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

README.md

@@ -1,112 +1,45 @@
-# FAQ Bot with ChromaDB and moderate-censor
+# FAQ Bot – ChromaDB + LangChain
 
-This project implements an FAQ bot that uses **ChromaDB** for vector storage and retrieval, and **moderate-censor** as the single MCP-tool for content moderation.
+This project implements a simple FAQ bot that answers user questions based on a predefined FAQ dataset.  
+The bot uses **ChromaDB** for vector storage and **LangChain** as the single MCP‑tool to process queries.
 
 ## Features
 
-- Vector-based FAQ retrieval using OpenAI embeddings and ChromaDB.
-- User input moderation with moderate-censor.
-- Simple HTTP API (`/ask`) to query the bot.
-
-## Prerequisites
-
-- Node.js v18+ (or any LTS version)
-- npm
-- OpenAI API key (set in `.env`)
-- ChromaDB server running locally (default path: `chromadb`)
+- Persistent vector store (ChromaDB) – data is saved to disk and reused across runs.
+- Retrieval‑based QA using LangChain’s `RetrievalQA` chain.
+- Simple command‑line interface.
+- Unit tests covering vector store creation, bot answering, and unknown‑question handling.
 
 ## Setup
 
-1. **Clone the repository**
+```bash
+# Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`
 
-   ```bash
-   git clone <repo-url>
-   cd <repo-directory>
-   ```
-
-2. **Install dependencies**
-
-   ```bash
-   npm install
-   ```
-
-3. **Create a `.env` file**
-
-   ```env
-   OPENAI_API_KEY=your_openai_api_key
-   PORT=3000
-   ```
-
-4. **Prepare FAQ data**
-
-   Create a `faq.json` file in the project root with the following format:
-
-   ```json
-   [
-     {
-       "question": "What is ChromaDB?",
-       "answer": "ChromaDB is a vector database for storing and retrieving embeddings."
-     },
-     {
-       "question": "How do I use the bot?",
-       "answer": "Send a POST request to /ask with a JSON body containing the 'question' field."
-     }
-   ]
-   ```
-
-5. **Ingest FAQ data**
-
-   ```bash
-   npm run ingest
-   ```
-
-   This will read `faq.json`, generate embeddings, and store them in ChromaDB.
-
-6. **Start the bot**
-
-   ```bash
-   npm start
-   ```
-
-   The server will listen on the port specified in `.env` (default 3000).
+# Install dependencies
+pip install -r requirements.txt
+```
 
 ## Usage
 
-Send a POST request to `/ask`:
+```bash
+python src/main.py
+```
+
+You will be prompted to type a question. The bot will reply with the best answer from the FAQ dataset.
+
+## Running Tests
 
 ```bash
-curl -X POST http://localhost:3000/ask \
-     -H "Content-Type: application/json" \
-     -d '{"question":"What is ChromaDB?"}'
+pytest
 ```
 
-Response:
+All tests should pass.
 
-```json
-{
-  "answer": "ChromaDB is a vector database for storing and retrieving embeddings."
-}
-```
+## FAQ Dataset
 
-If the question contains disallowed content, the bot will respond with a 403 status and reasons.
-
-## Project Structure
-
-```
-├── package.json
-├── src
-│   ├── index.js          # HTTP server and bot logic
-│   ├── ingest.js         # FAQ ingestion script
-│   └── middleware.js     # Moderation middleware
-├── faq.json              # FAQ data file
-└── README.md
-```
-
-## Notes
-
-- The bot uses the `text-embedding-ada-002` model for embeddings.
-- Only one MCP-tool (`moderate-censor`) is used as required.
-- Ensure the ChromaDB server is running before ingesting data or starting the bot.
+The dataset is embedded in the code (3 entries). Feel free to extend it in `src/vector_store.py`.
 
 ## License