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

# Install Python packages
pip install -r requirements.txt
```

## Usage
1. **Load documents** (optional):
   ```bash
   python init_documents.py
   ```
2. **Run the CLI**:
   ```bash
   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.