# RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that uses a local ChromaDB vector store for document retrieval and falls back to DuckDuckGo web search when the local store does not provide sufficient context.

## Features

- **Local Retrieval** – Store and query embeddings in a persistent ChromaDB collection.
- **Web Search Fallback** – If local retrieval fails to find relevant context, the agent performs a DuckDuckGo search and uses the snippets.
- **OpenAI Integration** – Uses OpenAI embeddings (`text-embedding-ada-002`) and the `gpt-3.5-turbo` model for generation.
- **CLI** – Simple command line interface for ingesting documents and asking questions.

## Prerequisites

- Python 3.9+
- An OpenAI API key
- (Optional) Internet access for web search

## 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 (recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`

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

`requirements.txt` contains:

```
openai
chromadb
duckduckgo-search
beautifulsoup4
requests
```

## Environment Variables

| Variable | Description | Example |
|----------|-------------|---------|
| `OPENAI_API_KEY` | Your OpenAI API key | `sk-...` |
| `CHROMA_DB_PATH` | Directory where ChromaDB stores data | `./chromadb` |
| `CHROMA_COLLECTION_NAME` | Name of the collection | `rag_collection` |
| `TOP_K` | Number of top documents to retrieve | `5` |
| `SIMILARITY_THRESHOLD` | Minimum similarity to consider a document relevant | `0.5` |
| `WEB_SEARCH_MAX_RESULTS` | Max number of web snippets to fetch | `3` |

Set them in your shell or create a `.env` file and load with `dotenv` (optional).

## Usage

### Ingest Documents

Place your plain text files (`.txt`) in a folder, then run:

```bash
python src/index.py ingest /path/to/text/files
```

The script will read all `.txt` files, split them into chunks, embed them, and store them in ChromaDB.

### Ask a Question

```bash
python src/index.py ask "What is the capital of France?"
```

The agent will:

1. Query the local vector store for relevant passages.
2. If none are found above the similarity threshold, perform a DuckDuckGo search.
3. Combine the retrieved context into a prompt.
4. Call OpenAI’s `gpt-3.5-turbo` to generate an answer.

## Example

```bash
$ python src/index.py ingest ./data
INFO:root:Added 12 documents to collection 'rag_collection'.

$ python src/index.py ask "Explain the theory of relativity."
Answer:
The theory of relativity, developed by Albert Einstein, consists of two parts: special relativity and general relativity. ...
```

## Testing

Unit tests are provided in the `tests/` directory. To run them:

```bash
pytest tests/
```

(If you don't have `pytest` installed, run `pip install pytest`.)

## Troubleshooting

- **No documents ingested** – Ensure the folder path is correct and contains `.txt` files.
- **OpenAI errors** – Verify that `OPENAI_API_KEY` is set and that you have sufficient quota.
- **Web search fails** – Check your internet connection and that DuckDuckGo is reachable.

## License

MIT License