library-desk/src/routers/vector.py

"""
Vector router for Library Desk API.

Endpoints for semantic search and vector operations.
"""

from fastapi import APIRouter, HTTPException, Depends, Query
from typing import Optional
import logging

from src.models.vector import (
    SearchRequest, SearchResponse,
    VectorUpdateRequest, VectorUpdateSummary,
    CollectionListResponse,
    DeletePageChunksRequest, DeletePageChunksResponse
)
from src.services.vector_service import VectorService
from src.clients.qdrant_client import QdrantClientWrapper
from src.clients.wikijs_client import WikiJSClient
from src.clients.ollama_client import OllamaClient
from src.core.dependencies import QdrantDep, WikiJSDep, OllamaDep, verify_api_key
from src.core.multi_tenancy import DEFAULT_USER

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/vector", tags=["Vector"])


# Dependency to get vector service
def get_vector_service(
    qdrant_client: QdrantDep,
    wiki_client: WikiJSDep,
    ollama_client: OllamaDep
) -> VectorService:
    """Get vector service instance."""
    return VectorService(qdrant_client, wiki_client, ollama_client)


@router.post("/search", response_model=SearchResponse)
async def semantic_search(
    request: SearchRequest,
    vector_service: VectorService = Depends(get_vector_service),
    api_key: str = Depends(verify_api_key)
):
    """
    Perform semantic search across user's documents.

    Uses Ollama to generate query embedding, then searches Qdrant
    for similar document chunks.

    **Example Request:**
    ```json
    {
        "query": "how to configure docker",
        "user": "jpmschweitzer",
        "limit": 10,
        "score_threshold": 0.5
    }
    ```

    **Returns:** List of matching chunks with similarity scores
    """
    try:
        return await vector_service.search(
            query=request.query,
            user=request.user,
            limit=request.limit,
            score_threshold=request.score_threshold
        )
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.error(f"Semantic search failed: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail="Search failed")


@router.post("/update-from-page/{page_id}", response_model=VectorUpdateSummary)
async def update_vectors_from_page(
    page_id: int,
    user: str = Query(default=DEFAULT_USER, description="User identifier"),
    force_refresh: bool = Query(default=False, description="Force re-embedding"),
    vector_service: VectorService = Depends(get_vector_service),
    api_key: str = Depends(verify_api_key)
):
    """
    Update vector embeddings from a wiki page.

    This endpoint:
    1. Fetches the page from Wiki.js
    2. Chunks the content (500 tokens with 50 token overlap)
    3. Generates embeddings via Ollama
    4. Upserts chunks to Qdrant with metadata

    **Use Cases:**
    - Called automatically after page creation/update (via BackgroundTasks)
    - Called manually by user/Librarian to refresh vectors
    - Called by Scheduler for batch processing

    **Example:** `POST /vector/update-from-page/5?user=jpmschweitzer`

    **Returns:** Summary with chunks created and processing time
    """
    try:
        summary = await vector_service.update_from_page(
            page_id=page_id,
            user=user,
            force_refresh=force_refresh
        )

        if not summary.success:
            raise HTTPException(
                status_code=500,
                detail=f"Vector update failed: {summary.error_message}"
            )

        return summary

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Failed to update vectors from page {page_id}: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail="Vector update failed")


@router.delete("/pages/{page_id}", response_model=DeletePageChunksResponse)
async def delete_page_chunks(
    page_id: int,
    user: str = Query(default=DEFAULT_USER, description="User identifier"),
    vector_service: VectorService = Depends(get_vector_service),
    api_key: str = Depends(verify_api_key)
):
    """
    Delete all vector chunks for a wiki page.

    This is automatically called when a page is deleted from the wiki.

    **Example:** `DELETE /vector/pages/5?user=jpmschweitzer`
    """
    try:
        deleted_count = await vector_service.delete_page_chunks(
            page_id=page_id,
            user=user
        )

        return DeletePageChunksResponse(
            page_id=page_id,
            chunks_deleted=deleted_count,
            success=deleted_count > 0,
            message=f"Deleted {deleted_count} chunks for page {page_id}"
        )

    except Exception as e:
        logger.error(f"Failed to delete chunks for page {page_id}: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail="Failed to delete chunks")


@router.get("/collections", response_model=CollectionListResponse)
async def list_collections(
    vector_service: VectorService = Depends(get_vector_service),
    api_key: str = Depends(verify_api_key)
):
    """
    List all Qdrant collections with statistics.

    Returns collection names, vector counts, and point counts.

    **Example:** `GET /vector/collections`
    """
    try:
        return await vector_service.list_collections()
    except Exception as e:
        logger.error(f"Failed to list collections: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail="Failed to list collections")