from __future__ import annotations

import os
import numpy as np
from dotenv import load_dotenv
from langchain_community.vectorstores import Chroma

from hf_text_embeddings import HFTextEmbeddings


DOCUMENT_PERSIST_DIR = "./chroma_db_docs"
DOCUMENT_COLLECTION = "document_context"

load_dotenv()


def _metadata_as_text(metadata: dict) -> str:
    searchable_fields = (
        "document_title",
        "document_type",
        "document_class",
        "keywords",
        "filename",
    )
    return " | ".join(
        str(metadata[field]).strip()
        for field in searchable_fields
        if metadata.get(field)
    )


def _combine_filters(first: dict | None, second: dict) -> dict:
    if not first:
        return second
    return {"$and": [first, second]}


def _classify_document_filter(
    vectorstore: Chroma,
    embeddings: HFTextEmbeddings,
    query: str,
    base_filter: dict | None = None,
) -> tuple[dict | None, dict | None, float | None]:
    get_kwargs = {"include": ["metadatas"]}
    if base_filter:
        get_kwargs["where"] = base_filter
    stored = vectorstore.get(**get_kwargs)

    candidates: dict[str, dict] = {}
    for metadata in stored.get("metadatas") or []:
        if not metadata:
            continue
        source = str(metadata.get("source") or "")
        if source and _metadata_as_text(metadata):
            candidates.setdefault(source, metadata)

    if not candidates:
        return base_filter, None, None

    sources = list(candidates)
    metadata_texts = [_metadata_as_text(candidates[source]) for source in sources]
    query_vector = np.asarray(embeddings.embed_query(query), dtype=np.float32)
    metadata_vectors = np.asarray(
        embeddings.embed_documents(metadata_texts),
        dtype=np.float32,
    )
    query_norm = np.linalg.norm(query_vector)
    metadata_norms = np.linalg.norm(metadata_vectors, axis=1)
    denominators = metadata_norms * query_norm
    scores = np.divide(
        metadata_vectors @ query_vector,
        denominators,
        out=np.zeros(len(metadata_vectors), dtype=np.float32),
        where=denominators != 0,
    )
    best_index = int(np.argmax(scores))
    best_source = sources[best_index]
    best_metadata = candidates[best_source]
    return (
        _combine_filters(base_filter, {"source": {"$eq": best_source}}),
        best_metadata,
        float(scores[best_index]),
    )


def retrieve_document_context(
    query: str,
    k: int = 4,
    filter_metadata: dict | None = None,
) -> str:
    try:
        embeddings = HFTextEmbeddings()
        vectorstore = Chroma(
            collection_name=os.getenv("DOCUMENT_CHROMA_COLLECTION", DOCUMENT_COLLECTION),
            embedding_function=embeddings,
            persist_directory=os.getenv("DOCUMENT_CHROMA_DIR", DOCUMENT_PERSIST_DIR),
        )
        selected_filter, selected_metadata, metadata_score = _classify_document_filter(
            vectorstore,
            embeddings,
            query,
            filter_metadata,
        )
        if selected_metadata is not None:
            print(
                "Metadata classifier | "
                f"score={metadata_score:.4f} | "
                f"document={selected_metadata.get('document_title')} | "
                f"type={selected_metadata.get('document_type')}",
                flush=True,
            )

        # Evita el query HNSW de Chroma, que puede bloquearse en algunos
        # contenedores. La coleccion ASTM es pequena, por lo que un ranking
        # coseno directo sobre los embeddings persistidos es rapido y estable.
        get_kwargs = {"include": ["documents", "metadatas", "embeddings"]}
        if selected_filter:
            get_kwargs["where"] = selected_filter
        stored = vectorstore.get(**get_kwargs)
        documents = stored.get("documents") or []
        metadatas = stored.get("metadatas") or []
        stored_embeddings = stored.get("embeddings")
        if not documents or stored_embeddings is None:
            print("Document Chroma returned no searchable chunks.", flush=True)
            return ""

        query_vector = np.asarray(embeddings.embed_query(query), dtype=np.float32)
        chunk_vectors = np.asarray(stored_embeddings, dtype=np.float32)
        query_norm = np.linalg.norm(query_vector)
        chunk_norms = np.linalg.norm(chunk_vectors, axis=1)
        denominators = chunk_norms * query_norm
        similarities = np.divide(
            chunk_vectors @ query_vector,
            denominators,
            out=np.zeros(len(chunk_vectors), dtype=np.float32),
            where=denominators != 0,
        )
        top_indices = np.argsort(similarities)[::-1][: min(k, len(documents))]
        blocks = []
        for rank, index in enumerate(top_indices, start=1):
            metadata = metadatas[index] if index < len(metadatas) else {}
            print(
                f"Document rank {rank} | similarity={similarities[index]:.4f} | "
                f"document={metadata.get('document_title')}",
                flush=True,
            )
            blocks.append(
                f"[Documento {rank}] metadata={metadata}\n{documents[index]}"
            )
        return "\n\n".join(blocks)
    except Exception as exc:
        print(f"Document Chroma retrieval skipped: {exc}", flush=True)
        return ""


def build_context_prompt(
    user_text: str,
    k: int = 4,
    conversation_history: str = "",
) -> str:
    document_context = retrieve_document_context(user_text, k=k)

    context_parts = []
    if document_context:
        context_parts.append("Contexto normativo/documental recuperado de documentos/Chroma:\n" + document_context)

    if not context_parts:
        return user_text

    context = "\n\n".join(context_parts)
    return f"""Sos el agente del proyecto Alberti/Metalurgia.
Usa como fuente principal el contexto técnico y normativo recuperado desde los documentos indexados en Chroma.
Cuando respondas sobre datos del proyecto, prioriza los registros recuperados y menciona información relacionada si ayudan pero no divulgues ID o datos propios de los registros.
Si el contexto recuperado no alcanza para responder con precision, dilo claramente y no inventes datos exactos.

{context}

Historial reciente de esta conversacion:
{conversation_history or "Sin mensajes anteriores."}

Pregunta del usuario:
{user_text}"""