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

README.md

@@ -1,87 +1,118 @@
-# FAQ Bot – ChromaDB + Ollama
+# FAQ Bot – QDrant Vector Store
 
-This repository contains a simple FAQ chatbot that uses:
-
-- **Ollama** for embeddings (`nomic-embed-text`) and text generation.
-- **ChromaDB** as the vector store.
-- **LangChain** to orchestrate the retrieval and generation pipeline.
+This project implements a simple FAQ bot that uses **QDrant** as the vector store instead of ChromaDB.  
+The bot can ingest a text file containing FAQ content, embed the text using OpenAI embeddings, store the embeddings in QDrant, and answer user questions by retrieving the most relevant passages.
 
 ## Features
 
-- Loads a small set of FAQ questions and answers.
-- Generates embeddings with the `nomic-embed-text` model.
-- Stores embeddings in a persistent ChromaDB collection.
-- Retrieves the most relevant answer to a user query.
-- Generates a natural language response using an Ollama LLM.
+- **Vector Store** – QDrant (via `qdrant-client`)
+- **Embeddings** – OpenAI `text-embedding-ada-002`
+- **CLI** – Ingest data, query the bot, delete the collection
+- **API** – `get_response(question: str, top_k: int = 5)` for integration with tools like MCP-tool
 
-## Requirements
+## Prerequisites
 
-- Python 3.10+
-- Ollama server running locally (default port 11434).  
-  Install from https://ollama.ai/ and pull the required models:
-  ```bash
-  ollama pull nomic-embed-text
-  ollama pull llama3   # or any other generation model you prefer
-  ```
+- Python 3.9+
+- QDrant server running locally or accessible via network
+- OpenAI API key
 
-## Installation
+## Setup
 
-```bash
-# Clone the repository
-git clone https://github.com/your-username/faq-bot.git
-cd faq-bot
+1. **Clone the repository**
 
-# Create a virtual environment (optional but recommended)
-python -m venv .venv
-source .venv/bin/activate   # On Windows: .venv\Scripts\activate
+   ```bash
+   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
+   cd povtornyy-ekzamen-faq-bot-chromadb-odin
+   ```
 
-# Install dependencies
-pip install -r requirements.txt
-```
+2. **Create a virtual environment (optional but recommended)**
+
+   ```bash
+   python -m venv venv
+   source venv/bin/activate   # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install dependencies**
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Set environment variables**
+
+   Create a `.env` file in the project root or export the variables directly:
+
+   ```bash
+   export OPENAI_API_KEY="your-openai-api-key"
+   export QDRANT_URL="http://localhost:6333"   # Adjust if your QDrant instance is elsewhere
+   export QDRANT_API_KEY=""   # Leave empty if no auth is required
+   export QDRANT_COLLECTION="faq_collection"
+   ```
+
+   If you prefer not to use a `.env` file, you can set the variables in your shell session.
 
 ## Usage
 
+### 1. Ingest Data
+
+Prepare a plain text file (`faq.txt`) containing your FAQ content. Then run:
+
 ```bash
-python src/main.py
+python src/index.py ingest faq.txt
 ```
 
-You will see a prompt:
+The script will:
 
-```
-FAQ Bot is ready. Type your question (or 'exit' to quit).
+- Split the text into chunks (max 500 characters per chunk)
+- Generate embeddings for each chunk
+- Store the embeddings in QDrant under the collection name defined by `QDRANT_COLLECTION`
+
+### 2. Query the Bot
+
+```bash
+python src/index.py query "What is the return policy?"
 ```
 
-Type any of the predefined FAQ questions or any other question, and the bot will respond with the most relevant answer.
+You can adjust the number of results returned with `--top_k`:
 
-## Project Structure
-
-```
-faq-bot/
-├── src/
-│   └── main.py          # Main application script
-├── requirements.txt     # Python dependencies
-└── README.md            # This file
+```bash
+python src.index.py query "What is the return policy?" --top_k 3
 ```
 
-## Customizing the FAQ
+### 3. Delete the Collection
 
-The FAQ data is currently hard‑coded in `src/main.py`. To add more questions:
+> **Warning:** This will permanently delete all data in the collection.
 
-1. Open `src/main.py`.
-2. Edit the `faq_pairs` list inside the `load_faq_data()` function.
-3. Restart the bot.
+```bash
+python src/index.py delete
+```
 
-## Persistence
+### 4. Integration via API
 
-The vector store is persisted in the `chroma_db/` directory. The next time you run the bot, it will reuse the existing embeddings instead of recomputing them.
+If you want to use the bot programmatically (e.g., from MCP-tool), import the `get_response` function:
+
+```python
+from src.index import get_response
+
+answer = get_response("How do I reset my password?")
+print(answer)
+```
 
 ## Troubleshooting
 
-- **Ollama not found**: Ensure the Ollama server is running and accessible at `http://localhost:11434`.  
-- **Embedding errors**: Verify that the `nomic-embed-text` model is pulled (`ollama list`).  
-- **Vector store errors**: Delete the `chroma_db/` directory if you suspect corruption.
+- **QDrant Connection Errors**  
+  Ensure the QDrant server is running and reachable at the URL specified by `QDRANT_URL`. Check firewall settings if accessing remotely.
+
+- **OpenAI API Errors**  
+  Verify that `OPENAI_API_KEY` is correct and has sufficient quota. Check the OpenAI dashboard for usage limits.
+
+- **Large Documents**  
+  The ingestion script splits documents into 500‑character chunks. Adjust `max_chunk_size` in `split_text_into_chunks` if you need larger or smaller chunks.
 
 ## License
 
-MIT License
----
+This project is provided under the MIT License. Feel free to modify and extend it for your own use cases.
+
+## Contact
+
+For questions or support, contact Artur Kuzakhmetov at `artur@example.com`.