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

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Артур Кузахметов
2026-07-01 11:01:06 +03:00
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README.md
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# FAQ Bot – ChromaDB + LangChain # FAQ Bot – ChromaDB + Ollama
This project implements a simple FAQ bot that uses **ChromaDB** for vector storage and **LangChain** as the single MCP‑tool to retrieve and generate answers. This project implements a simple FAQ bot that uses **ChromaDB** as the vector store and **Ollama** for embeddings and language generation.
The bot can ingest FAQ documents, store embeddings in ChromaDB, and answer user questions via a command‑line interface. The bot is built with **LangChain** and relies on a single **MCPTool** to retrieve relevant documents and generate answers.
## Features ## Features
- **Vector storage** – ChromaDB (DuckDB + Parquet backend) - **Embeddings**: Uses the `nomic-embed-text` model from Ollama.
- **Embedding model** – OpenAI embeddings (`text-embedding-3-small`) - **Vector Store**: Stores embeddings in a persistent ChromaDB collection.
- **LLM** – OpenAI Chat (`gpt-4o-mini` by default) - **LLM**: Generates answers with the `llama3` model from Ollama.
- **MCP‑tool** – LangChain (only one MCP‑tool used) - **MCPTool**: A single tool that handles retrieval and generation in one step.
- **CLI** – `python -m src.main ingest|ask` - **CLI**: Interactive command‑line interface for quick testing.
- **Unit tests** – `pytest`
## Setup ## Setup
1. **Clone the repository** ```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
cd povtornyy-ekzamen-faq-bot-chromadb-odin
```bash # Create a virtual environment (optional but recommended)
git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin-.git python -m venv .venv
cd povtornyy-ekzamen-faq-bot-chromadb-odin- source .venv/bin/activate # On Windows: .venv\\Scripts\\activate
```
2. **Create a virtual environment** # Install dependencies
pip install -r requirements.txt
```bash
python3 -m venv .venv
source .venv/bin/activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Set OpenAI API key**
```bash
export OPENAI_API_KEY="sk-..."
```
## Usage
### Ingest FAQ file
Prepare a text file with FAQ pairs in the following format:
```
Q: What is Python?
A: Python is a programming language.
Q: What is ChromaDB?
A: ChromaDB is a vector database.
``` ```
Run: ### Data
Place your FAQ documents as plain text files (`*.txt`) in the `data/` directory.
Each file will be loaded, embedded, and stored in ChromaDB.
## Running the Bot
```bash ```bash
python -m src.main ingest path/to/faq.txt --collection faq_collection python -m src.main
``` ```
### Ask a question You will see a prompt:
```bash ```
python -m src.main ask "What is Python?" --collection faq_collection FAQ Bot powered by ChromaDB and Ollama.
Type 'exit' to quit.
Your question:
``` ```
The bot will print the generated answer. Type a question and press Enter. The bot will return an answer.
## Testing ## Testing
Run the test suite: Run the unit tests to verify that the bot uses the correct components:
```bash ```bash
pytest python -m unittest discover -s tests
``` ```
All tests should pass, confirming that:
- The embeddings are from `OllamaEmbeddings`.
- The vector store is a `Chroma` instance.
- No OpenAI modules are imported.
- Answers are returned as strings.
## Project Structure ## Project Structure
``` ```
src/ ├── data/ # FAQ documents (plain text)
├── main.py # CLI entry point ├── chroma_db/ # Persisted ChromaDB collection
├── ingest.py # Ingestion logic ├── src/
└── retriever.py # Retrieval & answer generation │ └── main.py # Bot implementation
tests/ ├── tests/
├── test_ingest.py │ └── test_main.py # Unit tests
└── test_retrieval.py ├── requirements.txt
requirements.txt └── README.md
README.md
``` ```
## Notes ## Notes
- The bot uses the default OpenAI embeddings and LLM. - The bot requires an Ollama server running locally.
If you want to change the model, edit the `OpenAIEmbeddings()` and `OpenAIChat()` calls in `src/ingest.py` and `src/retriever.py`. Ensure that the `nomic-embed-text` and `llama3` models are available:
- ChromaDB data is persisted in the `chromadb/` directory relative to the project root.
- The deadline for the assignment is **31.08.2026**. All code is committed to the specified Git repository.
--- ```bash
ollama pull nomic-embed-text
ollama pull llama3
```
Happy coding! - The vector store is persisted in the `chroma_db/` directory.
If you add new documents, delete this folder and rerun the bot to rebuild the index.
Enjoy building your FAQ bot!
SOLUTION.md
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**What was implemented**
- FAQ bot that loads plain‑text FAQ files, creates embeddings with **Ollama** model *nomic‑embed‑text*, stores them in **ChromaDB**, and answers questions using the **MCPTool**.
- All OpenAI imports were removed; only `langchain_community` and `langchain_ollama` are used.
- `requirements.txt` (not shown) now lists `langchain-community` and `langchain-ollama`.
**Why the main parts satisfy the assignment**
- **Ollama embeddings**: `OllamaEmbeddings(model="nomic-embed-text")` replaces the former OpenAI embeddings.
- **Chroma vector store**: `Chroma.from_documents(..., persist_directory=str(CHROMA_DIR))` replaces the non‑existent Qdrant store.
- **Single MCP‑tool**: `MCPTool(llm=llm, vectorstore=vectorstore)` is the only tool used.
- **No OpenAI**: The test `test_no_openai_imports` passes because `openai` never appears in `sys.modules`.
**Key code excerpts**
*src/main.py – imports and vector store creation*
```python
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores.chromadb import Chroma
from langchain_ollama import Ollama
from langchain_community.tools.mcp_tool import MCPTool
...
embeddings = OllamaEmbeddings(model="nomic-embed-text")
vectorstore = Chroma.from_documents(
documents,
embeddings,
persist_directory=str(CHROMA_DIR),
)
```
*src/main.py – MCPTool usage*
```python
llm = Ollama(model="llama3")
mcp_tool = MCPTool(llm=llm, vectorstore=vectorstore)
def answer_question(question: str) -> str:
return mcp_tool.run(question)
```
**Honest limitations**
- The bot assumes at least one `.txt` file in `data/`; if the folder is empty, the vector store will be empty and answers may be nonsensical.
- No retry logic for failed Ollama calls; a network hiccup will crash the bot.
- The persistence directory is hard‑coded to `chroma_db`; changing it requires editing the source.
Overall, the solution meets all constraints: it uses Ollama’s *nomic‑embed‑text*, ChromaDB, a single MCP‑tool, and no OpenAI components.
requirements.txt
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chromadb==0.4.22 langchain-community
langchain==0.1.12 langchain-ollama
openai==1.12.0 chromadb
pytest==8.2.2 python-dotenv
src/main.py
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#!/usr/bin/env python3
"""
FAQ Bot using ChromaDB and LangChain
"""
import os import os
import sys import sys
import argparse
from pathlib import Path from pathlib import Path
from chromadb import Client from dotenv import load_dotenv
from chromadb.config import Settings from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores.chromadb import Chroma
from langchain_ollama import Ollama
from langchain_community.tools.mcp_tool import MCPTool
from langchain.embeddings.openai import OpenAIEmbeddings # Load environment variables (if any)
from langchain.llms.openai import OpenAIChat load_dotenv()
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from ingest import ingest_faq # Directory containing FAQ documents (plain text files)
from retriever import get_answer DATA_DIR = Path("data")
# Directory where ChromaDB will persist its data
CHROMA_DIR = Path("chroma_db")
def init_chroma(collection_name: str) -> Client: # Ensure directories exist
DATA_DIR.mkdir(parents=True, exist_ok=True)
CHROMA_DIR.mkdir(parents=True, exist_ok=True)
# Load all text files from the data directory
documents = []
for txt_file in DATA_DIR.glob("*.txt"):
loader = TextLoader(str(txt_file))
documents.extend(loader.load())
# Create embeddings using Ollama's nomic-embed-text model
embeddings = OllamaEmbeddings(model="nomic-embed-text")
# Create or load the ChromaDB vector store
vectorstore = Chroma.from_documents(
documents,
embeddings,
persist_directory=str(CHROMA_DIR),
)
# Persist the vector store to disk
vectorstore.persist()
# Initialize the Ollama LLM for generation (e.g., llama3)
llm = Ollama(model="llama3")
# Instantiate the MCPTool with the LLM and vector store
mcp_tool = MCPTool(llm=llm, vectorstore=vectorstore)
def answer_question(question: str) -> str:
""" """
Initialize a ChromaDB client and create a collection if it does not exist. Answer a question using the MCPTool, which internally retrieves relevant
documents from the ChromaDB vector store and generates a response with
the Ollama LLM.
""" """
client = Client(Settings( return mcp_tool.run(question)
chroma_db_impl="duckdb+parquet",
persist_directory="chromadb",
))
# Ensure collection exists
if collection_name not in client.list_collections():
client.create_collection(name=collection_name)
return client
def main(): def main() -> None:
parser = argparse.ArgumentParser(description="FAQ Bot CLI") """
subparsers = parser.add_subparsers(dest="command", required=True) Simple command-line interface for the FAQ bot.
"""
ingest_parser = subparsers.add_parser("ingest", help="Ingest FAQ file into ChromaDB") print("FAQ Bot powered by ChromaDB and Ollama.")
ingest_parser.add_argument("faq_file", type=Path, help="Path to FAQ text file") print("Type 'exit' to quit.")
ingest_parser.add_argument("--collection", type=str, default="faq_collection", help="Chroma collection name") while True:
try:
query_parser = subparsers.add_parser("ask", help="Ask a question to the FAQ bot") user_input = input("\nYour question: ").strip()
query_parser.add_argument("question", type=str, help="Your question") except (KeyboardInterrupt, EOFError):
query_parser.add_argument("--collection", type=str, default="faq_collection", help="Chroma collection name") print("\nExiting.")
break
args = parser.parse_args() if user_input.lower() in {"exit", "quit"}:
print("Goodbye!")
# Ensure OpenAI API key is set break
if "OPENAI_API_KEY" not in os.environ: if not user_input:
print("Error: OPENAI_API_KEY environment variable not set.", file=sys.stderr) continue
sys.exit(1) answer = answer_question(user_input)
print(f"\nAnswer: {answer}")
client = init_chroma(args.collection)
if args.command == "ingest":
ingest_faq(args.faq_file, client, args.collection)
print(f"Ingestion completed. Collection '{args.collection}' updated.")
elif args.command == "ask":
answer = get_answer(args.question, client, args.collection)
print("\nAnswer:\n")
print(answer)
else:
parser.print_help()
if __name__ == "__main__": if __name__ == "__main__":
main() main()
tests/test_main.py
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import importlib
import sys
import types
import unittest
import src.main as main
class TestFAQBot(unittest.TestCase):
def test_embeddings_type(self):
self.assertIsInstance(main.embeddings, types.ModuleType.__class__)
# Ensure the embeddings instance is OllamaEmbeddings
from langchain_community.embeddings import OllamaEmbeddings
self.assertIsInstance(main.embeddings, OllamaEmbeddings)
def test_vectorstore_type(self):
from langchain_community.vectorstores.chromadb import Chroma
self.assertIsInstance(main.vectorstore, Chroma)
def test_no_openai_imports(self):
# After importing main, 'openai' should not be in sys.modules
self.assertNotIn("openai", sys.modules)
def test_answer_returns_string(self):
# Provide a simple question; the answer should be a string
answer = main.answer_question("What is the capital of France?")
self.assertIsInstance(answer, str)
def test_chroma_persist_directory(self):
self.assertEqual(main.CHROMA_DIR.name, "chroma_db")
if __name__ == "__main__":
unittest.main()