import logging
import os
from contextlib import asynccontextmanager
from functools import lru_cache
from typing import Any

import httpx
from fastembed import SparseTextEmbedding
from fastapi import FastAPI, HTTPException, Request
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
from qdrant_client import AsyncQdrantClient, models

EMBEDDINGS_DENSE_MODEL = "Qwen/Qwen3-Embedding-0.6B"

# Ваш сервис должен считывать эти переменные из окружения (env), так как проверяющая система управляет ими
HOST = os.getenv("HOST", "0.0.0.0")
PORT = int(os.getenv("PORT", "8003"))

API_KEY = os.getenv("API_KEY")
EMBEDDINGS_DENSE_URL = os.getenv("EMBEDDINGS_DENSE_URL")
QDRANT_DENSE_VECTOR_NAME = os.getenv("QDRANT_DENSE_VECTOR_NAME", "dense")
QDRANT_SPARSE_VECTOR_NAME = os.getenv("QDRANT_SPARSE_VECTOR_NAME", "sparse")
SPARSE_MODEL_NAME = "Qdrant/bm25"
RERANKER_MODEL = "nvidia/llama-nemotron-rerank-1b-v2"
RERANKER_URL = os.getenv("RERANKER_URL")
OPEN_API_LOGIN = os.getenv("OPEN_API_LOGIN")
OPEN_API_PASSWORD = os.getenv("OPEN_API_PASSWORD")
QDRANT_URL = os.getenv("QDRANT_URL")
QDRANT_COLLECTION_NAME = os.getenv("QDRANT_COLLECTION_NAME", "evaluation")
REQUIRED_ENV_VARS = [
    "EMBEDDINGS_DENSE_URL",
    "RERANKER_URL",
    "QDRANT_URL",
]
 
logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
logger = logging.getLogger("search-service")


def validate_required_env() -> None:
    if bool(OPEN_API_LOGIN) != bool(OPEN_API_PASSWORD):
        raise RuntimeError("OPEN_API_LOGIN and OPEN_API_PASSWORD must be set together")

    if not API_KEY and not (OPEN_API_LOGIN and OPEN_API_PASSWORD):
        raise RuntimeError("Either API_KEY or OPEN_API_LOGIN and OPEN_API_PASSWORD must be set")

    missing_env_vars = [
        name for name in REQUIRED_ENV_VARS if os.getenv(name) is None or os.getenv(name) == ""
    ]
    if not missing_env_vars:
        return

    logger.error("Empty required env vars: %s", ", ".join(missing_env_vars))
    raise RuntimeError(f"Empty required env vars: {', '.join(missing_env_vars)}")


validate_required_env()


def get_upstream_request_kwargs() -> dict[str, Any]:
    headers = {"Content-Type": "application/json"}
    kwargs: dict[str, Any] = {"headers": headers}

    if OPEN_API_LOGIN and OPEN_API_PASSWORD:
        kwargs["auth"] = (OPEN_API_LOGIN, OPEN_API_PASSWORD)
        return kwargs

    if API_KEY:
        headers["Authorization"] = f"Bearer {API_KEY}"

    return kwargs


# Модель данных, которую мы предоставляем и рассчитываем получать от вас
class DateRange(BaseModel):
    from_: str = Field(alias="from")
    to: str


class Entities(BaseModel):
    people: list[str] | None = None
    emails: list[str] | None = None
    documents: list[str] | None = None
    names: list[str] | None = None
    links: list[str] | None = None


class Question(BaseModel):
    text: str
    asker: str = ""
    asked_on: str = ""
    variants: list[str] | None = None
    hyde: list[str] | None = None
    keywords: list[str] | None = None
    entities: Entities | None = None
    date_mentions: list[str] | None = None
    date_range: DateRange | None = None
    search_text: str = ""


class SearchAPIRequest(BaseModel):
    question: Question


class SearchAPIItem(BaseModel):
    message_ids: list[str]


class SearchAPIResponse(BaseModel):
    results: list[SearchAPIItem]


class DenseEmbeddingItem(BaseModel):
    index: int
    embedding: list[float]


class DenseEmbeddingResponse(BaseModel):
    data: list[DenseEmbeddingItem]


class SparseVector(BaseModel):
    indices: list[int] = Field(default_factory=list)
    values: list[float] = Field(default_factory=list)


class SparseEmbeddingResponse(BaseModel):
    vectors: list[SparseVector]

# Метадата чанков в Qdrant'e, по которой вы можете фильтровать
class ChunkMetadata(BaseModel):
    chat_name: str
    chat_type: str # channel, group, private, thread
    chat_id: str
    chat_sn: str
    thread_sn: str | None = None
    message_ids: list[str]
    start: str
    end: str
    participants: list[str] = Field(default_factory=list)
    mentions: list[str] = Field(default_factory=list)
    contains_forward: bool = False
    contains_quote: bool = False


@lru_cache(maxsize=1)
def get_sparse_model() -> SparseTextEmbedding:
    logger.info("Loading local sparse model %s", SPARSE_MODEL_NAME)
    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)


@asynccontextmanager
async def lifespan(app: FastAPI):
    app.state.http = httpx.AsyncClient()
    app.state.qdrant = AsyncQdrantClient(
        url=QDRANT_URL,
        api_key=API_KEY,
    )
    try:
        yield
    finally:
        await app.state.http.aclose()
        await app.state.qdrant.close()


app = FastAPI(title="Search Service", version="0.1.0", lifespan=lifespan)


# Внутри шаблона dense и rerank берутся из внешних HTTP endpoint'ов,
# которые предоставляет проверяющая система.
# Текущий код ниже — минимальный пример search pipeline.
DENSE_PREFETCH_K = 30
SPARSE_PREFETCH_K = 40
RETRIEVE_K = 80
RERANK_LIMIT = 20
FINAL_TOP_K = 50
MAX_DENSE_QUERIES = 4
MAX_SPARSE_QUERIES = 3

async def embed_dense(client: httpx.AsyncClient, text: str) -> list[float]:
    # Dense endpoint ожидает OpenAI-compatible body с input как списком строк.
    response = await client.post(
        EMBEDDINGS_DENSE_URL,
        **get_upstream_request_kwargs(),
        json={
            "model": os.getenv("EMBEDDINGS_DENSE_MODEL", EMBEDDINGS_DENSE_MODEL),
            "input": [text],
        },
    )
    response.raise_for_status()

    payload = DenseEmbeddingResponse.model_validate(response.json())
    if not payload.data:
        raise ValueError("Dense embedding response is empty")

    return payload.data[0].embedding


async def embed_sparse(text: str) -> SparseVector:
    vectors = list(get_sparse_model().embed([text]))
    if not vectors:
        raise ValueError("Sparse embedding response is empty")

    item = vectors[0]
    return SparseVector(
        indices=[int(index) for index in item.indices.tolist()],
        values=[float(value) for value in item.values.tolist()],
    )


def unique_non_empty(values: list[str | None]) -> list[str]:
    result: list[str] = []
    seen: set[str] = set()

    for value in values:
        text = (value or "").strip()
        if text and text not in seen:
            seen.add(text)
            result.append(text)

    return result


def question_entity_terms(question: Question) -> list[str]:
    entities = question.entities
    if entities is None:
        return []

    values: list[str | None] = []
    values.extend(entities.people or [])
    values.extend(entities.emails or [])
    values.extend(entities.documents or [])
    values.extend(entities.names or [])
    values.extend(entities.links or [])
    return unique_non_empty(values)


def build_query_texts(question: Question) -> tuple[str, list[str], list[str]]:
    primary_query = (question.search_text or question.text).strip()
    dense_queries = unique_non_empty(
        [
            primary_query,
            *(question.variants or []),
            *(question.hyde or []),
        ]
    )[:MAX_DENSE_QUERIES]

    keyword_query = " ".join(question.keywords or []).strip()
    entity_query = " ".join(question_entity_terms(question)).strip()
    sparse_queries = unique_non_empty(
        [
            keyword_query,
            entity_query,
            primary_query,
            *(question.variants or []),
        ]
    )[:MAX_SPARSE_QUERIES]

    return primary_query, dense_queries, sparse_queries


def build_search_filter(question: Question) -> models.Filter | None:
    must_conditions: list[Any] = []

    if question.date_range and hasattr(models, "DatetimeRange"):
        must_conditions.append(
            models.FieldCondition(
                key="metadata.start",
                range=models.DatetimeRange(
                    gte=question.date_range.from_,
                    lte=question.date_range.to,
                ),
            )
        )

    return models.Filter(must=must_conditions) if must_conditions else None

async def qdrant_search(
    client: AsyncQdrantClient,
    dense_vectors: list[list[float]],
    sparse_vectors: list[SparseVector],
    question_data: Question
) -> Any | None:
    search_filter = build_search_filter(question_data)
    prefetch: list[models.Prefetch] = []

    for dense_vector in dense_vectors:
        prefetch.append(
            models.Prefetch(
                query=dense_vector,
                using=QDRANT_DENSE_VECTOR_NAME,
                limit=DENSE_PREFETCH_K,
                filter=search_filter,
            )
        )

    for sparse_vector in sparse_vectors:
        if not sparse_vector.indices:
            continue

        prefetch.append(
            models.Prefetch(
                query=models.SparseVector(
                    indices=sparse_vector.indices,
                    values=sparse_vector.values,
                ),
                using=QDRANT_SPARSE_VECTOR_NAME,
                limit=SPARSE_PREFETCH_K,
                filter=search_filter,
            )
        )

    if not prefetch:
        return None

    response = await client.query_points(
        collection_name=QDRANT_COLLECTION_NAME,
        prefetch=prefetch,
        query=models.FusionQuery(fusion=models.Fusion.RRF),
        limit=RETRIEVE_K,
        with_payload=True,
    )

    if not response.points:
        return None

    return response.points


def extract_message_ids(point: Any) -> list[str]:
    payload = point.payload or {}
    metadata = payload.get("metadata") or {}
    message_ids = metadata.get("message_ids") or []

    return [str(message_id) for message_id in message_ids]


async def get_rerank_scores(
    client: httpx.AsyncClient,
    label: str,
    targets: list[str],
) -> list[float]:
    if not targets:
        return []

    # Rerank endpoint возвращает score для пары query -> candidate text.
    response = await client.post(
        RERANKER_URL,
        **get_upstream_request_kwargs(),
        json={
            "model": RERANKER_MODEL,
            "encoding_format": "float",
            "text_1": label,
            "text_2": targets,
        },
    )
    response.raise_for_status()

    payload = response.json()
    data = payload.get("data") or []

    return [float(sample["score"]) for sample in data]


async def rerank_points(
    client: httpx.AsyncClient,
    query: str,
    points: list[Any],
) -> list[Any]:
    rerank_candidates = points[:RERANK_LIMIT]
    retrieval_tail = points[RERANK_LIMIT:]
    rerank_targets = [
        str((point.payload or {}).get("page_content") or "")
        for point in rerank_candidates
    ]

    try:
        scores = await get_rerank_scores(client, query, rerank_targets)
    except Exception:
        logger.exception("Rerank failed, returning retrieval order")
        return points

    if len(scores) != len(rerank_candidates):
        logger.warning(
            "Rerank returned %d scores for %d candidates",
            len(scores),
            len(rerank_candidates),
        )
        return points

    reranked_candidates = [
        point
        for _, point in sorted(
            zip(scores, rerank_candidates),
            key=lambda item: item[0],
            reverse=True,
        )
    ]

    return reranked_candidates + retrieval_tail


def aggregate_message_ids(points: list[Any], top_k: int = FINAL_TOP_K) -> list[str]:
    best_scores: dict[str, float] = {}
    first_seen_rank: dict[str, int] = {}
    total_points = len(points)

    for rank, point in enumerate(points):
        retrieval_score = float(getattr(point, "score", 0.0) or 0.0)
        rank_score = float(total_points - rank)
        score = rank_score + retrieval_score

        for message_id in extract_message_ids(point):
            if message_id not in first_seen_rank:
                first_seen_rank[message_id] = rank

            if score > best_scores.get(message_id, float("-inf")):
                best_scores[message_id] = score

    ranked_ids = sorted(
        best_scores,
        key=lambda message_id: (-best_scores[message_id], first_seen_rank[message_id]),
    )
    return ranked_ids[:top_k]


# Ваш сервис должен имплементировать оба этих метода
@app.get("/health")
async def health() -> dict[str, str]:
    return {"status": "ok"}


@app.post("/search", response_model=SearchAPIResponse)
async def search(payload: SearchAPIRequest) -> SearchAPIResponse:
    query, dense_queries, sparse_queries = build_query_texts(payload.question)
    if not query:
        raise HTTPException(status_code=400, detail="question.search_text or question.text is required")

    client: httpx.AsyncClient = app.state.http
    qdrant: AsyncQdrantClient = app.state.qdrant

    dense_vectors = [await embed_dense(client, item) for item in dense_queries]
    sparse_vectors = [await embed_sparse(item) for item in sparse_queries]
    best_points = await qdrant_search(qdrant, dense_vectors, sparse_vectors, payload.question)

    if best_points is None:
        return SearchAPIResponse(results=[])

    best_points = await rerank_points(client, query, list(best_points))
    message_ids = aggregate_message_ids(best_points)

    return SearchAPIResponse(
        results=[SearchAPIItem(message_ids=message_ids)]
    )


@app.exception_handler(Exception)
async def exception_handler(request: Request, exc: Exception) -> JSONResponse:
    logger.exception(exc)
    detail = str(exc) or repr(exc)

    if isinstance(exc, RequestValidationError):
        return JSONResponse(status_code=422, content={"detail": exc.errors()})

    if isinstance(exc, HTTPException):
        return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})

    return JSONResponse(status_code=500, content={"detail": detail})


def main() -> None:
    import uvicorn

    uvicorn.run(
        "main:app",
        host=HOST,
        port=PORT,
        reload=False,
    )


if __name__ == "__main__":
    main()