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

2026-07-01 15:10:08 +03:00
parent f522dcfa80
commit 51ab1df383
6 changed files with 161 additions and 239 deletions
@@ -1,81 +1,45 @@
-# FAQ Bot – ChromaDB + Ollama Embeddings
+# FAQ Bot with ChromaDB

-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.
+This project implements a simple FAQ bot that uses **ChromaDB** as the vector store and **MCP-tool** for generating embeddings. The bot indexes a set of FAQ entries and can answer user questions by retrieving the most relevant entries from the vector store.

-## Features
+## Architecture

- **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
+- **ChromaDB** – the sole vector storage stack used for persisting embeddings and performing similarity queries.
+- **MCP-tool** – the only MCP-tool used for generating embeddings from text. No other vector store libraries or MCP-tools are included.

 ## Setup

-1. **Clone the repository**
+```bash
+# Install dependencies
+npm install

-   ```bash
-   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
-   cd povtornyy-ekzamen-faq-bot-chromadb-odin
-   ```
+# Run the bot
+npm start
+```

-2. **Create a virtual environment**
+## How It Works

-   ```bash
-   python -m venv .venv
-   source .venv/bin/activate   # On Windows: .venv\Scripts\activate
-   ```
+1. **VectorStore**  
+   - Connects to a local ChromaDB instance.  
+   - Adds documents with embeddings generated by MCP-tool.  
+   - Queries the collection for the top‑k most similar documents.  

-3. **Install dependencies**
+2. **Bot**  
+   - Initializes the vector store.  
+   - Indexes a predefined list of FAQs.  
+   - Answers user questions by querying the vector store and returning the top results.

-   ```bash
-   pip install -r requirements.txt
-   ```
+## Example

-4. **Set environment variables**
+Running the bot will output:

-   Create a `.env` file in the project root (or export variables manually):
+```
+Answer:
+ What is ChromaDB?
+ChromaDB is a vector database designed for storing and querying embeddings efficiently.
+---
+How do I use MCP-tool?
+MCP-tool is a utility that generates embeddings from text using a chosen model.
+```

-   ```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
---
+Feel free to extend the FAQ list or integrate the bot into a larger application.