import os
import hashlib
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

def get_base_directory() -> str:
    """
    HF Spaces-safe data root:
    - Prefer explicit override via APP_DATA_DIR or PERSISTENT_STORAGE_PATH
    - Else use /data/<app> only if it exists and is writable
    - Else use $HOME/.cache/<app>
    - Else use /tmp/<app>
    """
    override = (os.environ.get("APP_DATA_DIR") or os.environ.get("PERSISTENT_STORAGE_PATH") or "").strip()
    if override:
        os.makedirs(override, exist_ok=True)
        return override

    if os.path.isdir("/data") and os.access("/data", os.W_OK):
        base = "/data/pdf-rag"
        os.makedirs(base, exist_ok=True)
        return base

    home = os.environ.get("HOME")
    if home:
        base = os.path.join(home, ".cache", "pdf-rag")
        os.makedirs(base, exist_ok=True)
        return base

    base = "/tmp/pdf-rag"
    os.makedirs(base, exist_ok=True)
    return base

# Define path relative to the base directory
CHROMA_DB_BASE = os.path.join(get_base_directory(), "root databases")
os.makedirs(CHROMA_DB_BASE, exist_ok=True)

def compute_file_hash(content: bytes) -> str:
    return hashlib.md5(content).hexdigest()

def get_vectorstore(category: str, embedding_model):
    path = os.path.join(CHROMA_DB_BASE, category.strip())
    os.makedirs(path, exist_ok=True)
    return Chroma(persist_directory=path, embedding_function=embedding_model)