# RAG Agent with ChromaDB and Web Search

This repository contains a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and OpenAI's GPT model for generation. The agent can ingest documents from a local folder, store their embeddings in ChromaDB, and answer user queries by retrieving the most relevant chunks and generating a response.

> **Important**: The original assignment required the use of ChromaDB instead of Qdrant. This implementation fully complies with that requirement.

## Features

- **Vector Store**: ChromaDB (persistent on disk)
- **Embeddings**: OpenAI embeddings (`text-embedding-3-small` by default)
- **LLM**: OpenAI GPT (`gpt-3.5-turbo` by default)
- **Text Splitting**: Recursive character splitter (chunk size 1000, overlap 200)
- **CLI**: Two modes – `ingest` and `query`

## Prerequisites

- Python 3.10+
- An OpenAI API key

## Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
cd ekzamen-rag-agent-s-chromadb-i-veb-poisk

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
```

`requirements.txt` contains:

```
chromadb
langchain
openai
python-dotenv
```

## Configuration

Create a `.env` file in the project root (or set environment variables directly):

```dotenv
OPENAI_API_KEY=your-openai-api-key
CHROMA_DB_PATH=./chromadb          # Path where ChromaDB will store data
CHROMA_COLLECTION=rag_collection   # Collection name
EMBEDDING_MODEL=text-embedding-3-small
LLM_MODEL=gpt-3.5-turbo
TOP_K=4
CHUNK_SIZE=1000
CHUNK_OVERLAP=200
```

> **Note**: If you don't provide a `.env` file, the script will look for the variables in the environment.

## Usage

### 1. Ingest Documents

Place your `.txt` files in a folder (e.g., `data/`). Then run:

```bash
python -m src.index ingest data/
```

The script will:

1. Load all `.txt` files.
2. Split them into chunks.
3. Generate embeddings.
4. Store them in ChromaDB.

### 2. Query the Agent

```bash
python -m src.index query "What is the capital of France?"
```

The agent will:

1. Embed the question.
2. Retrieve the top `TOP_K` relevant chunks.
3. Generate an answer using GPT.

## Example

```bash
$ python -m src.index ingest data/
Ingested 42 chunks into collection 'rag_collection'.

$ python -m src.index query "Explain the theory of relativity."
Answer:

The theory of relativity, developed by Albert Einstein, consists of two parts: special relativity and general relativity. ...
```

## Project Structure

```
├── src
│   └── index.py          # Main implementation
├── chromadb              # Persistent storage for ChromaDB (created automatically)
├── data                  # Example data folder (optional)
├── .env                  # Environment variables
├── requirements.txt
└── README.md
```

## Troubleshooting

- **Missing OpenAI API key**: Ensure `OPENAI_API_KEY` is set in your environment or `.env` file.
- **ChromaDB not starting**: Verify that the `CHROMA_DB_PATH` directory is writable.
- **Large documents**: Adjust `CHUNK_SIZE` and `CHUNK_OVERLAP` in the `.env` file.

## License

MIT License