RagChat

RagChat enables interaction between large language models and unstructured data. It addresses challenges such as dynamic content updates, varied data sources, and retrieval accuracy by using an upsert-first architecture, filtering mechanisms, and a combination of knowledge graphs with vector search. It supports multi-user, custom models, and self-hosting to provide operational control.

Features

• Upsert-first design: Supports constant updates.
• Flexible metadata filtering: Information retrieval allows using custom fields.
• Efficient knowledge graph: Graph is built using small models, promoting efficiency and scalability.
• Multiuser support: Knowledge bases can be isolated or shared.
• Language consistency: Prompts and examples use the specified language, improving reliability.
• Async batch processing: Ingestion of multiple documents can be done in parallel with streaming progress updates.
• Pluggable LLMs and Embedding models: Supports the use of custom models or connection to API endpoints; providers are easily swappable.
• Open source & self-hostable: Operation occurs locally in Docker or directly on a machine, ensuring privacy.

Use Cases

• Casual chat sessions with memories
• Technical documentation search
• Chat+file hybrid RAG with citations
• Personal use
• Multi-user setups

Quick start

Docker Compose is required for the easiest setup.

Install RagChat with pip:

pip install ragchat-ai

Recommended local models:

• bge-m3 for embeddings
• qwen3:4b or qwen3:8b for LLM -- Make sure 8k context length is supported.

Configure environment variables:

git clone https://github.com/raul3820/ragchat.git
cd ragchat
cp .env.example .env

Example: Open Webui

Run dependencies with:

docker compose up --build

After startup, the web chat UI is accessible at http://localhost:3001 (refer to .env for port).

Retrieval will be applied to all models. Two flows are presented:

• Casual chat with memories (default)
• Formal RAG with citations (triggered by writing # in the chat and selecting a file)

For file ingestion use: http://localhost:3001/workspace/knowledge

Example: Lihua benchmark with Python SDK

Run dependencies with:

docker compose up neo4j qdrant --build

Once the DB has started, run the file ingestion with:

python -m examples.lihua.step0_index --full

and the Q&A with

python -m examples.lihua.step1_qa --test recall --limit 5

Contributing

Contributions welcomed! Please see the CONTRIBUTING.md file for details on how to contribute.

• Bug reports (issues)
• Feature suggestions
• Pull requests (inclusion of tests requested)

Open Source & License

RagChat is MIT-licensed (see LICENSE). Self-hosting and extension are permitted. Certain features may require user-provided LLM/API keys.