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-# 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.