GraphRAG + AutoGen + Ollama + Chainlit UI = Local Multi-Agent RAG Superbot

This application integrates GraphRAG with AutoGen agents, powered by local LLMs from Ollama, for free and offline embedding and inference. Key highlights include:

Agentic-RAG: - Integrating GraphRAG's knowledge search method with an AutoGen agent via function calling.
Offline LLM Support: - Configuring GraphRAG (local & global search) to support local models from Ollama for inference and embedding.
Non-OpenAI Function Calling: - Extending AutoGen to support function calling with non-OpenAI LLMs from Ollama via Lite-LLM proxy server.
Interactive UI: - Deploying Chainlit UI to handle continuous conversations, multi-threading, and user input settings.

Useful Links 🔗

Full Guide: Microsoft's GraphRAG + AutoGen + Ollama + Chainlit = Fully Local & Free Multi-Agent RAG Superbot Medium.com 📚

📦 Installation and Setup Linux

Follow these steps to set up and run AutoGen GraphRAG Local with Ollama and Chainlit UI:

Install LLMs:

Visit Ollama's website for installation files.

ollama pull mistral
ollama pull nomic-embed-text
ollama pull llama3
ollama serve

Create conda environment and install packages:

conda create -n RAG_agents python=3.12
conda activate RAG_agents
git clone https://github.com/karthik-codex/autogen_graphRAG.git
cd autogen_graphRAG
pip install -r requirements.txt

Initiate GraphRAG root folder:

mkdir -p ./input
python -m graphrag.index --init  --root .
mv ./utils/settings.yaml ./

Replace 'embedding.py' and 'openai_embeddings_llm.py' in the GraphRAG package folder using files from Utils folder:
```
sudo find / -name openai_embeddings_llm.py
sudo find / -name embedding.py
```
Create embeddings and knowledge graph:
```
python -m graphrag.index --root .
```
Start Lite-LLM proxy server:
```
litellm --model ollama_chat/llama3
```
Run app:
```
chainlit run appUI.py
```

📦 Installation and Setup Windows

Follow these steps to set up and run AutoGen GraphRAG Local with Ollama and Chainlit UI on Windows: