agent-s-rag-pamyatyu/SOLUTION.md

What was implemented

• Switched the embedding provider from OpenAIEmbeddings to OllamaEmbeddings (langchain‑community).
• Replaced the FAISS vector store with a Qdrant store.
• Updated all imports, configuration, and helper functions to use the new stack.
• Added the required dependencies (langchain-community, qdrant-client) to requirements.txt.
• Kept the LLM (OpenAI), prompt templates, chain structure, and memory unchanged.
• Provided local‑host configuration for both Ollama and Qdrant in config.py.

Why the main parts satisfy the requirements

• Embeddings – embeddings.py now returns an OllamaEmbeddings instance that talks to a local Ollama server (base_url=f"{OLLAMA_HOST}:{OLLAMA_PORT}").
• Vector store – vector_store.py creates a QdrantClient, ensures the collection exists, and returns a Qdrant vector store wired to the Ollama embeddings.
• Agent – agent.py builds a RetrievalQA chain that uses the Qdrant retriever, the same OpenAI LLM, and a conversation buffer memory.
• Configuration – config.py exposes host/port for both services, so the agent can connect to local instances without hard‑coding URLs.
• Dependencies – requirements.txt now lists langchain-community and qdrant-client, satisfying the “add dependencies” requirement.

Key code excerpts

embeddings.py – Ollama embeddings

from langchain_community.embeddings import OllamaEmbeddings
...
return OllamaEmbeddings(
    model=OLLAMA_EMBEDDING_MODEL,
    base_url=f"{OLLAMA_HOST}:{OLLAMA_PORT}"
)

vector_store.py – Qdrant store

from qdrant_client import QdrantClient
...
return Qdrant(
    client=client,
    collection_name=QDRANT_COLLECTION_NAME,
    embeddings=embeddings
)

agent.py – RetrievalQA chain unchanged except for the retriever

vector_store: Qdrant = get_vector_store()
retriever = vector_store.as_retriever(search_kwargs={"k": 5})
...
chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    memory=memory
)

config.py – local‑host settings

OLLAMA_HOST: str = "http://localhost"
OLLAMA_PORT: int = 11434
QDRANT_HOST: str = "http://localhost"
QDRANT_PORT: int = 6333

Honest limitations

• The solution assumes a running local Ollama server exposing the chosen embedding model (llama2) and a Qdrant instance listening on the default ports.
• The vector size is hard‑coded to 768; if the chosen Ollama model uses a different dimensionality, the collection creation will need adjustment.
• No automated tests were executed; the changes are based on the provided project structure and should satisfy the functional requirements.