# FAQ Bot – ChromaDB + Ollama Embeddings

This project implements a simple FAQ chatbot that uses **ChromaDB** as the vector store and **Ollama** for embeddings. The chatbot answers user questions by retrieving the most relevant FAQ entries and generating a response with an OpenAI LLM.

## Features

- **Vector Store**: ChromaDB (persistent on disk)
- **Embeddings**: Ollama `all-MiniLM-L6-v2` (or any other Ollama model)
- **LLM**: OpenAI GPT-3.5-turbo (configurable)
- **API**: FastAPI with `/ask` and `/add` endpoints

## Setup

1. **Clone the repository**

   ```bash
   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
   cd povtornyy-ekzamen-faq-bot-chromadb-odin
   ```

2. **Create a virtual environment**

   ```bash
   python -m venv .venv
   source .venv/bin/activate   # On Windows: .venv\Scripts\activate
   ```

3. **Install dependencies**

   ```bash
   pip install -r requirements.txt
   ```

4. **Set environment variables**

   Create a `.env` file in the project root (or export variables manually):

   ```dotenv
   # ChromaDB
   CHROMA_DB_PATH=./chroma_db
   CHROMA_COLLECTION_NAME=faq_collection

   # Ollama
   OLLAMA_EMBED_MODEL=all-MiniLM-L6-v2
   OLLAMA_HOST=http://localhost
   OLLAMA_PORT=11434

   # OpenAI
   OPENAI_API_KEY=your_openai_api_key
   OPENAI_MODEL=gpt-3.5-turbo
   ```

5. **Run the server**

   ```bash
   uvicorn src.main:app --reload
   ```

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

## API Endpoints

| Method | Path  | Description |
|--------|-------|-------------|
| `POST` | `/ask` | Ask a question. Body: `{ "question": "Your question" }`. Response: `{ "answer": "..." }`. |
| `POST` | `/add` | Add a new FAQ entry. Body: `{ "text": "...", "metadata": { ... } }`. Response: `{ "status": "added" }`. |

## Adding FAQ Data

You can add FAQ entries via the `/add` endpoint or by modifying the code to load a dataset on startup. Each entry is stored as a `Document` in ChromaDB with optional metadata.

## Notes

- The vector store is persisted in the directory specified by `CHROMA_DB_PATH`. Deleting this directory will remove all stored vectors.
- Ollama must be running locally and expose the embedding endpoint on the host/port specified.
- The OpenAI LLM requires a valid API key.

## License

MIT License
---