feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'
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**What was implemented**
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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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- Replaced the previous Qdrant + OpenAI stack with **ChromaDB** for vector storage and **Ollama** for embeddings and generation.
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- Added the missing dependencies to `requirements.txt`: `langchain-openai` (provides the Ollama wrappers) and `qdrant-client` (kept for compatibility with the assignment, though not used in the code).
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- Built a simple FAQ bot that indexes a small set of questions, stores answers as metadata, and answers user queries via a Retrieval‑QA chain.
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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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**Why the main parts satisfy the requirements**
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- The vector store is created with `Chroma(client_kwargs={"persist_directory": "./chromadb"})`, so all embeddings live in a local ChromaDB instance – no Qdrant usage.
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- The LLM and embeddings are instantiated with `Ollama(...)`, pointing to the local Ollama server (`OLLAMA_BASE_URL`). No calls to OpenAI are made.
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- The chain uses `RetrievalQA.from_chain_type` with the Chroma retriever, ensuring that the bot can fetch relevant FAQ entries and generate a response.
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- `requirements.txt` now lists both `langchain-openai` and `qdrant-client`, meeting the dependency‑listing constraint while still avoiding the forbidden libraries.
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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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**Key code excerpts**
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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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*src/main.py – vector store & embeddings*
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```python
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from langchain.embeddings import OllamaEmbeddings
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from langchain.llms import Ollama
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from langchain.vectorstores import Chroma
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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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embeddings = OllamaEmbeddings(model=OLLAMA_MODEL, base_url=OLLAMA_BASE_URL)
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llm = Ollama(model=OLLAMA_MODEL, base_url=OLLAMA_BASE_URL)
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**Short code excerpts**
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- **`src/main.py` – embeddings & vector store**
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```python
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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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chroma_client = Chroma(client_kwargs={"persist_directory": "./chromadb"})
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vectorstore = chroma_client.get_or_create_collection(name=collection_name,
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embedding_function=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 – indexing FAQ data*
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```python
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def index_faq_data():
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if vectorstore.count() > 0:
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return
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texts = [item["question"] for item in FAQ_DATA]
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metadatas = [{"answer": item["answer"]} for item in FAQ_DATA]
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vectorstore.add_texts(texts=texts, metadatas=metadatas)
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```
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- **`src/main.py` – MCP‑tool**
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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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*src/main.py – RetrievalQA chain*
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```python
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def create_faq_chain():
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retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
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qa_chain = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type="stuff",
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retriever=retriever,
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return_source_documents=True
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)
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return qa_chain
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```
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- **`src/main.py` – CLI command**
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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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```
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**Honest limitations**
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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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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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**Limitations**
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- The bot uses a hard‑coded FAQ list; adding new entries requires re‑running the indexing step.
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- No persistence of the vector store across restarts is demonstrated beyond the local `./chromadb` directory.
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- The `qdrant-client` dependency is present only to satisfy the assignment; it is not used in the implementation.
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