"""
RAG service using ChromaDB + LM Studio's /v1/embeddings endpoint.
No local ML libraries (torch, sentence-transformers, onnxruntime) needed —
embeddings are generated by the same LM Studio instance used for chat.
Each chunk is stored with metadata: user_id, source_id, source_type (doc|url).
"""

import re
from typing import Optional

from chromadb import EmbeddingFunction, Documents, Embeddings
from flask import current_app
from openai import OpenAI

_chroma_client = None
_collection = None


class LMStudioEmbeddingFunction(EmbeddingFunction):
    """ChromaDB-compatible embedding function that calls LM Studio's /v1/embeddings."""

    def __init__(self, base_url: str, model: str):
        self._client = OpenAI(base_url=f"{base_url}/v1", api_key="lm-studio")
        self._model = model

    def __call__(self, input: Documents) -> Embeddings:
        response = self._client.embeddings.create(model=self._model, input=input)
        return [item.embedding for item in response.data]


def _get_collection():
    global _chroma_client, _collection
    if _collection is None:
        import chromadb
        path = current_app.config["VECTORDB_PATH"]
        base_url = current_app.config["LM_STUDIO_URL"]
        model = current_app.config["LM_STUDIO_EMBEDDING_MODEL"]
        _chroma_client = chromadb.PersistentClient(path=path)
        _collection = _chroma_client.get_or_create_collection(
            name="ki_context",
            embedding_function=LMStudioEmbeddingFunction(base_url, model),
            metadata={"hnsw:space": "cosine"},
        )
    return _collection


def chunk_text(text: str, chunk_size: int, overlap: int) -> list[str]:
    words = text.split()
    chunks = []
    start = 0
    while start < len(words):
        end = start + chunk_size
        chunks.append(" ".join(words[start:end]))
        start += chunk_size - overlap
    return [c for c in chunks if c.strip()]


def index_source(
    text: str,
    user_id: int,
    source_id: int,
    source_type: str,  # "doc" | "url"
    chunk_size: int = 500,
    chunk_overlap: int = 50,
):
    """Chunk, embed via LM Studio and store in ChromaDB. Replaces existing chunks."""
    collection = _get_collection()

    delete_source(user_id, source_id, source_type)

    chunks = chunk_text(text, chunk_size, chunk_overlap)
    if not chunks:
        return

    ids = [f"{source_type}_{source_id}_chunk_{i}" for i in range(len(chunks))]
    metadatas = [
        {"user_id": str(user_id), "source_id": str(source_id), "source_type": source_type}
        for _ in chunks
    ]

    collection.add(documents=chunks, ids=ids, metadatas=metadatas)


def delete_source(user_id: int, source_id: int, source_type: str):
    """Remove all chunks belonging to a source from ChromaDB."""
    collection = _get_collection()
    try:
        collection.delete(
            where={
                "$and": [
                    {"user_id": {"$eq": str(user_id)}},
                    {"source_id": {"$eq": str(source_id)}},
                    {"source_type": {"$eq": source_type}},
                ]
            }
        )
    except Exception:
        pass


def similarity_search(
    query: str,
    user_id: int,
    source_ids: Optional[list[int]] = None,
    source_type: Optional[str] = None,
    top_k: int = 5,
) -> list[str]:
    """Search for relevant chunks via LM Studio embeddings."""
    collection = _get_collection()

    conditions = [{"user_id": {"$eq": str(user_id)}}]

    if source_ids is not None and len(source_ids) > 0:
        conditions.append(
            {"source_id": {"$in": [str(sid) for sid in source_ids]}}
        )

    if source_type:
        conditions.append({"source_type": {"$eq": source_type}})

    where = {"$and": conditions} if len(conditions) > 1 else conditions[0]

    try:
        results = collection.query(
            query_texts=[query],
            n_results=top_k,
            where=where,
        )
        return results["documents"][0] if results["documents"] else []
    except Exception:
        return []