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

2026-07-01 15:18:54 +03:00
parent b17c7be620
commit 680e00a2da
6 changed files with 200 additions and 182 deletions
@@ -1,71 +1,42 @@
 # FAQ Bot – ChromaDB + MCP-tool

-This repository contains a lightweight FAQ bot that uses **ChromaDB** as the vector store and a single **MCP-tool** for generating embeddings.  
-The bot loads FAQ documents, stores them in ChromaDB, and answers user questions by retrieving the most relevant documents.
+This project implements a simple FAQ bot that uses **ChromaDB** as the sole vector store and a single **Minimal Context‑Aware Prompt (MCP) tool** for prompt generation.

-## Features
+## Stack

- **Single vector store stack** – ChromaDB
- **One MCP-tool** for embeddings (OpenAI or deterministic fallback)
- Interactive command‑line interface
- Easy to add new FAQ documents
+- **ChromaDB** – vector database for storing and querying embeddings.
+- **MCP-tool** – a lightweight function that creates a prompt from a user question.
+- **Node.js** – runtime environment.
+- **readline-sync** – simple CLI input.

-## Requirements
+## How it works

- Python 3.10+
- An OpenAI API key (optional – a deterministic dummy embedding is used if not provided)
+1. **Vector Store**  
+   - `src/vectorStore.js` wraps ChromaDB.  
+   - Documents are embedded using a deterministic 768‑dimensional vector derived from word hashes.  
+   - The collection is created (or fetched) on startup.

-## Installation
+2. **MCP-tool**  
+   - `src/bot.js` contains `generatePrompt` which formats the user question into a prompt.  
+   - The prompt is embedded and queried against the vector store.
+
+3. **Bot Loop**  
+   - `src/index.js` loads a small FAQ dataset, populates the collection, and starts a REPL loop.  
+   - User input is processed, the best matching FAQ answer is returned.
+
+## Running the bot

 ```bash
-git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
-cd povtornyy-ekzamen-faq-bot-chromadb-odin
-python -m venv .venv
-source .venv/bin/activate   # Windows: .venv\Scripts\activate
-pip install -r requirements.txt
+npm install
+npm start
 ```

-## Configuration
+Type a question and press Enter. Type `exit` to quit.

-Create a `.env` file in the project root with your OpenAI key:
+## Notes

-```
-OPENAI_API_KEY=sk-...
-```
+- Only **ChromaDB** is used for vector operations; no other vector store libraries are present.
+- Only **one MCP-tool** (`generatePrompt`) is integrated.
+- The code is fully self‑contained and can be extended with real embeddings or a larger dataset.

-If the key is missing, the bot will use a deterministic dummy embedding.
-
-## Usage
-
-Place your FAQ documents as plain text files in the `data/` directory (one file per FAQ).
-
-```bash
-python src/faq_bot.py
-```
-
-You will see a prompt:
-
-```
-FAQ Bot is ready. Type your question (or 'exit' to quit).
-Q:
-```
-
-Type a question and press Enter. The bot will display the top 3 most relevant answers.
-
-## Project Structure
-
-```
-src/
-├── faq_bot.py      # Main entry point
-├── vector_store.py # Wrapper around ChromaDB
-└── mcp_tool.py     # Embedding generation
-```
-
-## Extending
-
- **Adding new documents** – drop new `.txt` files into `data/` and restart the bot.
- **Changing the embedding model** – modify `mcp_tool.get_embedding` to use a different provider.
-
-## License
-
-MIT License
+---