ObsiGate/backend/bookslm_routes.py

"""BooksLM API routes — directory-scoped AI chat for Obsidian vaults."""

import json
import logging
from pathlib import Path

from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field

from backend.auth.middleware import check_vault_access, require_auth
from backend.bookslm import build_system_prompt, collect_directory_context
from backend.indexer import get_vault_data

logger = logging.getLogger("obsigate.bookslm_routes")
router = APIRouter(prefix="/api/ai/bookslm", tags=["BooksLM"])


# ── Request models ──


class BooksLMContextRequest(BaseModel):
    vault: str = Field(description="Vault name")
    directory: str = Field(default="", description="Relative directory path within the vault")


class BooksLMChatRequest(BaseModel):
    vault: str = Field(description="Vault name")
    directory: str = Field(default="", description="Relative directory path within the vault")
    message: str = Field(description="User message")
    conversation_history: list[dict[str, str]] = Field(
        default_factory=list,
        description="Previous conversation turns [{role, content}]",
    )
    provider: str | None = Field(
        default=None,
        description="AI provider override (e.g. 'deepseek', 'openrouter', 'gemini', 'nvidia', 'xiaomi', 'mistral', 'qwencloud'). "
                    "If not set, uses DEFAULT_PROVIDER.",
    )
    model: str | None = Field(
        default=None,
        description="Model name to use for this request. If not set, uses the provider's default model.",
    )


# ── Endpoints ──


@router.post("/context")
async def api_bookslm_context(
    req: BooksLMContextRequest,
    current_user=Depends(require_auth),
):
    """Collect directory context for BooksLM.

    Returns file list, content, and metadata for the specified directory.
    """
    if not check_vault_access(req.vault, current_user):
        raise HTTPException(status_code=403, detail=f"Accès refusé à la vault '{req.vault}'")

    vault_data = get_vault_data(req.vault)
    if not vault_data:
        raise HTTPException(status_code=404, detail=f"Vault '{req.vault}' not found")

    vault_path = Path(vault_data["path"])
    context = collect_directory_context(vault_path, req.directory)
    return context


@router.post("/chat")
async def api_bookslm_chat(
    req: BooksLMChatRequest,
    current_user=Depends(require_auth),
):
    """Chat with AI about directory contents (BooksLM).

    Builds context from the directory, then sends the user message
    with a system prompt containing all file contents to the AI provider.
    Returns an SSE stream with the response.
    """
    if not check_vault_access(req.vault, current_user):
        raise HTTPException(status_code=403, detail=f"Accès refusé à la vault '{req.vault}'")

    vault_data = get_vault_data(req.vault)
    if not vault_data:
        raise HTTPException(status_code=404, detail=f"Vault '{req.vault}' not found")

    vault_path = Path(vault_data["path"])

    # Collect context
    context = collect_directory_context(vault_path, req.directory)
    if context["file_count"] == 0:
        raise HTTPException(status_code=404, detail="Aucun fichier markdown trouvé dans ce dossier")

    # Build system prompt
    system_prompt = build_system_prompt(context)

    # Call AI provider
    from backend.ai import DEFAULT_PROVIDER, PROVIDERS, _call_deepseek_openrouter, _call_gemini

    # Build messages with conversation history
    messages_text = ""
    if req.conversation_history:
        for turn in req.conversation_history:
            role = turn.get("role", "user")
            content = turn.get("content", "")
            if role == "user":
                messages_text += f"\n\nUtilisateur : {content}"
            elif role == "assistant":
                messages_text += f"\n\nAssistant : {content}"

    # Current message
    user_prompt = req.message
    if messages_text:
        user_prompt = f"Historique de la conversation :{messages_text}\n\nQuestion actuelle : {req.message}"

    async def generate_sse():
        try:
            # Resolve provider: explicit override wins, else default.
            # Fall back to first available if the requested one isn't configured.
            cfg_name = (req.provider or DEFAULT_PROVIDER).lower()
            if cfg_name not in PROVIDERS or not PROVIDERS[cfg_name].get("api_key"):
                # Try next available provider
                for pname, pcfg in PROVIDERS.items():
                    if pcfg.get("api_key") and pname != "gemini":
                        cfg_name = pname
                        break
                else:
                    # No provider available at all
                    err = "Aucun fournisseur AI configuré (clés API manquantes)"
                    error_data = json.dumps({"error": err}, ensure_ascii=False)
                    yield f"event: error\ndata: {error_data}\n\n"
                    return

            # Optional per-request model override
            original_model = None
            if req.model and cfg_name in PROVIDERS:
                original_model = PROVIDERS[cfg_name].get("model")
                PROVIDERS[cfg_name]["model"] = req.model
            try:
                if cfg_name == "gemini":
                    response = await _call_gemini(user_prompt, system_prompt, temperature=0.3, max_tokens=4096)
                else:
                    response = await _call_deepseek_openrouter(
                        user_prompt, system_prompt,
                        provider=cfg_name,
                        temperature=0.3,
                        max_tokens=4096,
                    )
            finally:
                # Restore the original model so other calls aren't affected
                if original_model is not None and cfg_name in PROVIDERS:
                    PROVIDERS[cfg_name]["model"] = original_model

            # Send the full response as a single SSE event
            data = json.dumps({"token": response, "provider": cfg_name, "model": req.model or PROVIDERS.get(cfg_name, {}).get("model", "")}, ensure_ascii=False)
            yield f"event: message\ndata: {data}\n\n"
            yield "event: done\ndata: {}\n\n"
        except Exception as e:
            logger.error(f"BooksLM chat error: {e}")
            error_data = json.dumps({"error": str(e)}, ensure_ascii=False)
            yield f"event: error\ndata: {error_data}\n\n"

    return StreamingResponse(
        generate_sse(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
        },
    )