feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

README.md

@@ -1,78 +1,81 @@
-# FAQ Bot – ChromaDB + Ollama
+# FAQ Bot – ChromaDB + Ollama Embeddings
 
-This project implements a simple FAQ bot that uses **ChromaDB** as the vector database and **Ollama** as the LLM provider.  
-The bot indexes a set of frequently asked questions (FAQ) and answers, then retrieves the most relevant answers to user queries using semantic similarity.
+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 (local, file‑based persistence)
-- **LLM**: Ollama (e.g., `llama3.1`)
-- **Embeddings**: Ollama embeddings
-- **Retrieval**: Semantic search over FAQ questions
-- **Answer generation**: Ollama LLM generates natural language responses
+- **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**  
+1. **Clone the repository**
+
    ```bash
-   git clone <repo-url>
-   cd <repo-directory>
+   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** (optional but recommended)  
+2. **Create a virtual environment**
+
    ```bash
-   python -m venv venv
-   source venv/bin/activate   # On Windows: venv\Scripts\activate
+   python -m venv .venv
+   source .venv/bin/activate   # On Windows: .venv\Scripts\activate
    ```
 
-3. **Install dependencies**  
+3. **Install dependencies**
+
    ```bash
    pip install -r requirements.txt
    ```
 
-4. **Configure Ollama**  
-   - Ensure Ollama is running locally (default port `11434`).  
-   - Optionally set environment variables in a `.env` file:
-     ```
-     OLLAMA_MODEL=llama3.1
-     OLLAMA_BASE_URL=http://localhost:11434
-     ```
+4. **Set environment variables**
 
-5. **Run the bot**  
-   ```bash
-   python src/main.py
+   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
    ```
 
-   Type your question in the console. Type `exit` or `quit` to stop.
+5. **Run the server**
 
-## Project Structure
+   ```bash
+   uvicorn src.main:app --reload
+   ```
 
-```
-.
-├── requirements.txt
-├── src
-│   └── main.py
-└── README.md
-```
+   The API will be available at `http://127.0.0.1:8000`.
 
-- `requirements.txt` – lists all Python dependencies, including `langchain-openai` and `qdrant-client` as required by the assignment (even though they are not used in the implementation).
-- `src/main.py` – main application logic:
-  - Initializes Ollama embeddings and LLM.
-  - Sets up a ChromaDB collection for FAQ data.
-  - Indexes sample FAQ entries.
-  - Builds a RetrievalQA chain.
-  - Provides a simple REPL for user interaction.
+## 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 FAQ data is hard‑coded in `src/main.py`. In a production setup, you would load this from a database or a file.
-- The vector store persists in the `./chromadb` directory. Delete this folder to re‑index from scratch.
-- The bot uses the `stuff` chain type, which concatenates retrieved documents before passing them to the LLM. This is suitable for short FAQ answers.
+- 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.
 
-## Troubleshooting
+## License
 
-- **Ollama not found**: Ensure the Ollama server is running and accessible at the URL specified in `OLLAMA_BASE_URL`.
-- **Missing dependencies**: Run `pip install -r requirements.txt` again.
-- **Indexing errors**: Delete the `./chromadb` folder and restart the bot to rebuild the index.
-
-Enjoy your FAQ bot!
+MIT License
+---