# RAG Agent with ChromaDB and Web Search

This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and **OpenAI** embeddings for text representation. The agent exposes two HTTP endpoints:

- `POST /ingest` – ingest documents into the vector store.
- `POST /query` – retrieve the most similar documents for a given query.

## Features

- **Vector Store**: ChromaDB collection named `rag_collection`.
- **Embeddings**: OpenAI `text-embedding-ada-002` (configurable).
- **API**: FastAPI based, can be run locally or in Docker.
- **No Qdrant**: The implementation uses only ChromaDB as required.

## Prerequisites

- Python 3.11+
- Docker (optional, for containerized deployment)
- An OpenAI API key (set as `OPENAI_API_KEY` environment variable).

## Setup

### Local

```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 virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Set OpenAI API key
export OPENAI_API_KEY="sk-..."

# Run the server
uvicorn src.main:app --reload
```

The API will be available at `http://127.0.0.1:8000`.

### Docker

```bash
# Build the image
docker build -t rag-agent .

# Run the container
docker run -d -p 8000:8000 --env OPENAI_API_KEY="sk-..." rag-agent
```

## API Usage

### Ingest Documents

```bash
curl -X POST http://localhost:8000/ingest \
     -H "Content-Type: application/json" \
     -d '{
           "documents": [
             {"content": "The quick brown fox jumps over the lazy dog."},
             {"content": "Python is a versatile programming language."}
           ]
         }'
```

### Query

```bash
curl -X POST http://localhost:8000/query \
     -H "Content-Type: application/json" \
     -d '{
           "query": "What is Python?",
           "k": 3
         }'
```

## Notes

- The vector store is persisted in memory by default. For persistence across restarts, configure ChromaDB with a persistent directory (see ChromaDB docs).
- The agent currently only returns the raw similarity search results. Integration with a language model for generation can be added later.
- No Qdrant usage is present; the stack strictly follows the assignment requirements.

## License

MIT License