def vector_search()

in python/src/tablestore_for_agent_memory/knowledge/knowledge_store.py [0:0]

• 28 lines of code
• 2 McCabe index (conditional complexity)

    def vector_search(self,
                      query_vector: List[float],
                      top_k: Optional[int] = 10,
                      tenant_id: Optional[Union[List[str], str]] = None,
                      metadata_filter: Optional[Filter] = None,
                      limit: Optional[int] = Field(default=None, le=1000, ge=1),
                      next_token: Optional[str] = None,
                      meta_data_to_get: Optional[List[str]] = None,
                      **kwargs: Any,
                      ) -> Response[DocumentHit]:
        if limit is None:
            limit = top_k
        metadata_filter = self._wrap_tenant_id_filter(tenant_id=tenant_id, metadata_filter=metadata_filter)
        vector_filter = Filters.vector_query(
            vector_field=self._embedding_field,
            query_vector=query_vector,
            top_k=top_k,
            metadata_filter=metadata_filter,
        )
        routing_keys = self._build_routing_keys(tenant_id=tenant_id)
        return self.search_documents(
            tenant_id=None,
            metadata_filter=vector_filter,
            limit=limit,
            next_token=next_token,
            meta_data_to_get=meta_data_to_get,
            routing_keys=routing_keys,
        )