Most RAG systems are stateless. Every query re-fetches, re-embeds, re-ranks.

ragy is different: index any topic across a full time window once, then use semantic similarity to retrieve the right days — not all days. Temporal memory for your AI agents, financial research, and knowledge workflows.

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Why ragy?

Standard RAG retrieves documents. ragy retrieves moments in time.

You define a query (e.g. "Fed interest rate signals") and a time window (e.g. 365 days). ragy fetches and embeds every day's content once, stores it in a local vector database, and lets you instantly answer: "which days in the past year most resembled this?" — ranked by cosine similarity, plotted on a timeline.

Key difference from other RAG projects:

	Standard RAG	ragy
Data model	Documents	Days in time
Re-fetch per query?	Yes	No — indexed once
Time-aware?	No	Yes — date is a first-class dimension
Visualization	None	Similarity timeline (xray)
Scheduling	Manual	Built-in APScheduler
Agent integration	Varies	Native MCP server

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What You Can Build

🌐 Enterprise data collection — Use Bright Data for large-scale web scraping, structured data extraction, and professional-grade data collection. Index competitive intelligence, market research, or any web data with advanced scraping capabilities.

📈 Financial research — Index a year of market news with yfinance, query "Fed pivot signals", get back the 10 most semantically similar trading days with similarity scores and related tickers on a timeline. Track multiple stocks with scheduled daily updates — no API costs.

🔍 Competitive intelligence — Schedule daily indexing of topics with Bright Data or Tavily. Ask "what weeks had the most activity around X?" Retrieve content ranked by relevance, not recency.

🤖 AI agent long-term memory — Give your Claude / LangGraph / n8n agent a persistent temporal knowledge base via MCP. No search API call on every turn — just semantic retrieval from your local index.

💹 Stock monitoring dashboard — Create collections for each stock you track (AAPL, NVDA, TSLA) using yfinance source. Query across time to find similar market conditions, earnings patterns, or news sentiment shifts.

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See It in Action

The xray command — your temporal similarity radar

ragy> xray
Collection: Stock_JNJ
Query: high price expected
Top K: 10

Returns a ranked list of dates with similarity scores, plotted on a timeline. This is the fastest way to understand when your topic was most relevant.

Financial Data Search

ragy> search_yfin
Search query: tesla stock
Max results: 3

Returns financial news articles with related ticker symbols (e.g., TSLA), publisher information, and direct links to Yahoo Finance. Perfect for quick market research or monitoring specific stocks.

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Quick Start

One-command install

curl -fsSL https://raw.githubusercontent.com/Arseni1919/ragy/main/install.sh | bash

Manual setup

git clone https://github.com/Arseni1919/ragy.git
cd ragy
uv sync

Create a .env file — API keys optional depending on data source:

# For Bright Data (advanced scraping)
BRIGHT_DATA_API_KEY="your-key-here"
BRIGHT_DATA_ZONE="your-zone"

# For Tavily (general web search)
TAVILY_API_KEY="your-key-here"   # Get free at tavily.com

# Note: yfinance (financial data) needs no API key

Start the stack:

# Terminal 1 — API
uv run uvicorn ragy_api.main:app --reload

# Terminal 2 — CLI
uv run ragy

First run note: embedding models (~80MB) download automatically in the background. First query may take 10–30 seconds; subsequent runs are instant.

Docker Quick Start

The fastest way to run RAGY:

git clone https://github.com/Arseni1919/ragy.git
cd ragy
cp .env.example .env
# Add your Tavily API key to .env
docker-compose up -d

Access at http://localhost:8000/docs

See README-DOCKER.md for complete Docker documentation.

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Core Workflow

1. Index a topic over time

ragy> create_index
Query: artificial intelligence news
Collection name: ai_2024
Number of days: 365

This fetches, embeds, and stores one entry per day for the past 365 days. Run once. Query forever.

2. Retrieve semantically relevant days

ragy> extract
Collection: ai_2024
Query: transformer architecture improvements
Top K: 5

Returns the 5 days most semantically similar to your query — not keyword matches, not most recent, but most relevant.

3. Visualize similarity over time

ragy> xray
Collection: ai_2024
Query: open source model releases
Top K: 10

Plots similarity scores as a timeline. Instantly see if your topic had a spike, a gradual trend, or scattered activity.

4. Schedule automatic updates

ragy> create_job
Query: tech news
Collection name: daily_tech
Data source: bright_data   # or 'tavily' or 'yfinance'
Interval type: day
Interval amount: 1

Example with yfinance for financial monitoring:

ragy> create_job
Query: nvidia stock news
Collection name: nvidia_tracker
Data source: yfinance       # financial news + quotes with tickers
Interval type: day
Interval amount: 1

ragy updates your collection every day at the scheduled hour. Your index stays current without manual work. Choose bright_data for advanced scraping, tavily for general web search, or yfinance for financial data (stocks, news, quotes) — no API key needed for yfinance.

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Data Sources

ragy supports multiple data sources for indexing and search. Choose based on your use case:

Bright Data (Advanced Web Scraping)

• Best for: Structured data extraction, large-scale web scraping, alternative search
• Requires: API key and zone credentials
• Content: Web pages, structured data, search results
• Use when: Need more control over scraping, bypassing restrictions, or professional-grade data collection

Tavily (General Web Search)

• Best for: News, articles, general web content, research
• Requires: API key (free tier available at tavily.com)
• Content: Web pages, news articles, blog posts
• Use when: Tracking general topics, news, or web content

yfinance (Financial Data)

• Best for: Stock market news, financial data, company information
• Requires: No API key needed (uses Yahoo Finance)
• Content: Financial news articles with related tickers, stock quotes, company data
• Use when: Monitoring stocks, tracking financial news, building investment research tools

Example comparison:

# Advanced scraping → use Bright Data
ragy> create_job
Query: competitor analysis
Data source: bright_data  # Advanced web scraping capabilities

# General tech news → use Tavily
ragy> create_job
Query: artificial intelligence breakthroughs
Data source: tavily

# Stock-specific news → use yfinance
ragy> create_job
Query: nvidia earnings
Data source: yfinance    # Returns news + NVDA ticker info

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CLI Reference

21 commands across 5 categories:

Index Management

Command	Description
create_index	Build temporal vector index for a query + time window
delete_index	Remove a collection
upload_csv	Import from CSV (date, content columns required)

Query & Extract

Command	Description
extract	Retrieve top-K days by semantic similarity
search	Live web search via Tavily
search_yfin	Search financial data via yfinance (stocks, news, quotes)

Inspect

Command	Description
list	All collections
status	Document count for a collection
sample	Inspect a single document by index
head_index / tail_index	Preview first / last 5 documents
stats	Full database overview
xray	Similarity timeline plot

Scheduling

Command	Description
jobs	List scheduled jobs
create_job	Set up recurring index updates
delete_job	Remove a job

System

Command	Description
health	API health check
info	Embedding model details
change_emb	Swap embedding model
help / exit / shutdown	Utility commands

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REST API

19 endpoints. Full Swagger UI at http://localhost:8000/docs.

Category	Key Endpoints
Index	POST /api/v1/index/create (SSE streaming)
Extract	POST /api/v1/extract/data · POST /api/v1/extract/all
Database	GET /api/v1/database/stats · GET /api/v1/database/collection/{name}/distribution
Search	POST /api/v1/search/web (Tavily) · POST /api/v1/search/yfinance (financial data)
Scheduler	POST /api/v1/system/scheduler/jobs/create
Upload	POST /api/v1/upload/csv

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MCP Integration

ragy ships a native MCP server, letting Claude Desktop (and any MCP-compatible agent) query your temporal knowledge base directly.

Available tools

Tool	Description
list_collections	See all indexed topics
extract_all	Retrieve relevant days by semantic similarity
search_web	Live web search via Tavily
get_database_stats	Database overview
health_check	API status

Setup

1. Start the API:

uv run uvicorn ragy_api.main:app --host 0.0.0.0 --port 8000

2. Add to claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "ragy": {
      "command": "uv",
      "args": ["run", "ragy-mcp"],
      "cwd": "/absolute/path/to/ragy"
    }
  }
}

3. Ask Claude:

• "Which days in my ai_2024 collection are most similar to 'GPT-4 level breakthroughs'?"
• "Show me database stats"
• "Search the web for recent LLM benchmark results"

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Architecture

graph TD
    D[Search Engines<br/>Bright Data / Tavily / yfinance] -->|fetch + index| B
    C[Embeddings<br/>HuggingFace / Ollama] -->|encode| B
    A[zvec<br/>local vector store] <-->|store / retrieve| B[FastAPI Backend]
    B --> E[CLI Client]
    B --> F[MCP Server]
    B --> G[HTTP / REST]
    H[APScheduler] -->|daily update| B

    style B fill:#0066FF,color:#fff
    style E fill:#00DDFF,color:#000
    style F fill:#00DDFF,color:#000

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Data flows:

1. Index: Bright Data/Tavily/yfinance → FastAPI → Embeddings → zvec (runs once)
2. Query: CLI / MCP / HTTP → FastAPI → Embeddings → zvec → ranked results
3. Schedule: APScheduler → FastAPI → Bright Data/Tavily/yfinance → zvec (runs daily)

All processing is local. Only search API calls (Bright Data, Tavily, yfinance) go to the network.

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Configuration

# Required (only for Tavily search)
TAVILY_API_KEY="..."           # Get free key at tavily.com
                               # Note: yfinance search works without any API key

# Optional — sensible defaults shown
HF_EMB_MODEL="google/embeddinggemma-300m"
DB_PATH="./ragy_db"
DB_PROVIDER="zvec"             # Vector database: "zvec" (default) or "chromadb"
RAGY_MAX_CONCURRENT=10
API_HOST="0.0.0.0"
API_PORT=8000
SCHEDULER_ENABLED=true
SCHEDULER_HOUR=2               # Daily update hour (UTC)
SCHEDULER_TIMEZONE="UTC"
JOBS_DB_PATH="./ragy_jobs.db"

# Optional — for Bright Data integration
BRIGHT_DATA_API_KEY="..."
BRIGHT_DATA_ZONE="..."

Data Source Selection:

• Use bright_data source for advanced scraping: requires BRIGHT_DATA_API_KEY and BRIGHT_DATA_ZONE in .env
• Use tavily source when creating jobs: requires TAVILY_API_KEY in .env
• Use yfinance source for financial data: no API key needed, works out of the box

Vector Database:

• zvec (default): High-performance C++ vector database from Alibaba, optimized for speed and memory
• chromadb: Python-based alternative, can be switched via DB_PROVIDER="chromadb" in .env

To use Ollama embeddings instead of HuggingFace:

ragy> change_emb
# Select: ollama
# Model: nomic-embed-text

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CSV Upload Format

Bring your own data — any time-series corpus works:

date,content,title,url
2024-01-15,Full text of article or document...,Optional title,https://...
2024-01-16,...

date and content are required. Any additional columns are stored as metadata and returned in query results.

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Project Structure

ragy/
├── ragy_api/          # FastAPI backend (19 endpoints)
│   ├── services/      # Business logic
│   └── routers/       # Route handlers
├── ragy_cli/          # Terminal interface (21 commands)
├── ragy_mcp/          # MCP server (5 tools)
├── conn_db/           # Database factory (zvec/chromadb)
├── conn_zvec/         # zvec connector (default)
├── conn_emb_hugging_face/
├── conn_emb_ollama/
├── conn_tavily/       # Tavily search API
├── conn_bright_data/  # Bright Data scraping API
├── sample_data/       # Sample datasets to try immediately
└── pyproject.toml

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Contributing

git fork https://github.com/Arseni1919/ragy
git checkout -b feature/your-feature
uv sync
# make changes
git commit -m "feat: your feature"
git push origin feature/your-feature
# open PR

See CLAUDE.md for code conventions, testing guidelines, and development workflows.

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Acknowledgments

• zvec — high-performance C++ vector database
• Bright Data — web scraping and data collection
• Tavily — web search API
• yfinance — Yahoo Finance data access
• Sentence Transformers — embedding models
• FastAPI — web framework
• Rich — terminal formatting

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