Agentic RAG

Multi-provider agentic RAG built with LangGraph and LangChain.

This project indexes a set of source documents, caches the vector index locally, and answers questions with a retrieval-first workflow that can rewrite queries, call retrieval as a tool, and generate grounded answers with citations.

Features

• LangGraph-based agentic RAG flow
• Multi-provider chat model support
• Multi-provider embedding support
• OpenAI-compatible endpoint support
• Local vector index cache in .cache/vectorstores
• CLI with step-by-step execution tracing
• Source-aware answers with citations

Supported Providers

Chat

• google
• openai
• openai-compatible
• anthropic
• litellm

Embeddings

• google
• openai
• openai-compatible
• litellm

Quickstart

git clone git@github.com:scldrn/RAGmain.git
cd RAGmain
python3 -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'

Create a .env file with your provider config. Minimal Google AI Studio example:

CHAT_PROVIDER=google
CHAT_MODEL=gemini-2.5-flash
EMBEDDING_PROVIDER=google
EMBEDDING_MODEL=gemini-embedding-2-preview
INDEX_CACHE_DIR=.cache/vectorstores
GOOGLE_API_KEY=your_api_key

Run a query:

python -m agentic_rag --question "What does Lilian Weng say about reward hacking?"

Run with trace output:

python -m agentic_rag \
  --question "What does Lilian Weng say about reward hacking?" \
  --show-steps

Provider Examples

OpenAI:

CHAT_PROVIDER=openai
CHAT_MODEL=gpt-4.1-mini
EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-small
OPENAI_API_KEY=your_api_key

Anthropic chat + OpenAI embeddings:

CHAT_PROVIDER=anthropic
CHAT_MODEL=claude-3-5-sonnet-20241022
EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-small
ANTHROPIC_API_KEY=your_api_key
OPENAI_API_KEY=your_api_key

OpenAI-compatible local endpoint:

CHAT_PROVIDER=openai-compatible
CHAT_MODEL=local-model-name
CHAT_API_BASE=http://localhost:1234/v1
CHAT_API_KEY=lm-studio
EMBEDDING_PROVIDER=openai-compatible
EMBEDDING_MODEL=text-embedding-3-small
EMBEDDING_API_BASE=http://localhost:1234/v1
EMBEDDING_API_KEY=lm-studio

CLI Options

Common options:

• --question
• --url
• --chat-provider
• --chat-model
• --embedding-provider
• --embedding-model
• --chat-api-base
• --embedding-api-base
• --show-steps
• --diagram

Show full help:

python -m agentic_rag --help

How It Works

1. Load and split source documents.
2. Build or reuse a cached vector index.
3. Let the graph decide whether to answer directly or retrieve.
4. Retrieve relevant chunks as a tool call.
5. Grade retrieved context.
6. Rewrite the question if retrieval quality is weak.
7. Generate a final cited answer.

Default Sources

The current default setup indexes a small set of Lilian Weng blog posts so the project works immediately as a reference RAG example.

You can add your own sources at runtime:

python -m agentic_rag \
  --url https://example.com/doc1 \
  --url https://example.com/doc2 \
  --question "Summarize the main ideas"

Development

Run tests:

pytest

Main files:

• src/agentic_rag/providers.py
• src/agentic_rag/app.py
• src/agentic_rag/graph.py
• src/agentic_rag/cli.py
• src/agentic_rag/settings.py

Notes

• The first run for a new source/config combination builds embeddings and writes a cache file.
• Later runs reuse the cached vector index and are much cheaper/faster.
• Python 3.10+ is recommended even though the project currently runs on 3.9.