import concurrent.futures

import opik
from loguru import logger
from qdrant_client.models import FieldCondition, Filter, MatchValue, Range, Distance

from llm_engineering.application import utils
from llm_engineering.application.preprocessing.dispatchers import EmbeddingDispatcher
from llm_engineering.domain.embedded_chunks import (
    EmbeddedArticleChunk,
    EmbeddedChunk,
    EmbeddedPostChunk,
    EmbeddedRepositoryChunk,
)
from llm_engineering.domain.queries import EmbeddedQuery, Query
from llm_engineering.domain.video_chunks import EmbeddedVideoChunk

from .query_expansion import QueryExpansion
from .reranking import Reranker
from .self_query import SelfQuery
from .multimodal_dispatcher import MultimodalEmbeddingDispatcher

from typing import Union


class ContextRetriever:
    def __init__(self, mock: bool = False) -> None:
        self._query_expander = QueryExpansion(mock=mock)
        self._metadata_extractor = SelfQuery(mock=mock)
        self._reranker = Reranker(mock=mock)

    @opik.track(name="ContextRetriever.search")
    def search(self, query: Union[str, Query], k: int = 3, expand_to_n_queries: int = 3) -> list:
        # Existing code
        query_model = Query.from_str(query) if isinstance(query, str) else query
        query_model = self._metadata_extractor.generate(query_model)
        
        n_generated_queries = self._query_expander.generate(query_model, expand_to_n=expand_to_n_queries)
        
        # Initialize n_k_documents with empty list
        n_k_documents = []
        
        with concurrent.futures.ThreadPoolExecutor() as executor:
            if n_generated_queries:
                search_tasks = [executor.submit(self._search, _query_model, k) 
                            for _query_model in n_generated_queries]
                
                # Handle potential None results from tasks
                n_k_documents = [task.result() or [] for task in concurrent.futures.as_completed(search_tasks)]
        
        # Ensure we're always working with a list of lists
        n_k_documents = n_k_documents or [[]]
        
        # Safe flattening with None filtering
        n_k_documents = utils.misc.flatten([docs for docs in n_k_documents if docs is not None])
        
        if n_k_documents:
            k_documents = self.rerank(query, chunks=n_k_documents, keep_top_k=k)
        else:
            k_documents = []
        
        return k_documents

    def _search(self, query: Query, k: int = 3) -> list[EmbeddedChunk]:
        assert k >= 3, "k should be >= 3"

        def _search_data_category(
            data_category_odm: type[EmbeddedChunk], embedded_query: EmbeddedQuery
        ) -> list[EmbeddedChunk]:
            if embedded_query.author_id:
                query_filter = Filter(
                    must=[
                        FieldCondition(
                            key="author_id",
                            match=MatchValue(
                                value=str(embedded_query.author_id),
                            ),
                        )
                    ]
                )
            else:
                query_filter = None

            return data_category_odm.search(
                query_vector=embedded_query.embedding,
                limit=k // 3,
                query_filter=query_filter,
            )

        embedded_query: EmbeddedQuery = EmbeddingDispatcher.dispatch(query)

        post_chunks = _search_data_category(EmbeddedPostChunk, embedded_query)
        articles_chunks = _search_data_category(EmbeddedArticleChunk, embedded_query)
        repositories_chunks = _search_data_category(EmbeddedRepositoryChunk, embedded_query)

        retrieved_chunks = post_chunks + articles_chunks + repositories_chunks

        return retrieved_chunks

    def rerank(self, query: str | Query, chunks: list[EmbeddedChunk], keep_top_k: int) -> list[EmbeddedChunk]:
        if isinstance(query, str):
            query = Query.from_str(query)

        reranked_documents = self._reranker.generate(query=query, chunks=chunks, keep_top_k=keep_top_k)

        logger.info(f"{len(reranked_documents)} documents reranked successfully.")

        return reranked_documents


class VideoContextRetriever(ContextRetriever):
    def _search(self, query: Query, k: int = 3) -> list[EmbeddedChunk]:
        def _search_video_category(self, embedded_query: EmbeddedQuery, k: int):
            return EmbeddedVideoChunk.search(
                query_vector=embedded_query.embedding,
                limit=k,
                query_filter=self._create_time_filter(embedded_query)
            )