facial_recog_7.py

def search_faces(self, request: SearchFacesRequest) -> SearchFacesResponse:
    all_results = []
    
    for query_face in request.query_faces:
        for model_name in query_face.model_names:
            collection_name = f"face_embeddings_{model_name.lower()}"
            
            # Perform vector similarity search
            search_results = self._client.search(
                collection_name=collection_name,
                query_vector=query_face.embeddings[model_name],
                limit=request.max_results,
                with_payload=True
            )
            
            # Convert results to our format
            for scored_point in search_results:
                face_record = self._point_to_face_record(scored_point)
                search_result = SearchResult(
                    face_record=face_record,
                    similarity_score=scored_point.score,
                    model_used=model_name,
                    query_face_id=query_face.face_id
                )
                all_results.append(search_result)
    
    # Sort by similarity score and return top results
    all_results.sort(key=lambda x: x.similarity_score, reverse=True)
    return SearchFacesResponse(success=True, results=all_results[:request.max_results])