sources.crawl import SessionFactory, crawl_registry +from c2a.sources.firecrawl import FirecrawlClient +from c2a.sources.registry import Registry +from c2a.train.data import read_jsonl + + +@dataclass +class PilotResult: + crawled: dict[str, int] = field(default_factory=dict) # store -> new/changed products + product_labels: int = 0 + pair_labels: int = 0 + to_review: int = 0 + jev_requests: int = 0 + skipped_budget: int = 0 + report: str = "" + + +def run_pilot( + registry: Registry, + settings: Settings, + store_ids: Sequence[str], + decider: Decider, + data_dir: Path = Path("data"), + firecrawl: FirecrawlClient | None = None, + max_products: int | None = 200, + pairs_k: int = 3, + max_jev_requests: int = 500, + session_factory: SessionFactory | None = None, +) -> PilotResult: + not_ok = [s for s in store_ids if s not in {x.id for x in registry.crawlable()}] + if not_ok: + raise ValueError(f"not approved/enabled: {not_ok} (c2a registry approve --by ...)") + raw_dir, labels_dir = data_dir / "raw", data_dir / "labels" + res = PilotResult() + thresholds = load_thresholds() + cache = ResponseCache(labels_dir / "cache") + model = settings.labeler.model + + results = crawl_registry( + registry, + raw_dir, + store_ids, + firecrawl=firecrawl, + decider=decider, + max_products=max_products, + qps=settings.budgets.default_qps_per_domain, + session_factory=session_factory, + ) + changed: list[Product] = [] + for m, ch in results: + res.crawled[m.store_id] = m.products_new_or_changed + changed += ch + + budget = max_jev_requests + if changed: + items = [LabelItem(id=p.id, state=product_state(p)) for p in changed] + run = label(items, decider, product_v1(), thresholds, cache, budget, model=model) + save_run(labels_dir / "product_v1", items, run) + budget -= run.requests_made + res.jev_requests += run.requests_made + res.product_labels = len(run.records) + res.to_review += len(run.review) + res.skipped_budget += len(run.skipped) + + if pairs_k > 0 and budget > 0: + by_store: dict[str, list[Product]] = defaultdict(list) + for sid in store_ids: + path = raw_dir / sid / "products.jsonl" + if path.exists(): + by_store[sid] = list(read_jsonl(path, Product)) + items = [ + it + for prods in by_store.values() + for it in pair_items(candidate_pairs(prods, k=pairs_k)) + ] + if items: + run = label(items, decider, pair_v1(), thresholds, cache, budget, model=model) + save_run(labels_dir / "pair_v1", items, run) + res.jev_requests += run.requests_made + res.pair_labels = len(run.records) + res.to_review += len(run.review) + res.skipped_budget += len(run.skipped) + + res.report = build_report(raw_dir, labels_dir) + report_path = data_dir / "reports" / "pilot.md" + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text(res.report) + return res diff --git a/src/c2a/report.py b/src/c2a/report.py new file mode 100644 index 0000000..b2dac18 --- /dev/null +++ b/src/c2a/report.py @@ -0,0 +1,119 @@ +"""Markdown summary of a crawl + label run, built from files on disk.""" + +from __future__ import annotations + +import json +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +from c2a.labeling.records import LabelItem, LabelRecord +from c2a.train.data import read_jsonl + +STORE_HEADER = [ + "| store | method | products | new/changed | errors | retries | warnings |", + "|---|---|---:|---:|---:|---:|---|", +] +LABEL_HEADER = [ + "| question | labels | accepted | human | mean confidence | top accepted values |", + "|---|---:|---:|---:|---:|---|", +] + + +def _manifests(raw_dir: Path) -> list[dict]: + return [json.loads(p.read_text()) for p in sorted(raw_dir.glob("*/manifest.json"))] + + +def _count_lines(path: Path) -> int: + if not path.exists(): + return 0 + return sum(1 for line in path.read_text().splitlines() if line.strip()) + + +def _store_rows(manifests: list[dict]) -> list[str]: + rows = [] + for m in manifests: + warnings = "; ".join(m.get("warnings", [])) or "-" + rows.append( + f"| {m['store_id']} | {m['method']} | {m['products_total']} | " + f"{m['products_new_or_changed']} | {len(m['errors'])} | {m.get('retries', 0)} | " + f"{warnings} |" + ) + return rows or ["| (no crawls yet) | | | | | | |"] + + +def _question_row(qid: str, rs: list[LabelRecord]) -> str: + acc = sum(r.status == "accepted" for r in rs) / len(rs) + human = sum(r.source == "human" for r in rs) + conf = sum(r.confidence for r in rs) / len(rs) + values = Counter(r.value for r in rs if r.status == "accepted").most_common(4) + top = ", ".join(f"{v} {n}" for v, n in values) or "-" + return f"| {qid} | {len(rs)} | {acc:.0%} | {human} | {conf:.2f} | {top} |" + + +def _title(state: Any, fallback: str) -> str: + if not isinstance(state, dict): + return fallback + if "title" in state: + return str(state["title"]) + if "candidate" in state: + anchor = state["anchor"].get("title") or state["anchor"].get("query") + return f"{anchor} ↔ {state['candidate'].get('title')}" + return fallback + + +def _samples(run: Path, records: list[LabelRecord], n: int) -> list[str]: + items_path = run / "items.jsonl" + if not items_path.exists() or n <= 0: + return [] + accepted: dict[str, dict[str, str]] = defaultdict(dict) + for r in records: + if r.status == "accepted": + accepted[r.item_id][r.question_id] = r.value + out = ["", "Samples:"] + for it in read_jsonl(items_path, LabelItem): + if it.id in accepted: + labels = ", ".join(f"{k}={v}" for k, v in accepted[it.id].items()) + out.append(f"- {_title(it.state, it.id)}: {labels}") + if len(out) - 2 >= n: + break + return out + + +def build_report(raw_dir: Path, labels_dir: Path, samples: int = 5) -> str: + manifests = _manifests(raw_dir) + out = ["# crawl2action pilot report", "", "## Stores", "", *STORE_HEADER] + out += _store_rows(manifests) + errors = [(m["store_id"], e) for m in manifests for e in m["errors"]][:10] + if errors: + out += ["", "First errors:"] + [f"- `{s}`: {e}" for s, e in errors] + + cache_dir = labels_dir / "cache" + paid = sum(1 for _ in cache_dir.rglob("*.json")) if cache_dir.exists() else 0 + credits = sum(m.get("firecrawl_credits", 0) for m in manifests) + out += [ + "", + "## Cost", + "", + f"- Jev requests paid so far (cached responses): {paid}", + f"- Firecrawl credits in the latest crawls: {credits}", + ] + + for run in sorted(p.parent for p in labels_dir.glob("*/labels.jsonl")): + records = list(read_jsonl(run / "labels.jsonl", LabelRecord)) + in_queue = _count_lines(run / "review_queue.jsonl") + n_items = len({r.item_id for r in records}) + out += [ + "", + f"## Labels: {run.name}", + "", + f"{n_items} items, {len(records)} labels, {in_queue} waiting for review.", + "", + *LABEL_HEADER, + ] + by_q: dict[str, list[LabelRecord]] = defaultdict(list) + for r in records: + by_q[r.question_id].append(r) + out += [_question_row(q, rs) for q, rs in sorted(by_q.items())] + out += _samples(run, records, samples) + return "\n".join(out) + "\n" diff --git a/src/c2a/schemas.py b/src/c2a/schemas.py new file mode 100644 index 0000000..6e90ca8 --- /dev/null +++ b/src/c2a/schemas.py @@ -0,0 +1,195 @@ +"""Core data model. Aligned with the UCP catalog (products/variants, minor-unit prices) +and schema.org Product so crawled, UCP and Shopify data normalize to one shape.""" + +from __future__ import annotations + +from datetime import datetime +from enum import StrEnum +from typing import Any + +from pydantic import BaseModel, Field, HttpUrl, field_validator, model_validator + + +class Money(BaseModel): + """Price in integer minor units (e.g. cents/öre) plus ISO-4217 currency.""" + + amount: int = Field(ge=0) + currency: str = Field(min_length=3, max_length=3) + + @field_validator("currency") + @classmethod + def _upper(cls, v: str) -> str: + return v.upper() + + +class Variant(BaseModel): + id: str + title: str = "" + sku: str | None = None + options: dict[str, str] = Field(default_factory=dict) + price: Money + compare_at_price: Money | None = None + available: bool = True + + +class Product(BaseModel): + id: str # globally unique: ":" + store_id: str + native_id: str + title: str + description: str = "" + brand: str | None = None + category: str | None = None + tags: list[str] = Field(default_factory=list) + attributes: dict[str, str] = Field(default_factory=dict) + url: HttpUrl | None = None + image_urls: list[str] = Field(default_factory=list) + variants: list[Variant] = Field(min_length=1) + locale: str | None = None + source: str = "unknown" # shopify | ucp | firecrawl | open_dataset + crawled_at: datetime | None = None + + @property + def min_price(self) -> Money: + return min((v.price for v in self.variants), key=lambda m: m.amount) + + @property + def in_stock(self) -> bool: + return any(v.available for v in self.variants) + + +class Review(BaseModel): + product_id: str + rating: float | None = Field(default=None, ge=0, le=5) + text: str = "" + locale: str | None = None + + +class Tier(StrEnum): + A = "A" # structured / permissioned: UCP, Shopify products.json, open datasets + B = "B" # Firecrawl extract, robots + ToS permitting + C = "C" # marketplaces: off unless explicitly cleared + + +class TosStatus(StrEnum): + unreviewed = "unreviewed" + approved = "approved" + denied = "denied" + + +class Store(BaseModel): + id: str + name: str + domain: str + country: str = Field(min_length=2, max_length=2) + locale: str + platform: str = "unknown" # shopify | ucp | woocommerce | custom | unknown + tier: Tier + tos_status: TosStatus = TosStatus.unreviewed + method: str = "firecrawl" # shopify | ucp | firecrawl + enabled: bool = False + verticals: list[str] = Field(default_factory=list) + currency: str | None = None # shop currency when the source does not state it (Shopify) + notes: str = "" + # Audit trail for ToS review; only set by a human via `c2a registry approve|deny`. + reviewed_by: str | None = None + reviewed_at: datetime | None = None + review_note: str | None = None + + +# ---------- Task I/O ---------- + + +class Constraints(BaseModel): + max_price: Money | None = None + in_stock_only: bool = True + sizes: list[str] = Field(default_factory=list) + locale: str | None = None + + +class Candidate(BaseModel): + product_id: str + title: str + price: Money + in_stock: bool = True + attributes: dict[str, str] = Field(default_factory=dict) + + +class RecRequest(BaseModel): + stage: str = Field(pattern="^(pre_checkout|post_checkout)$") + query: str | None = None + context_product_ids: list[str] = Field(default_factory=list) # anchor/cart/order + candidates: list[Candidate] = Field(default_factory=list) + constraints: Constraints = Field(default_factory=Constraints) + locale: str = "en-US" + k: int = Field(default=10, ge=1, le=50) + + +class RecItem(BaseModel): + product_id: str + reason: str = "" + + +class RecResponse(BaseModel): + items: list[RecItem] + + +class CopyRequest(BaseModel): + product: Product + kind: str = Field(pattern="^(title|description|bullets|seo|ad)$") + locale: str = "en-US" + brand_voice: str | None = None + + +class CopyResponse(BaseModel): + text: str + claims: list[str] = Field(default_factory=list) + + +class ImageBrief(BaseModel): + product_id: str + use_case: str = Field(pattern="^(hero|lifestyle|banner|edit)$") + scene: str + style: str = "" + aspect_ratio: str = "1:1" + reference_image_urls: list[str] = Field(default_factory=list) + negative_prompt: str = "" + + +class DecisionRequest(BaseModel): + evidence: str + question: str + labels: list[str] = Field(min_length=2) + + @field_validator("labels") + @classmethod + def _unique(cls, v: list[str]) -> list[str]: + if len(set(v)) != len(v): + raise ValueError("labels must be unique") + return v + + +class DecisionResult(BaseModel): + label: str + probs: dict[str, float] + + @model_validator(mode="after") + def _check(self) -> DecisionResult: + total = sum(self.probs.values()) + if abs(total - 1.0) > 1e-6: + raise ValueError(f"probabilities must sum to 1, got {total}") + if self.label not in self.probs: + raise ValueError("label must be one of probs") + return self + + +class TaskExample(BaseModel): + """One dataset item for any task family; `payload` holds the typed request.""" + + id: str + task: str # rec_pre | rec_post | copy | localize | image_brief | decide | agentic + locale: str + store_id: str | None = None + created_at: datetime | None = None + payload: dict[str, Any] + ground_truth: dict[str, Any] = Field(default_factory=dict) diff --git a/src/c2a/serve/__init__.py b/src/c2a/serve/__init__.py new file mode 100644 index 0000000..3d7881e --- /dev/null +++ b/src/c2a/serve/__init__.py @@ -0,0 +1 @@ +"""Serving on Modal: vLLM (student + LoRAs), image model, FastAPI gateway, semantic router.""" diff --git a/src/c2a/serve/gateway.py b/src/c2a/serve/gateway.py new file mode 100644 index 0000000..a9b4666 --- /dev/null +++ b/src/c2a/serve/gateway.py @@ -0,0 +1,78 @@ +"""Thin FastAPI gateway. In production vLLM Semantic Router sits in front of it and routes +to the fast reranker / 27B student / image pipeline (configs/router/semantic-router.yaml). + +M0 dev behaviour for /recommend: retrieve candidates, rank them with a pluggable ranker +(defaults to retrieval order), and validate with the same grader checks used in training +(no hallucinated IDs, constraints respected).""" + +from __future__ import annotations + +from collections.abc import Callable + +from c2a.decide.backends import KeywordDecider +from c2a.decide.base import Decider, decide +from c2a.decide.systemone import SystemOneRequest, SystemOneResponse +from c2a.graders.rec import constraint_violations, hallucinated_ids +from c2a.index import Retriever +from c2a.schemas import ( + Candidate, + DecisionRequest, + DecisionResult, + RecItem, + RecRequest, + RecResponse, +) + +Ranker = Callable[[RecRequest, list[Candidate]], list[RecItem]] + + +def retrieval_order_ranker(req: RecRequest, cands: list[Candidate]) -> list[RecItem]: + return [RecItem(product_id=c.product_id, reason="retrieval match") for c in cands] + + +def create_app( + retriever: Retriever, ranker: Ranker = retrieval_order_ranker, decider: Decider | None = None +): + try: + from fastapi import FastAPI, HTTPException + except ImportError as exc: # pragma: no cover + raise RuntimeError("Install the serve extra: uv sync --extra serve") from exc + + app = FastAPI(title="crawl2action gateway", version="0.1.0") + decider = decider or KeywordDecider() + + @app.get("/health") + def health() -> dict: + return {"status": "ok"} + + @app.post("/recommend", response_model=RecResponse) + def recommend(req: RecRequest) -> RecResponse: + cands = req.candidates or (retriever.search(req.query, k=50) if req.query else []) + items = ranker(req, cands) + ids = [it.product_id for it in items] + if hallucinated_ids(ids, cands): + raise HTTPException(502, "ranker returned ids outside the candidate set") + bad = {pid for pid, _ in constraint_violations(ids, cands, req.constraints)} + return RecResponse(items=[it for it in items if it.product_id not in bad][: req.k]) + + @app.post("/decide", response_model=DecisionResult) + def decide_one(req: DecisionRequest) -> DecisionResult: + return decide(decider, req.evidence, req.question, req.labels) + + @app.post("/v1/systemone", response_model=SystemOneResponse) + def systemone(req: SystemOneRequest) -> SystemOneResponse: + return decider.ask(req) + + @app.post("/generate/text") + def generate_text() -> None: + raise HTTPException(501, "text generation lands in M4") + + @app.post("/generate/image") + def generate_image() -> None: + raise HTTPException(501, "image generation lands in M4") + + @app.post("/feedback") + def feedback() -> None: + raise HTTPException(501, "feedback logging lands in M5") + + return app diff --git a/src/c2a/serve/image.py b/src/c2a/serve/image.py new file mode 100644 index 0000000..02e1daa --- /dev/null +++ b/src/c2a/serve/image.py @@ -0,0 +1,13 @@ +"""Modal app: Qwen-Image-2.1 (diffusers) with optional post-trained product LoRA. M4.""" + +from __future__ import annotations + +MODEL_ID = "Qwen/Qwen-Image-2.1" +GPU = "L40S" + +try: # pragma: no cover - optional dependency + import modal + + app = modal.App("c2a-image") +except ImportError: # pragma: no cover + app = None diff --git a/src/c2a/serve/llm.py b/src/c2a/serve/llm.py new file mode 100644 index 0000000..c01bf75 --- /dev/null +++ b/src/c2a/serve/llm.py @@ -0,0 +1,17 @@ +"""Modal app: vLLM OpenAI-compatible server for the post-trained student (+ task LoRAs). M4. + +Deploy: `modal deploy src/c2a/serve/llm.py` (requires the modal extra and exported weights +on the `c2a-models` Volume).""" + +from __future__ import annotations + +MODEL_VOLUME = "c2a-models" +GPU = "H100" # Qwen3.8-27B in FP8 fits on 1x H100; use "H100:2" for BF16 +VLLM_ARGS = ["--enable-lora", "--max-lora-rank", "64", "--guided-decoding-backend", "auto"] + +try: # pragma: no cover - optional dependency + import modal + + app = modal.App("c2a-llm") +except ImportError: # pragma: no cover + app = None diff --git a/src/c2a/sources/__init__.py b/src/c2a/sources/__init__.py new file mode 100644 index 0000000..12606ed --- /dev/null +++ b/src/c2a/sources/__init__.py @@ -0,0 +1 @@ +"""Data sources: store registry, compliance gate and per-platform ingesters.""" diff --git a/src/c2a/sources/compliance.py b/src/c2a/sources/compliance.py new file mode 100644 index 0000000..829a60b --- /dev/null +++ b/src/c2a/sources/compliance.py @@ -0,0 +1,160 @@ +"""Crawl compliance: ToS gate, robots.txt and per-domain rate limiting. + +Default-deny: a store is crawlable only if its ToS status is approved AND robots.txt +allows the path for our user agent. Unknown/unreachable robots.txt denies. +""" + +from __future__ import annotations + +import threading +import time +from urllib import robotparser +from urllib.parse import urlparse + +import httpx + +from c2a.schemas import Store, TosStatus + +USER_AGENT = "crawl2action-bot/0.1 (+https://github.com/umaitech/crawl2action)" + + +def parse_robots(text: str) -> robotparser.RobotFileParser: + rp = robotparser.RobotFileParser() + rp.parse(text.splitlines()) + return rp + + +def fetch_robots( + domain: str, client: httpx.Client | None = None +) -> robotparser.RobotFileParser | None: + own = client is None + client = client or httpx.Client(timeout=10, headers={"User-Agent": USER_AGENT}) + try: + resp = client.get(f"https://{domain}/robots.txt", follow_redirects=True) + except httpx.HTTPError: + return None + finally: + if own: + client.close() + if resp.status_code == 404: + return parse_robots("") # no robots.txt: allowed by convention + if resp.status_code != 200: + return None + return parse_robots(resp.text) + + +def robots_allows(rp: robotparser.RobotFileParser | None, url: str) -> bool: + return rp is not None and rp.can_fetch(USER_AGENT, url) + + +def _store_gate(store: Store) -> tuple[bool, str]: + if store.tos_status is not TosStatus.approved: + return False, f"tos_status={store.tos_status.value}" + if not store.enabled: + return False, "store disabled" + return True, "ok" + + +def can_crawl(store: Store, url: str, rp: robotparser.RobotFileParser | None) -> tuple[bool, str]: + ok, why = _store_gate(store) + if not ok: + return ok, why + if urlparse(url).netloc != store.domain: + return False, "url outside store domain" + if not robots_allows(rp, url): + return False, "robots.txt disallows (or unavailable)" + return True, "ok" + + +class RateLimiter: + """Simple per-domain minimum-interval limiter (thread-safe).""" + + def __init__(self, qps: float, clock=time.monotonic, sleep=time.sleep) -> None: + if qps <= 0: + raise ValueError("qps must be > 0") + self.interval = 1.0 / qps + self._next: dict[str, float] = {} + self._lock = threading.Lock() + self._clock, self._sleep = clock, sleep + + def wait(self, domain: str) -> None: + with self._lock: + now = self._clock() + start = max(now, self._next.get(domain, now)) + self._next[domain] = start + self.interval + if start > now: + self._sleep(start - now) + + +class CrawlSession: + """The only way fetchers touch a store: every URL passes `can_crawl` and the rate limiter. + + robots.txt is fetched once per session (unavailable robots -> everything denied). + """ + + def __init__( + self, + store: Store, + client: httpx.Client | None = None, + limiter: RateLimiter | None = None, + robots: robotparser.RobotFileParser | None = None, + max_retries: int = 3, + sleep=time.sleep, + ) -> None: + self.store = store + self.max_retries = max_retries + self._sleep = sleep + self.retries = 0 + self.warnings: list[str] = [] + self.client = client or httpx.Client( + timeout=20, headers={"User-Agent": USER_AGENT}, follow_redirects=True + ) + self.limiter = limiter or RateLimiter(qps=1.0) + self.robots = robots if robots is not None else fetch_robots(store.domain, self.client) + self.denied: list[tuple[str, str]] = [] + # Hosts of APIs the store itself advertises for programmatic access (UCP profile). + # They still need the store's ToS approval, but not a robots.txt entry. + self.api_hosts: set[str] = set() + + def allow_api_host(self, url: str) -> None: + self.api_hosts.add(urlparse(url).netloc) + + def allowed(self, url: str) -> bool: + if urlparse(url).netloc in self.api_hosts: + ok, why = _store_gate(self.store) + else: + ok, why = can_crawl(self.store, url, self.robots) + if not ok: + self.denied.append((url, why)) + return ok + + def _request(self, method: str, url: str, **kwargs) -> httpx.Response: + if not self.allowed(url): + raise PermissionError(f"crawl not allowed: {url} ({self.denied[-1][1]})") + for attempt in range(self.max_retries + 1): + self.limiter.wait(self.store.domain) + resp = self.client.request(method, url, **kwargs) + if resp.status_code not in RETRY_STATUS or attempt == self.max_retries: + return resp + self.retries += 1 + self._sleep(_retry_after(resp, default=2.0**attempt)) + raise AssertionError("unreachable") + + def get(self, url: str, **kwargs) -> httpx.Response: + return self._request("GET", url, **kwargs) + + def post(self, url: str, **kwargs) -> httpx.Response: + """For UCP catalog APIs (same gates as GET).""" + return self._request("POST", url, **kwargs) + + +RETRY_STATUS = {429, 500, 502, 503, 504} +MAX_RETRY_AFTER = 60.0 + + +def _retry_after(resp: httpx.Response, default: float) -> float: + """Seconds from a numeric Retry-After header (capped), else the default backoff.""" + try: + return min(float(resp.headers.get("Retry-After", "")), MAX_RETRY_AFTER) + except ValueError: + return default diff --git a/src/c2a/sources/crawl.py b/src/c2a/sources/crawl.py new file mode 100644 index 0000000..af7b423 --- /dev/null +++ b/src/c2a/sources/crawl.py @@ -0,0 +1,154 @@ +"""Crawl orchestration over the registry: default-deny, per-method dispatch, incremental +output under data/raw/{store_id}/ (products.jsonl, hashes.json, manifest.json).""" + +from __future__ import annotations + +import hashlib +import json +from collections.abc import Callable, Sequence +from dataclasses import asdict, dataclass, field +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +import httpx + +from c2a.decide.base import Decider +from c2a.schemas import Product, Store +from c2a.sources import firecrawl as fc +from c2a.sources import shopify, ucp +from c2a.sources.compliance import CrawlSession, RateLimiter, can_crawl +from c2a.sources.registry import Registry +from c2a.train.data import read_jsonl, write_jsonl + +SessionFactory = Callable[[Store], CrawlSession] + + +def content_hash(p: Product) -> str: + """Hash of the fields that matter for training; ignores crawl timestamps.""" + data = p.model_dump(mode="json", exclude={"crawled_at"}) + return hashlib.sha256(json.dumps(data, sort_keys=True).encode()).hexdigest() + + +@dataclass +class StoreManifest: + store_id: str + method: str + started_at: str + finished_at: str = "" + products_total: int = 0 + products_new_or_changed: int = 0 + products_unchanged: int = 0 + errors: list[str] = field(default_factory=list) + firecrawl_credits: int = 0 + jev_requests: int = 0 + denied_urls: int = 0 + retries: int = 0 + warnings: list[str] = field(default_factory=list) + budget_exhausted: bool = False + + +def plan(registry: Registry, store_ids: Sequence[str] | None = None) -> list[tuple[Store, str]]: + """(store, reason) for every selected store; reason 'ok' means it will be crawled.""" + stores = [registry.by_id(s) for s in store_ids] if store_ids else registry.stores + out = [] + for s in stores: + ok, why = can_crawl(s, f"https://{s.domain}/", _AllowAll()) + out.append((s, "ok" if ok else why)) + return out + + +class _AllowAll: + """Robots placeholder for planning (robots is checked per URL during the real crawl).""" + + def can_fetch(self, *_: Any) -> bool: + return True + + +def _default_session(store: Store, qps: float) -> CrawlSession: + return CrawlSession(store, limiter=RateLimiter(qps=qps)) + + +def crawl_one( + store: Store, + out_dir: Path, + session: CrawlSession, + firecrawl: fc.FirecrawlClient | None = None, + decider: Decider | None = None, + max_products: int | None = None, + seed_queries: Sequence[str] = (), +) -> tuple[StoreManifest, list[Product]]: + """Crawl one store; return its manifest and the new/changed products.""" + m = StoreManifest(store.id, store.method, datetime.now(UTC).isoformat()) + products: list[Product] = [] + try: + if store.method == "shopify": + products, m.errors = shopify.crawl(session, max_products=max_products) + elif store.method == "ucp": + profile = ucp.discover(session) + if profile is None: + raise ValueError("no UCP REST catalog advertised at /.well-known/ucp") + queries = list(seed_queries) or store.verticals or ["*"] + products, m.errors = ucp.iter_catalog(session, profile, queries, max_products) + elif store.method == "firecrawl": + if firecrawl is None: + raise ValueError("Firecrawl client not configured (FIRECRAWL_API_KEY)") + res = fc.crawl_store(session, firecrawl, decider, max_products=max_products) + products, m.errors = res.products, res.errors + m.firecrawl_credits, m.jev_requests = res.credits_spent, res.jev_requests + m.budget_exhausted = res.budget_exhausted + else: + raise ValueError(f"unknown method '{store.method}'") + except (PermissionError, ValueError, RuntimeError, httpx.HTTPError) as exc: + m.errors.append(f"{type(exc).__name__}: {exc}") + m.denied_urls = len(session.denied) + m.retries = session.retries + m.warnings = list(session.warnings) + changed = _write_incremental(out_dir / store.id, products, m) + m.finished_at = datetime.now(UTC).isoformat() + (out_dir / store.id).mkdir(parents=True, exist_ok=True) + (out_dir / store.id / "manifest.json").write_text(json.dumps(asdict(m), indent=2)) + return m, changed + + +def _write_incremental(store_dir: Path, products: list[Product], m: StoreManifest) -> list[Product]: + store_dir.mkdir(parents=True, exist_ok=True) + hashes_path = store_dir / "hashes.json" + products_path = store_dir / "products.jsonl" + old_hashes: dict[str, str] = json.loads(hashes_path.read_text()) if hashes_path.exists() else {} + existing: dict[str, Product] = ( + {p.id: p for p in read_jsonl(products_path, Product)} if products_path.exists() else {} + ) + changed = [] + for p in products: + h = content_hash(p) + if old_hashes.get(p.id) == h and p.id in existing: + m.products_unchanged += 1 + continue + old_hashes[p.id] = h + existing[p.id] = p + changed.append(p) + m.products_new_or_changed = len(changed) + m.products_total = len(existing) + write_jsonl(products_path, existing.values()) + hashes_path.write_text(json.dumps(old_hashes, indent=0, sort_keys=True)) + return changed + + +def crawl_registry( + registry: Registry, + out_dir: Path, + store_ids: Sequence[str] | None = None, + firecrawl: fc.FirecrawlClient | None = None, + decider: Decider | None = None, + max_products: int | None = None, + qps: float = 1.0, + session_factory: SessionFactory | None = None, +) -> list[tuple[StoreManifest, list[Product]]]: + results = [] + for store, reason in plan(registry, store_ids): + if reason != "ok": + continue + session = (session_factory or (lambda s: _default_session(s, qps)))(store) + results.append(crawl_one(store, out_dir, session, firecrawl, decider, max_products)) + return results diff --git a/src/c2a/sources/discover.py b/src/c2a/sources/discover.py new file mode 100644 index 0000000..0b81f93 --- /dev/null +++ b/src/c2a/sources/discover.py @@ -0,0 +1,146 @@ +"""Store discovery: platform fingerprinting and candidate expansion via Firecrawl search. + +Fingerprinting only reads public, lightweight endpoints (robots-checked) and may only update +descriptive registry fields (platform/method). Expansion writes *candidates* for human +review; it never edits the registry and never approves anything. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from urllib.parse import urlparse + +import httpx + +from c2a.schemas import Store, Tier, TosStatus +from c2a.sources.compliance import USER_AGENT, fetch_robots, robots_allows +from c2a.sources.firecrawl import FirecrawlClient +from c2a.sources.ucp import WELL_KNOWN_PATH, parse_profile + +METHOD_FOR_PLATFORM = {"ucp": "ucp", "shopify": "shopify"} # everything else -> firecrawl + +# Localized search phrasing per country (discovery queries only). +QUERY_TEMPLATES = { + "SE": "bästa nätbutik {category} Sverige", + "GB": "best online shop {category} UK", + "ES": "mejor tienda online {category} España", + "US": "best online store {category} USA", + "CA": "best online store {category} Canada", +} +LOCALE_FOR_COUNTRY = {"SE": "sv-SE", "GB": "en-GB", "ES": "es-ES", "US": "en-US", "CA": "en-CA"} +EXCLUDE_DOMAINS = re.compile( + r"(amazon|ebay|google|facebook|instagram|youtube|reddit|wikipedia|pinterest|tiktok|" + r"trustpilot|pricerunner|prisjakt|idealo)\.", + re.I, +) + + +@dataclass +class Fingerprint: + domain: str + platform: str # ucp | shopify | woocommerce | custom | unknown + method: str # ucp | shopify | firecrawl + evidence: str + currency: str | None = None # Shopify /meta.json currency when available + + +def _get(client: httpx.Client, url: str) -> httpx.Response | None: + try: + return client.get(url, follow_redirects=True) + except httpx.HTTPError: + return None + + +def fingerprint(domain: str, client: httpx.Client | None = None) -> Fingerprint: + own = client is None + client = client or httpx.Client(timeout=15, headers={"User-Agent": USER_AGENT}) + try: + rp = fetch_robots(domain, client) + base = f"https://{domain}" + resp = _get(client, base + WELL_KNOWN_PATH) + if resp is not None and resp.status_code == 200: + try: + if parse_profile(resp.json()) is not None: + return Fingerprint(domain, "ucp", "ucp", WELL_KNOWN_PATH) + except ValueError: + pass + if robots_allows(rp, base + "/products.json"): + resp = _get(client, base + "/products.json?limit=1") + if resp is not None and resp.status_code == 200: + try: + if "products" in resp.json(): + return Fingerprint( + domain, + "shopify", + "shopify", + "/products.json", + _shopify_currency(client, base, rp), + ) + except ValueError: + pass + if not robots_allows(rp, base + "/"): + return Fingerprint(domain, "unknown", "firecrawl", "robots disallows homepage") + resp = _get(client, base + "/") + if resp is None or resp.status_code != 200: + return Fingerprint(domain, "unknown", "firecrawl", "homepage unreachable") + html = resp.text.lower() + if "cdn.shopify.com" in html or "shopify.theme" in html: + return Fingerprint( + domain, "shopify", "firecrawl", "shopify markers; products.json closed" + ) + if "woocommerce" in html or "wp-content" in html: + return Fingerprint(domain, "woocommerce", "firecrawl", "woocommerce markers") + return Fingerprint(domain, "custom", "firecrawl", "no known platform markers") + finally: + if own: + client.close() + + +def _shopify_currency(client: httpx.Client, base: str, rp) -> str | None: + if not robots_allows(rp, base + "/meta.json"): + return None + resp = _get(client, base + "/meta.json") + if resp is None or resp.status_code != 200: + return None + try: + return resp.json().get("currency") or None + except ValueError: + return None + + +def _domain(url: str) -> str: + return urlparse(url).netloc.lower() + + +def expand( + country: str, + category: str, + firecrawl: FirecrawlClient, + known_domains: set[str], + limit: int = 20, +) -> list[Store]: + """Candidate stores from web search. Always tier B, unreviewed and disabled.""" + template = QUERY_TEMPLATES.get(country.upper()) + if template is None: + raise ValueError(f"no query template for country {country}") + results = firecrawl.search(template.format(category=category), limit=limit) + out: dict[str, Store] = {} + for r in results: + domain = _domain(r["url"]) + if not domain or domain in known_domains or domain in out or EXCLUDE_DOMAINS.search(domain): + continue + slug = re.sub(r"[^a-z0-9]+", "", domain.removeprefix("www.").split(".")[0]) + out[domain] = Store( + id=slug or domain, + name=(r.get("title") or domain)[:80], + domain=domain, + country=country.upper(), + locale=LOCALE_FOR_COUNTRY[country.upper()], + tier=Tier.B, + tos_status=TosStatus.unreviewed, + enabled=False, + verticals=[category], + notes=f"discovered via search: {r.get('description', '')[:160]}", + ) + return list(out.values()) diff --git a/src/c2a/sources/firecrawl.py b/src/c2a/sources/firecrawl.py new file mode 100644 index 0000000..5d74b6b --- /dev/null +++ b/src/c2a/sources/firecrawl.py @@ -0,0 +1,310 @@ +"""Firecrawl v2 ingestion (tier B): /map -> compliance filter -> URL heuristic -> +Jev page gate (markdown scrape) -> JSON extract for product pages only. + +Uses the REST API directly (httpx) so it is easy to mock and has no SDK dependency. +Every URL goes through `CrawlSession.allowed` (ToS + robots + domain) before Firecrawl +fetches it; Firecrawl's own request rate is bounded by the session's rate limiter. +""" + +from __future__ import annotations + +import os +import re +import time +from collections.abc import Callable +from dataclasses import dataclass, field +from datetime import UTC, datetime +from decimal import Decimal, InvalidOperation +from typing import Any + +import httpx +from pydantic import BaseModel, Field + +from c2a.decide.base import Decider +from c2a.labeling.gating import Thresholds, load_thresholds +from c2a.labeling.pipeline import label +from c2a.labeling.questions import page_v1 +from c2a.labeling.records import LabelItem +from c2a.labeling.state import page_state +from c2a.normalize import to_minor_units +from c2a.schemas import Money, Product, Store, Variant +from c2a.sources.compliance import CrawlSession + +FIRECRAWL_BASE_URL = "https://api.firecrawl.dev" +RETRY_STATUS = {429, 500, 502, 503, 504} +# Approximate credit costs (Firecrawl pricing); used for budgeting, not billing. +CREDITS = {"map": 1, "scrape_markdown": 1, "scrape_json": 5, "search": 2} + + +class BudgetExceeded(RuntimeError): + pass + + +class FirecrawlClient: + def __init__( + self, + api_key: str | None = None, + base_url: str = FIRECRAWL_BASE_URL, + credit_budget: int = 5000, + timeout: float = 60.0, + max_retries: int = 3, + sleep: Callable[[float], None] = time.sleep, + ) -> None: + api_key = api_key if api_key is not None else os.environ.get("FIRECRAWL_API_KEY") + if not api_key: + raise RuntimeError("FIRECRAWL_API_KEY is not set") + self.credit_budget = credit_budget + self.credits_spent = 0 + self.max_retries = max_retries + self._sleep = sleep + self._client = httpx.Client( + base_url=base_url.rstrip("/"), + timeout=timeout, + headers={"Authorization": f"Bearer {api_key}"}, + ) + + def _spend(self, op: str) -> None: + cost = CREDITS[op] + if self.credits_spent + cost > self.credit_budget: + raise BudgetExceeded(f"Firecrawl credit budget {self.credit_budget} exhausted") + self.credits_spent += cost + + def _post(self, path: str, body: dict[str, Any], op: str) -> dict[str, Any]: + self._spend(op) + for attempt in range(self.max_retries + 1): + resp = self._client.post(path, json=body) + if resp.status_code not in RETRY_STATUS or attempt == self.max_retries: + resp.raise_for_status() + data = resp.json() + if data.get("success") is False: + raise RuntimeError(f"firecrawl {path} failed: {data.get('error')}") + return data + self._sleep(2**attempt) + raise AssertionError("unreachable") + + def map(self, url: str, limit: int = 5000) -> list[str]: + data = self._post( + "/v2/map", + {"url": url, "limit": limit, "sitemap": "include", "ignoreQueryParameters": True}, + "map", + ) + links = data.get("links") or [] + return [link["url"] if isinstance(link, dict) else link for link in links] + + def scrape_markdown(self, url: str) -> str: + data = self._post( + "/v2/scrape", + {"url": url, "formats": ["markdown"], "onlyMainContent": True}, + "scrape_markdown", + ) + return (data.get("data") or {}).get("markdown") or "" + + def scrape_json(self, url: str, schema: dict[str, Any]) -> dict[str, Any] | None: + data = self._post( + "/v2/scrape", + {"url": url, "formats": [{"type": "json", "schema": schema}], "onlyMainContent": True}, + "scrape_json", + ) + return (data.get("data") or {}).get("json") + + def search(self, query: str, limit: int = 20) -> list[dict[str, Any]]: + """Web search results as [{url, title, description}]. Accepts both documented shapes + (data.web[] and data[]) since the exact v2 shape is unconfirmed here.""" + data = self._post("/v2/search", {"query": query, "limit": limit}, "search") + payload = data.get("data") + if isinstance(payload, dict): + payload = payload.get("web") or [] + return [r for r in payload or [] if isinstance(r, dict) and r.get("url")] + + +# ---------- extraction schema ---------- + + +class ExtractedVariant(BaseModel): + title: str = "" + sku: str | None = None + price: str | float | None = None + available: bool | None = None + options: dict[str, str] = Field(default_factory=dict) + + +class ExtractedProduct(BaseModel): + """What we ask Firecrawl JSON mode to extract from a product page.""" + + title: str + description: str = "" + brand: str | None = None + currency: str | None = Field(default=None, description="ISO 4217 code, e.g. SEK, GBP") + price: str | float | None = Field(default=None, description="Current price as shown") + images: list[str] = Field(default_factory=list) + variants: list[ExtractedVariant] = Field(default_factory=list) + attributes: dict[str, str] = Field(default_factory=dict) + breadcrumbs: list[str] = Field(default_factory=list) + sku: str | None = None + + +_NUM_RE = re.compile(r"\d[\d\s.,]*") + + +def parse_price(text: str | float | None) -> Decimal | None: + """'1 299,00 kr' -> 1299.00 ; '£19.99' -> 19.99 ; '1,299.50' -> 1299.50.""" + if text is None: + return None + if isinstance(text, int | float): + return Decimal(str(text)) + m = _NUM_RE.search(text) + if not m: + return None + s = m.group().replace(" ", "").replace(" ", "").rstrip(".,") + if "," in s and "." in s: + s = ( + s.replace(",", "") + if s.rfind(".") > s.rfind(",") + else s.replace(".", "").replace(",", ".") + ) + elif "," in s: + head, _, tail = s.rpartition(",") + s = f"{head.replace(',', '')}.{tail}" if len(tail) in (1, 2) else s.replace(",", "") + try: + return Decimal(s) + except InvalidOperation: + return None + + +def to_product(ex: ExtractedProduct, url: str, store: Store) -> Product: + currency = (ex.currency or store.currency or "").upper() + if len(currency) != 3: + raise ValueError("currency unknown") + variants = [] + for i, v in enumerate( + ex.variants or [ExtractedVariant(title=ex.title, sku=ex.sku, price=ex.price)] + ): + amount = parse_price(v.price if v.price is not None else ex.price) + if amount is None: + continue + variants.append( + Variant( + id=v.sku or f"v{i}", + title=v.title, + sku=v.sku, + options=v.options, + price=Money(amount=to_minor_units(amount, currency), currency=currency), + available=True if v.available is None else v.available, + ) + ) + if not variants: + raise ValueError("no parsable price") + native_id = ex.sku or re.sub(r"[^\w-]+", "-", url.split("//", 1)[-1].split("/", 1)[-1]).strip( + "-" + ) + return Product( + id=f"{store.id}:{native_id}", + store_id=store.id, + native_id=native_id, + title=ex.title, + description=ex.description, + brand=ex.brand, + category=" > ".join(ex.breadcrumbs) or None, + attributes=ex.attributes, + url=url, + image_urls=ex.images, + variants=variants, + locale=store.locale, + source="firecrawl", + crawled_at=datetime.now(UTC), + ) + + +# ---------- URL triage ---------- + +PRODUCT_URL_RE = re.compile(r"/products?/|/p/|/dp/|/item/|-p-\d|/produkt/|/producto/", re.I) +SKIP_URL_RE = re.compile( + r"/(blog|news|journal|cart|checkout|account|login|register|search|help|faq|pages|policies|" + r"collections|category|categories|kategori|stores?|careers|about|contact|kontakt)(/|$|\?)" + r"|\.(jpg|jpeg|png|gif|webp|svg|pdf|xml|css|js)$", + re.I, +) + + +def triage_url(url: str) -> str: + """product | skip | ambiguous (ambiguous pages go through the Jev page gate).""" + if PRODUCT_URL_RE.search(url): + return "product" + if SKIP_URL_RE.search(url): + return "skip" + return "ambiguous" + + +@dataclass +class FirecrawlCrawlResult: + products: list[Product] = field(default_factory=list) + errors: list[str] = field(default_factory=list) + urls_mapped: int = 0 + urls_denied: int = 0 + triage: dict[str, int] = field(default_factory=dict) + gated_out: int = 0 + jev_requests: int = 0 + credits_spent: int = 0 + budget_exhausted: bool = False + + +def crawl_store( + session: CrawlSession, + client: FirecrawlClient, + decider: Decider | None, + max_products: int | None = None, + max_urls: int = 5000, + thresholds: Thresholds | None = None, +) -> FirecrawlCrawlResult: + """Without a decider, ambiguous URLs are skipped (never extracted blind).""" + res = FirecrawlCrawlResult() + start_credits = client.credits_spent + schema = ExtractedProduct.model_json_schema() + thresholds = thresholds or load_thresholds() + try: + urls = client.map(f"https://{session.store.domain}", limit=max_urls) + res.urls_mapped = len(urls) + allowed = [u for u in dict.fromkeys(urls) if session.allowed(u)] + res.urls_denied = res.urls_mapped - len(allowed) + buckets: dict[str, list[str]] = {"product": [], "skip": [], "ambiguous": []} + for u in allowed: + buckets[triage_url(u)].append(u) + res.triage = {k: len(v) for k, v in buckets.items()} + + to_extract = list(buckets["product"]) + if decider is not None and buckets["ambiguous"]: + items = [] + for u in buckets["ambiguous"]: + session.limiter.wait(session.store.domain) + items.append(LabelItem(id=u, state=page_state(u, client.scrape_markdown(u)))) + run = label(items, decider, page_v1(), thresholds) + res.jev_requests = run.requests_made + product_pages = { + r.item_id + for r in run.records + if r.question_id == "page_type" + and r.value == "product_detail" + and r.status == "accepted" + } + res.gated_out = len(items) - len(product_pages) + to_extract += [u for u in buckets["ambiguous"] if u in product_pages] + + for u in to_extract: + if max_products is not None and len(res.products) >= max_products: + break + session.limiter.wait(session.store.domain) + raw = client.scrape_json(u, schema) + if not raw: + res.errors.append(f"{u}: empty extraction") + continue + try: + res.products.append( + to_product(ExtractedProduct.model_validate(raw), u, session.store) + ) + except ValueError as exc: + res.errors.append(f"{u}: {exc}") + except BudgetExceeded as exc: + res.budget_exhausted = True + res.errors.append(str(exc)) + res.credits_spent = client.credits_spent - start_credits + return res diff --git a/src/c2a/sources/open_datasets.py b/src/c2a/sources/open_datasets.py new file mode 100644 index 0000000..d7adf41 --- /dev/null +++ b/src/c2a/sources/open_datasets.py @@ -0,0 +1,200 @@ +"""Loaders for open behavioural datasets, reading files the user has downloaded locally. + +Licenses must be checked before use (open item in docs/PLAN.md). Parquet needs the `data` +extra (pyarrow), imported lazily. + +- ESCI / Shopping Queries (Amazon): query-product relevance judged by humans + (Exact/Substitute/Complement/Irrelevant) -> pair_v1 labels (gold + decider training). +- Amazon-M2 (KDD Cup 2023): multilingual sessions (incl. UK, ES) -> next-item ground truth. +- Amazon Reviews 2023: reviews + per-user purchase histories. +- H&M, RetailRocket, Diginetica: M2. +""" + +from __future__ import annotations + +import csv +import json +import re +from collections import defaultdict +from collections.abc import Iterable, Iterator +from pathlib import Path +from typing import Any + +from pydantic import BaseModel + +from c2a import NotYetImplemented +from c2a.labeling.records import LabelItem, LabelRecord +from c2a.schemas import Review + +DATASETS = ( + "esci", + "amazon_m2", + "amazon_reviews_2023", + "hm_fashion", + "retailrocket", + "diginetica", +) +ESCI_TO_PAIR = {"E": "exact", "S": "substitute", "C": "complement", "I": "irrelevant"} +ESCI_LOCALE = {"us": "en-US", "es": "es-ES", "jp": "ja-JP"} +M2_LOCALE = { + "UK": "en-GB", + "DE": "de-DE", + "JP": "ja-JP", + "ES": "es-ES", + "FR": "fr-FR", + "IT": "it-IT", +} + + +def _read_parquet(path: Path | str) -> list[dict[str, Any]]: + try: + import pyarrow.parquet as pq + except ImportError as exc: # pragma: no cover - optional extra + raise RuntimeError("Install the data extra: uv sync --extra data") from exc + return pq.read_table(path).to_pylist() + + +# ---------- ESCI ---------- + + +class EsciExample(BaseModel): + query_id: str + query: str + product_id: str + locale: str + label: str # pair_v1 value + split: str + product: dict[str, Any] + + +def load_esci( + examples_path: Path | str, + products_path: Path | str, + locales: Iterable[str] | None = None, + small_version_only: bool = True, +) -> Iterator[EsciExample]: + wanted = set(locales) if locales else None + products = {(r["product_id"], r["product_locale"]): r for r in _read_parquet(products_path)} + for r in _read_parquet(examples_path): + if small_version_only and not r.get("small_version", 1): + continue + loc = r["product_locale"] + if wanted and loc not in wanted: + continue + prod = products.get((r["product_id"], loc), {}) + yield EsciExample( + query_id=str(r["query_id"]), + query=r["query"], + product_id=r["product_id"], + locale=ESCI_LOCALE.get(loc, loc), + label=ESCI_TO_PAIR[r["esci_label"]], + split=r.get("split", "train"), + product={ + k.removeprefix("product_"): v + for k, v in prod.items() + if k + in ( + "product_title", + "product_brand", + "product_color", + "product_description", + "product_bullet_point", + ) + and v + }, + ) + + +def esci_to_labels(examples: Iterable[EsciExample]) -> tuple[list[LabelItem], list[LabelRecord]]: + """ESCI judgements are human labels -> pair@1 records with source='human'.""" + items, records = [], [] + for ex in examples: + item_id = f"esci:{ex.locale}:{ex.query_id}:{ex.product_id}" + items.append( + LabelItem(id=item_id, state={"anchor": {"query": ex.query}, "candidate": ex.product}) + ) + records.append( + LabelRecord( + item_id=item_id, + qset="pair@1", + question_id="relation", + qtype="choice", + value=ex.label, + probabilities={ex.label: 1.0}, + confidence=1.0, + source="human", + model="esci", + status="accepted", + reviewer="amazon-esci", + ) + ) + return items, records + + +# ---------- Amazon-M2 ---------- + + +class Session(BaseModel): + prev_items: list[str] + next_item: str | None + locale: str + + +_ITEM_RE = re.compile(r"'([^']+)'") + + +def load_amazon_m2( + sessions_csv: Path | str, locales: Iterable[str] | None = None +) -> Iterator[Session]: + """sessions_*.csv columns: prev_items ("['A' 'B']"), next_item (train only), locale.""" + wanted = set(locales) if locales else None + with Path(sessions_csv).open(newline="") as f: + for row in csv.DictReader(f): + if wanted and row["locale"] not in wanted: + continue + yield Session( + prev_items=_ITEM_RE.findall(row["prev_items"]), + next_item=row.get("next_item") or None, + locale=M2_LOCALE.get(row["locale"], row["locale"]), + ) + + +# ---------- Amazon Reviews 2023 ---------- + + +def load_amazon_reviews(reviews_jsonl: Path | str) -> Iterator[Review]: + with Path(reviews_jsonl).open() as f: + for line in f: + if not line.strip(): + continue + r = json.loads(line) + yield Review( + product_id=f"amazon:{r.get('parent_asin') or r['asin']}", + rating=r.get("rating"), + text=" ".join(x for x in (r.get("title"), r.get("text")) if x), + locale="en-US", + ) + + +def user_histories(reviews_jsonl: Path | str, verified_only: bool = True) -> dict[str, list[str]]: + """user_id -> parent_asins in time order (a proxy for purchase sequences).""" + events: dict[str, list[tuple[int, str]]] = defaultdict(list) + with Path(reviews_jsonl).open() as f: + for line in f: + if not line.strip(): + continue + r = json.loads(line) + if verified_only and not r.get("verified_purchase"): + continue + events[r["user_id"]].append( + (int(r.get("timestamp", 0)), r.get("parent_asin") or r["asin"]) + ) + return {u: [a for _, a in sorted(ev)] for u, ev in events.items()} + + +def load(name: str, split: str = "train"): + if name not in DATASETS: + raise KeyError(name) + raise NotYetImplemented( + f"open dataset loader '{name}' (use the specific load_* function)", "M2" + ) diff --git a/src/c2a/sources/registry.py b/src/c2a/sources/registry.py new file mode 100644 index 0000000..86d06f4 --- /dev/null +++ b/src/c2a/sources/registry.py @@ -0,0 +1,78 @@ +"""Load, validate, update and save the store registry. + +Approval (tos_status + enabled) is a human-only action via `approve`/`deny`; automated code +(discovery, fingerprinting) may only update descriptive fields such as platform/method. +""" + +from __future__ import annotations + +from collections import Counter +from datetime import UTC, datetime +from pathlib import Path + +import yaml +from pydantic import BaseModel + +from c2a.schemas import Store, TosStatus + +DEFAULT_REGISTRY = Path(__file__).with_name("registry.yaml") +AUTOMATABLE_FIELDS = frozenset({"platform", "method", "currency"}) + + +class Registry(BaseModel): + version: int = 1 + notes: str = "" + stores: list[Store] + + def by_id(self, store_id: str) -> Store: + for s in self.stores: + if s.id == store_id: + return s + raise KeyError(store_id) + + def crawlable(self) -> list[Store]: + """Stores that are both enabled and ToS-approved. Enabled-but-unreviewed never crawls.""" + return [s for s in self.stores if s.enabled and s.tos_status is TosStatus.approved] + + def approve(self, store_id: str, by: str, note: str = "") -> Store: + if not by.strip(): + raise ValueError("approval requires the reviewer's name (--by)") + s = self.by_id(store_id) + s.tos_status, s.enabled = TosStatus.approved, True + s.reviewed_by, s.reviewed_at, s.review_note = by, datetime.now(UTC), note or None + return s + + def deny(self, store_id: str, by: str, note: str = "") -> Store: + if not by.strip(): + raise ValueError("denial requires the reviewer's name (--by)") + s = self.by_id(store_id) + s.tos_status, s.enabled = TosStatus.denied, False + s.reviewed_by, s.reviewed_at, s.review_note = by, datetime.now(UTC), note or None + return s + + def update_descriptive(self, store_id: str, **fields: str) -> Store: + """Automated updates (fingerprinting). Refuses anything that affects crawl permission.""" + bad = set(fields) - AUTOMATABLE_FIELDS + if bad: + raise ValueError(f"not automatable: {sorted(bad)}") + s = self.by_id(store_id) + for k, v in fields.items(): + setattr(s, k, v) + return s + + +def load_registry(path: Path | str | None = None) -> Registry: + data = yaml.safe_load(Path(path or DEFAULT_REGISTRY).read_text()) + reg = Registry.model_validate(data) + dupes = [k for k, n in Counter(s.id for s in reg.stores).items() if n > 1] + if dupes: + raise ValueError(f"duplicate store ids: {dupes}") + return reg + + +def save_registry(reg: Registry, path: Path | str | None = None) -> Path: + path = Path(path or DEFAULT_REGISTRY) + data = reg.model_dump(mode="json", exclude_none=True) + header = "# Store registry for crawl2action. See docs/PLAN.md section 1.\n" + path.write_text(header + yaml.safe_dump(data, sort_keys=False, allow_unicode=True)) + return path diff --git a/src/c2a/sources/registry.yaml b/src/c2a/sources/registry.yaml new file mode 100644 index 0000000..72d894e --- /dev/null +++ b/src/c2a/sources/registry.yaml @@ -0,0 +1,369 @@ +# Store registry for crawl2action. See docs/PLAN.md section 1. +version: 1 +notes: 'Seed candidates only. Platform/UCP are verified by `c2a discover` (M1); tos_status + must be set to ''approved'' after legal review before enabled: true takes effect.' +stores: +- id: happysocks + name: Happy Socks + domain: www.happysocks.com + country: SE + locale: sv-SE + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: djerfavenue + name: Djerf Avenue + domain: djerfavenue.com + country: SE + locale: sv-SE + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: cdlp + name: CDLP + domain: cdlp.com + country: SE + locale: sv-SE + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: apotea + name: Apotea + domain: www.apotea.se + country: SE + locale: sv-SE + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - health + - beauty +- id: lyko + name: Lyko + domain: lyko.com + country: SE + locale: sv-SE + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - beauty +- id: elgiganten + name: Elgiganten + domain: www.elgiganten.se + country: SE + locale: sv-SE + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - electronics +- id: cdon + name: CDON + domain: cdon.se + country: SE + locale: sv-SE + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - marketplace +- id: gymshark + name: Gymshark + domain: www.gymshark.com + country: GB + locale: en-GB + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: represent + name: Represent + domain: representclo.com + country: GB + locale: en-GB + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: huel + name: Huel + domain: huel.com + country: GB + locale: en-GB + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - grocery + - health +- id: oliverbonas + name: Oliver Bonas + domain: www.oliverbonas.com + country: GB + locale: en-GB + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - home + - apparel +- id: argos + name: Argos + domain: www.argos.co.uk + country: GB + locale: en-GB + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - marketplace +- id: johnlewis + name: John Lewis + domain: www.johnlewis.com + country: GB + locale: en-GB + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - department +- id: hawkers + name: Hawkers + domain: www.hawkersco.com + country: ES + locale: es-ES + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: scalpers + name: Scalpers + domain: scalperscompany.com + country: ES + locale: es-ES + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: mrwonderful + name: Mr Wonderful + domain: www.mrwonderfulshop.es + country: ES + locale: es-ES + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - home + - gifts +- id: pccomponentes + name: PcComponentes + domain: www.pccomponentes.com + country: ES + locale: es-ES + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - electronics +- id: elcorteingles + name: El Corte Inglés + domain: www.elcorteingles.es + country: ES + locale: es-ES + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - department +- id: allbirds + name: Allbirds + domain: www.allbirds.com + country: US + locale: en-US + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: glossier + name: Glossier + domain: www.glossier.com + country: US + locale: en-US + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - beauty +- id: brooklinen + name: Brooklinen + domain: www.brooklinen.com + country: US + locale: en-US + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - home +- id: bombas + name: Bombas + domain: bombas.com + country: US + locale: en-US + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: bestbuy + name: Best Buy + domain: www.bestbuy.com + country: US + locale: en-US + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - electronics +- id: target + name: Target + domain: www.target.com + country: US + locale: en-US + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - department +- id: kotn + name: Kotn + domain: kotn.com + country: CA + locale: en-CA + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: frankandoak + name: Frank And Oak + domain: www.frankandoak.com + country: CA + locale: en-CA + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: knix + name: Knix + domain: knix.com + country: CA + locale: en-CA + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: tentree + name: Tentree + domain: www.tentree.com + country: CA + locale: en-CA + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: peacecollective + name: Peace Collective + domain: peacecollective.ca + country: CA + locale: en-CA + platform: unknown + tier: B + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - apparel +- id: canadiantire + name: Canadian Tire + domain: www.canadiantire.ca + country: CA + locale: en-CA + platform: unknown + tier: C + tos_status: unreviewed + method: firecrawl + enabled: false + verticals: + - department diff --git a/src/c2a/sources/shopify.py b/src/c2a/sources/shopify.py new file mode 100644 index 0000000..52166a4 --- /dev/null +++ b/src/c2a/sources/shopify.py @@ -0,0 +1,129 @@ +"""Shopify storefront `/products.json` ingestion (tier A: structured, no LLM extraction).""" + +from __future__ import annotations + +from datetime import UTC, datetime +from decimal import Decimal +from typing import Any + +from c2a.normalize import to_minor_units +from c2a.schemas import Money, Product, Store, Variant +from c2a.sources.compliance import CrawlSession + + +def parse_product(raw: dict[str, Any], store: Store, currency: str) -> Product: + variants = [ + Variant( + id=str(v["id"]), + title=v.get("title") or "", + sku=v.get("sku") or None, + options={ + opt["name"]: v.get(f"option{i + 1}") + for i, opt in enumerate(raw.get("options", [])) + if v.get(f"option{i + 1}") is not None + }, + price=Money( + amount=to_minor_units(Decimal(str(v["price"])), currency), currency=currency + ), + compare_at_price=( + Money( + amount=to_minor_units(Decimal(str(v["compare_at_price"])), currency), + currency=currency, + ) + if v.get("compare_at_price") + else None + ), + available=bool(v.get("available", True)), + ) + for v in raw.get("variants", []) + ] + tags = raw.get("tags") or [] + if isinstance(tags, str): + tags = [t.strip() for t in tags.split(",") if t.strip()] + return Product( + id=f"{store.id}:{raw['id']}", + store_id=store.id, + native_id=str(raw["id"]), + title=raw["title"], + description=raw.get("body_html") or "", + brand=raw.get("vendor") or None, + category=raw.get("product_type") or None, + tags=tags, + url=f"https://{store.domain}/products/{raw['handle']}" if raw.get("handle") else None, + image_urls=[img["src"] for img in raw.get("images", []) if img.get("src")], + variants=variants, + locale=store.locale, + source="shopify", + crawled_at=datetime.now(UTC), + ) + + +def parse_products_json( + payload: dict[str, Any], store: Store, currency: str +) -> tuple[list[Product], list[str]]: + """Return (products, errors). Products without variants are skipped with an error.""" + products, errors = [], [] + for raw in payload.get("products", []): + try: + products.append(parse_product(raw, store, currency)) + except Exception as exc: # collect and continue + errors.append(f"{raw.get('id')}: {exc}") + return products, errors + + +COUNTRY_CURRENCY = {"SE": "SEK", "GB": "GBP", "ES": "EUR", "US": "USD", "CA": "CAD"} + + +def shop_currency(session: CrawlSession) -> str: + """Registry currency, else Shopify's public /meta.json, else the country's currency + (recorded as a warning because multi-currency shops may differ).""" + if session.store.currency: + return session.store.currency + meta_url = f"https://{session.store.domain}/meta.json" + if session.allowed(meta_url): + resp = session.get(meta_url) + if resp.status_code == 200: + try: + currency = resp.json().get("currency") + except ValueError: + currency = None + if currency: + return currency + fallback = COUNTRY_CURRENCY.get(session.store.country) + if not fallback: + raise ValueError(f"{session.store.id}: currency unknown; set `currency` in the registry") + session.warnings.append(f"currency not found; assumed {fallback} from country") + return fallback + + +def crawl( + session: CrawlSession, max_products: int | None = None, limit: int = 250, max_pages: int = 100 +) -> tuple[list[Product], list[str]]: + """Crawl a Shopify storefront through the compliance session. + + Stops on an empty page, a short page (fewer than `limit` products) or a page identical + to the previous one (some themes ignore `page`). + """ + currency = shop_currency(session) + products: list[Product] = [] + errors: list[str] = [] + prev_ids: list | None = None + for page in range(1, max_pages + 1): + resp = session.get( + f"https://{session.store.domain}/products.json", + params={"limit": limit, "page": page}, + ) + resp.raise_for_status() + raw = resp.json().get("products") or [] + ids = [p.get("id") for p in raw] + if not raw or ids == prev_ids: + break + prev_ids = ids + got, errs = parse_products_json({"products": raw}, session.store, currency) + products += got + errors += errs + if max_products is not None and len(products) >= max_products: + return products[:max_products], errors + if len(raw) < limit: + break + return products, errors diff --git a/src/c2a/sources/ucp.py b/src/c2a/sources/ucp.py new file mode 100644 index 0000000..088c8f7 --- /dev/null +++ b/src/c2a/sources/ucp.py @@ -0,0 +1,160 @@ +"""UCP (Universal Commerce Protocol) catalog client, REST binding (spec version 2026-01-01). + +Discovery: GET https:///.well-known/ucp -> profile advertising the shopping service +endpoint and catalog capabilities. Catalog: POST {endpoint}/catalog/search (cursor +pagination) and POST {endpoint}/catalog/lookup. Prices are already integer minor units. +Spec: https://ucp.dev/latest/specification/shopping/catalog/ +""" + +from __future__ import annotations + +from collections.abc import Iterable, Iterator +from datetime import UTC, datetime +from typing import Any + +from pydantic import BaseModel + +from c2a.schemas import Money, Product, Store, Variant +from c2a.sources.compliance import CrawlSession + +WELL_KNOWN_PATH = "/.well-known/ucp" +SHOPPING_SERVICE = "dev.ucp.shopping" +CAP_SEARCH = "dev.ucp.shopping.catalog.search" +CAP_LOOKUP = "dev.ucp.shopping.catalog.lookup" + + +class UcpProfile(BaseModel): + version: str + endpoint: str # REST base URL of the shopping service + capabilities: list[str] + + @property + def can_search(self) -> bool: + return CAP_SEARCH in self.capabilities + + @property + def can_lookup(self) -> bool: + return CAP_LOOKUP in self.capabilities + + +def parse_profile(doc: dict[str, Any]) -> UcpProfile | None: + """Return the REST shopping profile, or None if the store offers no REST catalog.""" + ucp = doc.get("ucp") or {} + services = (ucp.get("services") or {}).get(SHOPPING_SERVICE) or [] + rest = next((s for s in services if s.get("transport") == "rest" and s.get("endpoint")), None) + if rest is None: + return None + caps = sorted((ucp.get("capabilities") or {}).keys()) + return UcpProfile( + version=str(ucp.get("version", "")), + endpoint=rest["endpoint"].rstrip("/"), + capabilities=caps, + ) + + +def discover(session: CrawlSession) -> UcpProfile | None: + resp = session.get(f"https://{session.store.domain}{WELL_KNOWN_PATH}") + if resp.status_code != 200: + return None + profile = parse_profile(resp.json()) + if profile is not None: + session.allow_api_host(profile.endpoint) + return profile + + +def _money(raw: dict[str, Any] | None) -> Money | None: + if not raw or "amount" not in raw or "currency" not in raw: + return None + return Money(amount=int(raw["amount"]), currency=raw["currency"]) + + +def parse_ucp_product(raw: dict[str, Any], store: Store) -> Product: + variants = [] + for v in raw.get("variants") or []: + price = _money(v.get("price")) + if price is None: + continue + variants.append( + Variant( + id=str(v["id"]), + title=v.get("title") or "", + sku=v.get("sku") or None, + options={o["name"]: o["label"] for o in v.get("options") or [] if "label" in o}, + price=price, + compare_at_price=_money(v.get("list_price")), + available=bool((v.get("availability") or {}).get("available", True)), + ) + ) + if not variants: # search results may carry only a price range + price = _money((raw.get("price_range") or {}).get("min")) + if price is None: + raise ValueError(f"product {raw.get('id')} has no priced variant") + variants = [Variant(id=str(raw["id"]), price=price)] + categories = raw.get("categories") or [] + merchant_cat = next((c["value"] for c in categories if c.get("taxonomy") == "merchant"), None) + attributes = { + f"category_{c['taxonomy']}": str(c["value"]) + for c in categories + if c.get("taxonomy") and c.get("value") and c.get("taxonomy") != "merchant" + } + tags = sorted({t for v in raw.get("variants") or [] for t in v.get("tags") or []}) + return Product( + id=f"{store.id}:{raw['id']}", + store_id=store.id, + native_id=str(raw["id"]), + title=raw["title"], + description=(raw.get("description") or {}).get("plain") or "", + category=merchant_cat, + tags=tags, + attributes=attributes, + url=raw.get("url"), + image_urls=[m["url"] for m in raw.get("media") or [] if m.get("type") == "image"], + variants=variants, + locale=store.locale, + source="ucp", + crawled_at=datetime.now(UTC), + ) + + +def search_catalog( + session: CrawlSession, profile: UcpProfile, query: str, limit: int = 50, max_pages: int = 20 +) -> Iterator[dict[str, Any]]: + """Yield raw products for one query, following cursor pagination.""" + cursor = None + for _ in range(max_pages): + body: dict[str, Any] = {"query": query, "pagination": {"limit": limit}} + if cursor: + body["pagination"]["cursor"] = cursor + resp = session.post(f"{profile.endpoint}/catalog/search", json=body) + resp.raise_for_status() + data = resp.json() + yield from data.get("products") or [] + page = data.get("pagination") or {} + cursor = page.get("cursor") + if not page.get("has_next_page") or not cursor: + return + + +def iter_catalog( + session: CrawlSession, + profile: UcpProfile, + seed_queries: Iterable[str], + max_products: int | None = None, +) -> tuple[list[Product], list[str]]: + """Enumerate a catalog via seed queries (UCP search needs a query), dedupe by id.""" + if not profile.can_search: + raise ValueError(f"{session.store.id}: UCP profile has no catalog.search capability") + seen: dict[str, Product] = {} + errors: list[str] = [] + for q in seed_queries: + for raw in search_catalog(session, profile, q): + pid = str(raw.get("id")) + if pid in seen: + continue + try: + seen[pid] = parse_ucp_product(raw, session.store) + except (KeyError, ValueError) as exc: + errors.append(f"{pid}: {exc}") + if max_products is not None and len(seen) >= max_products: + return list(seen.values()), errors + return list(seen.values()), errors diff --git a/src/c2a/train/__init__.py b/src/c2a/train/__init__.py new file mode 100644 index 0000000..cdd686e --- /dev/null +++ b/src/c2a/train/__init__.py @@ -0,0 +1 @@ +"""Post-training: data formats, rewards, and training loops (Tinker primary, Modal fallback).""" diff --git a/src/c2a/train/ac2/__init__.py b/src/c2a/train/ac2/__init__.py new file mode 100644 index 0000000..7fedf11 --- /dev/null +++ b/src/c2a/train/ac2/__init__.py @@ -0,0 +1,7 @@ +"""Applied Compute AC2 trainer (added once access is granted; follows ac2-cookbook layout).""" + +from c2a import NotYetImplemented + + +def submit(cfg) -> str: + raise NotYetImplemented("AC2 trainer", "post-M3 (pending AC2 access)") diff --git a/src/c2a/train/base.py b/src/c2a/train/base.py new file mode 100644 index 0000000..96d1cf5 --- /dev/null +++ b/src/c2a/train/base.py @@ -0,0 +1,55 @@ +"""Trainer interface and run configuration shared by all training platforms.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Literal, Protocol + +import yaml +from pydantic import BaseModel, Field + +Stage = Literal["sft", "rl", "opd", "dpo", "genrm"] +Platform = Literal["tinker", "modal", "ac2"] + + +class RunConfig(BaseModel): + name: str + stage: Stage + platform: Platform = "tinker" + base_model: str + dataset: Path + log_dir: Path = Path("runs") + lora_rank: int = Field(default=32, ge=1) + learning_rate: float = Field(default=1e-4, gt=0) + batch_size: int = Field(default=32, ge=1) + steps: int | None = Field(default=None, ge=1) + save_every: int = Field(default=20, ge=0) + max_length: int = 8192 + # RL / OPD + group_size: int = Field(default=8, ge=1) + max_tokens: int = 512 + temperature: float = 1.0 + advantage: Literal["grpo", "dr_grpo", "rank_grpo"] = "dr_grpo" + drop_zero_variance_groups: bool = True + reward: str | None = None # key in c2a.train.rewards.REWARD_FNS + fail_penalty: float = -0.5 + teacher_model: str | None = None # OPD teacher (same tokenizer family as base_model) + kl_coef: float = 1.0 + + @property + def run_dir(self) -> Path: + return self.log_dir / self.name + + +def load_run_config(path: Path | str) -> RunConfig: + return RunConfig.model_validate(yaml.safe_load(Path(path).read_text())) + + +class Trainer(Protocol): + def submit(self, cfg: RunConfig) -> str: + """Start a run; return a run id.""" + ... + + def export(self, run_id: str, dest: Path) -> Path: + """Download final weights / LoRA adapter for serving (vLLM on Modal).""" + ... diff --git a/src/c2a/train/data.py b/src/c2a/train/data.py new file mode 100644 index 0000000..65ada20 --- /dev/null +++ b/src/c2a/train/data.py @@ -0,0 +1,74 @@ +"""Training record formats (JSONL) for every post-training stage.""" + +from __future__ import annotations + +from collections.abc import Iterable, Iterator +from pathlib import Path +from typing import Any, Literal, TypeVar + +from pydantic import BaseModel, Field, model_validator + +T = TypeVar("T", bound=BaseModel) + + +class Message(BaseModel): + role: Literal["system", "user", "assistant"] + content: str + + +class SFTExample(BaseModel): + id: str + messages: list[Message] = Field(min_length=2) + + @model_validator(mode="after") + def _ends_with_assistant(self) -> SFTExample: + if self.messages[-1].role != "assistant": + raise ValueError("SFT example must end with an assistant message") + return self + + +class PreferencePair(BaseModel): + """For DPO / reward-model training (human or AI-labelled).""" + + id: str + prompt: list[Message] + chosen: str + rejected: str + source: Literal["human", "teacher", "implicit"] = "human" + + +class RLPrompt(BaseModel): + """RL prompt; `example` carries what the reward function needs (ground truth, candidates).""" + + id: str + messages: list[Message] = Field(min_length=1) + reward: str # key in c2a.train.rewards.REWARD_FNS + example: dict[str, Any] = Field(default_factory=dict) + + +class DistillPrompt(BaseModel): + """On-policy distillation prompt: the student samples, the teacher scores every token.""" + + id: str + messages: list[Message] = Field(min_length=1) + + +def write_jsonl(path: Path | str, records: Iterable[BaseModel]) -> int: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + n = 0 + with path.open("w") as f: + for r in records: + f.write(r.model_dump_json() + "\n") + n += 1 + return n + + +def read_jsonl(path: Path | str, model: type[T]) -> Iterator[T]: + with Path(path).open() as f: + for line_no, line in enumerate(f, 1): + if line.strip(): + try: + yield model.model_validate_json(line) + except ValueError as exc: + raise ValueError(f"{path}:{line_no}: {exc}") from exc diff --git a/src/c2a/train/envs/__init__.py b/src/c2a/train/envs/__init__.py new file mode 100644 index 0000000..71a5319 --- /dev/null +++ b/src/c2a/train/envs/__init__.py @@ -0,0 +1,8 @@ +"""Modal-hosted rollout environments / reward endpoints called from Tinker loops (M3): +candidate retrieval, graders, judge/decider scoring and image rendering for rewards.""" + +from c2a import NotYetImplemented + + +def remote_reward(name: str, output: str, example: dict) -> float: + raise NotYetImplemented(f"remote reward endpoint '{name}'", "M3") diff --git a/src/c2a/train/fallback/__init__.py b/src/c2a/train/fallback/__init__.py new file mode 100644 index 0000000..b99f10a --- /dev/null +++ b/src/c2a/train/fallback/__init__.py @@ -0,0 +1,8 @@ +"""Self-run fallback on Modal GPUs: TRL + PEFT (SFT, DPO, GenRM) and verl (GRPO/DAPO/GSPO, +Rank-GRPO, OPD). Used when a model or loss is not available on Tinker. M3.""" + +from c2a import NotYetImplemented + + +def submit(cfg) -> str: + raise NotYetImplemented("Modal fallback trainer", "M3") diff --git a/src/c2a/train/rewards.py b/src/c2a/train/rewards.py new file mode 100644 index 0000000..96be6e3 --- /dev/null +++ b/src/c2a/train/rewards.py @@ -0,0 +1,131 @@ +"""RL rewards and advantage estimators. Rewards reuse the graders, so eval and RL agree. + +Advantages +- grpo: (r - mean) / (std + eps) per group +- dr_grpo: r - mean per group (no std/length normalization; Dr.GRPO) +- rank_grpo: per-rank advantages for ranked lists (Rank-GRPO-style): each rank position k + gets the normalized DCG reward-to-go from k onward, group-centered per position. +Dynamic sampling (DAPO): groups with zero reward variance carry no signal and are dropped. +""" + +from __future__ import annotations + +import json +import math +import re +import statistics +from collections.abc import Callable, Sequence +from typing import Any + +from pydantic import ValidationError + +from c2a.graders.ranking import dedupe_preserving_order +from c2a.graders.rec import RecGrader, hallucinated_ids, parse_rec_output +from c2a.schemas import Candidate, CopyResponse, DecisionResult + +RewardFn = Callable[[str, dict[str, Any], float], float] + + +def rec_reward(output: str, example: dict[str, Any], fail_penalty: float) -> float: + res = RecGrader(k=example.get("k", 10)).grade(output, example) + return res.score if res.passed else fail_penalty + + +def decide_reward(output: str, example: dict[str, Any], fail_penalty: float) -> float: + """Calibration-aware: 1 - Brier/2 in [0, 1]. Output must be a DecisionResult JSON + over exactly the example's labels.""" + try: + res = DecisionResult.model_validate(json.loads(output)) + except (json.JSONDecodeError, ValidationError, TypeError): + return fail_penalty + if set(res.probs) != set(example["labels"]): + return fail_penalty + truth = example["label"] + b = sum((p - (1.0 if lbl == truth else 0.0)) ** 2 for lbl, p in res.probs.items()) + return 1.0 - b / 2.0 + + +def copy_reward(output: str, example: dict[str, Any], fail_penalty: float) -> float: + """Placeholder faithfulness reward until the post-trained decider/GenRM lands (M2): + fraction of claims whose numbers are all present in the product attributes.""" + try: + res = CopyResponse.model_validate(json.loads(output)) + except (json.JSONDecodeError, ValidationError, TypeError): + return fail_penalty + if not res.text.strip(): + return fail_penalty + if not res.claims: + return 0.5 + attrs = " ".join(f"{k} {v}" for k, v in example.get("attributes", {}).items()).lower() + supported = 0 + for claim in res.claims: + numbers = re.findall(r"\d+(?:[.,]\d+)?", claim) + if all(n in attrs for n in numbers): + supported += 1 + return supported / len(res.claims) + + +REWARD_FNS: dict[str, RewardFn] = { + "rec": rec_reward, + "decide": decide_reward, + "copy": copy_reward, +} + + +def group_advantages( + rewards: Sequence[float], mode: str = "dr_grpo", eps: float = 1e-6 +) -> list[float]: + if not rewards: + return [] + mean = statistics.fmean(rewards) + centered = [r - mean for r in rewards] + if mode == "dr_grpo": + return centered + if mode == "grpo": + std = statistics.pstdev(rewards) + return [c / (std + eps) for c in centered] + raise ValueError(f"unknown advantage mode: {mode}") + + +def has_signal(rewards: Sequence[float], tol: float = 1e-9) -> bool: + return len(rewards) > 1 and (max(rewards) - min(rewards)) > tol + + +def rank_rewards_to_go( + output: str, example: dict[str, Any], k: int = 10, fail_penalty: float = -0.5 +) -> list[float]: + """Per-rank normalized DCG reward-to-go for one ranked output. + + Position i gets sum_{j>=i} gain_j / IDCG@k, so credit only flows from a rank to the + items at or after it (no non-causal credit). Hallucinated items get `fail_penalty` + as their gain; invalid output returns [fail_penalty]. + """ + parsed = parse_rec_output(output) + if parsed is None: + return [fail_penalty] + ranked = dedupe_preserving_order([it.product_id for it in parsed.items])[:k] + if not ranked: + return [fail_penalty] + candidates = [Candidate.model_validate(c) for c in example["candidates"]] + bad = set(hallucinated_ids(ranked, candidates)) + relevance: dict[str, float] = example["relevance"] + ideal = sorted(relevance.values(), reverse=True)[:k] + idcg = sum((2**r - 1) / math.log2(i + 2) for i, r in enumerate(ideal)) or 1.0 + gains = [ + fail_penalty if pid in bad else (2 ** relevance.get(pid, 0.0) - 1) / math.log2(i + 2) / idcg + for i, pid in enumerate(ranked) + ] + out, acc = [0.0] * len(gains), 0.0 + for i in range(len(gains) - 1, -1, -1): + acc += gains[i] + out[i] = acc + return out + + +def rank_group_advantages(per_rank: Sequence[Sequence[float]]) -> list[list[float]]: + """Center each rank position across the group (positions absent in a sample count as 0).""" + width = max((len(r) for r in per_rank), default=0) + means = [ + statistics.fmean([r[i] if i < len(r) else 0.0 for r in per_rank]) for i in range(width) + ] + return [[r[i] - means[i] for i in range(len(r))] for r in per_rank] diff --git a/src/c2a/train/tinker/__init__.py b/src/c2a/train/tinker/__init__.py new file mode 100644 index 0000000..44f8b5f --- /dev/null +++ b/src/c2a/train/tinker/__init__.py @@ -0,0 +1,5 @@ +"""Tinker (Thinking Machines) training loops: SFT, RL (GRPO family) and on-policy distillation. + +Loops are platform-agnostic orchestration over `TrainingBackend`; `TinkerSDKBackend` +adapts the real tinker SDK + tinker-cookbook renderers. Tests use a fake backend. +""" diff --git a/src/c2a/train/tinker/client.py b/src/c2a/train/tinker/client.py new file mode 100644 index 0000000..f6ca715 --- /dev/null +++ b/src/c2a/train/tinker/client.py @@ -0,0 +1,183 @@ +"""Backend protocol + Tinker SDK adapter. + +The SDK adapter follows the public tinker-cookbook recipes (sl_loop.py, rl_loop.py, +distillation/train_on_policy.py). All SDK calls are isolated here so API drift is a +one-file fix. It requires the `train` extra and TINKER_API_KEY. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Protocol + +from c2a import NotYetImplemented +from c2a.train.data import Message + + +@dataclass +class Sample: + text: str + tokens: list[int] = field(default_factory=list) + logprobs: list[float] = field(default_factory=list) + + +@dataclass +class RLDatum: + """One sampled completion with its advantage: a scalar (sequence-level) or one value + per sampled token (OPD / token-level credit).""" + + messages: list[Message] + sample: Sample + advantage: float | list[float] + + +class TrainingBackend(Protocol): + def sft_step(self, batch: list[list[Message]], learning_rate: float) -> dict[str, float]: ... + + def sample( + self, messages: list[Message], n: int, max_tokens: int, temperature: float + ) -> list[Sample]: ... + + def rl_step(self, batch: list[RLDatum], learning_rate: float) -> dict[str, float]: ... + + def teacher_logprobs(self, messages: list[Message], sample: Sample) -> list[float]: + """Teacher log-prob of each sampled token (same tokenizer as the student).""" + ... + + def save(self, name: str) -> str: ... + + +def _msgs(messages: list[Message]) -> list[dict[str, str]]: + return [m.model_dump() for m in messages] + + +class TinkerSDKBackend: + """Adapter over `tinker` + `tinker_cookbook`. Not exercised in CI (needs credentials).""" + + def __init__( + self, + base_model: str, + lora_rank: int = 32, + teacher_model: str | None = None, + max_length: int = 8192, + ) -> None: + try: + import tinker + from tinker_cookbook import model_info, renderers + from tinker_cookbook.tokenizer_utils import get_tokenizer + except ImportError as exc: # pragma: no cover - depends on optional extra + raise RuntimeError("Install the train extra: uv sync --extra train") from exc + + self._tinker = tinker + self._renderers = renderers + self.max_length = max_length + tokenizer = get_tokenizer(base_model) + self.renderer = renderers.get_renderer( + model_info.get_recommended_renderer_name(base_model), tokenizer + ) + self.service = tinker.ServiceClient() + self.training = self.service.create_lora_training_client( + base_model=base_model, rank=lora_rank + ) + self._sampler = None + self._teacher = ( + self.service.create_sampling_client(base_model=teacher_model) if teacher_model else None + ) + + def _adam(self, lr: float): + return self._tinker.AdamParams(learning_rate=lr, beta1=0.9, beta2=0.95, eps=1e-8) + + def sft_step(self, batch: list[list[Message]], learning_rate: float) -> dict[str, float]: + from tinker_cookbook.supervised.data import conversation_to_datum + + datums = [ + conversation_to_datum( + _msgs(conv), + self.renderer, + self.max_length, + self._renderers.TrainOnWhat.ALL_ASSISTANT_MESSAGES, + ) + for conv in batch + ] + fb = self.training.forward_backward(datums, loss_fn="cross_entropy") + opt = self.training.optim_step(self._adam(learning_rate)) + fb.result() + res = opt.result() + self._sampler = None + return dict(res.metrics or {}) + + def _sampling_client(self): + if self._sampler is None: + self._sampler = self.training.save_weights_and_get_sampling_client() + return self._sampler + + def sample( + self, messages: list[Message], n: int, max_tokens: int, temperature: float + ) -> list[Sample]: + prompt = self.renderer.build_generation_prompt(_msgs(messages)) + params = self._tinker.types.SamplingParams( + max_tokens=max_tokens, temperature=temperature, stop=self.renderer.get_stop_sequences() + ) + result = ( + self._sampling_client() + .sample(prompt=prompt, num_samples=n, sampling_params=params) + .result() + ) + out = [] + for seq in result.sequences: + parsed, _ = self.renderer.parse_response(seq.tokens) + out.append( + Sample( + text=self._renderers.get_text_content(parsed), + tokens=list(seq.tokens), + logprobs=list(seq.logprobs or []), + ) + ) + return out + + def _datum(self, d: RLDatum) -> Any: + import torch + from tinker import types + from tinker.types.tensor_data import TensorData + + prompt = self.renderer.build_generation_prompt(_msgs(d.messages)) + toks, lps = d.sample.tokens, d.sample.logprobs + ob_len = prompt.length - 1 + model_input = prompt.append(types.EncodedTextChunk(tokens=toks[:-1])) + adv = d.advantage if isinstance(d.advantage, list) else [d.advantage] * len(toks) + if len(adv) != len(toks): + raise ValueError("per-token advantages must match sampled token count") + return types.Datum( + model_input=model_input, + loss_fn_inputs={ + "target_tokens": TensorData.from_torch(torch.tensor([0] * ob_len + toks)), + "logprobs": TensorData.from_torch(torch.tensor([0.0] * ob_len + lps)), + "advantages": TensorData.from_torch(torch.tensor([0.0] * ob_len + adv)), + }, + ) + + def rl_step(self, batch: list[RLDatum], learning_rate: float) -> dict[str, float]: + datums = [self._datum(d) for d in batch] + fb = self.training.forward_backward(datums, loss_fn="importance_sampling") + opt = self.training.optim_step(self._adam(learning_rate)) + fb.result() + res = opt.result() + self._sampler = None + return dict(res.metrics or {}) + + def teacher_logprobs(self, messages: list[Message], sample: Sample) -> list[float]: + if self._teacher is None: + raise RuntimeError("teacher_model not configured") + from tinker import types + + prompt = self.renderer.build_generation_prompt(_msgs(messages)) + full = prompt.append(types.EncodedTextChunk(tokens=sample.tokens)) + lps = self._teacher.compute_logprobs(full).result() + # logprobs are per position of `full`; keep those of the sampled tokens + return [float(x) for x in lps[-len(sample.tokens) :]] + + def save(self, name: str) -> str: + return self.training.save_weights_for_sampler(name).result().path + + def export(self, tinker_path: str, dest: str) -> str: # pragma: no cover + raise NotYetImplemented("checkpoint archive download for vLLM serving", "M3") diff --git a/src/c2a/train/tinker/opd.py b/src/c2a/train/tinker/opd.py new file mode 100644 index 0000000..89dfb18 --- /dev/null +++ b/src/c2a/train/tinker/opd.py @@ -0,0 +1,53 @@ +"""On-policy distillation: the student samples, the teacher scores every sampled token; +per-token advantage = -kl_coef * (log p_student - log p_teacher) (negative reverse KL). +Teacher and student must share a tokenizer (e.g. RL'd Qwen3.8-27B -> small Qwen, or +Inkling -> Inkling-Small).""" + +from __future__ import annotations + +import statistics +from collections.abc import Sequence + +from c2a.train.base import RunConfig +from c2a.train.data import DistillPrompt +from c2a.train.tinker.client import RLDatum, TrainingBackend +from c2a.train.tinker.sft import _write_summary + + +def reverse_kl_advantages( + student_lps: Sequence[float], teacher_lps: Sequence[float], kl_coef: float +) -> list[float]: + if len(student_lps) != len(teacher_lps): + raise ValueError("student and teacher logprobs must align token-by-token") + return [-kl_coef * (s - t) for s, t in zip(student_lps, teacher_lps, strict=True)] + + +def run_opd(backend: TrainingBackend, prompts: Sequence[DistillPrompt], cfg: RunConfig) -> dict: + if not cfg.teacher_model: + raise ValueError("OPD requires teacher_model") + n_batches = len(prompts) // cfg.batch_size + if n_batches == 0: + raise ValueError(f"need at least batch_size={cfg.batch_size} prompts, got {len(prompts)}") + steps = min(n_batches, cfg.steps or n_batches) + history, checkpoints = [], [] + for step in range(steps): + batch = prompts[step * cfg.batch_size : (step + 1) * cfg.batch_size] + datums, kls = [], [] + for p in batch: + for s in backend.sample(p.messages, cfg.group_size, cfg.max_tokens, cfg.temperature): + t_lps = backend.teacher_logprobs(p.messages, s) + adv = reverse_kl_advantages(s.logprobs, t_lps, cfg.kl_coef) + kls.append( + statistics.fmean(a - b for a, b in zip(s.logprobs, t_lps, strict=True)) + if t_lps + else 0.0 + ) + datums.append(RLDatum(p.messages, s, adv)) + metrics = backend.rl_step(datums, cfg.learning_rate) + history.append({"step": step, "reverse_kl": statistics.fmean(kls), **metrics}) + if cfg.save_every and step > 0 and step % cfg.save_every == 0: + checkpoints.append(backend.save(f"{cfg.name}-{step:06d}")) + checkpoints.append(backend.save(f"{cfg.name}-final")) + summary = {"stage": "opd", "steps": steps, "checkpoints": checkpoints, "history": history} + _write_summary(cfg, summary) + return summary diff --git a/src/c2a/train/tinker/rl.py b/src/c2a/train/tinker/rl.py new file mode 100644 index 0000000..012935b --- /dev/null +++ b/src/c2a/train/tinker/rl.py @@ -0,0 +1,72 @@ +"""RL loop (GRPO family) with grader-backed rewards. + +Per step: sample `group_size` completions per prompt -> reward -> group advantages +(dr_grpo / grpo) -> drop zero-variance groups (DAPO dynamic sampling) -> importance- +sampling policy-gradient step. `rank_grpo` computes per-rank advantages; mapping ranks to +token spans in the SDK backend is scheduled for M3, so here it falls back to the rank-0 +advantage (the full-list reward-to-go) as a sequence-level signal. +""" + +from __future__ import annotations + +import statistics +from collections.abc import Sequence + +from c2a.train.base import RunConfig +from c2a.train.data import RLPrompt +from c2a.train.rewards import ( + REWARD_FNS, + group_advantages, + has_signal, + rank_group_advantages, + rank_rewards_to_go, +) +from c2a.train.tinker.client import RLDatum, TrainingBackend +from c2a.train.tinker.sft import _write_summary + + +def run_rl(backend: TrainingBackend, prompts: Sequence[RLPrompt], cfg: RunConfig) -> dict: + n_batches = len(prompts) // cfg.batch_size + if n_batches == 0: + raise ValueError(f"need at least batch_size={cfg.batch_size} prompts, got {len(prompts)}") + steps = min(n_batches, cfg.steps or n_batches) + history, checkpoints = [], [] + for step in range(steps): + batch = prompts[step * cfg.batch_size : (step + 1) * cfg.batch_size] + datums: list[RLDatum] = [] + mean_rewards, dropped = [], 0 + for p in batch: + reward_fn = REWARD_FNS[cfg.reward or p.reward] + samples = backend.sample(p.messages, cfg.group_size, cfg.max_tokens, cfg.temperature) + rewards = [reward_fn(s.text, p.example, cfg.fail_penalty) for s in samples] + mean_rewards.append(statistics.fmean(rewards)) + if cfg.advantage == "rank_grpo": + per_rank = [ + rank_rewards_to_go(s.text, p.example, p.example.get("k", 10), cfg.fail_penalty) + for s in samples + ] + advs = [a[0] if a else 0.0 for a in rank_group_advantages(per_rank)] + signal = has_signal([r[0] for r in per_rank]) + else: + advs = group_advantages(rewards, cfg.advantage) + signal = has_signal(rewards) + if cfg.drop_zero_variance_groups and not signal: + dropped += 1 + continue + datums += [RLDatum(p.messages, s, a) for s, a in zip(samples, advs, strict=True)] + metrics = backend.rl_step(datums, cfg.learning_rate) if datums else {"skipped": 1.0} + history.append( + { + "step": step, + "reward_mean": statistics.fmean(mean_rewards), + "groups_dropped": dropped, + "datums": len(datums), + **metrics, + } + ) + if cfg.save_every and step > 0 and step % cfg.save_every == 0: + checkpoints.append(backend.save(f"{cfg.name}-{step:06d}")) + checkpoints.append(backend.save(f"{cfg.name}-final")) + summary = {"stage": "rl", "steps": steps, "checkpoints": checkpoints, "history": history} + _write_summary(cfg, summary) + return summary diff --git a/src/c2a/train/tinker/sft.py b/src/c2a/train/tinker/sft.py new file mode 100644 index 0000000..62659cd --- /dev/null +++ b/src/c2a/train/tinker/sft.py @@ -0,0 +1,37 @@ +"""Supervised fine-tuning loop (cold start: grader-filtered teacher distillation).""" + +from __future__ import annotations + +import json +import logging +from collections.abc import Sequence + +from c2a.train.base import RunConfig +from c2a.train.data import SFTExample +from c2a.train.tinker.client import TrainingBackend + +log = logging.getLogger(__name__) + + +def run_sft(backend: TrainingBackend, examples: Sequence[SFTExample], cfg: RunConfig) -> dict: + n_batches = len(examples) // cfg.batch_size # drop last partial batch + if n_batches == 0: + raise ValueError(f"need at least batch_size={cfg.batch_size} examples, got {len(examples)}") + steps = min(n_batches, cfg.steps or n_batches) + history, checkpoints = [], [] + for step in range(steps): + lr = cfg.learning_rate * max(0.0, 1.0 - step / steps) # linear decay + batch = examples[step * cfg.batch_size : (step + 1) * cfg.batch_size] + metrics = backend.sft_step([ex.messages for ex in batch], lr) + history.append({"step": step, "lr": lr, **metrics}) + if cfg.save_every and step > 0 and step % cfg.save_every == 0: + checkpoints.append(backend.save(f"{cfg.name}-{step:06d}")) + checkpoints.append(backend.save(f"{cfg.name}-final")) + summary = {"stage": "sft", "steps": steps, "checkpoints": checkpoints, "history": history} + _write_summary(cfg, summary) + return summary + + +def _write_summary(cfg: RunConfig, summary: dict) -> None: + cfg.run_dir.mkdir(parents=True, exist_ok=True) + (cfg.run_dir / "summary.json").write_text(json.dumps(summary, indent=2, default=str)) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..9901a19 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,52 @@ +import json +from pathlib import Path + +import pytest + +from c2a.schemas import Store, Tier, TosStatus + +FIXTURES = Path(__file__).parent / "fixtures" + + +@pytest.fixture +def store() -> Store: + return Store( + id="acme", + name="Acme", + domain="shop.example.com", + country="SE", + locale="sv-SE", + platform="shopify", + tier=Tier.A, + tos_status=TosStatus.approved, + method="shopify", + enabled=True, + ) + + +@pytest.fixture +def shopify_payload() -> dict: + return json.loads((FIXTURES / "shopify_products.json").read_text()) + + +@pytest.fixture +def rec_example() -> dict: + def cand(pid, amount, in_stock=True): + return { + "product_id": pid, + "title": pid, + "price": {"amount": amount, "currency": "SEK"}, + "in_stock": in_stock, + } + + return { + "candidates": [ + cand("a", 100), + cand("b", 200), + cand("c", 300), + cand("d", 50, in_stock=False), + ], + "constraints": {"max_price": {"amount": 250, "currency": "SEK"}, "in_stock_only": True}, + "relevance": {"a": 2, "b": 1}, + "k": 3, + } diff --git a/tests/fixtures/robots.txt b/tests/fixtures/robots.txt new file mode 100644 index 0000000..704b7ee --- /dev/null +++ b/tests/fixtures/robots.txt @@ -0,0 +1,4 @@ +User-agent: * +Disallow: /checkout +Disallow: /cart +Allow: / diff --git a/tests/fixtures/shopify_products.json b/tests/fixtures/shopify_products.json new file mode 100644 index 0000000..6c1e162 --- /dev/null +++ b/tests/fixtures/shopify_products.json @@ -0,0 +1,15 @@ +{"products": [ + {"id": 101, "title": "Merino Runner", "handle": "merino-runner", "body_html": "

Soft merino sneaker.

", + "vendor": "Acme", "product_type": "Shoes", "tags": ["sneakers", "wool"], + "options": [{"name": "Size"}, {"name": "Color"}], + "variants": [ + {"id": 1001, "title": "42 / Grey", "sku": "MR-42-G", "price": "129.00", "compare_at_price": "149.00", "available": true, "option1": "42", "option2": "Grey"}, + {"id": 1002, "title": "43 / Grey", "sku": "", "price": "129.00", "compare_at_price": null, "available": false, "option1": "43", "option2": "Grey"} + ], + "images": [{"src": "https://cdn.example.com/mr.jpg"}]}, + {"id": 102, "title": "Wool Socks", "handle": "wool-socks", "body_html": "", "vendor": "Acme", "product_type": "Socks", + "tags": "socks, wool", "options": [{"name": "Title"}], + "variants": [{"id": 1003, "title": "Default Title", "price": "19.99", "available": true, "option1": "Default Title"}], + "images": []}, + {"id": 103, "title": "Broken", "handle": "broken", "variants": []} +]} diff --git a/tests/test_calibration.py b/tests/test_calibration.py new file mode 100644 index 0000000..9ee5625 --- /dev/null +++ b/tests/test_calibration.py @@ -0,0 +1,24 @@ +import pytest + +from c2a.decide.calibration import accuracy, brier, ece +from c2a.schemas import DecisionResult + + +def r(label, p): + other = "b" if label == "a" else "a" + return DecisionResult(label=label, probs={label: p, other: 1 - p}) + + +def test_perfect_confident_predictions(): + res = [r("a", 1.0), r("b", 1.0)] + assert ( + brier(res, ["a", "b"]) == 0.0 + and ece(res, ["a", "b"]) == 0.0 + and accuracy(res, ["a", "b"]) == 1 + ) + + +def test_overconfident_wrong_is_penalized(): + res = [r("a", 0.9), r("a", 0.9)] + assert ece(res, ["a", "b"]) == pytest.approx(0.4) + assert brier(res, ["a", "b"]) == pytest.approx((0.02 + 1.62) / 2) diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..25a43fa --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,39 @@ +from typer.testing import CliRunner + +from c2a.bench import CANARY, TRACKS, contains_canary +from c2a.cli import app + +runner = CliRunner() + + +def test_registry_validate(): + res = runner.invoke(app, ["registry", "validate"]) + assert res.exit_code == 0 and "crawlable (enabled + ToS approved): 0" in res.output + + +def test_decide_keyword_backend(): + res = runner.invoke( + app, + ["decide", "--evidence", "running socks for shoes", "--labels", "complement,substitute"], + ) + assert res.exit_code == 0 and '"probs"' in res.output + + +def test_train_dry_run_and_stage_mismatch(): + res = runner.invoke( + app, ["train", "sft", "--config", "configs/train/sft_student.yaml", "--dry-run"] + ) + assert res.exit_code == 0 and "sft-student-proxy" in res.output + res = runner.invoke( + app, ["train", "rl", "--config", "configs/train/sft_student.yaml", "--dry-run"] + ) + assert res.exit_code != 0 + + +def test_stubs_exit_nonzero_with_milestone(): + res = runner.invoke(app, ["build"]) + assert res.exit_code == 2 and "M2" in res.output + + +def test_bench_tracks_and_canary(): + assert len(TRACKS) == 7 and contains_canary(f"x {CANARY} y") diff --git a/tests/test_compliance.py b/tests/test_compliance.py new file mode 100644 index 0000000..0f5add7 --- /dev/null +++ b/tests/test_compliance.py @@ -0,0 +1,40 @@ +import httpx +import pytest +import respx + +from c2a.schemas import TosStatus +from c2a.sources.compliance import RateLimiter, can_crawl, fetch_robots, parse_robots + +from .conftest import FIXTURES + + +def rp(): + return parse_robots((FIXTURES / "robots.txt").read_text()) + + +def test_can_crawl_gates(store): + url = "https://shop.example.com/products/x" + assert can_crawl(store, url, rp()) == (True, "ok") + assert not can_crawl(store, "https://shop.example.com/checkout", rp())[0] + assert not can_crawl(store, "https://other.example.com/products/x", rp())[0] + assert not can_crawl(store, url, None)[0] # robots unavailable -> deny + store.tos_status = TosStatus.unreviewed + assert can_crawl(store, url, rp()) == (False, "tos_status=unreviewed") + + +@respx.mock +def test_fetch_robots_statuses(): + respx.get("https://a.example/robots.txt").mock(return_value=httpx.Response(404)) + respx.get("https://b.example/robots.txt").mock(return_value=httpx.Response(503)) + assert fetch_robots("a.example").can_fetch("x", "https://a.example/anything") + assert fetch_robots("b.example") is None + + +def test_rate_limiter_spaces_requests(): + t = [0.0] + sleeps = [] + rl = RateLimiter(qps=2, clock=lambda: t[0], sleep=sleeps.append) + rl.wait("d") + rl.wait("d") + rl.wait("other") + assert sleeps == [pytest.approx(0.5)] diff --git a/tests/test_decide.py b/tests/test_decide.py new file mode 100644 index 0000000..fc563c7 --- /dev/null +++ b/tests/test_decide.py @@ -0,0 +1,117 @@ +import httpx +import pytest +import respx + +from c2a.decide.backends import KeywordDecider, SystemOneClient +from c2a.decide.base import decide, normalize_probs +from c2a.decide.systemone import ( + Choice, + ChoiceAnswer, + Noul, + Score, + ScoreAnswer, + SystemOneRequest, + SystemOneResponse, + top_label, + top_prob, +) + +# Documented System One example response (Kev README / TypeSafe contract). +EXAMPLE_RESPONSE = { + "model": "kev-latest", + "answers": { + "department": { + "type": "choice", + "choice": "returns", + "confidence": 0.21, + "probabilities": {"returns": 0.47, "shipping": 0.28, "billing": 0.25}, + }, + "escalate": {"type": "noul", "noul": 0.93}, + "frustration": { + "type": "score", + "score": 1.44, + "confidence": 0.78, + "legend": {"0": "Calm", "1": "Frustrated", "2": "Very angry"}, + "probabilities": {"0": 0.00, "1": 0.56, "2": 0.44}, + }, + }, + "usage": {"input_tokens": 101, "output_tokens": 161}, + "latency_ms": 495, +} + +REQUEST = SystemOneRequest( + state="Charged twice for order 123, where is my refund?", + questions={ + "department": Choice(criteria={"returns": None, "shipping": None, "billing": "Payments"}), + "escalate": Noul(instructions="Should a human take over?"), + "frustration": Score(criteria=["Calm", "Frustrated", "Very angry"]), + }, +) + + +def test_example_response_roundtrip_and_helpers(): + resp = SystemOneResponse.model_validate(EXAMPLE_RESPONSE) + assert SystemOneResponse.model_validate_json(resp.model_dump_json()) == resp + assert top_label(resp.answers["department"]) == "returns" + assert top_label(resp.answers["escalate"]) == "true" + assert top_label(resp.answers["frustration"]) == "Frustrated" + assert top_prob(resp.answers["escalate"]) == pytest.approx(0.93) + + +def test_request_serializes_typed_questions(): + body = REQUEST.model_dump(mode="json", exclude_none=True) + assert body["model"] == "jev-latest" + assert body["questions"]["frustration"] == { + "type": "score", + "criteria": ["Calm", "Frustrated", "Very angry"], + } + with pytest.raises(ValueError): + Choice(criteria={}) + + +@respx.mock +def test_client_posts_with_auth_and_retries_on_429(): + route = respx.post("https://api.typesafe.ai/v1/systemone").mock( + side_effect=[httpx.Response(429), httpx.Response(200, json=EXAMPLE_RESPONSE)] + ) + sleeps = [] + client = SystemOneClient(api_key="k", sleep=sleeps.append) + resp = client.ask(REQUEST) + assert resp.answers["escalate"].noul == 0.93 + assert route.call_count == 2 and sleeps == [1.0] + req = route.calls.last.request + assert req.headers["Authorization"] == "Bearer k" + assert b'"model":"jev-latest"' in req.content.replace(b" ", b"") + + +@respx.mock +def test_client_raises_after_retries_and_on_4xx(): + respx.post("https://api.typesafe.ai/v1/systemone").mock(return_value=httpx.Response(401)) + with pytest.raises(httpx.HTTPStatusError): + SystemOneClient(api_key="k", sleep=lambda s: None).ask(REQUEST) + + +def test_client_requires_key(monkeypatch): + monkeypatch.delenv("TYPESAFE_API_KEY", raising=False) + with pytest.raises(RuntimeError, match="TYPESAFE_API_KEY"): + SystemOneClient() + + +def test_keyword_decider_answers_every_question(): + resp = KeywordDecider().ask(REQUEST) + assert set(resp.answers) == {"department", "escalate", "frustration"} + assert isinstance(resp.answers["frustration"], ScoreAnswer) + dep = resp.answers["department"] + assert isinstance(dep, ChoiceAnswer) and sum(dep.probabilities.values()) == pytest.approx(1.0) + billing = KeywordDecider().ask(REQUEST.model_copy(update={"state": "billing issue"})) + assert billing.answers["department"].choice == "billing" + + +def test_decide_helper_wraps_single_choice(): + res = decide(KeywordDecider(), "running socks for shoes", "relation?", ["complement", "socks"]) + assert res.label == "socks" and sum(res.probs.values()) == pytest.approx(1.0) + + +def test_normalize_probs_sums_to_one_and_argmax(): + res = normalize_probs({"a": 0.0, "b": 2.0}, ["a", "b", "c"]) + assert res.label == "b" and sum(res.probs.values()) == pytest.approx(1.0) diff --git a/tests/test_discover_crawl.py b/tests/test_discover_crawl.py new file mode 100644 index 0000000..d2b0206 --- /dev/null +++ b/tests/test_discover_crawl.py @@ -0,0 +1,127 @@ +import json + +import httpx +import respx + +from c2a.schemas import TosStatus +from c2a.sources.compliance import CrawlSession, RateLimiter, parse_robots +from c2a.sources.crawl import crawl_registry, plan +from c2a.sources.discover import expand, fingerprint +from c2a.sources.firecrawl import FirecrawlClient +from c2a.sources.registry import Registry + +from .test_ucp import PROFILE + + +def mock_site(domain, well_known=None, products_json=None, home="", meta=None): + respx.get(f"https://{domain}/robots.txt").mock(return_value=httpx.Response(404)) + respx.get(f"https://{domain}/meta.json").mock( + return_value=httpx.Response(200, json=meta) if meta else httpx.Response(404) + ) + respx.get(f"https://{domain}/.well-known/ucp").mock( + return_value=httpx.Response(200, json=well_known) if well_known else httpx.Response(404) + ) + respx.get(f"https://{domain}/products.json").mock( + return_value=httpx.Response(200, json=products_json) + if products_json + else httpx.Response(404) + ) + respx.get(f"https://{domain}/").mock(return_value=httpx.Response(200, text=home)) + + +@respx.mock +def test_fingerprint_platforms(): + mock_site("u.test", well_known=PROFILE) + mock_site("s.test", products_json={"products": []}, meta={"currency": "SEK"}) + mock_site("w.test", home="") + mock_site("c.test", home="hello") + got = {d: fingerprint(d) for d in ("u.test", "s.test", "w.test", "c.test")} + assert {d: (f.platform, f.method) for d, f in got.items()} == { + "u.test": ("ucp", "ucp"), + "s.test": ("shopify", "shopify"), + "w.test": ("woocommerce", "firecrawl"), + "c.test": ("custom", "firecrawl"), + } + assert got["s.test"].currency == "SEK" and got["c.test"].currency is None + + +@respx.mock +def test_expand_emits_unreviewed_disabled_candidates(): + respx.post("https://api.firecrawl.dev/v2/search").mock( + return_value=httpx.Response( + 200, + json={ + "success": True, + "data": { + "web": [ + {"url": "https://www.nyshop.se/kläder", "title": "Nyshop"}, + {"url": "https://www.amazon.se/x"}, + {"url": "https://known.se/"}, + {"url": "https://www.nyshop.se/other"}, + ] + }, + }, + ) + ) + stores = expand("SE", "apparel", FirecrawlClient(api_key="k"), {"known.se"}) + assert [s.domain for s in stores] == ["www.nyshop.se"] + s = stores[0] + assert s.id == "nyshop" and s.locale == "sv-SE" and s.tier.value == "B" + assert s.tos_status is TosStatus.unreviewed and not s.enabled + + +def shopify_page(n): + return { + "products": [ + { + "id": i, + "title": f"P{i}", + "handle": f"p{i}", + "variants": [{"id": i * 10, "price": "10.00", "available": True}], + } + for i in range(n) + ] + } + + +@respx.mock +def test_crawl_registry_incremental_and_manifest(tmp_path, store): + store.currency = "SEK" + other = store.model_copy( + update={"id": "other", "domain": "o.test", "tos_status": TosStatus.unreviewed} + ) + reg = Registry(stores=[store, other]) + assert [(s.id, r) for s, r in plan(reg)] == [("acme", "ok"), ("other", "tos_status=unreviewed")] + + route = respx.get("https://shop.example.com/products.json") + route.mock( + side_effect=lambda req: httpx.Response( + 200, json=shopify_page(3) if req.url.params["page"] == "1" else {"products": []} + ) + ) + + def factory(s): + return CrawlSession( + s, + client=httpx.Client(), + limiter=RateLimiter(1000, sleep=lambda x: None), + robots=parse_robots(""), + ) + + ((m, changed),) = crawl_registry(reg, tmp_path, session_factory=factory) + assert m.products_new_or_changed == 3 and len(changed) == 3 and m.errors == [] + manifest = json.loads((tmp_path / "acme" / "manifest.json").read_text()) + assert manifest["products_total"] == 3 + + ((m2, changed2),) = crawl_registry(reg, tmp_path, session_factory=factory) + assert m2.products_unchanged == 3 and changed2 == [] + assert not (tmp_path / "other").exists() # never crawled + + +def test_crawl_cli_dry_run_reports_skips(): + from typer.testing import CliRunner + + from c2a.cli import app + + res = CliRunner().invoke(app, ["crawl", "--dry-run", "--store", "allbirds"]) + assert res.exit_code == 0 and "skip: tos_status=unreviewed" in res.output diff --git a/tests/test_firecrawl.py b/tests/test_firecrawl.py new file mode 100644 index 0000000..cfdcb38 --- /dev/null +++ b/tests/test_firecrawl.py @@ -0,0 +1,164 @@ +import json + +import httpx +import pytest +import respx + +from c2a.decide.systemone import ChoiceAnswer, SystemOneResponse +from c2a.sources import firecrawl as fc +from c2a.sources.compliance import CrawlSession, RateLimiter, parse_robots + +API = "https://api.firecrawl.dev" +EXTRACT = { + "title": "Wool Runner", + "brand": "Acme", + "currency": "SEK", + "price": "1 299,00 kr", + "images": ["https://cdn/x.jpg"], + "breadcrumbs": ["Shoes", "Runners"], + "sku": "WR-1", +} + + +class PageGate: + """Scripted Jev: pages whose URL contains 'wool' are product pages.""" + + def __init__(self): + self.calls = 0 + + def ask(self, request): + self.calls += 1 + is_product = "wool" in request.state["url"] + label = "product_detail" if is_product else "category_listing" + opts = list(request.questions["page_type"].criteria) + probs = {o: (0.97 if o == label else 0.01) for o in opts} + return SystemOneResponse( + model="jev-latest", + answers={"page_type": ChoiceAnswer(choice=label, confidence=0.95, probabilities=probs)}, + ) + + +def session(store): + return CrawlSession( + store, + client=httpx.Client(), + limiter=RateLimiter(1000, sleep=lambda s: None), + robots=parse_robots("User-agent: *\nDisallow: /checkout\n"), + ) + + +def scrape_router(request): + body = json.loads(request.content) + if body["formats"] == ["markdown"]: + return httpx.Response(200, json={"success": True, "data": {"markdown": "# page"}}) + return httpx.Response(200, json={"success": True, "data": {"json": EXTRACT}}) + + +@pytest.mark.parametrize( + ("text", "want"), + [ + ("1 299,00 kr", "1299.00"), + ("£19.99", "19.99"), + ("1,299.50", "1299.50"), + ("1.299,50 €", "1299.50"), + ("$1,299", "1299"), + (129.0, "129.0"), + ("free", None), + ], +) +def test_parse_price(text, want): + got = fc.parse_price(text) + assert (str(got) if got is not None else None) == want + + +def test_triage_url(): + assert fc.triage_url("https://s.com/products/wool-runner") == "product" + assert fc.triage_url("https://s.com/collections/shoes/products/x") == "product" + assert fc.triage_url("https://s.com/blog/how-to") == "skip" + assert fc.triage_url("https://s.com/img/a.jpg") == "skip" + assert fc.triage_url("https://s.com/wool-runner-grey") == "ambiguous" + + +def test_to_product_minor_units(store): + p = fc.to_product(fc.ExtractedProduct.model_validate(EXTRACT), "https://s/x", store) + assert p.variants[0].price.amount == 129900 and p.category == "Shoes > Runners" + assert p.id == "acme:WR-1" and p.source == "firecrawl" + with pytest.raises(ValueError): + fc.to_product( + fc.ExtractedProduct(title="x", price="n/a", currency="SEK"), "https://s/x", store + ) + + +@respx.mock +def test_crawl_store_page_gate_controls_extract_spend(store): + respx.post(f"{API}/v2/map").mock( + return_value=httpx.Response( + 200, + json={ + "success": True, + "links": [ + {"url": "https://shop.example.com/products/a"}, + "https://shop.example.com/wool-runner", # ambiguous -> gate says product + "https://shop.example.com/sale-shoes", # ambiguous -> gate says listing + "https://shop.example.com/blog/post", # skip + "https://shop.example.com/checkout", # robots-denied + ], + }, + ) + ) + scrape = respx.post(f"{API}/v2/scrape").mock(side_effect=scrape_router) + client = fc.FirecrawlClient(api_key="k") + gate = PageGate() + res = fc.crawl_store(session(store), client, gate) + formats = [json.loads(c.request.content)["formats"] for c in scrape.calls] + json_calls = [f for f in formats if f != ["markdown"]] + assert formats.count(["markdown"]) == 2 and len(json_calls) == 2 # /products/a + wool-runner + assert res.triage == {"product": 1, "skip": 1, "ambiguous": 2} and res.urls_denied == 1 + assert res.gated_out == 1 and gate.calls == 2 and len(res.products) == 2 + assert res.credits_spent == 1 + 2 * 1 + 2 * 5 + + +@respx.mock +def test_no_decider_never_extracts_ambiguous(store): + respx.post(f"{API}/v2/map").mock( + return_value=httpx.Response( + 200, json={"success": True, "links": ["https://shop.example.com/x"]} + ) + ) + scrape = respx.post(f"{API}/v2/scrape").mock(side_effect=scrape_router) + res = fc.crawl_store(session(store), fc.FirecrawlClient(api_key="k"), None) + assert scrape.call_count == 0 and res.products == [] + + +@respx.mock +def test_budget_stops_crawl(store): + respx.post(f"{API}/v2/map").mock( + return_value=httpx.Response( + 200, + json={ + "success": True, + "links": [f"https://shop.example.com/products/{i}" for i in range(5)], + }, + ) + ) + respx.post(f"{API}/v2/scrape").mock(side_effect=scrape_router) + res = fc.crawl_store(session(store), fc.FirecrawlClient(api_key="k", credit_budget=12), None) + assert res.budget_exhausted and len(res.products) == 2 and res.credits_spent == 11 + + +@respx.mock +def test_search_accepts_both_shapes(): + route = respx.post(f"{API}/v2/search") + route.mock( + return_value=httpx.Response( + 200, json={"success": True, "data": {"web": [{"url": "https://a.se"}]}} + ) + ) + client = fc.FirecrawlClient(api_key="k") + assert client.search("q") == [{"url": "https://a.se"}] + route.mock( + return_value=httpx.Response( + 200, json={"success": True, "data": [{"url": "https://b.se"}, {}]} + ) + ) + assert client.search("q") == [{"url": "https://b.se"}] diff --git a/tests/test_gateway.py b/tests/test_gateway.py new file mode 100644 index 0000000..2f66d73 --- /dev/null +++ b/tests/test_gateway.py @@ -0,0 +1,69 @@ +from fastapi.testclient import TestClient + +from c2a.index import InMemoryRetriever +from c2a.schemas import Money, Product, RecItem, Variant +from c2a.serve.gateway import create_app + + +def product(pid, title, amount, available=True): + return Product( + id=f"s:{pid}", + store_id="s", + native_id=pid, + title=title, + variants=[Variant(id=pid, price=Money(amount=amount, currency="SEK"), available=available)], + ) + + +PRODUCTS = [ + product("1", "wool socks", 100), + product("2", "wool sweater", 900), + product("3", "wool hat", 50, False), +] + + +def test_recommend_filters_constraints(): + client = TestClient(create_app(InMemoryRetriever(PRODUCTS))) + res = client.post( + "/recommend", + json={ + "stage": "pre_checkout", + "query": "wool", + "constraints": {"max_price": {"amount": 500, "currency": "SEK"}}, + }, + ) + assert res.status_code == 200 + assert [i["product_id"] for i in res.json()["items"]] == ["s:1"] + + +def test_recommend_rejects_hallucinating_ranker(): + app = create_app( + InMemoryRetriever(PRODUCTS), ranker=lambda req, c: [RecItem(product_id="nope")] + ) + res = TestClient(app).post("/recommend", json={"stage": "pre_checkout", "query": "wool"}) + assert res.status_code == 502 + + +def test_health_and_decide(): + client = TestClient(create_app(InMemoryRetriever(PRODUCTS))) + assert client.get("/health").json() == {"status": "ok"} + res = client.post("/decide", json={"evidence": "e", "question": "q", "labels": ["x", "y"]}) + assert res.status_code == 200 and abs(sum(res.json()["probs"].values()) - 1) < 1e-9 + + +def test_systemone_passthrough(): + client = TestClient(create_app(InMemoryRetriever(PRODUCTS))) + body = { + "state": "wool socks", + "questions": { + "vertical": { + "type": "choice", + "criteria": {"apparel": "socks and clothes", "home": None}, + }, + "bundle": {"type": "noul"}, + }, + } + res = client.post("/v1/systemone", json=body) + assert res.status_code == 200 + answers = res.json()["answers"] + assert answers["vertical"]["choice"] == "apparel" and answers["bundle"]["type"] == "noul" diff --git a/tests/test_labeling.py b/tests/test_labeling.py new file mode 100644 index 0000000..d4fdc28 --- /dev/null +++ b/tests/test_labeling.py @@ -0,0 +1,206 @@ +import json + +import pytest +from typer.testing import CliRunner + +from c2a.cli import app +from c2a.decide.systemone import ( + ChoiceAnswer, + NoulAnswer, + ScoreAnswer, + SystemOneRequest, + SystemOneResponse, +) +from c2a.labeling.cache import ResponseCache +from c2a.labeling.escalation import apply_review +from c2a.labeling.export import to_decider_train, to_gold +from c2a.labeling.gating import Thresholds, is_confident, load_thresholds +from c2a.labeling.pipeline import label +from c2a.labeling.questions import get_question_set, pair_v1, product_v1 +from c2a.labeling.records import LabelItem +from c2a.labeling.state import pair_state, product_state +from c2a.sources.shopify import parse_products_json +from c2a.train.data import write_jsonl + + +class ScriptedDecider: + """Returns fixed answers for product_v1 and counts calls.""" + + model = "jev-latest" + + def __init__(self, confident: bool = True): + self.calls = 0 + self.confident = confident + + def ask(self, request: SystemOneRequest) -> SystemOneResponse: + self.calls += 1 + c = 0.9 if self.confident else 0.3 + answers = {} + for qid, q in request.questions.items(): + if q.type == "noul": + answers[qid] = NoulAnswer(noul=0.02 if self.confident else 0.6) + elif q.type == "choice": + opts = list(q.criteria) + probs = { + o: (c if i == 0 else (1 - c) / (len(opts) - 1)) for i, o in enumerate(opts) + } + answers[qid] = ChoiceAnswer(choice=opts[0], confidence=c, probabilities=probs) + else: + keys = [str(i) for i in range(len(q.criteria))] + probs = {k: (c if k == "1" else (1 - c) / (len(keys) - 1)) for k in keys} + answers[qid] = ScoreAnswer( + score=1.0, + confidence=c, + legend=dict(zip(keys, q.criteria, strict=True)), + probabilities=probs, + ) + return SystemOneResponse(model=self.model, answers=answers) + + +@pytest.fixture +def products(store, shopify_payload): + return parse_products_json(shopify_payload, store, "SEK")[0] + + +@pytest.fixture +def items(products): + return [LabelItem(id=p.id, state=product_state(p)) for p in products] + + +def test_question_sets_are_valid_and_versioned(): + for name in ("page_v1", "product_v1", "pair_v1"): + qs = get_question_set(name) + assert qs.questions and "@" in qs.id + assert "apparel" in product_v1().questions["vertical"].criteria + assert list(pair_v1().questions["relation"].criteria) == [ + "exact", + "substitute", + "complement", + "irrelevant", + ] + with pytest.raises(KeyError): + get_question_set("nope") + + +def test_product_state_is_compact(products): + s = product_state(products[0]) + assert s["price"] == "129.00 SEK" and s["description"] == "Soft merino sneaker." + assert s["options"] == {"Color": ["Grey"], "Size": ["42", "43"]} + assert "attributes" not in s # empty fields dropped + assert pair_state("wool sneakers", products[0])["anchor"] == {"query": "wool sneakers"} + + +def test_gating_per_type_and_overrides(): + t = Thresholds( + min_confidence=0.6, + min_top_prob=0.7, + noul_min=0.85, + per_question={"restricted": {"noul_min": 0.95}}, + ) + assert is_confident(NoulAnswer(noul=0.1), t) + assert not is_confident(NoulAnswer(noul=0.1), t.for_question("restricted")) + good = ChoiceAnswer(choice="a", confidence=0.7, probabilities={"a": 0.8, "b": 0.2}) + low_conf = good.model_copy(update={"confidence": 0.5}) + assert is_confident(good, t) and not is_confident(low_conf, t) + assert load_thresholds().per_question["contains_pii"]["noul_min"] == 0.95 + + +def test_confident_labels_are_accepted_and_cached(tmp_path, items): + cache = ResponseCache(tmp_path / "cache") + dec = ScriptedDecider() + run = label(items, dec, product_v1(), load_thresholds(), cache=cache) + assert dec.calls == 2 and run.requests_made == 2 + assert len(run.records) == 2 * len(product_v1().questions) + assert all(r.status == "accepted" and r.source == "jev" for r in run.records) + price = next(r for r in run.records if r.question_id == "price_tier") + assert price.value == "mid_market" and "mid_market" in price.probabilities + bundle = next(r for r in run.records if r.question_id == "is_bundle") + assert bundle.value == "false" + rerun = label(items, dec, product_v1(), load_thresholds(), cache=cache) + assert dec.calls == 2 and rerun.cache_hits == 2 # no second payment + + +def test_budget_cap_skips_items(items): + dec = ScriptedDecider() + run = label(items, dec, product_v1(), load_thresholds(), max_requests=1) + assert dec.calls == 1 and run.budget_exhausted and run.skipped == [items[1].id] + + +def test_escalation_teacher_agree_and_disagree(items): + qs = product_v1() + agree = label( + items[:1], + ScriptedDecider(False), + qs, + load_thresholds(), + teacher=lambda state, qid, q: ( + "true" + if q.type == "noul" + else (list(q.criteria)[0] if q.type == "choice" else q.criteria[1]) + ), + ) + assert all(r.status == "accepted" and r.source == "jev+teacher" for r in agree.records) + assert agree.review == [] + + disagree = label( + items[:1], + ScriptedDecider(False), + qs, + load_thresholds(), + teacher=lambda state, qid, q: "something else", + ) + assert all(r.status == "review" for r in disagree.records) + assert len(disagree.review) == len(qs.questions) + assert disagree.review[0].record.teacher_value == "something else" + + no_teacher = label(items[:1], ScriptedDecider(False), qs, load_thresholds()) + assert all(r.status == "review" for r in no_teacher.records) + + +def test_exports_kev_format_and_exclude_jev_only(items): + qs = product_v1() + run = label(items, ScriptedDecider(), qs, load_thresholds()) + states = {it.id: it.state for it in items} + assert to_decider_train(states, run.records, qs) == [] # Jev-only excluded by default + rows = to_decider_train(states, run.records, qs, sources={"jev"}) + q = rows[0]["questions"] + assert q["is_bundle"]["label"] is False and q["price_tier"]["label"] == 1 + assert q["vertical"]["label"] == "apparel" and q["vertical"]["type"] == "choice" + + reviewed = [ + apply_review(r, "premium", "ann") for r in run.records if r.question_id == "price_tier" + ] + gold = to_gold(reviewed) + assert gold[0]["label"] == "premium" and gold[0]["reviewer"] == "ann" + human_rows = to_decider_train(states, reviewed, qs) + assert human_rows[0]["questions"]["price_tier"]["label"] == 2 + + +def test_cli_dry_run_and_offline_run(tmp_path, products): + runner = CliRunner() + inp = tmp_path / "products.jsonl" + write_jsonl(inp, products) + res = runner.invoke(app, ["label", "run", "--input", str(inp), "--dry-run"]) + assert res.exit_code == 0 and '"questions"' in res.output and "product@1" in res.output + res = runner.invoke( + app, + [ + "label", + "run", + "--input", + str(inp), + "--backend", + "keyword", + "--out", + str(tmp_path / "labels"), + ], + ) + assert res.exit_code == 0, res.output + run_dir = tmp_path / "labels" / "product_v1" + assert (run_dir / "labels.jsonl").exists() and (run_dir / "items.jsonl").exists() + out = tmp_path / "gold.jsonl" + res = runner.invoke( + app, ["label", "export", "--run-dir", str(run_dir), "--kind", "gold", "--out", str(out)] + ) + assert res.exit_code == 0 and out.read_text() == "" # no human labels yet + json.loads((run_dir / "labels.jsonl").read_text().splitlines()[0]) diff --git a/tests/test_open_datasets.py b/tests/test_open_datasets.py new file mode 100644 index 0000000..68f2a6d --- /dev/null +++ b/tests/test_open_datasets.py @@ -0,0 +1,150 @@ +import json + +import pyarrow as pa +import pyarrow.parquet as pq + +from c2a.labeling.export import to_decider_train +from c2a.labeling.questions import pair_v1 +from c2a.sources import open_datasets as od + + +def write_esci(tmp_path): + ex = tmp_path / "examples.parquet" + pr = tmp_path / "products.parquet" + pq.write_table( + pa.Table.from_pylist( + [ + { + "example_id": 1, + "query": "wool socks", + "query_id": 7, + "product_id": "B1", + "product_locale": "us", + "esci_label": "E", + "small_version": 1, + "large_version": 1, + "split": "train", + }, + { + "example_id": 2, + "query": "wool socks", + "query_id": 7, + "product_id": "B2", + "product_locale": "us", + "esci_label": "C", + "small_version": 1, + "large_version": 1, + "split": "test", + }, + { + "example_id": 3, + "query": "calcetines", + "query_id": 8, + "product_id": "B3", + "product_locale": "es", + "esci_label": "S", + "small_version": 0, + "large_version": 1, + "split": "train", + }, + ] + ), + ex, + ) + pq.write_table( + pa.Table.from_pylist( + [ + { + "product_id": "B1", + "product_title": "Merino socks", + "product_description": None, + "product_bullet_point": "Warm", + "product_brand": "Acme", + "product_color": "grey", + "product_locale": "us", + }, + { + "product_id": "B2", + "product_title": "Boot dryer", + "product_description": "Dries boots", + "product_bullet_point": None, + "product_brand": None, + "product_color": None, + "product_locale": "us", + }, + ] + ), + pr, + ) + return ex, pr + + +def test_esci_to_human_pair_labels(tmp_path): + ex, pr = write_esci(tmp_path) + rows = list(od.load_esci(ex, pr)) + assert [(r.product_id, r.label, r.locale) for r in rows] == [ + ("B1", "exact", "en-US"), + ("B2", "complement", "en-US"), + ] + assert rows[0].product == { + "title": "Merino socks", + "bullet_point": "Warm", + "brand": "Acme", + "color": "grey", + } + assert len(list(od.load_esci(ex, pr, small_version_only=False))) == 3 + items, records = od.esci_to_labels(rows) + assert all(r.source == "human" and r.qset == "pair@1" for r in records) + train = to_decider_train({i.id: i.state for i in items}, records, pair_v1()) + assert train[1]["questions"]["relation"]["label"] == "complement" + assert train[0]["state"]["anchor"] == {"query": "wool socks"} + + +def test_amazon_m2_sessions(tmp_path): + p = tmp_path / "sessions.csv" + p.write_text( + "prev_items,next_item,locale\n" + "\"['B09W9FND7K' 'B09JSPLN1M']\",B076THCGSG,UK\n" + "\"['X1']\",X2,DE\n" + ) + rows = list(od.load_amazon_m2(p, locales=["UK"])) + assert len(rows) == 1 and rows[0].prev_items == ["B09W9FND7K", "B09JSPLN1M"] + assert rows[0].next_item == "B076THCGSG" and rows[0].locale == "en-GB" + + +def test_amazon_reviews_and_histories(tmp_path): + p = tmp_path / "reviews.jsonl" + lines = [ + { + "rating": 5.0, + "title": "Great", + "text": "Love it", + "asin": "A1", + "parent_asin": "P1", + "user_id": "u1", + "timestamp": 200, + "verified_purchase": True, + }, + { + "rating": 2.0, + "title": "", + "text": "Meh", + "asin": "A2", + "parent_asin": "P2", + "user_id": "u1", + "timestamp": 100, + "verified_purchase": True, + }, + { + "rating": 4.0, + "text": "ok", + "asin": "A3", + "user_id": "u2", + "timestamp": 1, + "verified_purchase": False, + }, + ] + p.write_text("\n".join(json.dumps(x) for x in lines)) + reviews = list(od.load_amazon_reviews(p)) + assert reviews[0].product_id == "amazon:P1" and reviews[0].text == "Great Love it" + assert od.user_histories(p) == {"u1": ["P2", "P1"]} diff --git a/tests/test_pilot.py b/tests/test_pilot.py new file mode 100644 index 0000000..45ecf23 --- /dev/null +++ b/tests/test_pilot.py @@ -0,0 +1,255 @@ +import json +import os + +import httpx +import pytest +import respx +from typer.testing import CliRunner + +from c2a.cli import app +from c2a.config import Settings, load_dotenv +from c2a.decide.systemone import ChoiceAnswer, NoulAnswer, ScoreAnswer, SystemOneResponse +from c2a.doctor import format_checks, run_doctor +from c2a.labeling.pairs import candidate_pairs, pair_items +from c2a.labeling.review import apply_decision, format_item, load_queue +from c2a.pilot import run_pilot +from c2a.schemas import Money, Product, TosStatus, Variant +from c2a.sources import shopify +from c2a.sources.compliance import CrawlSession, RateLimiter, parse_robots +from c2a.sources.registry import Registry + + +class UnsureDecider: + """Answers every question with low confidence, so everything goes to review.""" + + model = "jev-latest" + + def __init__(self): + self.calls = 0 + + def ask(self, request): + self.calls += 1 + answers = {} + for qid, q in request.questions.items(): + if q.type == "noul": + answers[qid] = NoulAnswer(noul=0.55) + elif q.type == "choice": + opts = list(q.criteria) + p = 1 / len(opts) + answers[qid] = ChoiceAnswer( + choice=opts[0], confidence=0.1, probabilities=dict.fromkeys(opts, p) + ) + else: + keys = [str(i) for i in range(len(q.criteria))] + answers[qid] = ScoreAnswer( + score=1.0, + confidence=0.1, + legend=dict(zip(keys, q.criteria, strict=True)), + probabilities=dict.fromkeys(keys, 1 / len(keys)), + ) + return SystemOneResponse(model=self.model, answers=answers) + + +def product(pid, title, category): + return Product( + id=f"s:{pid}", + store_id="s", + native_id=pid, + title=title, + category=category, + variants=[Variant(id=pid, price=Money(amount=100, currency="SEK"))], + ) + + +def session(store, **kw): + return CrawlSession( + store, + client=httpx.Client(), + limiter=RateLimiter(1000, sleep=lambda s: None), + robots=parse_robots(""), + sleep=lambda s: None, + **kw, + ) + + +def test_load_dotenv_does_not_override(tmp_path, monkeypatch): + env = tmp_path / ".env" + env.write_text('# c\nexport C2A_A="one"\nC2A_B=two\nC2A_C=\nbad line\n') + monkeypatch.setenv("C2A_B", "keep") + monkeypatch.delenv("C2A_A", raising=False) + assert load_dotenv(env) == ["C2A_A"] + assert os.environ["C2A_A"] == "one" and os.environ["C2A_B"] == "keep" + monkeypatch.delenv("C2A_A") + + +@respx.mock +def test_session_retries_on_429_with_retry_after(store): + route = respx.get("https://shop.example.com/products/x").mock( + side_effect=[httpx.Response(429, headers={"Retry-After": "7"}), httpx.Response(200)] + ) + sleeps = [] + s = CrawlSession( + store, + client=httpx.Client(), + limiter=RateLimiter(1000, sleep=lambda x: None), + robots=parse_robots(""), + sleep=sleeps.append, + ) + assert s.get("https://shop.example.com/products/x").status_code == 200 + assert route.call_count == 2 and sleeps == [7.0] and s.retries == 1 + + +@respx.mock +def test_shopify_currency_chain_and_page_guards(store): + store.currency = None + respx.get("https://shop.example.com/meta.json").mock(return_value=httpx.Response(404)) + page = { + "products": [ + {"id": 1, "title": "A", "handle": "a", "variants": [{"id": 10, "price": "1.00"}]} + ] + } + route = respx.get("https://shop.example.com/products.json").mock( + return_value=httpx.Response(200, json=page) # theme ignores ?page -> same page forever + ) + s = session(store) + products, _ = shopify.crawl(s, limit=1) + assert len(products) == 1 and route.call_count == 2 # stops on the repeated page + assert products[0].variants[0].price.currency == "SEK" + assert "assumed SEK" in s.warnings[0] + + +def test_candidate_pairs_deterministic_no_dupes(): + prods = [ + product("1", "wool socks grey", "socks"), + product("2", "wool socks black", "socks"), + product("3", "merino wool socks", "socks"), + product("4", "running shoes", "shoes"), + ] + a = candidate_pairs(prods, k=2, other=1) + assert a == candidate_pairs(list(reversed(prods)), k=2, other=1) + keys = [frozenset((x.id, y.id)) for x, y in a] + assert len(keys) == len(set(keys)) and all(x.id != y.id for x, y in a) + assert any({x.category, y.category} == {"socks", "shoes"} for x, y in a) + items = pair_items(a) + assert items[0].id.startswith("pair:") and "candidate" in items[0].state + + +@respx.mock +def test_doctor_reports_fixes(store): + respx.get("https://api.typesafe.ai").mock(return_value=httpx.Response(404)) + respx.get("https://api.firecrawl.dev").mock(side_effect=httpx.ConnectError("no")) + respx.get("https://shop.example.com/robots.txt").mock( + return_value=httpx.Response(200, text="User-agent: *\nDisallow: /products.json\n") + ) + os.environ.pop("TYPESAFE_API_KEY", None) + store.currency = None + checks = { + c.name: c for c in run_doctor(Registry(stores=[store]), Settings(), client=httpx.Client()) + } + assert checks["TYPESAFE_API_KEY"].status == "fail" + assert checks["Jev API reachable"].status == "ok" + assert checks["Firecrawl API reachable"].status == "warn" # no Firecrawl stores + assert checks["acme: /products.json allowed"].status == "fail" + assert checks["acme: currency"].status == "warn" + empty = run_doctor(Registry(stores=[]), Settings(), client=httpx.Client()) + assert "c2a registry approve" in format_checks(empty) + + +@respx.mock +def test_doctor_live_shows_raw_body_on_format_mismatch(store, monkeypatch): + monkeypatch.setenv("TYPESAFE_API_KEY", "k") + respx.get("https://api.typesafe.ai").mock(return_value=httpx.Response(404)) + respx.get("https://api.firecrawl.dev").mock(return_value=httpx.Response(404)) + respx.get("https://shop.example.com/robots.txt").mock(return_value=httpx.Response(404)) + respx.post("https://api.typesafe.ai/v1/systemone").mock( + return_value=httpx.Response(200, json={"result": {"unexpected": True}}) + ) + checks = run_doctor(Registry(stores=[store]), Settings(), live=True, client=httpx.Client()) + bad = next(c for c in checks if c.name == "Jev response format") + assert bad.status == "fail" and "unexpected" in bad.detail + + +@respx.mock +def test_pilot_end_to_end_then_review(tmp_path, store): + store.currency = "SEK" + respx.get("https://shop.example.com/products.json").mock( + return_value=httpx.Response( + 200, + json={ + "products": [ + { + "id": i, + "title": t, + "handle": f"h{i}", + "product_type": c, + "variants": [{"id": i * 10, "price": "99.00"}], + } + for i, (t, c) in enumerate( + [ + ("wool socks", "socks"), + ("wool socks thick", "socks"), + ("trail shoes", "shoes"), + ], + 1, + ) + ] + }, + ) + ) + other = store.model_copy(update={"id": "other", "tos_status": TosStatus.unreviewed}) + reg = Registry(stores=[store, other]) + with pytest.raises(ValueError, match="not approved"): + run_pilot(reg, Settings(), ["other"], UnsureDecider(), data_dir=tmp_path) + + dec = UnsureDecider() + res = run_pilot( + reg, + Settings(), + ["acme"], + dec, + data_dir=tmp_path, + pairs_k=1, + session_factory=lambda s: session(s), + ) + assert res.crawled == {"acme": 3} and res.product_labels == 3 * 8 + assert res.pair_labels > 0 and res.to_review == res.product_labels + res.pair_labels + assert "## Labels: product_v1" in res.report and "## Labels: pair_v1" in res.report + assert (tmp_path / "reports" / "pilot.md").exists() + + # human review: answering one item replaces its record and shrinks the queue + run_dir = tmp_path / "labels" / "product_v1" + queue = load_queue(run_dir) + item = next(q for q in queue if q.record.question_id == "vertical") + assert "Q: Which vertical" in format_item(item, 1, len(queue)) + decided = apply_decision(run_dir, item, "apparel", "marcus") + assert decided.source == "human" and len(load_queue(run_dir)) == len(queue) - 1 + labels = [json.loads(line) for line in (run_dir / "labels.jsonl").read_text().splitlines()] + mine = [ + r for r in labels if r["item_id"] == item.record.item_id and r["question_id"] == "vertical" + ] + assert len(mine) == 1 and mine[0]["value"] == "apparel" and mine[0]["reviewer"] == "marcus" + with pytest.raises(ValueError): + apply_decision(run_dir, load_queue(run_dir)[0], "not-an-option", "marcus") + + # rerun: nothing changed, no new Jev calls for products, cached pairs + calls = dec.calls + res2 = run_pilot( + reg, + Settings(), + ["acme"], + dec, + data_dir=tmp_path, + pairs_k=1, + session_factory=lambda s: session(s), + ) + assert res2.crawled == {"acme": 0} and dec.calls == calls + + out = CliRunner().invoke(app, ["report", "--data", str(tmp_path)]) + assert out.exit_code == 0 and "| vertical |" in out.output and "| relation |" in out.output + + +def test_review_cli_skip_and_quit_leave_files(tmp_path): + run_dir = tmp_path / "product_v1" + run_dir.mkdir() + res = CliRunner().invoke(app, ["label", "review", "--run-dir", str(run_dir), "--by", "m"]) + assert res.exit_code == 0 and "nothing to review" in res.output diff --git a/tests/test_ranking.py b/tests/test_ranking.py new file mode 100644 index 0000000..0aca40f --- /dev/null +++ b/tests/test_ranking.py @@ -0,0 +1,15 @@ +import pytest + +from c2a.graders.ranking import dedupe_preserving_order, ndcg_at_k, recall_at_k + + +def test_ndcg_perfect_and_reversed(): + rel = {"a": 2, "b": 1} + assert ndcg_at_k(["a", "b"], rel, 2) == pytest.approx(1.0) + assert 0 < ndcg_at_k(["b", "a"], rel, 2) < 1 + assert ndcg_at_k(["x"], {}, 5) == 0.0 + + +def test_recall_and_dedupe(): + assert recall_at_k(["a", "x", "b"], {"a", "b"}, 2) == 0.5 + assert dedupe_preserving_order(["a", "b", "a", "c"]) == ["a", "b", "c"] diff --git a/tests/test_rec_graders.py b/tests/test_rec_graders.py new file mode 100644 index 0000000..cb3854a --- /dev/null +++ b/tests/test_rec_graders.py @@ -0,0 +1,32 @@ +import json + +import pytest + +from c2a.graders.base import composite +from c2a.graders.rec import RecGrader + + +def out(*ids): + return json.dumps({"items": [{"product_id": i} for i in ids]}) + + +def test_perfect_ranking_scores_high(rec_example): + res = RecGrader(k=3).grade(out("a", "b"), rec_example) + assert res.passed and res.components["ndcg"] == 1.0 and res.components["recall"] == 1.0 + assert res.score == 1.0 + + +def test_hard_failures(rec_example): + assert not RecGrader().grade("not json", rec_example).passed + res = RecGrader().grade(out("a", "zzz"), rec_example) + assert not res.passed and res.score == 0 and "zzz" in res.reasons[0] + + +def test_constraint_violations_reduce_score(rec_example): + res = RecGrader(k=3).grade(out("a", "c", "d"), rec_example) # c too pricey, d out of stock + assert res.passed and res.components["constraints"] == pytest.approx(1 / 3) + assert any("over max price" in r for r in res.reasons) + + +def test_composite_weights(): + assert composite({"x": 1.0, "y": 0.0}, {"x": 3, "y": 1}) == 0.75 diff --git a/tests/test_registry.py b/tests/test_registry.py new file mode 100644 index 0000000..48c7532 --- /dev/null +++ b/tests/test_registry.py @@ -0,0 +1,19 @@ +from c2a.schemas import TosStatus +from c2a.sources.registry import load_registry + + +def test_seed_registry_is_valid_and_default_deny(): + reg = load_registry() + assert len(reg.stores) >= 25 + assert {s.country for s in reg.stores} == {"SE", "GB", "ES", "US", "CA"} + assert all(not s.enabled and s.tos_status is TosStatus.unreviewed for s in reg.stores) + assert reg.crawlable() == [] + + +def test_crawlable_requires_enabled_and_approved(): + reg = load_registry() + s = reg.stores[0] + s.enabled = True + assert reg.crawlable() == [] # still unreviewed + s.tos_status = TosStatus.approved + assert reg.crawlable() == [s] diff --git a/tests/test_registry_session.py b/tests/test_registry_session.py new file mode 100644 index 0000000..8daa624 --- /dev/null +++ b/tests/test_registry_session.py @@ -0,0 +1,73 @@ +import httpx +import pytest +import respx + +from c2a.schemas import TosStatus +from c2a.sources.compliance import CrawlSession, RateLimiter, parse_robots +from c2a.sources.registry import load_registry, save_registry + +from .conftest import FIXTURES + + +def test_approve_deny_roundtrip_with_audit(tmp_path): + reg = load_registry() + path = tmp_path / "registry.yaml" + s = reg.approve("allbirds", by="marcus", note="ToS allows crawling product pages") + assert s.enabled and s.tos_status is TosStatus.approved and s.reviewed_at is not None + save_registry(reg, path) + again = load_registry(path) + a = again.by_id("allbirds") + assert a.reviewed_by == "marcus" and a.review_note.startswith("ToS") + assert [x.id for x in again.crawlable()] == ["allbirds"] + again.deny("allbirds", by="legal") + assert again.crawlable() == [] and again.by_id("allbirds").tos_status is TosStatus.denied + with pytest.raises(ValueError): + again.approve("allbirds", by=" ") + + +def test_automated_updates_cannot_touch_permissions(): + reg = load_registry() + reg.update_descriptive("kotn", platform="shopify", method="shopify") + assert reg.by_id("kotn").method == "shopify" + with pytest.raises(ValueError): + reg.update_descriptive("kotn", enabled=True) + with pytest.raises(ValueError): + reg.update_descriptive("kotn", tos_status="approved") + + +def _session(store, robots_text=None): + calls = [] + rl = RateLimiter(qps=1000, sleep=lambda s: None) + rl.wait = lambda d: calls.append(d) # type: ignore[method-assign] + rp = parse_robots(robots_text or (FIXTURES / "robots.txt").read_text()) + return CrawlSession(store, client=httpx.Client(), limiter=rl, robots=rp), calls + + +@respx.mock +def test_crawl_session_gates_every_request(store): + respx.get("https://shop.example.com/products/x").mock(return_value=httpx.Response(200)) + session, calls = _session(store) + assert session.get("https://shop.example.com/products/x").status_code == 200 + assert calls == ["shop.example.com"] + for url in ("https://shop.example.com/checkout", "https://evil.example.com/products/x"): + with pytest.raises(PermissionError): + session.get(url) + assert len(session.denied) == 2 and calls == ["shop.example.com"] + store.tos_status = TosStatus.unreviewed + with pytest.raises(PermissionError, match="tos_status"): + session.get("https://shop.example.com/products/x") + + +@respx.mock +def test_api_host_needs_tos_but_not_robots(store): + respx.post("https://api.platform.example/ucp/catalog/search").mock( + return_value=httpx.Response(200, json={}) + ) + session, _ = _session(store, robots_text="User-agent: *\nDisallow: /") + with pytest.raises(PermissionError): + session.post("https://api.platform.example/ucp/catalog/search") + session.allow_api_host("https://api.platform.example/ucp") + assert session.post("https://api.platform.example/ucp/catalog/search").status_code == 200 + store.enabled = False + with pytest.raises(PermissionError): + session.post("https://api.platform.example/ucp/catalog/search") diff --git a/tests/test_rewards.py b/tests/test_rewards.py new file mode 100644 index 0000000..3c29f5e --- /dev/null +++ b/tests/test_rewards.py @@ -0,0 +1,62 @@ +import json + +import pytest + +from c2a.train.rewards import ( + copy_reward, + decide_reward, + group_advantages, + has_signal, + rank_group_advantages, + rank_rewards_to_go, + rec_reward, +) + + +def out(*ids): + return json.dumps({"items": [{"product_id": i} for i in ids]}) + + +def test_rec_reward_penalizes_hard_failures(rec_example): + assert rec_reward(out("a", "b"), rec_example, -0.5) == 1.0 + assert rec_reward(out("zzz"), rec_example, -0.5) == -0.5 + assert rec_reward("oops", rec_example, -0.5) == -0.5 + + +def test_decide_reward_is_calibration_aware(): + ex = {"labels": ["a", "b"], "label": "a"} + confident_right = decide_reward( + json.dumps({"label": "a", "probs": {"a": 0.9, "b": 0.1}}), ex, -1 + ) + hedged = decide_reward(json.dumps({"label": "a", "probs": {"a": 0.6, "b": 0.4}}), ex, -1) + confident_wrong = decide_reward( + json.dumps({"label": "b", "probs": {"a": 0.1, "b": 0.9}}), ex, -1 + ) + assert confident_right > hedged > confident_wrong >= 0 + assert decide_reward(json.dumps({"label": "a", "probs": {"a": 1.0}}), ex, -1) == -1 + + +def test_copy_reward_checks_numbers_against_attributes(): + ex = {"attributes": {"weight": "250 g", "material": "merino"}} + good = json.dumps({"text": "t", "claims": ["Weighs 250 g"]}) + bad = json.dumps({"text": "t", "claims": ["Weighs 300 g", "Weighs 250 g"]}) + assert copy_reward(good, ex, -1) == 1.0 and copy_reward(bad, ex, -1) == 0.5 + + +def test_group_advantages_modes(): + assert group_advantages([1, 0, 0, 1], "dr_grpo") == [0.5, -0.5, -0.5, 0.5] + assert group_advantages([1, 0], "grpo") == pytest.approx([1.0, -1.0], abs=1e-4) + assert not has_signal([0.3, 0.3, 0.3]) and has_signal([0, 1]) + + +def test_rank_rewards_to_go_are_causal(rec_example): + rtg = rank_rewards_to_go(out("x_bad", "a"), rec_example, k=3, fail_penalty=-0.5) + assert len(rtg) == 2 and rtg[0] < rtg[1] # hallucinated top item lowers rank-0 return only + perfect = rank_rewards_to_go(out("a", "b"), rec_example, k=3) + assert perfect[0] == pytest.approx(1.0) and perfect[0] > perfect[1] > 0 + assert rank_rewards_to_go("nope", rec_example) == [-0.5] + + +def test_rank_group_advantages_center_per_position(): + advs = rank_group_advantages([[1.0, 0.5], [0.0]]) + assert advs == [[0.5, 0.25], [-0.5]] diff --git a/tests/test_schemas.py b/tests/test_schemas.py new file mode 100644 index 0000000..b0d9fe9 --- /dev/null +++ b/tests/test_schemas.py @@ -0,0 +1,32 @@ +import pytest +from pydantic import ValidationError + +from c2a.schemas import DecisionRequest, DecisionResult, Money, Product, Variant + + +def test_money_uppercases_currency(): + assert Money(amount=100, currency="sek").currency == "SEK" + + +def test_product_requires_variant_and_helpers(): + with pytest.raises(ValidationError): + Product(id="s:1", store_id="s", native_id="1", title="x", variants=[]) + p = Product( + id="s:1", + store_id="s", + native_id="1", + title="x", + variants=[ + Variant(id="1", price=Money(amount=500, currency="EUR"), available=False), + Variant(id="2", price=Money(amount=300, currency="EUR"), available=True), + ], + ) + assert p.min_price.amount == 300 and p.in_stock + + +def test_decision_schemas_validate(): + with pytest.raises(ValidationError): + DecisionRequest(evidence="e", question="q", labels=["a", "a"]) + with pytest.raises(ValidationError): + DecisionResult(label="a", probs={"a": 0.5, "b": 0.4}) + DecisionResult(label="a", probs={"a": 0.6, "b": 0.4}) diff --git a/tests/test_shopify.py b/tests/test_shopify.py new file mode 100644 index 0000000..b406c0c --- /dev/null +++ b/tests/test_shopify.py @@ -0,0 +1,15 @@ +from c2a.sources.shopify import parse_products_json + + +def test_parse_products_json(store, shopify_payload): + products, errors = parse_products_json(shopify_payload, store, "SEK") + assert [p.id for p in products] == ["acme:101", "acme:102"] + assert len(errors) == 1 and errors[0].startswith("103") + runner, socks = products + v = runner.variants[0] + assert v.price.amount == 12900 and v.compare_at_price.amount == 14900 + assert v.options == {"Size": "42", "Color": "Grey"} + assert runner.variants[1].sku is None and not runner.variants[1].available + assert str(runner.url) == "https://shop.example.com/products/merino-runner" + assert socks.tags == ["socks", "wool"] and socks.variants[0].price.amount == 1999 + assert runner.source == "shopify" and runner.locale == "sv-SE" diff --git a/tests/test_splits.py b/tests/test_splits.py new file mode 100644 index 0000000..ea95d41 --- /dev/null +++ b/tests/test_splits.py @@ -0,0 +1,30 @@ +from datetime import UTC, datetime + +from c2a.datasets.splits import pick_holdout_stores, split +from c2a.schemas import TaskExample + + +def ex(i, store, day): + return TaskExample( + id=str(i), + task="rec_pre", + locale="en-US", + store_id=store, + created_at=datetime(2026, 9, day, tzinfo=UTC), + payload={}, + ) + + +def test_holdout_is_deterministic(): + ids = [f"s{i}" for i in range(100)] + a, b = pick_holdout_stores(ids, 0.2), pick_holdout_stores(ids, 0.2) + assert a == b and 5 < len(a) < 40 + + +def test_split_by_store_and_time(): + out = split( + [ex(1, "s1", 1), ex(2, "s2", 1), ex(3, "s1", 20)], {"s2"}, datetime(2026, 9, 15, tzinfo=UTC) + ) + assert [e.id for e in out["train"]] == ["1"] + assert sorted(e.id for e in out["test"]) == ["2", "3"] + assert not {e.store_id for e in out["train"]} & {"s2"} diff --git a/tests/test_tinker_loops.py b/tests/test_tinker_loops.py new file mode 100644 index 0000000..ed46c3b --- /dev/null +++ b/tests/test_tinker_loops.py @@ -0,0 +1,112 @@ +import json + +import pytest + +from c2a.train.base import RunConfig +from c2a.train.data import DistillPrompt, Message, RLPrompt, SFTExample +from c2a.train.tinker.client import Sample +from c2a.train.tinker.opd import reverse_kl_advantages, run_opd +from c2a.train.tinker.rl import run_rl +from c2a.train.tinker.sft import run_sft + + +class FakeBackend: + """Records calls; samples alternate between a perfect and a hallucinated ranking.""" + + def __init__(self): + self.sft_batches, self.rl_batches, self.saved = [], [], [] + self._i = 0 + + def sft_step(self, batch, learning_rate): + self.sft_batches.append((len(batch), learning_rate)) + return {"loss": 1.0 / (len(self.sft_batches))} + + def sample(self, messages, n, max_tokens, temperature): + outs = [] + for _ in range(n): + ids = ["a", "b"] if self._i % 2 == 0 else ["zzz"] + self._i += 1 + outs.append( + Sample( + text=json.dumps({"items": [{"product_id": i} for i in ids]}), + tokens=[1, 2, 3], + logprobs=[-0.1, -0.2, -0.3], + ) + ) + return outs + + def rl_step(self, batch, learning_rate): + self.rl_batches.append(batch) + return {"loss": 0.0} + + def teacher_logprobs(self, messages, sample): + return [-0.2, -0.2, -0.2] + + def save(self, name): + self.saved.append(name) + return f"tinker://fake/{name}" + + +def cfg(tmp_path, **kw): + base = dict( + name="t", + stage="sft", + base_model="m", + dataset=tmp_path / "d.jsonl", + log_dir=tmp_path, + batch_size=2, + save_every=1, + ) + return RunConfig(**{**base, **kw}) + + +def msgs(): + return [Message(role="user", content="q"), Message(role="assistant", content="a")] + + +def test_sft_loop(tmp_path): + be = FakeBackend() + exs = [SFTExample(id=str(i), messages=msgs()) for i in range(5)] + summary = run_sft(be, exs, cfg(tmp_path)) + assert summary["steps"] == 2 and [n for n, _ in be.sft_batches] == [2, 2] + assert be.sft_batches[0][1] > be.sft_batches[1][1] # lr decays + assert be.saved[-1] == "t-final" and (tmp_path / "t" / "summary.json").exists() + + +@pytest.mark.parametrize("mode", ["dr_grpo", "grpo", "rank_grpo"]) +def test_rl_loop_uses_grader_rewards(tmp_path, rec_example, mode): + be = FakeBackend() + prompts = [ + RLPrompt(id=str(i), messages=msgs()[:1], reward="rec", example=rec_example) + for i in range(2) + ] + summary = run_rl(be, prompts, cfg(tmp_path, stage="rl", group_size=4, advantage=mode, steps=1)) + batch = be.rl_batches[0] + assert len(batch) == 8 + good = [d.advantage for d in batch if "zzz" not in d.sample.text] + bad = [d.advantage for d in batch if "zzz" in d.sample.text] + assert min(good) > 0 > max(bad) + assert summary["history"][0]["groups_dropped"] == 0 + + +def test_rl_loop_drops_zero_variance_groups(tmp_path, rec_example): + be = FakeBackend() + be.sample = lambda m, n, t, temp: [Sample(text="bad")] * n + prompts = [RLPrompt(id="1", messages=msgs()[:1], reward="rec", example=rec_example)] + summary = run_rl(be, prompts, cfg(tmp_path, stage="rl", batch_size=1, steps=1)) + assert be.rl_batches == [] and summary["history"][0]["groups_dropped"] == 1 + + +def test_opd_loop(tmp_path): + be = FakeBackend() + prompts = [DistillPrompt(id=str(i), messages=msgs()[:1]) for i in range(2)] + run_opd(be, prompts, cfg(tmp_path, stage="opd", teacher_model="t", group_size=2, steps=1)) + adv = be.rl_batches[0][0].advantage + assert adv == pytest.approx([-0.1, 0.0, 0.1]) # -(student - teacher) + with pytest.raises(ValueError): + run_opd(be, prompts, cfg(tmp_path, stage="opd")) + + +def test_reverse_kl_advantages_alignment(): + with pytest.raises(ValueError): + reverse_kl_advantages([0.0], [0.0, 1.0], 1.0) diff --git a/tests/test_train_data.py b/tests/test_train_data.py new file mode 100644 index 0000000..7c8f5b5 --- /dev/null +++ b/tests/test_train_data.py @@ -0,0 +1,28 @@ +import pytest +from pydantic import ValidationError + +from c2a.train.data import Message, RLPrompt, SFTExample, read_jsonl, write_jsonl + + +def test_sft_must_end_with_assistant(): + with pytest.raises(ValidationError): + SFTExample( + id="1", messages=[Message(role="user", content="hi"), Message(role="user", content="?")] + ) + + +def test_jsonl_roundtrip(tmp_path): + recs = [ + RLPrompt(id=str(i), messages=[Message(role="user", content="q")], reward="rec") + for i in range(3) + ] + path = tmp_path / "x" / "rl.jsonl" + assert write_jsonl(path, recs) == 3 + assert list(read_jsonl(path, RLPrompt)) == recs + + +def test_read_jsonl_reports_line(tmp_path): + p = tmp_path / "bad.jsonl" + p.write_text('{"id": "1"}\n') + with pytest.raises(ValueError, match="bad.jsonl:1"): + list(read_jsonl(p, RLPrompt)) diff --git a/tests/test_ucp.py b/tests/test_ucp.py new file mode 100644 index 0000000..1b92473 --- /dev/null +++ b/tests/test_ucp.py @@ -0,0 +1,108 @@ +import httpx +import respx + +from c2a.sources import ucp +from c2a.sources.compliance import CrawlSession, RateLimiter, parse_robots + +PROFILE = { + "ucp": { + "version": "2026-01-01", + "services": { + "dev.ucp.shopping": [ + {"version": "2026-01-01", "transport": "mcp", "endpoint": "https://x/mcp"}, + { + "version": "2026-01-01", + "transport": "rest", + "endpoint": "https://api.shop.test/ucp/", + }, + ] + }, + "capabilities": { + "dev.ucp.shopping.catalog.search": [{"version": "2026-01-01"}], + "dev.ucp.shopping.catalog.lookup": [{"version": "2026-01-01"}], + }, + "payment_handlers": {}, + } +} + +# From the UCP spec (catalog REST binding example), trimmed. +PRODUCT = { + "id": "prod_abc123", + "handle": "blue-runner-pro", + "title": "Blue Runner Pro", + "description": {"plain": "Lightweight running shoes with responsive cushioning."}, + "url": "https://shop.example.com/products/blue-runner-pro", + "categories": [ + {"value": "187", "taxonomy": "google_product_category"}, + {"value": "Footwear > Running", "taxonomy": "merchant"}, + ], + "price_range": { + "min": {"amount": 12000, "currency": "USD"}, + "max": {"amount": 12000, "currency": "USD"}, + }, + "media": [{"type": "image", "url": "https://cdn.example.com/brp.jpg"}], + "variants": [ + { + "id": "prod_abc123_size10", + "sku": "BRP-BLU-10", + "title": "Size 10", + "price": {"amount": 12000, "currency": "USD"}, + "availability": {"available": True}, + "options": [{"name": "Size", "label": "10"}], + "tags": ["running", "road"], + } + ], +} + + +def session(store): + return CrawlSession( + store, + client=httpx.Client(), + limiter=RateLimiter(1000, sleep=lambda s: None), + robots=parse_robots(""), + ) + + +def test_parse_profile_prefers_rest(): + p = ucp.parse_profile(PROFILE) + assert p.endpoint == "https://api.shop.test/ucp" and p.can_search and p.can_lookup + assert ucp.parse_profile({"ucp": {"services": {}}}) is None + + +def test_parse_ucp_product(store): + p = ucp.parse_ucp_product(PRODUCT, store) + assert p.id == "acme:prod_abc123" and p.source == "ucp" + v = p.variants[0] + assert v.price.amount == 12000 and v.price.currency == "USD" and v.options == {"Size": "10"} + assert ( + p.category == "Footwear > Running" + and p.attributes["category_google_product_category"] == "187" + ) + assert p.image_urls == ["https://cdn.example.com/brp.jpg"] and p.tags == ["road", "running"] + bare = {k: v for k, v in PRODUCT.items() if k != "variants"} + assert ucp.parse_ucp_product(bare, store).variants[0].price.amount == 12000 + + +@respx.mock +def test_discover_and_paginated_catalog(store): + respx.get("https://shop.example.com/.well-known/ucp").mock( + return_value=httpx.Response(200, json=PROFILE) + ) + p2 = {**PRODUCT, "id": "prod_2"} + route = respx.post("https://api.shop.test/ucp/catalog/search").mock( + side_effect=[ + httpx.Response( + 200, + json={"products": [PRODUCT], "pagination": {"cursor": "c1", "has_next_page": True}}, + ), + httpx.Response( + 200, json={"products": [p2, PRODUCT], "pagination": {"has_next_page": False}} + ), + ] + ) + s = session(store) + profile = ucp.discover(s) + products, errors = ucp.iter_catalog(s, profile, ["shoes"]) + assert [p.native_id for p in products] == ["prod_abc123", "prod_2"] and errors == [] + assert b'"cursor":"c1"' in route.calls[1].request.content.replace(b" ", b"") diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..38011d1 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4461 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and 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