# Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Code generated by the Google Gen AI SDK generator DO NOT EDIT. import functools import importlib import json import logging from typing import Any, Iterator, Optional, Union from urllib.parse import urlencode from google.genai import _api_module from google.genai import _common from google.genai._common import get_value_by_path as getv from google.genai._common import set_value_by_path as setv from google.genai.pagers import AsyncPager, Pager from . import _agent_engines_utils from . import types logger = logging.getLogger("vertexai_genai.memories") logger.setLevel(logging.INFO) def _AgentEngineMemoryConfig_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["display_name"]) is not None: setv(parent_object, ["displayName"], getv(from_object, ["display_name"])) if getv(from_object, ["description"]) is not None: setv(parent_object, ["description"], getv(from_object, ["description"])) if getv(from_object, ["ttl"]) is not None: setv(parent_object, ["ttl"], getv(from_object, ["ttl"])) if getv(from_object, ["expire_time"]) is not None: setv(parent_object, ["expireTime"], getv(from_object, ["expire_time"])) if getv(from_object, ["revision_expire_time"]) is not None: setv( parent_object, ["revisionExpireTime"], getv(from_object, ["revision_expire_time"]), ) if getv(from_object, ["revision_ttl"]) is not None: setv(parent_object, ["revisionTtl"], getv(from_object, ["revision_ttl"])) if getv(from_object, ["disable_memory_revisions"]) is not None: setv( parent_object, ["disableMemoryRevisions"], getv(from_object, ["disable_memory_revisions"]), ) if getv(from_object, ["topics"]) is not None: setv( parent_object, ["topics"], [item for item in getv(from_object, ["topics"])] ) return to_object def _CreateAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["fact"]) is not None: setv(to_object, ["fact"], getv(from_object, ["fact"])) if getv(from_object, ["scope"]) is not None: setv(to_object, ["scope"], getv(from_object, ["scope"])) if getv(from_object, ["config"]) is not None: setv( to_object, ["config"], _AgentEngineMemoryConfig_to_vertex( getv(from_object, ["config"]), to_object ), ) return to_object def _DeleteAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _GenerateAgentEngineMemoriesConfig_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["disable_consolidation"]) is not None: setv( parent_object, ["disableConsolidation"], getv(from_object, ["disable_consolidation"]), ) if getv(from_object, ["revision_labels"]) is not None: setv(parent_object, ["revisionLabels"], getv(from_object, ["revision_labels"])) if getv(from_object, ["revision_expire_time"]) is not None: setv( parent_object, ["revisionExpireTime"], getv(from_object, ["revision_expire_time"]), ) if getv(from_object, ["revision_ttl"]) is not None: setv(parent_object, ["revisionTtl"], getv(from_object, ["revision_ttl"])) if getv(from_object, ["disable_memory_revisions"]) is not None: setv( parent_object, ["disableMemoryRevisions"], getv(from_object, ["disable_memory_revisions"]), ) return to_object def _GenerateAgentEngineMemoriesRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["vertex_session_source"]) is not None: setv( to_object, ["vertexSessionSource"], getv(from_object, ["vertex_session_source"]), ) if getv(from_object, ["direct_contents_source"]) is not None: setv( to_object, ["directContentsSource"], getv(from_object, ["direct_contents_source"]), ) if getv(from_object, ["direct_memories_source"]) is not None: setv( to_object, ["directMemoriesSource"], getv(from_object, ["direct_memories_source"]), ) if getv(from_object, ["scope"]) is not None: setv(to_object, ["scope"], getv(from_object, ["scope"])) if getv(from_object, ["config"]) is not None: setv( to_object, ["config"], _GenerateAgentEngineMemoriesConfig_to_vertex( getv(from_object, ["config"]), to_object ), ) return to_object def _GetAgentEngineGenerateMemoriesOperationParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["operation_name"]) is not None: setv( to_object, ["_url", "operationName"], getv(from_object, ["operation_name"]) ) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _GetAgentEngineMemoryOperationParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["operation_name"]) is not None: setv( to_object, ["_url", "operationName"], getv(from_object, ["operation_name"]) ) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _GetAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _ListAgentEngineMemoryConfig_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["page_size"]) is not None: setv(parent_object, ["_query", "pageSize"], getv(from_object, ["page_size"])) if getv(from_object, ["page_token"]) is not None: setv(parent_object, ["_query", "pageToken"], getv(from_object, ["page_token"])) if getv(from_object, ["filter"]) is not None: setv(parent_object, ["_query", "filter"], getv(from_object, ["filter"])) if getv(from_object, ["order_by"]) is not None: setv(parent_object, ["_query", "orderBy"], getv(from_object, ["order_by"])) return to_object def _ListAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["config"]) is not None: setv( to_object, ["config"], _ListAgentEngineMemoryConfig_to_vertex( getv(from_object, ["config"]), to_object ), ) return to_object def _PurgeAgentEngineMemoriesRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["filter"]) is not None: setv(to_object, ["filter"], getv(from_object, ["filter"])) if getv(from_object, ["force"]) is not None: setv(to_object, ["force"], getv(from_object, ["force"])) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _RetrieveAgentEngineMemoriesConfig_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["filter"]) is not None: setv(parent_object, ["filter"], getv(from_object, ["filter"])) return to_object def _RetrieveAgentEngineMemoriesRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["scope"]) is not None: setv(to_object, ["scope"], getv(from_object, ["scope"])) if getv(from_object, ["similarity_search_params"]) is not None: setv( to_object, ["similaritySearchParams"], getv(from_object, ["similarity_search_params"]), ) if getv(from_object, ["simple_retrieval_params"]) is not None: setv( to_object, ["simpleRetrievalParams"], getv(from_object, ["simple_retrieval_params"]), ) if getv(from_object, ["config"]) is not None: setv( to_object, ["config"], _RetrieveAgentEngineMemoriesConfig_to_vertex( getv(from_object, ["config"]), to_object ), ) return to_object def _RollbackAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["target_revision_id"]) is not None: setv(to_object, ["targetRevisionId"], getv(from_object, ["target_revision_id"])) if getv(from_object, ["config"]) is not None: setv(to_object, ["config"], getv(from_object, ["config"])) return to_object def _UpdateAgentEngineMemoryConfig_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["display_name"]) is not None: setv(parent_object, ["displayName"], getv(from_object, ["display_name"])) if getv(from_object, ["description"]) is not None: setv(parent_object, ["description"], getv(from_object, ["description"])) if getv(from_object, ["ttl"]) is not None: setv(parent_object, ["ttl"], getv(from_object, ["ttl"])) if getv(from_object, ["expire_time"]) is not None: setv(parent_object, ["expireTime"], getv(from_object, ["expire_time"])) if getv(from_object, ["revision_expire_time"]) is not None: setv( parent_object, ["revisionExpireTime"], getv(from_object, ["revision_expire_time"]), ) if getv(from_object, ["revision_ttl"]) is not None: setv(parent_object, ["revisionTtl"], getv(from_object, ["revision_ttl"])) if getv(from_object, ["disable_memory_revisions"]) is not None: setv( parent_object, ["disableMemoryRevisions"], getv(from_object, ["disable_memory_revisions"]), ) if getv(from_object, ["topics"]) is not None: setv( parent_object, ["topics"], [item for item in getv(from_object, ["topics"])] ) if getv(from_object, ["update_mask"]) is not None: setv( parent_object, ["_query", "updateMask"], getv(from_object, ["update_mask"]) ) return to_object def _UpdateAgentEngineMemoryRequestParameters_to_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, ) -> dict[str, Any]: to_object: dict[str, Any] = {} if getv(from_object, ["name"]) is not None: setv(to_object, ["_url", "name"], getv(from_object, ["name"])) if getv(from_object, ["fact"]) is not None: setv(to_object, ["fact"], getv(from_object, ["fact"])) if getv(from_object, ["scope"]) is not None: setv(to_object, ["scope"], getv(from_object, ["scope"])) if getv(from_object, ["config"]) is not None: setv( to_object, ["config"], _UpdateAgentEngineMemoryConfig_to_vertex( getv(from_object, ["config"]), to_object ), ) return to_object class Memories(_api_module.BaseModule): def _create( self, *, name: str, fact: str, scope: dict[str, str], config: Optional[types.AgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """ Creates a new memory in the Agent Engine. """ parameter_model = types._CreateAgentEngineMemoryRequestParameters( name=name, fact=fact, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _CreateAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories".format_map(request_url_dict) else: path = "{name}/memories" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("post", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def delete( self, *, name: str, config: Optional[types.DeleteAgentEngineMemoryConfigOrDict] = None, ) -> types.DeleteAgentEngineMemoryOperation: """ Delete an Agent Engine memory. Args: name (str): Required. The name of the Agent Engine memory to be deleted. Format: `projects/{project}/locations/{location}/reasoningEngines/{resource_id}/memories/{memory}`. config (DeleteAgentEngineMemoryConfig): Optional. Additional configurations for deleting the Agent Engine. """ parameter_model = types._DeleteAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _DeleteAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("delete", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.DeleteAgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _generate( self, *, name: str, vertex_session_source: Optional[ types.GenerateMemoriesRequestVertexSessionSourceOrDict ] = None, direct_contents_source: Optional[ types.GenerateMemoriesRequestDirectContentsSourceOrDict ] = None, direct_memories_source: Optional[ types.GenerateMemoriesRequestDirectMemoriesSourceOrDict ] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.GenerateAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: """ Generates memories for an Agent Engine. """ parameter_model = types._GenerateAgentEngineMemoriesRequestParameters( name=name, vertex_session_source=vertex_session_source, direct_contents_source=direct_contents_source, direct_memories_source=direct_memories_source, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GenerateAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:generate".format_map(request_url_dict) else: path = "{name}/memories:generate" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("post", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineGenerateMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def get( self, *, name: str, config: Optional[types.GetAgentEngineMemoryConfigOrDict] = None, ) -> types.Memory: """ Gets an agent engine memory. Args: name (str): Required. A fully-qualified resource name or ID such as "projects/123/locations/us-central1/reasoningEngines/456/memories/789" or a shortened name such as "reasoningEngines/456/memories/789". """ parameter_model = types._GetAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("get", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.Memory._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _list( self, *, name: str, config: Optional[types.ListAgentEngineMemoryConfigOrDict] = None, ) -> types.ListReasoningEnginesMemoriesResponse: """ Lists Agent Engine memories. """ parameter_model = types._ListAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _ListAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories".format_map(request_url_dict) else: path = "{name}/memories" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("get", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.ListReasoningEnginesMemoriesResponse._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _get_memory_operation( self, *, operation_name: str, config: Optional[types.GetAgentEngineOperationConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: parameter_model = types._GetAgentEngineMemoryOperationParameters( operation_name=operation_name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineMemoryOperationParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{operationName}".format_map(request_url_dict) else: path = "{operationName}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("get", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _get_generate_memories_operation( self, *, operation_name: str, config: Optional[types.GetAgentEngineOperationConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: parameter_model = types._GetAgentEngineGenerateMemoriesOperationParameters( operation_name=operation_name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineGenerateMemoriesOperationParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{operationName}".format_map(request_url_dict) else: path = "{operationName}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("get", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineGenerateMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _retrieve( self, *, name: str, scope: dict[str, str], similarity_search_params: Optional[ types.RetrieveMemoriesRequestSimilaritySearchParamsOrDict ] = None, simple_retrieval_params: Optional[ types.RetrieveMemoriesRequestSimpleRetrievalParamsOrDict ] = None, config: Optional[types.RetrieveAgentEngineMemoriesConfigOrDict] = None, ) -> types.RetrieveMemoriesResponse: """ Retrieves memories for an Agent Engine. """ parameter_model = types._RetrieveAgentEngineMemoriesRequestParameters( name=name, scope=scope, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _RetrieveAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:retrieve".format_map(request_url_dict) else: path = "{name}/memories:retrieve" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("post", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.RetrieveMemoriesResponse._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _rollback( self, *, name: str, target_revision_id: str, config: Optional[types.RollbackAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineRollbackMemoryOperation: """ Rollback a memory to a previous revision. """ parameter_model = types._RollbackAgentEngineMemoryRequestParameters( name=name, target_revision_id=target_revision_id, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _RollbackAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}:rollback".format_map(request_url_dict) else: path = "{name}:rollback" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("post", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineRollbackMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _update( self, *, name: str, fact: Optional[str] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.UpdateAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """ Updates an Agent Engine memory. """ parameter_model = types._UpdateAgentEngineMemoryRequestParameters( name=name, fact=fact, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _UpdateAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("patch", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value def _purge( self, *, name: str, filter: str, force: Optional[bool] = None, config: Optional[types.PurgeAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEnginePurgeMemoriesOperation: """ Purges memories from an Agent Engine. """ parameter_model = types._PurgeAgentEngineMemoriesRequestParameters( name=name, filter=filter, force=force, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _PurgeAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:purge".format_map(request_url_dict) else: path = "{name}/memories:purge" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = self._api_client.request("post", path, request_dict, http_options) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEnginePurgeMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value _revisions = None @property def revisions(self): if self._revisions is None: try: # We need to lazy load the revisions module to handle the # possibility of ImportError when dependencies are not installed. self._revisions = importlib.import_module( ".memory_revisions", __package__ ) except ImportError as e: raise ImportError( "The 'agent_engines.memories.revisions' module requires " "additional packages. Please install them using pip install " "google-cloud-aiplatform[agent_engines]" ) from e return self._revisions.MemoryRevisions(self._api_client) def create( self, *, name: str, fact: str, scope: dict[str, str], config: Optional[types.AgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """Creates a new memory in the Agent Engine. Args: name (str): Required. The name of the memory to create. fact (str): Required. The fact to be stored in the memory. scope (dict[str, str]): Required. The scope of the memory. For example, {"user_id": "123"}. config (AgentEngineMemoryConfigOrDict): Optional. The configuration for the memory. Returns: AgentEngineMemoryOperation: The operation for creating the memory. """ if config is None: config = types.AgentEngineMemoryConfig() elif isinstance(config, dict): config = types.AgentEngineMemoryConfig.model_validate(config) operation = self._create( name=name, fact=fact, scope=scope, config=config, ) if config.wait_for_completion: if not operation.done: operation = _agent_engines_utils._await_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) # We need to make a call to get the memory because the operation # response might not contain the relevant fields. if operation.response: operation.response = self.get(name=operation.response.name) elif operation.error: raise RuntimeError(f"Failed to create memory: {operation.error}") else: raise RuntimeError("Error creating memory.") return operation def generate( self, *, name: str, vertex_session_source: Optional[ types.GenerateMemoriesRequestVertexSessionSourceOrDict ] = None, direct_contents_source: Optional[ types.GenerateMemoriesRequestDirectContentsSourceOrDict ] = None, direct_memories_source: Optional[ types.GenerateMemoriesRequestDirectMemoriesSourceOrDict ] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.GenerateAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: """Generates memories for the agent engine. Args: name (str): Required. The name of the agent engine to generate memories for. vertex_session_source (GenerateMemoriesRequestVertexSessionSource): Optional. The vertex session source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. direct_contents_source(GenerateMemoriesRequestDirectContentsSource): Optional. The direct contents source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. direct_memories_source (GenerateMemoriesRequestDirectMemoriesSource): Optional. The direct memories source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. scope (dict[str, str]): Optional. The scope of the memories to generate. This is optional if vertex_session_source is used, otherwise it must be specified. config (GenerateMemoriesConfig): Optional. The configuration for the memories to generate. Returns: AgentEngineGenerateMemoriesOperation: The operation for generating the memories. """ if config is None: config = types.GenerateAgentEngineMemoriesConfig() elif isinstance(config, dict): config = types.GenerateAgentEngineMemoriesConfig.model_validate(config) operation = self._generate( name=name, vertex_session_source=vertex_session_source, direct_contents_source=direct_contents_source, direct_memories_source=direct_memories_source, scope=scope, config=config, ) if config.wait_for_completion and not operation.done: operation = _agent_engines_utils._await_operation( operation_name=operation.name, get_operation_fn=self._get_generate_memories_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to generate memory: {operation.error}") return operation def list( self, *, name: str, config: Optional[types.ListAgentEngineMemoryConfigOrDict] = None, ) -> Iterator[types.Memory]: """Lists Agent Engine memories. Args: name (str): Required. The name of the agent engine to list memories for. config (ListAgentEngineMemoryConfig): Optional. The configuration for the memories to list. Returns: Iterable[Memory]: An iterable of memories. """ return Pager( "memories", functools.partial(self._list, name=name), self._list(name=name, config=config), config, ) def retrieve( self, *, name: str, scope: dict[str, str], similarity_search_params: Optional[ types.RetrieveMemoriesRequestSimilaritySearchParamsOrDict ] = None, simple_retrieval_params: Optional[ types.RetrieveMemoriesRequestSimpleRetrievalParamsOrDict ] = None, config: Optional[types.RetrieveAgentEngineMemoriesConfigOrDict] = None, ) -> Iterator[types.RetrieveMemoriesResponseRetrievedMemory]: """Retrieves memories for the agent. Args: name (str): Required. The name of the agent engine to retrieve memories for. scope (dict[str, str]): Required. The scope of the memories to retrieve. For example, {"user_id": "123"}. similarity_search_params (RetrieveMemoriesRequestSimilaritySearchParams): Optional. The similarity search parameters to use for retrieving memories. simple_retrieval_params (RetrieveMemoriesRequestSimpleRetrievalParams): Optional. The simple retrieval parameters to use for retrieving memories. config (RetrieveAgentEngineMemoriesConfig): Optional. The configuration for the memories to retrieve. Returns: Iterator[RetrieveMemoriesResponseRetrievedMemory]: An iterable of retrieved memories. """ return Pager( "retrieved_memories", lambda config: self._retrieve( name=name, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, scope=scope, config=config, ), self._retrieve( name=name, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, scope=scope, config=config, ), config, ) def rollback( self, *, name: str, target_revision_id: str, config: Optional[types.RollbackAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineRollbackMemoryOperation: """Rolls back a memory to a previous revision. Args: name (str): Required. The name of the memory to rollback. target_revision_id (str): Required. The revision ID to roll back to config (RollbackAgentEngineMemoryConfig): Optional. The configuration for the rollback. Returns: AgentEngineRollbackMemoryOperation: The operation for rolling back the memory. """ if config is None: config = types.RollbackAgentEngineMemoryConfig() elif isinstance(config, dict): config = types.RollbackAgentEngineMemoryConfig.model_validate(config) operation = self._rollback( name=name, target_revision_id=target_revision_id, config=config, ) if config.wait_for_completion and not operation.done: operation = _agent_engines_utils._await_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to rollback memory: {operation.error}") return operation def purge( self, *, name: str, filter: str, force: bool = False, config: Optional[types.PurgeAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEnginePurgeMemoriesOperation: """Purges memories from an Agent Engine. Args: name (str): Required. The name of the Agent Engine to purge memories from. filter (str): Required. The standard list filter to determine which memories to purge. force (bool): Optional. Whether to force the purge operation. If false, the operation will be staged but not executed. config (PurgeAgentEngineMemoriesConfig): Optional. The configuration for the purge operation. Returns: AgentEnginePurgeMemoriesOperation: The operation for purging the memories. """ if config is None: config = types.PurgeAgentEngineMemoriesConfig() elif isinstance(config, dict): config = types.PurgeAgentEngineMemoriesConfig.model_validate(config) operation = self._purge( name=name, filter=filter, force=force, config=config, ) if config.wait_for_completion and not operation.done: operation = _agent_engines_utils._await_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to purge memories: {operation.error}") return operation class AsyncMemories(_api_module.BaseModule): async def _create( self, *, name: str, fact: str, scope: dict[str, str], config: Optional[types.AgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """ Creates a new memory in the Agent Engine. """ parameter_model = types._CreateAgentEngineMemoryRequestParameters( name=name, fact=fact, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _CreateAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories".format_map(request_url_dict) else: path = "{name}/memories" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "post", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def delete( self, *, name: str, config: Optional[types.DeleteAgentEngineMemoryConfigOrDict] = None, ) -> types.DeleteAgentEngineMemoryOperation: """ Delete an Agent Engine memory. Args: name (str): Required. The name of the Agent Engine memory to be deleted. Format: `projects/{project}/locations/{location}/reasoningEngines/{resource_id}/memories/{memory}`. config (DeleteAgentEngineMemoryConfig): Optional. Additional configurations for deleting the Agent Engine. """ parameter_model = types._DeleteAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _DeleteAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "delete", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.DeleteAgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _generate( self, *, name: str, vertex_session_source: Optional[ types.GenerateMemoriesRequestVertexSessionSourceOrDict ] = None, direct_contents_source: Optional[ types.GenerateMemoriesRequestDirectContentsSourceOrDict ] = None, direct_memories_source: Optional[ types.GenerateMemoriesRequestDirectMemoriesSourceOrDict ] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.GenerateAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: """ Generates memories for an Agent Engine. """ parameter_model = types._GenerateAgentEngineMemoriesRequestParameters( name=name, vertex_session_source=vertex_session_source, direct_contents_source=direct_contents_source, direct_memories_source=direct_memories_source, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GenerateAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:generate".format_map(request_url_dict) else: path = "{name}/memories:generate" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "post", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineGenerateMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def get( self, *, name: str, config: Optional[types.GetAgentEngineMemoryConfigOrDict] = None, ) -> types.Memory: """ Gets an agent engine memory. Args: name (str): Required. A fully-qualified resource name or ID such as "projects/123/locations/us-central1/reasoningEngines/456/memories/789" or a shortened name such as "reasoningEngines/456/memories/789". """ parameter_model = types._GetAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "get", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.Memory._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _list( self, *, name: str, config: Optional[types.ListAgentEngineMemoryConfigOrDict] = None, ) -> types.ListReasoningEnginesMemoriesResponse: """ Lists Agent Engine memories. """ parameter_model = types._ListAgentEngineMemoryRequestParameters( name=name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _ListAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories".format_map(request_url_dict) else: path = "{name}/memories" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "get", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.ListReasoningEnginesMemoriesResponse._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _get_memory_operation( self, *, operation_name: str, config: Optional[types.GetAgentEngineOperationConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: parameter_model = types._GetAgentEngineMemoryOperationParameters( operation_name=operation_name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineMemoryOperationParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{operationName}".format_map(request_url_dict) else: path = "{operationName}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "get", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _get_generate_memories_operation( self, *, operation_name: str, config: Optional[types.GetAgentEngineOperationConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: parameter_model = types._GetAgentEngineGenerateMemoriesOperationParameters( operation_name=operation_name, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _GetAgentEngineGenerateMemoriesOperationParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{operationName}".format_map(request_url_dict) else: path = "{operationName}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "get", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineGenerateMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _retrieve( self, *, name: str, scope: dict[str, str], similarity_search_params: Optional[ types.RetrieveMemoriesRequestSimilaritySearchParamsOrDict ] = None, simple_retrieval_params: Optional[ types.RetrieveMemoriesRequestSimpleRetrievalParamsOrDict ] = None, config: Optional[types.RetrieveAgentEngineMemoriesConfigOrDict] = None, ) -> types.RetrieveMemoriesResponse: """ Retrieves memories for an Agent Engine. """ parameter_model = types._RetrieveAgentEngineMemoriesRequestParameters( name=name, scope=scope, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _RetrieveAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:retrieve".format_map(request_url_dict) else: path = "{name}/memories:retrieve" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "post", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.RetrieveMemoriesResponse._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _rollback( self, *, name: str, target_revision_id: str, config: Optional[types.RollbackAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineRollbackMemoryOperation: """ Rollback a memory to a previous revision. """ parameter_model = types._RollbackAgentEngineMemoryRequestParameters( name=name, target_revision_id=target_revision_id, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _RollbackAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}:rollback".format_map(request_url_dict) else: path = "{name}:rollback" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "post", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineRollbackMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _update( self, *, name: str, fact: Optional[str] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.UpdateAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """ Updates an Agent Engine memory. """ parameter_model = types._UpdateAgentEngineMemoryRequestParameters( name=name, fact=fact, scope=scope, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _UpdateAgentEngineMemoryRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}".format_map(request_url_dict) else: path = "{name}" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "patch", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEngineMemoryOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value async def _purge( self, *, name: str, filter: str, force: Optional[bool] = None, config: Optional[types.PurgeAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEnginePurgeMemoriesOperation: """ Purges memories from an Agent Engine. """ parameter_model = types._PurgeAgentEngineMemoriesRequestParameters( name=name, filter=filter, force=force, config=config, ) request_url_dict: Optional[dict[str, str]] if not self._api_client.vertexai: raise ValueError("This method is only supported in the Vertex AI client.") else: request_dict = _PurgeAgentEngineMemoriesRequestParameters_to_vertex( parameter_model ) request_url_dict = request_dict.get("_url") if request_url_dict: path = "{name}/memories:purge".format_map(request_url_dict) else: path = "{name}/memories:purge" query_params = request_dict.get("_query") if query_params: path = f"{path}?{urlencode(query_params)}" # TODO: remove the hack that pops config. request_dict.pop("config", None) http_options: Optional[types.HttpOptions] = None if ( parameter_model.config is not None and parameter_model.config.http_options is not None ): http_options = parameter_model.config.http_options request_dict = _common.convert_to_dict(request_dict) request_dict = _common.encode_unserializable_types(request_dict) response = await self._api_client.async_request( "post", path, request_dict, http_options ) response_dict = {} if not response.body else json.loads(response.body) return_value = types.AgentEnginePurgeMemoriesOperation._from_response( response=response_dict, kwargs=parameter_model.model_dump() ) self._api_client._verify_response(return_value) return return_value _revisions = None @property def revisions(self): if self._revisions is None: try: # We need to lazy load the revisions module to handle the # possibility of ImportError when dependencies are not installed. self._revisions = importlib.import_module( ".memory_revisions", __package__ ) except ImportError as e: raise ImportError( "The 'agent_engines.memories.revisions' module requires " "additional packages. Please install them using pip install " "google-cloud-aiplatform[agent_engines]" ) from e return self._revisions.AsyncMemoryRevisions(self._api_client) async def create( self, *, name: str, fact: str, scope: dict[str, str], config: Optional[types.AgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineMemoryOperation: """Creates a new memory in the Agent Engine. Args: name (str): Required. The name of the memory to create. fact (str): Required. The fact to be stored in the memory. scope (dict[str, str]): Required. The scope of the memory. For example, {"user_id": "123"}. config (AgentEngineMemoryConfigOrDict): Optional. The configuration for the memory. Returns: AgentEngineMemoryOperation: The operation for creating the memory. """ if config is None: config = types.AgentEngineMemoryConfig() elif isinstance(config, dict): config = types.AgentEngineMemoryConfig.model_validate(config) operation = await self._create( name=name, fact=fact, scope=scope, config=config, ) if config.wait_for_completion: if not operation.done: operation = await _agent_engines_utils._await_async_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) # We need to make a call to get the memory because the operation # response might not contain the relevant fields. if operation.response: operation.response = await self.get(name=operation.response.name) elif operation.error: raise RuntimeError(f"Failed to create memory: {operation.error}") else: raise RuntimeError("Error creating memory.") return operation async def generate( self, *, name: str, vertex_session_source: Optional[ types.GenerateMemoriesRequestVertexSessionSourceOrDict ] = None, direct_contents_source: Optional[ types.GenerateMemoriesRequestDirectContentsSourceOrDict ] = None, direct_memories_source: Optional[ types.GenerateMemoriesRequestDirectMemoriesSourceOrDict ] = None, scope: Optional[dict[str, str]] = None, config: Optional[types.GenerateAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEngineGenerateMemoriesOperation: """Generates memories for the agent engine. Args: name (str): Required. The name of the agent engine to generate memories for. vertex_session_source (GenerateMemoriesRequestVertexSessionSource): Optional. The vertex session source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. direct_contents_source(GenerateMemoriesRequestDirectContentsSource): Optional. The direct contents source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. direct_memories_source (GenerateMemoriesRequestDirectMemoriesSource): Optional. The direct memories source to use for generating memories. Only one of vertex_session_source, direct_contents_source, or direct_memories_source can be specified. scope (dict[str, str]): Optional. The scope of the memories to generate. This is optional if vertex_session_source is used, otherwise it must be specified. config (GenerateMemoriesConfig): Optional. The configuration for the memories to generate. Returns: AgentEngineGenerateMemoriesOperation: The operation for generating the memories. """ if config is None: config = types.GenerateAgentEngineMemoriesConfig() elif isinstance(config, dict): config = types.GenerateAgentEngineMemoriesConfig.model_validate(config) operation = await self._generate( name=name, vertex_session_source=vertex_session_source, direct_contents_source=direct_contents_source, direct_memories_source=direct_memories_source, scope=scope, config=config, ) if config.wait_for_completion and not operation.done: operation = await _agent_engines_utils._await_async_operation( operation_name=operation.name, get_operation_fn=self._get_generate_memories_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to generate memory: {operation.error}") return operation async def list( self, *, name: str, config: Optional[types.ListAgentEngineMemoryConfigOrDict] = None, ) -> AsyncPager[types.Memory]: """Lists Agent Engine memories. Args: name (str): Required. The name of the agent engine to list memories for. config (ListAgentEngineMemoryConfig): Optional. The configuration for the memories to list. Returns: AsyncPager[Memory]: An async pager of memories. """ return AsyncPager( "memories", functools.partial(self._list, name=name), await self._list(name=name, config=config), config, ) async def retrieve( self, *, name: str, scope: dict[str, str], similarity_search_params: Optional[ types.RetrieveMemoriesRequestSimilaritySearchParamsOrDict ] = None, simple_retrieval_params: Optional[ types.RetrieveMemoriesRequestSimpleRetrievalParamsOrDict ] = None, config: Optional[types.RetrieveAgentEngineMemoriesConfigOrDict] = None, ) -> AsyncPager[types.RetrieveMemoriesResponseRetrievedMemory]: """Retrieves memories for the agent. Args: name (str): Required. The name of the agent engine to retrieve memories for. scope (dict[str, str]): Required. The scope of the memories to retrieve. For example, {"user_id": "123"}. similarity_search_params (RetrieveMemoriesRequestSimilaritySearchParams): Optional. The similarity search parameters to use for retrieving memories. simple_retrieval_params (RetrieveMemoriesRequestSimpleRetrievalParams): Optional. The simple retrieval parameters to use for retrieving memories. config (RetrieveAgentEngineMemoriesConfig): Optional. The configuration for the memories to retrieve. Returns: AsyncPager[RetrieveMemoriesResponseRetrievedMemory]: An async pager of retrieved memories. """ return AsyncPager( "retrieved_memories", lambda config: self._retrieve( name=name, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, scope=scope, config=config, ), await self._retrieve( name=name, similarity_search_params=similarity_search_params, simple_retrieval_params=simple_retrieval_params, scope=scope, config=config, ), config, ) async def rollback( self, *, name: str, target_revision_id: str, config: Optional[types.RollbackAgentEngineMemoryConfigOrDict] = None, ) -> types.AgentEngineRollbackMemoryOperation: """Rolls back a memory to a previous revision. Args: name (str): Required. The name of the memory to rollback. target_revision_id (str): Required. The revision ID to roll back to config (RollbackAgentEngineMemoryConfig): Optional. The configuration for the rollback. Returns: AgentEngineRollbackMemoryOperation: The operation for rolling back the memory. """ if config is None: config = types.RollbackAgentEngineMemoryConfig() elif isinstance(config, dict): config = types.RollbackAgentEngineMemoryConfig.model_validate(config) operation = await self._rollback( name=name, target_revision_id=target_revision_id, config=config, ) if config.wait_for_completion and not operation.done: operation = await _agent_engines_utils._await_async_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to rollback memory: {operation.error}") return operation async def purge( self, *, name: str, filter: str, force: bool = False, config: Optional[types.PurgeAgentEngineMemoriesConfigOrDict] = None, ) -> types.AgentEnginePurgeMemoriesOperation: """Purges memories from an Agent Engine. Args: name (str): Required. The name of the Agent Engine to purge memories from. filter (str): Required. The standard list filter to determine which memories to purge. force (bool): Optional. Whether to force the purge operation. If false, the operation will be staged but not executed. config (PurgeAgentEngineMemoriesConfig): Optional. The configuration for the purge operation. Returns: AgentEnginePurgeMemoriesOperation: The operation for purging the memories. """ if config is None: config = types.PurgeAgentEngineMemoriesConfig() elif isinstance(config, dict): config = types.PurgeAgentEngineMemoriesConfig.model_validate(config) operation = await self._purge( name=name, filter=filter, force=force, config=config, ) if config.wait_for_completion and not operation.done: operation = await _agent_engines_utils._await_async_operation( operation_name=operation.name, get_operation_fn=self._get_memory_operation, poll_interval_seconds=0.5, ) if operation.error: raise RuntimeError(f"Failed to purge memories: {operation.error}") return operation