from pathlib import Path
from uuid import uuid4

from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_ollama import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

CHROMA_DIR = "./chroma_db"
COLLECTION_NAME = "local_kb"
EMBED_MODEL = "nomic-embed-text"
OLLAMA_BASE_URL = "http://127.0.0.1:11434"


def create_vectorstore(persist_directory: str = CHROMA_DIR) -> Chroma:
    embeddings = OllamaEmbeddings(
        model=EMBED_MODEL,
        base_url=OLLAMA_BASE_URL,
    )
    return Chroma(
        collection_name=COLLECTION_NAME,
        embedding_function=embeddings,
        persist_directory=persist_directory,
    )


def load_documents(directory: str, vectorstore: Chroma) -> int:
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        add_start_index=True,
    )

    base_path = Path(directory)
    if not base_path.exists():
        raise FileNotFoundError(f"Directory not found: {directory}")

    raw_docs: list[Document] = []
    for pattern in ("*.txt", "*.md"):
        for path in sorted(base_path.rglob(pattern)):
            content = path.read_text(encoding="utf-8")
            raw_docs.append(
                Document(
                    page_content=content,
                    metadata={"source": str(path)},
                )
            )

    if not raw_docs:
        return 0

    chunks = splitter.split_documents(raw_docs)
    ids = [str(uuid4()) for _ in chunks]
    vectorstore.add_documents(documents=chunks, ids=ids)
    return len(chunks)