from llama_index.core import VectorStoreIndex, Settings
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.response_synthesizers import get_response_synthesizer, ResponseMode
from llama_index.core.prompts import PromptTemplate
from my_logging import log_message
from config import PROMPT_SIMPLE_POISK

def create_vector_index(documents):
    log_message("Строю векторный индекс")
    return VectorStoreIndex.from_documents(documents)

def create_query_engine(vector_index):
    try:
        # --- Semantic-only retriever ---
        vector_retriever = VectorIndexRetriever(
            index=vector_index,
            similarity_top_k=30,      # recommended default
            similarity_cutoff=0.78    # filter weak matches
        )

        custom_prompt_template = PromptTemplate(PROMPT_SIMPLE_POISK)

        response_synthesizer = get_response_synthesizer(
            response_mode=ResponseMode.TREE_SUMMARIZE,
            text_qa_template=custom_prompt_template
        )

        query_engine = RetrieverQueryEngine(
            retriever=vector_retriever,
            response_synthesizer=response_synthesizer
        )

        log_message("Semantic-only query engine успешно создан")
        return query_engine

    except Exception as e:
        log_message(f"Ошибка создания query engine: {str(e)}")
        raise