povtornyy-ekzamen-faq-bot-chromadb-odin-/SOLUTION.md

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

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

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.