RAG Agent with ChromaDB

Overview

This repository implements a simple RAG (Retrieval‑Augmented Generation) agent that uses ChromaDB as the vector store and Ollama for embeddings and LLM inference. The agent can:

1. Add documents to the knowledge base.
2. Search the knowledge base semantically.
3. Interact via a lightweight CLI.

File Structure

• requirements.txt – Python dependencies.
• chunker.py – Text chunking utilities (RecursiveCharacterTextSplitter).
• vector_store.py – Wrapper around ChromaDB collection.
• tools.py – LangChain tools for search and add operations.
• agent.py – Agent creation with LangChain create_agent.
• cli.py – Simple command‑line interface.
• init_documents.py – Helper to load all .txt files from a directory into the vector store.

Installation

# Pull Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python packages
pip install -r requirements.txt

Usage

1. Load documents (optional):

   python init_documents.py
   

2. Run the CLI:

   python cli.py
   

   Type any question or /quit to exit.

Architecture

• Chunking: chunker.split_text() splits large texts into 500‑char chunks with 100‑char overlap using LangChain’s RecursiveCharacterTextSplitter.
• Vector Store: vector_store.ChromaVectorStore handles adding documents and similarity search. Embeddings are generated by langchain_ollama.OllamaEmbeddings (nomic-embed-text).
• Tools: Two tools decorated with @tool: search_knowledge_base and add_to_knowledge_base. They interact with the vector store.
• Agent: Created via LangChain’s create_agent, configured to use the two tools and a simple system prompt. The LLM is an Ollama llama3 instance.

Testing

Run the CLI and try:

/quit
Hello, what can you do?

The agent should respond using the knowledge base or add new documents if prompted.