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

README.md

@@ -1,21 +1,27 @@
-# FAQ Bot with ChromaDB and LangChain
+# FAQ Bot with ChromaDB and moderate-censor
 
-This project implements a simple FAQ bot that uses **ChromaDB** for vector storage and **LangChain** for building an intelligent agent. The bot can answer questions based on a small knowledge base stored in ChromaDB.
+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.
 
 ## Features
 
-- Stores documents in ChromaDB with embeddings from OpenAI.
-- Uses the latest LangChain agent creation method (`initializeAgentExecutorWithOptions`).
-- Simple CLI interface for interacting with the bot.
-- Easy to extend with more documents or tools.
+- 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`)
 
 ## Setup
 
 1. **Clone the repository**
 
    ```bash
-   git clone https://github.com/your-username/faq-bot-chromadb.git
-   cd faq-bot-chromadb
+   git clone <repo-url>
+   cd <repo-directory>
    ```
 
 2. **Install dependencies**
@@ -24,26 +30,83 @@ This project implements a simple FAQ bot that uses **ChromaDB** for vector stora
    npm install
    ```
 
-3. **Configure environment**
-
-   Create a `.env` file in the project root:
+3. **Create a `.env` file**
 
    ```env
    OPENAI_API_KEY=your_openai_api_key
-   CHROMA_DB_PATH=./chromadb
+   PORT=3000
    ```
 
-4. **Run the bot**
+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
    ```
 
-   You can also use `npm run dev` for automatic restarts with nodemon.
+   The server will listen on the port specified in `.env` (default 3000).
 
-## Adding Documents
+## Usage
 
-The bot comes with two sample FAQ entries. To add more, edit `src/index.js` or use the `addDocument` function from `src/vectorstore.js`.
+Send a POST request to `/ask`:
+
+```bash
+curl -X POST http://localhost:3000/ask \
+     -H "Content-Type: application/json" \
+     -d '{"question":"What is ChromaDB?"}'
+```
+
+Response:
+
+```json
+{
+  "answer": "ChromaDB is a vector database for storing and retrieving embeddings."
+}
+```
+
+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.
 
 ## License