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

SOLUTION.md

What was implemented

• Switched from OpenAI embeddings/LLM to Ollama’s nomic-embed-text for vector generation.
• Replaced the non‑existent QdrantVectorStore with a persistent ChromaDB store (langchain.vectorstores.Chroma).
• Added the missing dependencies langchain-community and langchain-ollama to requirements.txt.
• Updated the bot to use the Ollama model for both embeddings and text generation (llama3).
• Kept the interactive FAQ loop and retrieval‑QA chain intact.

Why the main parts satisfy the requirements

• Embeddings: OllamaEmbeddings(model="nomic-embed-text") guarantees the required Ollama model is used.
• Vector store: Chroma is imported from langchain.vectorstores and wrapped around a persistent Chroma client, fulfilling the ChromaDB constraint.
• Dependencies: requirements.txt now lists langchain-community and langchain-ollama, ensuring the environment can install the needed packages.
• LLM: The generation step uses Ollama(model="llama3"), an Ollama model, keeping the entire pipeline within the specified ecosystem.

Key code excerpts

src/main.py – embeddings and vector store

# 1. Set up embeddings using Ollama's "nomic-embed-text" model
embeddings = OllamaEmbeddings(model="nomic-embed-text")

def create_vectorstore(embeddings, persist_directory: str = "chroma_db") -> Chroma:
    ...
    vectorstore = Chroma(
        client=client,
        collection_name="faq",
        embedding_function=embeddings
    )
    return vectorstore

src/main.py – retrieval‑QA chain

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

requirements.txt (excerpt)

langchain-community
langchain-ollama

Limitations

• The bot currently uses a hard‑coded FAQ list; adding dynamic data sources would require further changes.
• Error handling around the vector store is minimal; in a production setting more robust checks would be advisable.

This implementation meets all assignment constraints while keeping the original interactive FAQ functionality.