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-# task-6a1d75c5fd30e81cf3126ae7
+# FAQ‑Bot – ChromaDB + MCP Tool
 
-Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool
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+## Overview
+This project implements an FAQ‑bot that answers questions about local course notes stored in Markdown files using **ChromaDB** for retrieval and a single **MCP‑style tool** for metadata queries.
+
+- **Local knowledge** – 2–3 `.md` files in `data/`.
+- **Vector store** – ChromaDB with Ollama embeddings.
+- **Tool** – `fetch_course_meta` that performs a GET request to a local JSON file (mock MCP server).
+- **Agent** – LangGraph agent that routes the query to the appropriate source and labels the answer with `source: chroma | mcp_meta`.
+- **CLI** – 3 example questions (2 for Chroma, 1 for meta) and an interactive mode.
+
+## Setup
+```bash
+# Pull Ollama model
+ollama pull nomic-embed-text
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Start local mock MCP server (JSON file)
+python -m http.server 8000 --directory mock
+```
+
+## Running the Bot
+```bash
+python main.py --question "What is the course schedule?"
+```
+
+## Example Questions
+1. **Chroma** – *"What topics are covered in week 3?"*
+2. **Chroma** – *"How many hours per week are required?"*
+3. **Meta** – *"What is the course schedule?"*
+
+## Project Structure
+```
+├── data/                 # Markdown FAQ files
+├── mock/                 # JSON mock MCP server
+├── src/
+│   ├── agent.py          # LangGraph agent definition
+│   ├── chunker.py        # Chroma loader
+│   ├── cli.py            # CLI interface
+│   ├── config.py         # Config & constants
+│   ├── init_loader.py    # Bulk loader
+│   ├── qdrant_store.py   # Vector store helper
+│   ├── rag_tools.py      # Search & meta tools
+│   ├── tools.py          # MCP wrapper
+│   └── vector_store.py   # Chroma store
+└── requirements.txt
+```
+
+## License
+MIT
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