from typing import List, Union, Generator
from schemas import OpenAIChatMessage

from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

ollama_embedding = OllamaEmbedding(
    model_name="nomic-embed-text",
    base_url="http://localhost:11434",
)

Settings.embed_model = ollama_embedding
Settings.llm = OpenAI(
    temperature=0, model="llama3", api_key="none", api_base="http://localhost:11434"
)

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)


def get_response(
    user_message: str, messages: List[OpenAIChatMessage]
) -> Union[str, Generator]:
    # This is where you can add your custom RAG pipeline.
    # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.

    print(messages)
    print(user_message)

    query_engine = index.as_query_engine(streaming=True)
    response = query_engine.query(user_message)

    return response.response_gen