# RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector database and performs live web searches to provide up‑to‑date information.

## Features

- **Vector store** – Documents are ingested, split into chunks, embedded with OpenAI embeddings, and stored in a persistent ChromaDB collection.
- **Web search** – Uses DuckDuckGo scraping to fetch recent web snippets for a query.
- **RAG pipeline** – Combines local document context and web results, then generates an answer with OpenAI GPT‑3.5‑Turbo.
- **CLI** – Simple command line interface for ingestion and querying.

## Prerequisites

- Python 3.10+
- An OpenAI API key with access to `text-embedding-ada-002` and `gpt-3.5-turbo`.

## Setup

```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 use `.venv\Scripts\activate`

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

## Configuration

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

```
OPENAI_API_KEY=sk-...
CHROMA_DB_PATH=./chromadb
CHROMA_COLLECTION_NAME=rag_collection
```

> **Note**: Do not commit your `.env` file or API key to version control.

## Usage

### 1. Ingest Documents

```bash
python src/main.py ingest path/to/doc1.txt path/to/doc2.txt
```

The script will read each file, split it into chunks, generate embeddings, and store them in ChromaDB.

### 2. Query the Agent

```bash
python src/main.py query "What is the capital of France?"
```

The agent will:

1. Retrieve relevant chunks from the local vector store.
2. Perform a DuckDuckGo web search for the query.
3. Combine both sources of information.
4. Generate a response using OpenAI GPT‑3.5‑Turbo.

## Project Structure

```
src/
├── main.py          # CLI entry point
├── vector_store.py  # ChromaDB ingestion & retrieval
├── web_search.py    # DuckDuckGo web search
requirements.txt
README.md
```

## Testing

The project can be tested with `pytest` (tests are not included in this minimal example).  
If you add tests, run:

```bash
pytest
```

## License

MIT License
--- 
Feel free to extend the agent with additional features such as custom embeddings, different LLMs, or alternative search APIs.