# RAG Agent with ChromaDB and Web Search

This repository contains a lightweight Retrieval‑Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs a simple web search to augment the retrieved context before generating an answer with OpenAI's GPT model.

> **Deadline**: 31.08.2026  
> **Version**: 14

## Features

- **Vector Store** – ChromaDB (local, no external service required)
- **Embeddings** – OpenAI `text-embedding-ada-002`
- **LLM** – OpenAI `gpt-3.5-turbo`
- **Web Search** – DuckDuckGo (no API key needed)
- **Command‑line interface** for adding documents and asking questions

## Prerequisites

- Python 3.9+
- An OpenAI API key

## Installation

```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/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:

```
openai>=1.0.0
chromadb>=0.4.0
requests>=2.31.0
beautifulsoup4>=4.12.0
```

## Configuration

Set your OpenAI API key as an environment variable:

```bash
export OPENAI_API_KEY="sk-..."
```

On Windows:

```cmd
set OPENAI_API_KEY=sk-...
```

## Usage

### 1. Add Documents

Add a text file to the vector store. The file will be split into chunks (≈500 tokens each) and embedded.

```bash
python -m src.index add path/to/document.txt
```

Example:

```bash
python -m src.index add data/biology.txt
```

### 2. Ask a Question

Query the RAG agent. It will:

1. Retrieve the top‑5 nearest chunks from ChromaDB.
2. Perform a DuckDuckGo web search for the query.
3. Combine the retrieved context and web snippets.
4. Generate an answer with GPT.

```bash
python -m src.index ask "What is the function of mitochondria?"
```

### 3. Help

```bash
python -m src.index
```

## Example

```bash
$ python -m src.index add sample.txt
Added 4 chunks from sample.txt to the collection.

$ python -m src.index ask "Explain the water cycle."
Answer:
The water cycle, also known as the hydrologic cycle, describes the continuous movement of water on, above, and below the surface of the Earth. ...
```

## Project Structure

```
src/
├── index.py          # Main script
README.md
requirements.txt
```

## Notes

- **ChromaDB Persistence** – The vector store is persisted in `./chromadb`. Delete this folder to reset the store.
- **Token Limits** – The embedding model `text-embedding-ada-002` supports up to 8191 tokens per request. The chunking logic approximates a 500‑token limit per chunk.
- **Web Search** – DuckDuckGo is used for simplicity. For production use, consider a dedicated search API (e.g., SerpAPI, Bing Search API).

## License

MIT License