"""Векторное хранилище Qdrant + эмбеддинги Ollama."""
from __future__ import annotations

import os
from pathlib import Path

from langchain_core.documents import Document
from langchain_ollama import OllamaEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams

COLLECTION_NAME = "knowledge_base"
QDRANT_PATH = os.getenv("QDRANT_PATH", "./qdrant_data")
OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
EMBED_MODEL = os.getenv("OLLAMA_EMBED_MODEL", "nomic-embed-text")


def get_embeddings() -> OllamaEmbeddings:
    return OllamaEmbeddings(
        model=EMBED_MODEL,
        base_url=OLLAMA_BASE_URL,
    )


def get_qdrant_client() -> QdrantClient:
    Path(QDRANT_PATH).mkdir(parents=True, exist_ok=True)
    return QdrantClient(path=QDRANT_PATH)


def _ensure_collection(client: QdrantClient, embeddings: OllamaEmbeddings) -> None:
    if client.collection_exists(COLLECTION_NAME):
        return
    vector_size = len(embeddings.embed_query("dimension probe"))
    client.create_collection(
        collection_name=COLLECTION_NAME,
        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
    )


def get_vector_store() -> QdrantVectorStore:
    client = get_qdrant_client()
    embeddings = get_embeddings()
    _ensure_collection(client, embeddings)
    return QdrantVectorStore(
        client=client,
        collection_name=COLLECTION_NAME,
        embedding=embeddings,
    )


def chunk_document(content: str, title: str) -> list[Document]:
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=100,
    )
    documents = splitter.create_documents(
        texts=[content],
        metadatas=[{"title": title}],
    )
    for index, doc in enumerate(documents):
        doc.metadata["chunk_index"] = index
        doc.metadata["source"] = title
    return documents


def add_document_to_store(content: str, title: str) -> int:
    store = get_vector_store()
    chunks = chunk_document(content, title)
    store.add_documents(chunks)
    return len(chunks)


def search_store(query: str, max_results: int = 4) -> list[tuple[Document, float]]:
    store = get_vector_store()
    return store.similarity_search_with_score(query, k=max_results)