remove unused function

README.md

@@ -411,7 +411,6 @@ The API provides the following main endpoints with their authentication and pagi
       - `collection_id` (optional) - Filter by collection
     - **Response includes:** `results`, `total`, `limit`, `threshold`, `query`
   - `POST /api/v1/search` - Advanced search with same pagination
-  - `GET /api/v1/search/similar/{image_id}` - Find similar images with pagination
 
 ### 🔑 **Authentication Model**
 

src/api/v1/search.py

@@ -237,122 +237,3 @@ async def search_images_advanced(
     except Exception as e:
         logger.error(f"Error in advanced search: {e}")
         raise HTTPException(status_code=500, detail="Advanced search failed")
-
-@router.get("/similar/{image_id}", response_model=SearchResponse)
-async def find_similar_images(
-    image_id: str,
-    request: Request,
-    limit: int = Query(10, ge=1, le=50, description="Number of similar images to return"),
-    threshold: float = Query(0.65, ge=0.0, le=1.0, description="Similarity threshold"),
-    current_user: UserModel = Depends(get_current_user)
-):
-    """
-    Find images similar to a given image
-    """
-    log_request(
-        {
-            "path": request.url.path, 
-            "method": request.method, 
-            "image_id": image_id,
-            "limit": limit,
-            "threshold": threshold
-        },
-        user_id=str(current_user.id),
-        team_id=str(current_user.team_id)
-    )
-    
-    try:
-        from bson import ObjectId
-        
-        # Get the reference image
-        try:
-            obj_id = ObjectId(image_id)
-        except:
-            raise HTTPException(status_code=400, detail="Invalid image ID")
-        
-        reference_image = await image_repository.get_by_id(obj_id)
-        if not reference_image:
-            raise HTTPException(status_code=404, detail="Reference image not found")
-        
-        # Check team access
-        if reference_image.team_id != current_user.team_id:
-            raise HTTPException(status_code=403, detail="Not authorized to access this image")
-        
-        # Check if the image has embeddings
-        if not reference_image.has_embedding or not reference_image.embedding_id:
-            raise HTTPException(status_code=400, detail="Reference image does not have embeddings")
-        
-        # Get the embedding for the reference image
-        reference_data = get_vector_db_service().get_image_vector(image_id)
-        if not reference_data or not reference_data.get('vector'):
-            raise HTTPException(status_code=400, detail="Failed to get reference image embedding")
-        
-        reference_embedding = reference_data['vector']
-        
-        # Search for similar images
-        search_results = get_vector_db_service().search_similar_images(
-            query_vector=reference_embedding,
-            limit=limit + 1,  # +1 to account for the reference image itself
-            score_threshold=threshold,
-            filter_conditions={"team_id": str(current_user.team_id)} if current_user.team_id else None
-        )
-        
-        # Remove the reference image from results
-        search_results = [result for result in search_results if result['image_id'] != image_id][:limit]
-        
-        if not search_results:
-            return SearchResponse(
-                query=f"Similar to image {image_id}",
-                results=[],
-                total=0,
-                limit=limit,
-                threshold=threshold
-            )
-        
-        # Get image IDs and scores from search results
-        image_ids = [result['image_id'] for result in search_results if result['image_id']]
-        scores = {result['image_id']: result['score'] for result in search_results if result['image_id']}
-        
-        # Get image metadata from database
-        images = await image_repository.get_by_ids(image_ids)
-        
-        # Convert to response format with similarity scores
-        results = []
-        for image in images:
-            image_id_str = str(image.id)
-            similarity_score = scores.get(image_id_str, 0.0)
-            
-            result = ImageResponse(
-                id=image_id_str,
-                filename=image.filename,
-                original_filename=image.original_filename,
-                file_size=image.file_size,
-                content_type=image.content_type,
-                storage_path=image.storage_path,
-                team_id=str(image.team_id),
-                uploader_id=str(image.uploader_id),
-                upload_date=image.upload_date,
-                description=image.description,
-                metadata=image.metadata,
-                has_embedding=image.has_embedding,
-                collection_id=str(image.collection_id) if image.collection_id else None,
-                similarity_score=similarity_score
-            )
-            results.append(result)
-        
-        # Sort by similarity score (highest first)
-        results.sort(key=lambda x: x.similarity_score or 0, reverse=True)
-        
-        return SearchResponse(
-            query=f"Similar to image {image_id}",
-            results=results,
-            total=len(results),
-            limit=limit,
-            threshold=threshold
-        )
-        
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error finding similar images: {e}")
-        raise HTTPException(status_code=500, detail="Similar image search failed")