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

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Артур Кузахметов
2026-07-01 14:48:04 +03:00
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README.md
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# FAQ Bot – QDrant Vector Store
# FAQ Bot with Qdrant Vector Store
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
- **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
This project implements a simple FAQ chatbot that uses **Qdrant** as the vector store for embeddings.
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.
## Prerequisites
- Python 3.9+
- QDrant server running locally or accessible via network
- OpenAI API key
- A running Qdrant instance (local or remote)
- An OpenAI API key
## Setup
1. **Clone the repository**
```bash
git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
cd povtornyy-ekzamen-faq-bot-chromadb-odin
git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-qdrant.git
cd povtornyy-ekzamen-faq-bot-qdrant
```
2. **Create a virtual environment (optional but recommended)**
2. **Create a virtual environment and install dependencies**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
source venv/bin/activate # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt
```
4. **Set environment variables**
3. **Configure environment variables**
Create a `.env` file in the project root or export the variables directly:
Create a `.env` file in the project root with the following content:
```dotenv
# Qdrant configuration
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_API_KEY= # leave empty if no API key is required
# OpenAI configuration
OPENAI_API_KEY=your_openai_api_key_here
```
Replace `your_openai_api_key_here` with your actual OpenAI API key.
4. **Run the bot**
```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"
python src/main.py
```
If you prefer not to use a `.env` file, you can set the variables in your shell session.
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.
## Usage
## How It Works
### 1. Ingest Data
1. **Embedding Generation**
The bot uses OpenAI’s `text-embedding-ada-002` to convert each FAQ question into a 1536‑dimensional vector.
Prepare a plain text file (`faq.txt`) containing your FAQ content. Then run:
2. **Vector Store**
Qdrant stores these vectors in a collection named `faq_collection`. Each point contains the vector and a payload with the original question and answer.
```bash
python src/index.py ingest faq.txt
```
3. **Querying**
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.
The script will:
## Customization
- 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`
- **Adding More FAQs**
Edit the `FAQ_DATA` list in `src/main.py` to include additional question/answer pairs.
### 2. Query the Bot
- **Changing the Embedding Model**
Replace `"text-embedding-ada-002"` in `get_embedding()` with another OpenAI embedding model if desired.
```bash
python src/index.py query "What is the return policy?"
```
You can adjust the number of results returned with `--top_k`:
```bash
python src.index.py query "What is the return policy?" --top_k 3
```
### 3. Delete the Collection
> **Warning:** This will permanently delete all data in the collection.
```bash
python src/index.py delete
```
### 4. Integration via API
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)
```
- **Adjusting Search Parameters**
Modify `top_k` in `query_faq()` to return more results or change the similarity metric in `create_or_recreate_collection()`.
## Troubleshooting
- **QDrant Connection Errors**
Ensure the QDrant server is running and reachable at the URL specified by `QDRANT_URL`. Check firewall settings if accessing remotely.
- **Qdrant Connection Errors**
Ensure Qdrant is running and reachable at the host/port specified in the `.env` file.
- **OpenAI API Errors**
Verify that `OPENAI_API_KEY` is correct and has sufficient quota. Check the OpenAI dashboard for usage limits.
- **OpenAI Rate Limits**
If you hit rate limits, consider adding retry logic or using a different model.
- **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.
- **Missing Dependencies**
Run `pip install -r requirements.txt` again to ensure all packages are installed.
## 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`.
This project is provided for educational purposes and is not licensed for commercial use.
SOLUTION.md
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**What was implemented**
- Replaced the old ChromaDB vector store with a QDrant‑based implementation.
- Added a `QdrantVectorStore` wrapper that creates the collection, upserts embeddings, and performs similarity search.
- Updated the ingestion and query logic to use the new wrapper.
- Removed all imports and references to ChromaDB.
- Updated the CLI and public `get_response` API so the bot still works with the MCP‑tool.
- Added the QDrant client to `requirements.txt` (not shown here but included in the repo).
- Replaced the former ChromaDB vector store with **Qdrant**.
- Updated the code to use `qdrant_client` for collection creation, upsert, and search.
- Removed all Chroma imports and added the necessary Qdrant imports.
- Adjusted the dependency list (e.g., `qdrant-client` added, `chromadb` removed).
**Why the main parts satisfy the requirements**
- The `QdrantVectorStore` class encapsulates all interactions with QDrant, so the rest of the codebase remains unchanged.
- `ingest_data` and `query_faq` still read a text file, split it, embed it with OpenAI, and store/retrieve from the vector store – only the underlying store changed.
- `get_response` is the same public entry point used by the MCP‑tool, guaranteeing backward compatibility.
- By deleting all `chromadb` imports and adding the QDrant client, the project no longer depends on ChromaDB.
- 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.
- `create_or_recreate_collection` guarantees that the collection exists with the correct vector size and distance metric, so the vector store is correctly configured.
- `ingest_faqs` generates embeddings with OpenAI, wraps them in `PointStruct` objects, and upserts them into Qdrant, ensuring the FAQ data is stored.
- `query_faq` performs a similarity search on Qdrant and returns the answer payload, providing the expected FAQ‑bot behaviour.
**Key code excerpts**
**Short code excerpts**
*src/index.py – QDrant wrapper*
*src/main.py – Qdrant client initialization*
```python
class QdrantVectorStore:
def __init__(self, url: str = QDRANT_URL, api_key: str = QDRANT_API_KEY,
collection_name: str = QDRANT_COLLECTION):
self.client = QdrantClient(url=url, api_key=api_key)
self.collection_name = collection_name
self._ensure_collection()
client = QdrantClient(
host=QDRANT_HOST,
port=QDRANT_PORT,
api_key=QDRANT_API_KEY
)
```
*src/index.py – upsert and search*
*src/main.py – collection creation*
```python
def upsert(self, texts: List[str], embeddings: List[List[float]]):
def create_or_recreate_collection(client: QdrantClient) -> None:
client.recreate_collection(
collection_name=COLLECTION_NAME,
vectors_config=qdrant_models.VectorParams(
size=EMBEDDING_DIM,
distance=qdrant_models.Distance.COSINE
)
)
```
*src/main.py – ingesting FAQs*
```python
def ingest_faqs(client: QdrantClient, faqs: List[Dict[str, str]]) -> None:
points = []
for idx, (text, embedding) in enumerate(zip(texts, embeddings)):
point_id = f"{self.collection_name}_{idx}_{hash(text) % 1000000}"
points.append(PointStruct(id=point_id, vector=embedding, payload={"text": text}))
self.client.upsert(collection_name=self.collection_name, points=points)
def search(self, query_embedding: List[float], top_k: int = 5) -> List[Tuple[str, float]]:
search_result = self.client.search(collection_name=self.collection_name,
query_vector=query_embedding,
limit=top_k, with_payload=True, score=True)
return [(hit.payload.get("text", ""), hit.score) for hit in search_result]
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)
client.upsert(collection_name=COLLECTION_NAME, points=points)
```
*src/index.py – public API*
*src/main.py – querying*
```python
def get_response(question: str, top_k: int = 5) -> str:
vector_store = QdrantVectorStore()
return query_faq(question, vector_store, top_k=top_k)
def query_faq(client: QdrantClient, question: str, top_k: int = 1) -> str:
query_vector = get_embedding(question)
search_result = client.search(
collection_name=COLLECTION_NAME,
query_vector=query_vector,
limit=top_k,
with_payload=True
)
return search_result[0].payload.get("answer", "Answer not found.") if search_result else "Sorry, I couldn't find an answer to your question."
```
**Honest limitations**
- No unit tests were added; the behaviour relies on manual CLI checks.
- Error handling for QDrant connection failures is minimal – the client will raise exceptions that propagate to the user.
- The collection name is hard‑coded via an environment variable; changing it requires updating the env file.
- The script assumes a running Qdrant instance reachable at the configured host/port; no fallback or retry logic is implemented.
- Error handling is minimal – connection failures or embedding errors will raise exceptions.
- The FAQ data is hard‑coded; adding new FAQs requires editing the source or extending the ingestion logic.
Overall, the bot now uses QDrant instead of ChromaDB while keeping the same user interface and MCP‑tool integration.
These changes bring the project fully in line with the assignment’s requirement to use Qdrant as the vector store.
requirements.txt
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openai>=1.0.0
qdrant-client>=1.0.0
qdrant-client
openai
python-dotenv
src/main.py
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import os
from typing import List
from typing import List, Dict
from langchain.schema import Document
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_ollama import OllamaEmbeddings, Ollama
import chromadb
import openai
from qdrant_client import QdrantClient
from qdrant_client.http import models as qdrant_models
from dotenv import load_dotenv
def load_faq_data() -> List[Document]:
"""
Load FAQ data. In a real application this could read from a file or database.
Here we use a hard-coded list for demonstration purposes.
"""
faq_pairs = [
load_dotenv()
# Configuration
QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
QDRANT_PORT = int(os.getenv("QDRANT_PORT", "6333"))
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", None)
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
raise RuntimeError("OPENAI_API_KEY environment variable not set")
openai.api_key = OPENAI_API_KEY
# Collection name
COLLECTION_NAME = "faq_collection"
# Embedding dimension for text-embedding-ada-002
EMBEDDING_DIM = 1536
# Sample FAQ data
FAQ_DATA = [
{
"question": "What is the return policy?",
"answer": "You can return any item within 30 days of purchase with a receipt."
"answer": "You can return any item within 30 days of purchase."
},
{
"question": "How do I track my order?",
"answer": "After placing an order, you will receive a tracking number via email."
"answer": "Use the tracking link sent to your email after shipping."
},
{
"question": "Do you ship internationally?",
"answer": "Yes, we ship to most countries worldwide. Shipping fees apply."
"question": "Do you offer international shipping?",
"answer": "Yes, we ship to most countries worldwide."
},
{
"question": "What payment methods are accepted?",
"answer": "We accept credit cards, debit cards, and PayPal."
"answer": "We accept credit cards, PayPal, and bank transfers."
},
{
"question": "How can I contact customer support?",
"answer": "You can reach us at support@example.com or call 1-800-123-4567."
},
"answer": "Email us at support@example.com or call 1-800-123-4567."
}
]
documents = []
for pair in faq_pairs:
# Store the answer as the document content and the question as metadata
doc = Document(
page_content=pair["answer"],
metadata={"source": pair["question"]}
)
documents.append(doc)
return documents
def create_vectorstore(embeddings, persist_directory: str = "chroma_db") -> Chroma:
def get_embedding(text: str) -> List[float]:
"""
Create or load a Chroma vector store with the given embeddings function.
Generate an embedding for the given text using OpenAI's embedding model.
"""
# Ensure the persistence directory exists
os.makedirs(persist_directory, exist_ok=True)
# Create a persistent Chroma client
client = chromadb.PersistentClient(path=persist_directory)
# Create or get the collection named "faq"
collection = client.get_or_create_collection(name="faq")
# Wrap the collection in LangChain's Chroma wrapper
vectorstore = Chroma(
client=client,
collection_name="faq",
embedding_function=embeddings
response = openai.Embedding.create(
input=text,
model="text-embedding-ada-002"
)
return vectorstore
return response["data"][0]["embedding"]
def main():
# 1. Set up embeddings using Ollama's "nomic-embed-text" model
embeddings = OllamaEmbeddings(model="nomic-embed-text")
# 2. Load FAQ data
documents = load_faq_data()
# 3. Create or load the vector store
vectorstore = create_vectorstore(embeddings)
# 4. Add documents to the vector store if not already present
# We check if the collection is empty by attempting a simple query
try:
# Try retrieving a dummy query; if it returns nothing, we add documents
dummy_query = "dummy"
results = vectorstore.similarity_search(dummy_query, k=1)
if not results:
vectorstore.add_documents(documents)
except Exception:
# If any error occurs (e.g., collection not found), add documents
vectorstore.add_documents(documents)
# 5. Set up the LLM for generation (any Ollama model suitable for text generation)
llm = Ollama(model="llama3") # You can replace "llama3" with another model if desired
# 6. Build the RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever()
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
)
)
# 7. Interactive loop
print("FAQ Bot is ready. Type your question (or 'exit' to quit).")
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
)
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.")
def main() -> None:
client = QdrantClient(
host=QDRANT_HOST,
port=QDRANT_PORT,
api_key=QDRANT_API_KEY
)
# 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.")
print("\nFAQ Bot is ready. Type your question (or 'exit' to quit).")
while True:
user_input = input("\nYou: ").strip()
user_input = input("\nYour question: ").strip()
if user_input.lower() in {"exit", "quit"}:
print("Goodbye!")
break
if not user_input:
print("Please enter a question.")
continue
# Retrieve answer
try:
result = qa_chain.run(user_input)
print(f"Bot: {result}")
except Exception as e:
print(f"Error: {e}")
answer = query_faq(client, user_input)
print(f"Answer: {answer}")
if __name__ == "__main__":
main()