feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'
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# FAQ Bot – ChromaDB + LangChain
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# FAQ Bot – ChromaDB + Ollama
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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.
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The bot can ingest FAQ documents, store embeddings in ChromaDB, and answer user questions via a command‑line interface.
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This project implements a simple FAQ bot that uses **ChromaDB** as the vector store and **Ollama** for embeddings and language generation.
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The bot is built with **LangChain** and relies on a single **MCPTool** to retrieve relevant documents and generate answers.
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## Features
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- **Vector storage** – ChromaDB (DuckDB + Parquet backend)
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- **Embedding model** – OpenAI embeddings (`text-embedding-3-small`)
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- **LLM** – OpenAI Chat (`gpt-4o-mini` by default)
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- **MCP‑tool** – LangChain (only one MCP‑tool used)
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- **CLI** – `python -m src.main ingest|ask`
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- **Unit tests** – `pytest`
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- **Embeddings**: Uses the `nomic-embed-text` model from Ollama.
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- **Vector Store**: Stores embeddings in a persistent ChromaDB collection.
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- **LLM**: Generates answers with the `llama3` model from Ollama.
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- **MCPTool**: A single tool that handles retrieval and generation in one step.
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- **CLI**: Interactive command‑line interface for quick testing.
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## Setup
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1. **Clone the repository**
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```bash
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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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```bash
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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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```
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# Create a virtual environment (optional but recommended)
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\\Scripts\\activate
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2. **Create a virtual environment**
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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```
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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4. **Set OpenAI API key**
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```bash
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export OPENAI_API_KEY="sk-..."
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```
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## Usage
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### Ingest FAQ file
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Prepare a text file with FAQ pairs in the following format:
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```
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Q: What is Python?
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A: Python is a programming language.
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Q: What is ChromaDB?
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A: ChromaDB is a vector database.
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# Install dependencies
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pip install -r requirements.txt
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```
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Run:
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### Data
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Place your FAQ documents as plain text files (`*.txt`) in the `data/` directory.
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Each file will be loaded, embedded, and stored in ChromaDB.
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## Running the Bot
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```bash
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python -m src.main ingest path/to/faq.txt --collection faq_collection
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python -m src.main
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```
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### Ask a question
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You will see a prompt:
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```bash
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python -m src.main ask "What is Python?" --collection faq_collection
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```
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FAQ Bot powered by ChromaDB and Ollama.
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Type 'exit' to quit.
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Your question:
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```
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The bot will print the generated answer.
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Type a question and press Enter. The bot will return an answer.
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## Testing
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Run the test suite:
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Run the unit tests to verify that the bot uses the correct components:
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```bash
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pytest
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python -m unittest discover -s tests
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```
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All tests should pass, confirming that:
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- The embeddings are from `OllamaEmbeddings`.
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- The vector store is a `Chroma` instance.
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- No OpenAI modules are imported.
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- Answers are returned as strings.
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## Project Structure
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```
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src/
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├── main.py # CLI entry point
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├── ingest.py # Ingestion logic
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└── retriever.py # Retrieval & answer generation
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tests/
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├── test_ingest.py
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└── test_retrieval.py
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requirements.txt
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README.md
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├── data/ # FAQ documents (plain text)
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├── chroma_db/ # Persisted ChromaDB collection
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├── src/
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│ └── main.py # Bot implementation
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├── tests/
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│ └── test_main.py # Unit tests
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├── requirements.txt
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└── README.md
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```
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## Notes
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- The bot uses the default OpenAI embeddings and LLM.
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If you want to change the model, edit the `OpenAIEmbeddings()` and `OpenAIChat()` calls in `src/ingest.py` and `src/retriever.py`.
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- ChromaDB data is persisted in the `chromadb/` directory relative to the project root.
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- The deadline for the assignment is **31.08.2026**. All code is committed to the specified Git repository.
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- The bot requires an Ollama server running locally.
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Ensure that the `nomic-embed-text` and `llama3` models are available:
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---
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```bash
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ollama pull nomic-embed-text
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ollama pull llama3
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```
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Happy coding!
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- The vector store is persisted in the `chroma_db/` directory.
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If you add new documents, delete this folder and rerun the bot to rebuild the index.
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Enjoy building your FAQ bot!
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