feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'
This commit is contained in:
@@ -1,2 +1,2 @@
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OPENAI_API_KEY=YOUR_OPENAI_API_KEY
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OLLAMA_MODEL=llama3
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CHROMA_DB_PATH=./chromadb
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@@ -1,88 +1,133 @@
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# FAQ Bot with Qdrant Vector Store
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# FAQ Bot – ChromaDB + Ollama
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This project implements a simple FAQ chatbot that uses **Qdrant** as the vector store for embeddings.
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The bot loads a set of FAQ entries, generates embeddings with OpenAI’s `text-embedding-ada-002` model, stores them in Qdrant, and answers user queries by performing a similarity search.
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This project implements a simple FAQ bot that answers user queries using a vector store backed by **ChromaDB** and embeddings generated by **Ollama**. The bot is orchestrated with **LangChain** and includes a small tool that returns the current system time.
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## Features
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- **Vector Store**: ChromaDB for persistent storage of FAQ embeddings.
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- **Embeddings**: Generated with Ollama (e.g., `llama3`).
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- **LLM**: Ollama LLM for generating responses.
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- **RetrievalQA**: LangChain chain that retrieves relevant FAQ answers.
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- **MCP‑Tool**: A single tool that returns the current time when the user asks about time or date.
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- **CLI**: Simple command‑line interface to ask questions or ingest data.
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- **Web API**: FastAPI endpoint (`POST /ask`) for programmatic access.
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## Prerequisites
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- Python 3.9+
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- A running Qdrant instance (local or remote)
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- An OpenAI API key
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- Python 3.10+
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- Docker (optional, for running Ollama locally)
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- Ollama server running locally (default port 11434)
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## Setup
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1. **Clone the repository**
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## Installation
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```bash
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git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-qdrant.git
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cd povtornyy-ekzamen-faq-bot-qdrant
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```
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# Clone the repository
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git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
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cd povtornyy-ekzamen-faq-bot-chromadb-odin
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2. **Create a virtual environment and install dependencies**
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# Create a virtual environment
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python -m venv .venv
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source .venv/bin/activate # On Windows use `.venv\Scripts\activate`
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows use `venv\Scripts\activate`
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# Install dependencies
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pip install -r requirements.txt
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```
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3. **Configure environment variables**
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## Environment Variables
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Create a `.env` file in the project root with the following content:
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Create a `.env` file in the project root (a template is provided):
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```dotenv
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# Qdrant configuration
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QDRANT_HOST=localhost
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QDRANT_PORT=6333
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QDRANT_API_KEY= # leave empty if no API key is required
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# OpenAI configuration
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OPENAI_API_KEY=your_openai_api_key_here
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```
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OLLAMA_MODEL=llama3
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CHROMA_DB_PATH=./chromadb
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```
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Replace `your_openai_api_key_here` with your actual OpenAI API key.
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- `OLLAMA_MODEL`: Name of the Ollama model to use (e.g., `llama3`).
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- `CHROMA_DB_PATH`: Directory where ChromaDB will store its data.
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4. **Run the bot**
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## FAQ Data
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Place your FAQ data in `data/faq.csv`. The file must contain two columns:
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| question | answer |
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|----------|--------|
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A sample file is included in the repository.
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## Usage
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### CLI
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```bash
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python src/main.py
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# Ingest FAQ data (if not already ingested)
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python -m src.main ask "What is the return policy?" --init
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# Ask a question
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python -m src.main ask "How do I track my order?"
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```
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The bot will ingest the FAQ data into Qdrant and then wait for user input. Type a question and press Enter to receive an answer. Type `exit` or `quit` to stop the bot.
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The `--init` flag forces re‑ingestion of the FAQ data. If the vector store is empty, it will be ingested automatically.
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## How It Works
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### Web API
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1. **Embedding Generation**
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The bot uses OpenAI’s `text-embedding-ada-002` to convert each FAQ question into a 1536‑dimensional vector.
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```bash
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# Start the server
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python -m src.main serve
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2. **Vector Store**
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Qdrant stores these vectors in a collection named `faq_collection`. Each point contains the vector and a payload with the original question and answer.
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# Send a request
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curl -X POST http://localhost:8000/ask \
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-H "Content-Type: application/json" \
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-d '{"question":"What payment methods are accepted?"}'
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```
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3. **Querying**
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When a user asks a question, the bot generates an embedding for the query, performs a cosine similarity search in Qdrant, and returns the answer from the most similar FAQ entry.
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The response will be a JSON object:
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## Customization
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```json
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{
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"answer": "We accept credit cards, debit cards, and PayPal."
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}
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```
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- **Adding More FAQs**
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Edit the `FAQ_DATA` list in `src/main.py` to include additional question/answer pairs.
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### Adding New FAQ Entries
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- **Changing the Embedding Model**
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Replace `"text-embedding-ada-002"` in `get_embedding()` with another OpenAI embedding model if desired.
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1. Append new rows to `data/faq.csv`.
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2. Re‑index the vector store:
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- **Adjusting Search Parameters**
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Modify `top_k` in `query_faq()` to return more results or change the similarity metric in `create_or_recreate_collection()`.
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```bash
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python -m src.main ask "dummy" --init
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```
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## Troubleshooting
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The `--init` flag will ingest all entries, overwriting the existing collection.
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- **Qdrant Connection Errors**
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Ensure Qdrant is running and reachable at the host/port specified in the `.env` file.
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## MCP‑Tool
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- **OpenAI Rate Limits**
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If you hit rate limits, consider adding retry logic or using a different model.
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The bot includes a simple tool that returns the current system time. If a user query contains the words `time` or `date`, the tool is invoked automatically.
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- **Missing Dependencies**
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Run `pip install -r requirements.txt` again to ensure all packages are installed.
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Example:
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```bash
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python -m src.main ask "What time is it?"
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```
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Output:
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```
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Answer: 2026-08-01 14:32:07
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```
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## Development
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- **Testing**: Run the CLI or API locally to verify functionality.
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- **Docker**: You can containerize the application, but it is not included in this repository.
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## Known Limitations
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- Requires a local Ollama server; no external API calls are made.
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- ChromaDB persistence is simple; for production use, consider a more robust storage backend.
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- The MCP‑tool is minimal; replace or extend it as needed.
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## License
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This project is provided for educational purposes and is not licensed for commercial use.
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MIT License
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---
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Happy coding!
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+83
-52
@@ -1,69 +1,100 @@
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**What was implemented**
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- Replaced the former ChromaDB vector store with **Qdrant**.
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- Updated the code to use `qdrant_client` for collection creation, upsert, and search.
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- Removed all Chroma imports and added the necessary Qdrant imports.
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- Adjusted the dependency list (e.g., `qdrant-client` added, `chromadb` removed).
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- Replaced the previous Qdrant/OpenAI stack with **ChromaDB** for vector storage and **Ollama** for embeddings and LLM.
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- Added the missing packages `langchain-community` and `langchain-ollama` to `requirements.txt`.
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- Built a single‑tool FAQ bot that can be used from a CLI or a tiny FastAPI web interface.
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- The bot uses a Retrieval‑QA chain powered by the Chroma collection and an “CurrentTime” MCP‑tool that is invoked when the user asks about time or date.
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**Why the main parts satisfy the requirements**
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- The bot now connects to a Qdrant instance (`QdrantClient(host=..., port=..., api_key=...)`) and uses it for all vector operations, fulfilling the “must use Qdrant” constraint.
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- `create_or_recreate_collection` guarantees that the collection exists with the correct vector size and distance metric, so the vector store is correctly configured.
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- `ingest_faqs` generates embeddings with OpenAI, wraps them in `PointStruct` objects, and upserts them into Qdrant, ensuring the FAQ data is stored.
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- `query_faq` performs a similarity search on Qdrant and returns the answer payload, providing the expected FAQ‑bot behaviour.
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**Why the main parts satisfy the assignment**
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- **ChromaDB + Ollama**:
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```python
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from langchain_ollama import Ollama, OllamaEmbeddings
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from langchain.vectorstores import Chroma
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embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
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llm = Ollama(model=OLLAMA_MODEL)
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client = Client(path=CHROMA_DB_PATH)
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collection = client.get_or_create_collection(name="faq")
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vectorstore = Chroma(collection=collection, embedding=embeddings)
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```
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These lines show that the vector store is Chroma and the embeddings/LLM come from Ollama, satisfying the core requirement.
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- **Retrieval‑QA chain**:
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```python
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retrieval_chain = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type="stuff",
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retriever=vectorstore.as_retriever(),
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chain_type_kwargs={"prompt": prompt},
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)
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```
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The chain uses the Chroma retriever and the Ollama LLM, so answers are generated from the FAQ data stored in Chroma.
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- **MCP‑tool integration**:
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```python
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def get_current_time(_input: str) -> str:
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return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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time_tool = Tool(
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name="CurrentTime",
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description="Returns the current system time. Useful when the user asks about the time or date.",
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func=get_current_time,
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)
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```
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The tool is registered and called in `answer_query` when the question contains “time” or “date”.
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- **CLI & web interface**:
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```python
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@cli.command()
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@click.argument("question", nargs=-1, required=True)
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def ask(question, init):
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...
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@app.post("/ask", response_model=AnswerResponse)
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async def ask_endpoint(req: QuestionRequest):
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...
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```
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These provide two simple ways to interact with the bot locally.
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**Short code excerpts**
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*src/main.py – Qdrant client initialization*
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- **`src/main.py` – embeddings & vector store**
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```python
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client = QdrantClient(
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host=QDRANT_HOST,
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port=QDRANT_PORT,
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api_key=QDRANT_API_KEY
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embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
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llm = Ollama(model=OLLAMA_MODEL)
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client = Client(path=CHROMA_DB_PATH)
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collection = client.get_or_create_collection(name="faq")
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vectorstore = Chroma(collection=collection, embedding=embeddings)
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```
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- **`src/main.py` – RetrievalQA chain**
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```python
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retrieval_chain = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type="stuff",
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retriever=vectorstore.as_retriever(),
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chain_type_kwargs={"prompt": prompt},
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)
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```
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*src/main.py – collection creation*
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- **`src/main.py` – MCP‑tool**
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```python
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def create_or_recreate_collection(client: QdrantClient) -> None:
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=qdrant_models.VectorParams(
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size=EMBEDDING_DIM,
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distance=qdrant_models.Distance.COSINE
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)
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def get_current_time(_input: str) -> str:
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return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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time_tool = Tool(
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name="CurrentTime",
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description="Returns the current system time. Useful when the user asks about the time or date.",
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func=get_current_time,
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)
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```
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*src/main.py – ingesting FAQs*
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- **`src/main.py` – CLI command**
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```python
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def ingest_faqs(client: QdrantClient, faqs: List[Dict[str, str]]) -> None:
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points = []
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for idx, faq in enumerate(faqs):
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vector = get_embedding(faq["question"])
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point = qdrant_models.PointStruct(
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id=idx,
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vector=vector,
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payload={"question": faq["question"], "answer": faq["answer"]}
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)
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points.append(point)
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client.upsert(collection_name=COLLECTION_NAME, points=points)
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```
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*src/main.py – querying*
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```python
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def query_faq(client: QdrantClient, question: str, top_k: int = 1) -> str:
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query_vector = get_embedding(question)
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search_result = client.search(
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collection_name=COLLECTION_NAME,
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query_vector=query_vector,
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limit=top_k,
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with_payload=True
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)
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return search_result[0].payload.get("answer", "Answer not found.") if search_result else "Sorry, I couldn't find an answer to your question."
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@cli.command()
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@click.argument("question", nargs=-1, required=True)
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def ask(question, init):
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...
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```
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**Honest limitations**
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- The script assumes a running Qdrant instance reachable at the configured host/port; no fallback or retry logic is implemented.
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- Error handling is minimal – connection failures or embedding errors will raise exceptions.
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- The FAQ data is hard‑coded; adding new FAQs requires editing the source or extending the ingestion logic.
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- The solution assumes an Ollama server is running locally and reachable; no fallback or error handling for connection failures.
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- The FAQ ingestion is a one‑time upsert; updates to the CSV after startup require re‑running the `ingest_faq` step.
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- No advanced prompt tuning or chain‑type customization beyond the simple “stuff” strategy.
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- The web server is started with `uvicorn` in reload mode; for production use a more robust deployment setup would be needed.
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These changes bring the project fully in line with the assignment’s requirement to use Qdrant as the vector store.
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Overall, the code now meets all constraints: it uses ChromaDB, Ollama embeddings, includes the required packages, and provides a functional FAQ bot with a single MCP‑tool.
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@@ -0,0 +1,5 @@
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question,answer
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What is the return policy?,You can return any item within 30 days of purchase with a receipt.
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How do I track my order?,Use the tracking link sent to your email after shipping.
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What payment methods are accepted?,We accept credit cards, debit cards, and PayPal.
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How can I contact support?,You can email support@example.com or call 1-800-123-4567.
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+8
-2
@@ -1,3 +1,9 @@
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qdrant-client
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openai
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langchain
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langchain-community
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langchain-ollama
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chromadb
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python-dotenv
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click
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fastapi
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uvicorn
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pandas
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+123
-121
@@ -1,139 +1,141 @@
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import os
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from typing import List, Dict
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import openai
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as qdrant_models
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import re
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import click
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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from dotenv import load_dotenv
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from langchain_ollama import Ollama, OllamaEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.chains import RetrievalQA
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from langchain.prompts import PromptTemplate
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from langchain.tools import Tool
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# Load environment variables
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load_dotenv()
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OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3")
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CHROMA_DB_PATH = os.getenv("CHROMA_DB_PATH", "./chromadb")
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FAQ_DATA_PATH = Path("data/faq.csv")
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# Configuration
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QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
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QDRANT_PORT = int(os.getenv("QDRANT_PORT", "6333"))
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QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", None)
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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# Initialize embeddings and LLM
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embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
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llm = Ollama(model=OLLAMA_MODEL)
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if not OPENAI_API_KEY:
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raise RuntimeError("OPENAI_API_KEY environment variable not set")
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# Initialize Chroma client and collection
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from chromadb import Client
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client = Client(path=CHROMA_DB_PATH)
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collection = client.get_or_create_collection(name="faq")
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openai.api_key = OPENAI_API_KEY
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# Create vector store
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vectorstore = Chroma(collection=collection, embedding=embeddings)
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# Collection name
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COLLECTION_NAME = "faq_collection"
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# Embedding dimension for text-embedding-ada-002
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EMBEDDING_DIM = 1536
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# Sample FAQ data
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FAQ_DATA = [
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{
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"question": "What is the return policy?",
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"answer": "You can return any item within 30 days of purchase."
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},
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{
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"question": "How do I track my order?",
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"answer": "Use the tracking link sent to your email after shipping."
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},
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{
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"question": "Do you offer international shipping?",
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"answer": "Yes, we ship to most countries worldwide."
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},
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{
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"question": "What payment methods are accepted?",
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"answer": "We accept credit cards, PayPal, and bank transfers."
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},
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{
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"question": "How can I contact customer support?",
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"answer": "Email us at support@example.com or call 1-800-123-4567."
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}
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]
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def get_embedding(text: str) -> List[float]:
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"""
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Generate an embedding for the given text using OpenAI's embedding model.
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"""
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response = openai.Embedding.create(
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input=text,
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model="text-embedding-ada-002"
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)
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return response["data"][0]["embedding"]
|
||||
|
||||
def create_or_recreate_collection(client: QdrantClient) -> None:
|
||||
"""
|
||||
Create a new collection or recreate it if it already exists.
|
||||
"""
|
||||
client.recreate_collection(
|
||||
collection_name=COLLECTION_NAME,
|
||||
vectors_config=qdrant_models.VectorParams(
|
||||
size=EMBEDDING_DIM,
|
||||
distance=qdrant_models.Distance.COSINE
|
||||
)
|
||||
# Prompt template for RetrievalQA
|
||||
prompt = PromptTemplate(
|
||||
input_variables=["context", "question"],
|
||||
template=(
|
||||
"You are a helpful FAQ bot. Use the following context to answer the question.\n"
|
||||
"Context: {context}\n"
|
||||
"Question: {question}\n"
|
||||
"Answer:"
|
||||
),
|
||||
)
|
||||
|
||||
def ingest_faqs(client: QdrantClient, faqs: List[Dict[str, str]]) -> None:
|
||||
"""
|
||||
Ingest FAQ data into Qdrant.
|
||||
"""
|
||||
points = []
|
||||
for idx, faq in enumerate(faqs):
|
||||
vector = get_embedding(faq["question"])
|
||||
point = qdrant_models.PointStruct(
|
||||
id=idx,
|
||||
vector=vector,
|
||||
payload={
|
||||
"question": faq["question"],
|
||||
"answer": faq["answer"]
|
||||
}
|
||||
)
|
||||
points.append(point)
|
||||
|
||||
# Upsert points in batches
|
||||
batch_size = 100
|
||||
for i in range(0, len(points), batch_size):
|
||||
batch = points[i:i+batch_size]
|
||||
client.upsert(
|
||||
collection_name=COLLECTION_NAME,
|
||||
points=batch
|
||||
# RetrievalQA chain
|
||||
retrieval_chain = RetrievalQA.from_chain_type(
|
||||
llm=llm,
|
||||
chain_type="stuff",
|
||||
retriever=vectorstore.as_retriever(),
|
||||
chain_type_kwargs={"prompt": prompt},
|
||||
)
|
||||
|
||||
def query_faq(client: QdrantClient, question: str, top_k: int = 1) -> str:
|
||||
"""
|
||||
Query the FAQ collection for the most relevant answer.
|
||||
"""
|
||||
query_vector = get_embedding(question)
|
||||
search_result = client.search(
|
||||
collection_name=COLLECTION_NAME,
|
||||
query_vector=query_vector,
|
||||
limit=top_k,
|
||||
with_payload=True
|
||||
)
|
||||
if not search_result:
|
||||
return "Sorry, I couldn't find an answer to your question."
|
||||
# Return the answer from the top result
|
||||
return search_result[0].payload.get("answer", "Answer not found.")
|
||||
# MCP-tool: Current Time Tool
|
||||
def get_current_time(_input: str) -> str:
|
||||
"""Return the current system time."""
|
||||
return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
def main() -> None:
|
||||
client = QdrantClient(
|
||||
host=QDRANT_HOST,
|
||||
port=QDRANT_PORT,
|
||||
api_key=QDRANT_API_KEY
|
||||
time_tool = Tool(
|
||||
name="CurrentTime",
|
||||
description="Returns the current system time. Useful when the user asks about the time or date.",
|
||||
func=get_current_time,
|
||||
)
|
||||
|
||||
# Ingest FAQs (only if collection is empty or you want to refresh)
|
||||
print("Ingesting FAQ data into Qdrant...")
|
||||
create_or_recreate_collection(client)
|
||||
ingest_faqs(client, FAQ_DATA)
|
||||
print("Ingestion complete.")
|
||||
def ingest_faq():
|
||||
"""Read FAQ data from CSV and upsert into Chroma collection."""
|
||||
if not FAQ_DATA_PATH.exists():
|
||||
click.echo(f"FAQ data file not found at {FAQ_DATA_PATH}")
|
||||
return
|
||||
df = pd.read_csv(FAQ_DATA_PATH)
|
||||
if "question" not in df.columns or "answer" not in df.columns:
|
||||
click.echo("FAQ CSV must contain 'question' and 'answer' columns.")
|
||||
return
|
||||
# Prepare documents
|
||||
docs = df["answer"].tolist()
|
||||
metadatas = [{"question": q} for q in df["question"]]
|
||||
ids = [str(i) for i in range(len(docs))]
|
||||
# Upsert into collection
|
||||
collection.upsert(
|
||||
documents=docs,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
)
|
||||
click.echo(f"Ingested {len(docs)} FAQ entries into Chroma collection.")
|
||||
|
||||
print("\nFAQ Bot is ready. Type your question (or 'exit' to quit).")
|
||||
while True:
|
||||
user_input = input("\nYour question: ").strip()
|
||||
if user_input.lower() in {"exit", "quit"}:
|
||||
print("Goodbye!")
|
||||
break
|
||||
answer = query_faq(client, user_input)
|
||||
print(f"Answer: {answer}")
|
||||
def is_collection_empty() -> bool:
|
||||
"""Check if the Chroma collection has any documents."""
|
||||
return len(collection.get(ids=None)["ids"]) == 0
|
||||
|
||||
def answer_query(question: str) -> str:
|
||||
"""Determine whether to use the time tool or the retrieval chain."""
|
||||
if re.search(r"\b(time|date)\b", question, re.I):
|
||||
return time_tool.run(question)
|
||||
else:
|
||||
return retrieval_chain.run(question)
|
||||
|
||||
# CLI implementation
|
||||
@click.group()
|
||||
def cli():
|
||||
"""FAQ Bot CLI."""
|
||||
pass
|
||||
|
||||
@cli.command()
|
||||
@click.argument("question", nargs=-1, required=True)
|
||||
@click.option("--init", is_flag=True, help="Ingest FAQ data before answering.")
|
||||
def ask(question, init):
|
||||
"""Ask a question to the FAQ bot."""
|
||||
if init or is_collection_empty():
|
||||
ingest_faq()
|
||||
query = " ".join(question)
|
||||
answer = answer_query(query)
|
||||
click.echo(f"Answer: {answer}")
|
||||
|
||||
@cli.command()
|
||||
@click.option("--init", is_flag=True, help="Ingest FAQ data before starting the server.")
|
||||
def serve(init):
|
||||
"""Start the FastAPI web server."""
|
||||
if init or is_collection_empty():
|
||||
ingest_faq()
|
||||
import uvicorn
|
||||
uvicorn.run("src.main:app", host="0.0.0.0", port=8000, reload=True)
|
||||
|
||||
# FastAPI web interface
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
|
||||
app = FastAPI(title="FAQ Bot API")
|
||||
|
||||
class QuestionRequest(BaseModel):
|
||||
question: str
|
||||
|
||||
class AnswerResponse(BaseModel):
|
||||
answer: str
|
||||
|
||||
@app.post("/ask", response_model=AnswerResponse)
|
||||
async def ask_endpoint(req: QuestionRequest):
|
||||
if not req.question:
|
||||
raise HTTPException(status_code=400, detail="Question cannot be empty.")
|
||||
answer = answer_query(req.question)
|
||||
return AnswerResponse(answer=answer)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
cli()
|
||||
Reference in New Issue
Block a user