import asyncio
import tempfile
import time
from fastapi import FastAPI, HTTPException, Depends
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse, FileResponse
from fastapi import UploadFile, File, Form
from pydantic import BaseModel
from app.schemas import NodeRunRequest, NodeRunResponse, MergeStrategy, Role, Message, Context, LLMConfig, ModelProvider, ReasoningEffort
from app.services.llm import llm_streamer, generate_title, get_openai_client
from app.auth import auth_router, get_current_user, get_current_user_optional, init_db, User, get_db
from app.auth.utils import get_password_hash
from dotenv import load_dotenv
import os
import json
import shutil
from typing import List, Literal, Optional
from uuid import uuid4
from google import genai
from sqlalchemy.orm import Session

load_dotenv()

app = FastAPI(title="ContextFlow Backend")

# Include authentication router
app.include_router(auth_router)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize database on startup
@app.on_event("startup")
async def startup_event():
    """Initialize database and create default test user if not exists"""
    init_db()
    
    # Create test user if not exists
    from app.auth.models import SessionLocal
    db = SessionLocal()
    try:
        existing = db.query(User).filter(User.username == "test").first()
        if not existing:
            test_user = User(
                username="test",
                email="test@contextflow.local",
                hashed_password=get_password_hash("114514")
            )
            db.add(test_user)
            db.commit()
            print("[startup] Created default test user (test/114514)")
        else:
            print("[startup] Test user already exists")
    finally:
        db.close()

# --------- Project / Blueprint storage ---------
DATA_ROOT = os.path.abspath(os.getenv("DATA_ROOT", os.path.join(os.getcwd(), "data")))
DEFAULT_USER = "test"
ARCHIVE_FILENAME = "archived_nodes.json"
VALID_FILE_PROVIDERS = {"local", "openai", "google"}
OPENAI_MAX_FILE_SIZE = 50 * 1024 * 1024  # 50MB limit per OpenAI docs
OPENAI_DEFAULT_FILE_PURPOSE = os.getenv("OPENAI_FILE_PURPOSE", "user_data")

def get_user_api_key(user: User | None, provider: str) -> str | None:
    """
    Get API key for a provider from user's saved settings.
    Falls back to environment variable if user has no key set.
    """
    if user:
        if provider == "openai" and user.openai_api_key:
            return user.openai_api_key
        if provider in ("google", "gemini") and user.gemini_api_key:
            return user.gemini_api_key
    # Fallback to environment variables
    if provider == "openai":
        return os.getenv("OPENAI_API_KEY")
    if provider in ("google", "gemini"):
        return os.getenv("GOOGLE_API_KEY")
    return None

def ensure_user_root(user: str) -> str:
    """
    Ensures the new data root structure:
    data/<user>/projects
    data/<user>/archive
    """
    user_root = os.path.join(DATA_ROOT, user)
    projects_root = os.path.join(user_root, "projects")
    archive_root = os.path.join(user_root, "archive")
    os.makedirs(projects_root, exist_ok=True)
    os.makedirs(archive_root, exist_ok=True)
    return user_root


def projects_root(user: str) -> str:
    return os.path.join(ensure_user_root(user), "projects")


def archive_root(user: str) -> str:
    return os.path.join(ensure_user_root(user), "archive")


def files_root(user: str) -> str:
    root = os.path.join(ensure_user_root(user), "files")
    os.makedirs(root, exist_ok=True)
    return root


def migrate_legacy_layout(user: str):
    """
    Migrate from legacy ./projects/<user> and legacy archive folders to the new data/<user>/ structure.
    """
    legacy_root = os.path.abspath(os.path.join(os.getcwd(), "projects", user))
    new_projects = projects_root(user)
    if os.path.exists(legacy_root) and not os.listdir(new_projects):
        try:
            for name in os.listdir(legacy_root):
                src = os.path.join(legacy_root, name)
                dst = os.path.join(new_projects, name)
                if not os.path.exists(dst):
                    shutil.move(src, dst)
        except Exception:
            pass
    # migrate legacy archive (archived/ or .cf_archived/)
    legacy_archives = [
        os.path.join(legacy_root, "archived", ARCHIVE_FILENAME),
        os.path.join(legacy_root, ".cf_archived", ARCHIVE_FILENAME),
    ]
    new_archive_file = archived_path(user)
    if not os.path.exists(new_archive_file):
        for legacy in legacy_archives:
            if os.path.exists(legacy):
                os.makedirs(os.path.dirname(new_archive_file), exist_ok=True)
                try:
                    shutil.move(legacy, new_archive_file)
                except Exception:
                    pass

def safe_path(user: str, relative_path: str) -> str:
    root = projects_root(user)
    norm = os.path.normpath(relative_path).lstrip(os.sep)
    full = os.path.abspath(os.path.join(root, norm))
    if not full.startswith(root):
        raise HTTPException(status_code=400, detail="Invalid path")
    return full

class FSItem(BaseModel):
    name: str
    path: str  # path relative to user root
    type: Literal["file", "folder"]
    size: Optional[int] = None
    mtime: Optional[float] = None
    children: Optional[List["FSItem"]] = None

FSItem.model_rebuild()

def list_tree(user: str, relative_path: str = ".") -> List[FSItem]:
    migrate_legacy_layout(user)
    root = safe_path(user, relative_path)
    items: List[FSItem] = []
    for name in sorted(os.listdir(root)):
        full = os.path.join(root, name)
        rel = os.path.relpath(full, projects_root(user))
        stat = os.stat(full)
        if os.path.isdir(full):
            items.append(FSItem(
                name=name,
                path=rel,
                type="folder",
                size=None,
                mtime=stat.st_mtime,
                children=list_tree(user, rel)
            ))
        else:
            items.append(FSItem(
                name=name,
                path=rel,
                type="file",
                size=stat.st_size,
                mtime=stat.st_mtime,
                children=None
            ))
    return items

class SaveBlueprintRequest(BaseModel):
    user: str = DEFAULT_USER
    path: str  # relative path including filename.json
    content: dict

class RenameRequest(BaseModel):
    user: str = DEFAULT_USER
    path: str
    new_name: Optional[str] = None
    new_path: Optional[str] = None

class FileMeta(BaseModel):
    id: str
    name: str
    size: int
    mime: str
    created_at: float
    provider: Optional[str] = None
    provider_file_id: Optional[str] = None
    openai_file_id: Optional[str] = None
    openai_vector_store_id: Optional[str] = None
    # Scopes for filtering: "project_path/node_id" composite keys
    scopes: List[str] = []

class FolderRequest(BaseModel):
    user: str = DEFAULT_USER
    path: str  # relative folder path

class DeleteRequest(BaseModel):
    user: str = DEFAULT_USER
    path: str
    is_folder: bool = False

# -----------------------------------------------

@app.get("/")
def read_root():
    return {"message": "ContextFlow Backend is running"}

def smart_merge_messages(messages: list[Message]) -> list[Message]:
    """
    Merges messages using two steps:
    1. Deduplication by ID (to handle diamond dependencies).
    2. Merging consecutive messages from the same role.
    """
    if not messages:
        return []

    # 1. Deduplicate by ID, keeping order
    seen_ids = set()
    deduplicated = []
    for msg in messages:
        if msg.id not in seen_ids:
            deduplicated.append(msg)
            seen_ids.add(msg.id)
    
    # 2. Merge consecutive roles
    if not deduplicated:
        return []
        
    merged = []
    current_msg = deduplicated[0].model_copy()
    
    for next_msg in deduplicated[1:]:
        if next_msg.role == current_msg.role:
            # Merge content
            current_msg.content += f"\n\n{next_msg.content}"
            # Keep the latest timestamp
            current_msg.timestamp = next_msg.timestamp
        else:
            merged.append(current_msg)
            current_msg = next_msg.model_copy()
            
    merged.append(current_msg)
    return merged

@app.post("/api/run_node_stream")
async def run_node_stream(
    request: NodeRunRequest,
    current_user: User | None = Depends(get_current_user_optional)
):
    """
    Stream the response from the LLM.
    """
    # Get API key from user settings if not provided in request
    provider_name = request.config.provider.value if hasattr(request.config.provider, 'value') else str(request.config.provider)
    if not request.config.api_key:
        user_key = get_user_api_key(current_user, provider_name.lower())
        if user_key:
            request.config.api_key = user_key
    
    # Get username for file operations
    username = current_user.username if current_user else DEFAULT_USER
    
    # 1. Concatenate all incoming contexts first
    raw_messages = []
    for ctx in request.incoming_contexts:
        raw_messages.extend(ctx.messages)
    
    # 2. Apply Merge Strategy
    final_messages = []
    if request.merge_strategy == MergeStrategy.SMART:
        final_messages = smart_merge_messages(raw_messages)
    else:
        # RAW strategy: just keep them as is
        final_messages = raw_messages

    execution_context = Context(messages=final_messages)

    tools: List[dict] = []
    attachments: List[dict] = []

    if request.config.provider == ModelProvider.OPENAI:
        vs_ids, debug_refs, filters = await prepare_openai_vector_search(
            user=username,
            attached_ids=request.attached_file_ids,
            scopes=request.scopes,
            llm_config=request.config,
        )
        # Always enable file_search if vector store exists (even without explicit attachments)
        # This allows nodes to access files attached in previous nodes of the trace
        if not vs_ids:
            # Try to get user's vector store anyway
            try:
                client = get_openai_client(request.config.api_key)
                vs_id = await ensure_user_vector_store(username, client)
                if vs_id:
                    vs_ids = [vs_id]
            except Exception as e:
                print(f"[warn] Could not get vector store: {e}")
        
        if vs_ids:
            tool_def = {"type": "file_search", "vector_store_ids": vs_ids}
            if filters:
                tool_def["filters"] = filters
            tools.append(tool_def)
        print(f"[openai file_search] vs_ids={vs_ids} refs={debug_refs} filters={filters}")
    elif request.config.provider == ModelProvider.GOOGLE:
        attachments = await prepare_attachments(
            user=username,
            target_provider=request.config.provider,
            attached_ids=request.attached_file_ids,
            llm_config=request.config,
        )

    return StreamingResponse(
        llm_streamer(execution_context, request.user_prompt, request.config, attachments, tools),
        media_type="text/event-stream"
    )

class TitleRequest(BaseModel):
    user_prompt: str
    response: str

class TitleResponse(BaseModel):
    title: str

@app.post("/api/generate_title", response_model=TitleResponse)
async def generate_title_endpoint(
    request: TitleRequest,
    current_user: User | None = Depends(get_current_user_optional)
):
    """
    Generate a short title for a Q-A pair using gpt-5-nano.
    Returns 3-4 short English words summarizing the topic.
    """
    api_key = get_user_api_key(current_user, "openai")
    title = await generate_title(request.user_prompt, request.response, api_key)
    return TitleResponse(title=title)


class SummarizeRequest(BaseModel):
    content: str
    model: str  # Model to use for summarization

class SummarizeResponse(BaseModel):
    summary: str

@app.post("/api/summarize", response_model=SummarizeResponse)
async def summarize_endpoint(
    request: SummarizeRequest,
    current_user: User | None = Depends(get_current_user_optional)
):
    """
    Summarize the given content using the specified model.
    """
    from app.services.llm import summarize_content
    openai_key = get_user_api_key(current_user, "openai")
    gemini_key = get_user_api_key(current_user, "gemini")
    summary = await summarize_content(request.content, request.model, openai_key, gemini_key)
    return SummarizeResponse(summary=summary)

# ---------------- Project / Blueprint APIs ----------------
@app.get("/api/projects/tree", response_model=List[FSItem])
def get_project_tree(user: str = DEFAULT_USER):
    """
    List all files/folders for the user under the projects root.
    """
    ensure_user_root(user)
    return list_tree(user)


@app.post("/api/projects/create_folder")
def create_folder(req: FolderRequest):
    """
    Create a folder (and parents) under the user's project root.
    """
    try:
        folder_path = safe_path(req.user, req.path)
        os.makedirs(folder_path, exist_ok=True)
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/projects/save_blueprint")
def save_blueprint(req: SaveBlueprintRequest):
    """
    Save a blueprint JSON to disk.
    """
    try:
        full_path = safe_path(req.user, req.path)
        os.makedirs(os.path.dirname(full_path), exist_ok=True)
        with open(full_path, "w", encoding="utf-8") as f:
            json.dump(req.content, f, ensure_ascii=False, indent=2)
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/api/projects/file")
def read_blueprint(user: str = DEFAULT_USER, path: str = ""):
    """
    Read a blueprint JSON file.
    """
    if not path:
        raise HTTPException(status_code=400, detail="path is required")
    full_path = safe_path(user, path)
    if not os.path.isfile(full_path):
        raise HTTPException(status_code=404, detail="file not found")
    try:
        with open(full_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        return {"content": data}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/api/projects/download")
def download_blueprint(user: str = DEFAULT_USER, path: str = ""):
    """
    Download a blueprint file.
    """
    if not path:
        raise HTTPException(status_code=400, detail="path is required")
    full_path = safe_path(user, path)
    if not os.path.isfile(full_path):
        raise HTTPException(status_code=404, detail="file not found")
    return FileResponse(full_path, filename=os.path.basename(full_path), media_type="application/json")


@app.post("/api/projects/rename")
def rename_item(req: RenameRequest):
    """
    Rename or move a file or folder.
    - If new_path is provided, it is treated as the target relative path (move).
    - Else, new_name is used within the same directory.
    """
    try:
        src = safe_path(req.user, req.path)
        if not os.path.exists(src):
            raise HTTPException(status_code=404, detail="source not found")
        if req.new_path:
            dst = safe_path(req.user, req.new_path)
        else:
            if not req.new_name:
                raise HTTPException(status_code=400, detail="new_name or new_path required")
            base_dir = os.path.dirname(src)
            dst = os.path.join(base_dir, req.new_name)
            # Ensure still inside user root
            safe_path(req.user, os.path.relpath(dst, ensure_user_root(req.user)))
        os.rename(src, dst)
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/projects/delete")
def delete_item(req: DeleteRequest):
    """
    Delete a file or folder.
    """
    try:
        target = safe_path(req.user, req.path)
        if not os.path.exists(target):
            raise HTTPException(status_code=404, detail="not found")
        if os.path.isdir(target):
            if not req.is_folder:
                # Prevent deleting folder accidentally unless flagged
                raise HTTPException(status_code=400, detail="set is_folder=True to delete folder")
            shutil.rmtree(target)
        else:
            os.remove(target)
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
# ----------------------------------------------------------

# --------------- Archived Nodes APIs ----------------------
def archived_path(user: str) -> str:
    root = archive_root(user)
    return os.path.join(root, ARCHIVE_FILENAME)

# ---------------- Files (uploads) ----------------
def files_index_path(user: str) -> str:
    return os.path.join(files_root(user), "index.json")

def user_vector_store_path(user: str) -> str:
    return os.path.join(files_root(user), "vector_store.json")

async def ensure_user_vector_store(user: str, client=None) -> str:
    """
    Ensure there is a vector store for the user (OpenAI).
    Persist the id under data/<user>/files/vector_store.json.
    """
    path = user_vector_store_path(user)
    if client is None:
        client = get_openai_client()

    # Try existing cached ID
    if os.path.exists(path):
        try:
            with open(path, "r", encoding="utf-8") as f:
                data = json.load(f)
                vs_id_cached = data.get("id")
                if vs_id_cached:
                    try:
                        await client.vector_stores.retrieve(vector_store_id=vs_id_cached)
                        return vs_id_cached
                    except Exception:
                        # Possibly deleted; recreate below
                        pass
        except Exception:
            pass

    # create new
    vs = await client.vector_stores.create(name=f"{user}-vs")
    vs_id = getattr(vs, "id", None)
    if not vs_id:
        raise HTTPException(status_code=500, detail="Failed to create vector store")
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, "w", encoding="utf-8") as f:
        json.dump({"id": vs_id}, f)
    return vs_id

async def ensure_openai_file_and_index(user: str, meta: FileMeta, path: str, llm_config: Optional[LLMConfig] = None) -> tuple[str, str]:
    """
    Ensure the file is uploaded to OpenAI Files and added to the user's vector store.
    Returns (openai_file_id, vector_store_id).
    """
    client = get_openai_client(llm_config.api_key if llm_config else None)
    vs_id = await ensure_user_vector_store(user, client)

    file_id = meta.openai_file_id or (meta.provider_file_id if meta.provider == "openai" else None)
    if not file_id:
        with open(path, "rb") as f:
            content = f.read()
        resp = await client.files.create(
            file=(meta.name or "upload.bin", content),
            purpose="assistants",
        )
        file_id = getattr(resp, "id", None)
        if not file_id:
            raise HTTPException(status_code=500, detail="OpenAI file upload returned no file_id")

    await add_file_to_vector_store(vs_id, file_id, client=client)
    return file_id, vs_id

async def remove_file_from_vector_store(vs_id: str, file_id: str, client=None):
    if not vs_id or not file_id:
        return
    if client is None:
        client = get_openai_client()
    try:
        await client.vector_stores.files.delete(vector_store_id=vs_id, file_id=file_id)
    except Exception as e:
        print(f"[warn] remove_file_from_vector_store failed: {e}")

async def add_file_to_vector_store(vs_id: str, file_id: str, client=None):
    """
    Add a file to vector store with file_id as attribute for filtering.
    We use file_id as the attribute so we can filter by specific files at query time.
    """
    if client is None:
        client = get_openai_client()
    
    # Use file_id as attribute for filtering
    create_params = {
        "vector_store_id": vs_id, 
        "file_id": file_id,
        "attributes": {"file_id": file_id}  # Enable filtering by file_id
    }
    
    await client.vector_stores.files.create(**create_params)
    # Poll until completed (limit capped at 100 per API spec)
    for _ in range(20):
        listing = await client.vector_stores.files.list(vector_store_id=vs_id, limit=100)
        found = None
        for item in getattr(listing, "data", []):
            if getattr(item, "id", None) == file_id or getattr(item, "file_id", None) == file_id:
                found = item
                break
        status = getattr(found, "status", None) if found else None
        if status == "completed":
            return
        await asyncio.sleep(0.5)
    # If not confirmed, still continue
    return

def load_files_index(user: str) -> List[FileMeta]:
    path = files_index_path(user)
    if not os.path.exists(path):
        return []
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    return [FileMeta(**item) for item in data]


def save_files_index(user: str, items: List[FileMeta]):
    path = files_index_path(user)
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, "w", encoding="utf-8") as f:
        json.dump([item.model_dump() for item in items], f, ensure_ascii=False, indent=2)


async def prepare_attachments(
    user: str,
    target_provider: str,
    attached_ids: List[str],
    llm_config: LLMConfig,
) -> list[dict]:
    """
    For each attached file ID:
    - If already uploaded to the target provider, reuse provider_file_id/uri.
    - Otherwise, upload with the original filename (required by OpenAI).
    Returns a list of dicts describing attachment references for the provider.
    """
    if not attached_ids:
        return []

    items = load_files_index(user)
    items_map = {item.id: item for item in items}
    attachments: list[dict] = []

    if isinstance(target_provider, ModelProvider):
        provider_norm = target_provider.value.lower()
    else:
        provider_norm = str(target_provider).lower()

    for fid in attached_ids:
        meta = items_map.get(fid)
        if not meta:
            print(f"[warn] Attached file id not found, skipping: {fid}")
            continue

        path = os.path.join(files_root(user), fid)
        if not os.path.exists(path):
            raise HTTPException(status_code=404, detail=f"Attached file missing on disk: {meta.name}")

        if provider_norm == ModelProvider.OPENAI or provider_norm == "openai":
            # Reuse provider file id if available
            if meta.provider == "openai" and meta.provider_file_id:
                attachments.append({
                    "provider": "openai",
                    "file_id": meta.provider_file_id,
                    "name": meta.name,
                    "mime": meta.mime,
                })
                continue

            # Upload to OpenAI with original filename
            with open(path, "rb") as f:
                content = f.read()
            size = len(content)
            if size > OPENAI_MAX_FILE_SIZE:
                raise HTTPException(status_code=400, detail=f"File {meta.name} exceeds OpenAI 50MB limit")

            try:
                client = get_openai_client(llm_config.api_key)
                resp = await client.files.create(
                    file=(meta.name or "upload.bin", content),
                    purpose=OPENAI_DEFAULT_FILE_PURPOSE,
                )
                openai_file_id = getattr(resp, "id", None)
                if not openai_file_id:
                    raise HTTPException(status_code=500, detail="OpenAI file upload returned no file_id")
                attachments.append({
                    "provider": "openai",
                    "file_id": openai_file_id,
                    "name": meta.name,
                    "mime": meta.mime,
                })
            except HTTPException:
                raise
            except Exception as e:
                raise HTTPException(status_code=500, detail=f"OpenAI upload failed: {str(e)}")

        elif provider_norm == ModelProvider.GOOGLE or provider_norm == "google":
            # Reuse uri/name if available and looks like a URI
            if meta.provider == "google" and meta.provider_file_id and "://" in meta.provider_file_id:
                attachments.append({
                    "provider": "google",
                    "uri": meta.provider_file_id,
                    "name": meta.name,
                    "mime": meta.mime,
                })
                continue

            key = llm_config.api_key or os.getenv("GOOGLE_API_KEY")
            if not key:
                raise HTTPException(status_code=500, detail="Google API Key not found")
            client = genai.Client(api_key=key)

            tmp_path = None
            try:
                with open(path, "rb") as f:
                    content = f.read()
                with tempfile.NamedTemporaryFile(delete=False) as tmp:
                    tmp.write(content)
                    tmp_path = tmp.name

                google_resp = await asyncio.to_thread(
                    client.files.upload,
                    file=tmp_path,
                    config={"mimeType": meta.mime or "application/octet-stream"},
                )
                google_name = getattr(google_resp, "name", None)
                google_uri = getattr(google_resp, "uri", None)

                # Poll for ACTIVE and uri if missing
                if google_name:
                    for _ in range(10):
                        try:
                            info = await asyncio.to_thread(client.files.get, name=google_name)
                            state = getattr(info, "state", None)
                            google_uri = getattr(info, "uri", google_uri)
                            if str(state).upper().endswith("ACTIVE") or state == "ACTIVE":
                                break
                            await asyncio.sleep(1)
                        except Exception:
                            await asyncio.sleep(1)
                print(f"[google upload] name={google_name} uri={google_uri}")

                uri = google_uri or google_name
                if not uri:
                    raise HTTPException(status_code=500, detail="Google upload returned no uri/name")
                attachments.append({
                    "provider": "google",
                    "uri": uri,
                    "name": meta.name,
                    "mime": meta.mime,
                })
            except HTTPException:
                raise
            except Exception as e:
                raise HTTPException(status_code=500, detail=f"Google upload failed: {str(e)}")
            finally:
                if tmp_path and os.path.exists(tmp_path):
                    os.remove(tmp_path)

        else:
            raise HTTPException(status_code=400, detail=f"Unsupported provider for attachments: {target_provider}")

    # Debug log
    print(f"[attachments] provider={provider_norm} count={len(attachments)} detail={[{'name': a.get('name'), 'id': a.get('file_id', a.get('uri'))} for a in attachments]}")
    return attachments


async def prepare_openai_vector_search(
    user: str,
    attached_ids: List[str],
    scopes: List[str],
    llm_config: LLMConfig,
) -> tuple[List[str], List[dict], Optional[dict]]:
    """
    Ensure all attached files are uploaded to OpenAI Files (purpose=assistants) and added to the user's vector store.
    Returns (vector_store_ids, openai_file_refs_for_debug, filters).
    
    Filtering logic:
    - Include files whose scopes intersect with requested scopes
    - ALSO include explicitly attached files (attached_ids)
    - Deduplicate to avoid double-processing
    - Filters are constructed using file_id attribute in vector store
    """
    items = load_files_index(user)
    items_map = {item.id: item for item in items}
    
    # Determine which files to include - combine scopes AND attached_ids
    relevant_files_map: dict[str, FileMeta] = {}
    
    # First: add files matching scopes
    if scopes:
        for item in items:
            if item.scopes and any(s in scopes for s in item.scopes):
                relevant_files_map[item.id] = item
        print(f"[file_search] scopes={scopes} matched_files={[f.name for f in relevant_files_map.values()]}")
    
    # Second: also add explicitly attached files (they should always be searchable)
    if attached_ids:
        for fid in attached_ids:
            meta = items_map.get(fid)
            if meta and fid not in relevant_files_map:
                relevant_files_map[fid] = meta
                print(f"[file_search] adding explicitly attached file: {meta.name}")
    
    relevant_files = list(relevant_files_map.values())
    
    if not relevant_files:
        return [], [], None

    changed = False
    vs_ids: List[str] = []
    debug_refs: List[dict] = []
    file_ids_for_filter: List[str] = []

    for meta in relevant_files:
        path = os.path.join(files_root(user), meta.id)
        if not os.path.exists(path):
            print(f"[warn] Attached file missing on disk, skipping: {meta.id}")
            continue
        # Enforce 50MB OpenAI limit
        file_size = os.path.getsize(path)
        if file_size > OPENAI_MAX_FILE_SIZE:
            print(f"[warn] File {meta.name} exceeds OpenAI 50MB limit, skipping")
            continue

        openai_file_id, vs_id = await ensure_openai_file_and_index(user, meta, path, llm_config)
        if meta.openai_file_id != openai_file_id or meta.openai_vector_store_id != vs_id:
            meta.openai_file_id = openai_file_id
            meta.openai_vector_store_id = vs_id
            changed = True
        vs_ids.append(vs_id)
        debug_refs.append({"name": meta.name, "file_id": openai_file_id, "vs_id": vs_id})
        if openai_file_id:
            file_ids_for_filter.append(openai_file_id)

    if changed:
        save_files_index(user, list(items_map.values()))

    # deduplicate
    vs_ids_unique = list({vid for vid in vs_ids if vid})
    
    # Build filters to only search relevant files
    filters = None
    if file_ids_for_filter:
        filters = {"type": "in", "key": "file_id", "value": file_ids_for_filter}
    
    return vs_ids_unique, debug_refs, filters

# -------------------------------------------------

@app.get("/api/projects/archived")
def get_archived_nodes(user: str = DEFAULT_USER):
    migrate_legacy_layout(user)
    path = archived_path(user)
    if not os.path.exists(path):
        return {"archived": []}
    try:
        with open(path, "r", encoding="utf-8") as f:
            return {"archived": json.load(f)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/projects/archived")
def save_archived_nodes(payload: dict):
    user = payload.get("user", DEFAULT_USER)
    data = payload.get("archived", [])
    try:
        path = archived_path(user)
        os.makedirs(os.path.dirname(path), exist_ok=True)
        with open(path, "w", encoding="utf-8") as f:
            json.dump(data, f, ensure_ascii=False, indent=2)
        return {"ok": True}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/api/files")
def list_files(user: str = DEFAULT_USER):
    migrate_legacy_layout(user)
    items = load_files_index(user)
    return {"files": [item.model_dump() for item in items]}


@app.post("/api/files/upload")
async def upload_file(
    user: str = DEFAULT_USER,
    file: UploadFile = File(...),
    provider: str = Form("local"),
    purpose: Optional[str] = Form(None),
):
    migrate_legacy_layout(user)
    items = load_files_index(user)
    file_id = str(uuid4())
    dest_root = files_root(user)
    dest_path = os.path.join(dest_root, file_id)
    file_name = file.filename or "upload.bin"
    provider_normalized = (provider or "local").lower()
    if provider_normalized not in VALID_FILE_PROVIDERS:
        raise HTTPException(status_code=400, detail="Unsupported provider")

    try:
        content = await file.read()
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

    size = len(content)
    if provider_normalized == "openai" and size > OPENAI_MAX_FILE_SIZE:
        raise HTTPException(status_code=400, detail="OpenAI provider limit: max 50MB per file")

    provider_file_id: Optional[str] = None
    provider_created_at: Optional[float] = None

    if provider_normalized == "openai":
        try:
            client = get_openai_client()
            upload_purpose = purpose or OPENAI_DEFAULT_FILE_PURPOSE
            resp = await client.files.create(
                file=(file_name, content),
                purpose=upload_purpose,
            )
            provider_file_id = getattr(resp, "id", None)
            provider_created_at = getattr(resp, "created_at", None)
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"OpenAI upload failed: {str(e)}")
    elif provider_normalized == "google":
        try:
            key = os.getenv("GOOGLE_API_KEY")
            if not key:
                raise HTTPException(status_code=500, detail="Google API Key not found")
            client = genai.Client(api_key=key)
            # The Google GenAI SDK upload is synchronous; run in thread to avoid blocking the event loop.
            tmp_path = None
            try:
                with tempfile.NamedTemporaryFile(delete=False) as tmp:
                    tmp.write(content)
                    tmp_path = tmp.name
                google_resp = await asyncio.to_thread(
                    client.files.upload,
                    file=tmp_path,
                    config={"mimeType": file.content_type or "application/octet-stream"},
                )
                google_name = getattr(google_resp, "name", None)
                google_uri = getattr(google_resp, "uri", None)

                # Poll for ACTIVE and uri if missing
                if google_name:
                    for _ in range(10):
                        try:
                            info = await asyncio.to_thread(client.files.get, name=google_name)
                            state = getattr(info, "state", None)
                            google_uri = getattr(info, "uri", google_uri)
                            if str(state).upper().endswith("ACTIVE") or state == "ACTIVE":
                                break
                            await asyncio.sleep(1)
                        except Exception:
                            await asyncio.sleep(1)

                provider_file_id = google_uri or google_name
            finally:
                if tmp_path and os.path.exists(tmp_path):
                    os.remove(tmp_path)

            provider_created_at = time.time()
        except HTTPException:
            raise
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Google upload failed: {str(e)}")

    try:
        os.makedirs(dest_root, exist_ok=True)
        with open(dest_path, "wb") as f:
            f.write(content)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

    created_at = provider_created_at or os.path.getmtime(dest_path)

    meta = FileMeta(
        id=file_id,
        name=file_name,
        size=size,
        mime=file.content_type or "application/octet-stream",
        created_at=created_at,
        provider=provider_normalized if provider_normalized != "local" else None,
        provider_file_id=provider_file_id,
        openai_file_id=None,
        openai_vector_store_id=None,
    )

    # Always try to index into OpenAI vector store (if <=50MB)
    if size <= OPENAI_MAX_FILE_SIZE:
        try:
            openai_file_id, vs_id = await ensure_openai_file_and_index(user, meta, dest_path, None)
            meta.openai_file_id = openai_file_id
            meta.openai_vector_store_id = vs_id
            if provider_normalized == "openai" and not meta.provider_file_id:
                meta.provider_file_id = openai_file_id
        except Exception as e:
            print(f"[warn] OpenAI indexing failed for {file_name}: {e}")
    else:
        print(f"[warn] Skipping OpenAI indexing for {file_name}: exceeds 50MB")

    items.append(meta)
    save_files_index(user, items)
    return {"file": meta}


@app.get("/api/files/download")
def download_file(user: str = DEFAULT_USER, file_id: str = ""):
    migrate_legacy_layout(user)
    items = load_files_index(user)
    meta = next((i for i in items if i.id == file_id), None)
    if not meta:
        raise HTTPException(status_code=404, detail="file not found")
    path = os.path.join(files_root(user), file_id)
    if not os.path.exists(path):
        raise HTTPException(status_code=404, detail="file missing on disk")
    return FileResponse(path, filename=meta.name, media_type=meta.mime)


@app.post("/api/files/delete")
async def delete_file(user: str = DEFAULT_USER, file_id: str = ""):
    migrate_legacy_layout(user)
    items = load_files_index(user)
    meta = next((i for i in items if i.id == file_id), None)
    if not meta:
        raise HTTPException(status_code=404, detail="file not found")

    # Remove from vector store and OpenAI Files if present
    if meta.openai_vector_store_id and meta.openai_file_id:
        await remove_file_from_vector_store(meta.openai_vector_store_id, meta.openai_file_id)
    if meta.provider == "openai" and meta.provider_file_id:
        try:
            client = get_openai_client()
            await client.files.delete(meta.provider_file_id)
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"OpenAI delete failed: {str(e)}")
    if meta.provider == "google" and meta.provider_file_id:
        try:
            key = os.getenv("GOOGLE_API_KEY")
            if not key:
                raise HTTPException(status_code=500, detail="Google API Key not found")
            client = genai.Client(api_key=key)
            await asyncio.to_thread(client.files.delete, meta.provider_file_id)
        except HTTPException:
            raise
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Google delete failed: {str(e)}")

    path = os.path.join(files_root(user), file_id)
    if os.path.exists(path):
        os.remove(path)
    items = [i for i in items if i.id != file_id]
    save_files_index(user, items)
    return {"ok": True}


class AddScopeRequest(BaseModel):
    user: str = DEFAULT_USER
    file_id: str
    scope: str  # "project_path/node_id" composite key


@app.post("/api/files/add_scope")
def add_file_scope(request: AddScopeRequest):
    """
    Add a scope to a file's scopes list.
    Called when user attaches a file to a node.
    """
    migrate_legacy_layout(request.user)
    items = load_files_index(request.user)
    meta = next((i for i in items if i.id == request.file_id), None)
    if not meta:
        raise HTTPException(status_code=404, detail="file not found")
    
    if request.scope not in meta.scopes:
        meta.scopes.append(request.scope)
        save_files_index(request.user, items)
    
    return {"file": meta.model_dump()}


class RemoveScopeRequest(BaseModel):
    user: str = DEFAULT_USER
    file_id: str
    scope: str


@app.post("/api/files/remove_scope")
def remove_file_scope(request: RemoveScopeRequest):
    """
    Remove a scope from a file's scopes list.
    Called when user detaches a file from a node.
    """
    migrate_legacy_layout(request.user)
    items = load_files_index(request.user)
    meta = next((i for i in items if i.id == request.file_id), None)
    if not meta:
        raise HTTPException(status_code=404, detail="file not found")
    
    if request.scope in meta.scopes:
        meta.scopes.remove(request.scope)
        save_files_index(request.user, items)
    
    return {"file": meta.model_dump()}