from __future__ import annotations import typing import json import uuid import base64 import aiohttp from .. import runner from ...core import entities as core_entities from .. import entities as llm_entities from ...utils import image from libs.dify_service_api.v1 import client, errors @runner.runner_class("dify-service-api") class DifyServiceAPIRunner(runner.RequestRunner): """Dify Service API 对话请求器""" dify_client: client.AsyncDifyServiceClient async def initialize(self): """初始化""" valid_app_types = ["chat", "agent", "workflow"] if ( self.ap.provider_cfg.data["dify-service-api"]["app-type"] not in valid_app_types ): raise errors.DifyAPIError( f"不支持的 Dify 应用类型: {self.ap.provider_cfg.data['dify-service-api']['app-type']}" ) api_key = self.ap.provider_cfg.data["dify-service-api"][ self.ap.provider_cfg.data["dify-service-api"]["app-type"] ]["api-key"] self.dify_client = client.AsyncDifyServiceClient( api_key=api_key, base_url=self.ap.provider_cfg.data["dify-service-api"]["base-url"], ) async def _preprocess_user_message( self, query: core_entities.Query ) -> tuple[str, list[str]]: """预处理用户消息，提取纯文本，并将图片上传到 Dify 服务 Returns: tuple[str, list[str]]: 纯文本和图片的 Dify 服务图片 ID """ plain_text = "" image_ids = [] if isinstance(query.user_message.content, list): for ce in query.user_message.content: if ce.type == "text": plain_text += ce.text elif ce.type == "image_base64": image_b64, image_format = await image.extract_b64_and_format(ce.image_base64) file_bytes = base64.b64decode(image_b64) file = ("img.png", file_bytes, f"image/{image_format}") file_upload_resp = await self.dify_client.upload_file( file, f"{query.session.launcher_type.value}_{query.session.launcher_id}", ) image_id = file_upload_resp["id"] image_ids.append(image_id) elif isinstance(query.user_message.content, str): plain_text = query.user_message.content return plain_text, image_ids async def _chat_messages( self, query: core_entities.Query ) -> typing.AsyncGenerator[llm_entities.Message, None]: """调用聊天助手""" cov_id = query.session.using_conversation.uuid or "" plain_text, image_ids = await self._preprocess_user_message(query) files = [ { "type": "image", "transfer_method": "local_file", "upload_file_id": image_id, } for image_id in image_ids ] mode = "basic" # 标记是基础编排还是工作流编排 basic_mode_pending_chunk = '' async for chunk in self.dify_client.chat_messages( inputs={}, query=plain_text, user=f"{query.session.launcher_type.value}_{query.session.launcher_id}", conversation_id=cov_id, files=files, timeout=self.ap.provider_cfg.data["dify-service-api"]["chat"]["timeout"], ): self.ap.logger.debug("dify-chat-chunk: " + str(chunk)) if chunk['event'] == 'workflow_started': mode = "workflow" if mode == "workflow": if chunk['event'] == 'node_finished': if chunk['data']['node_type'] == 'answer': yield llm_entities.Message( role="assistant", content=chunk['data']['outputs']['answer'], ) elif mode == "basic": if chunk['event'] == 'message': basic_mode_pending_chunk += chunk['answer'] elif chunk['event'] == 'message_end': yield llm_entities.Message( role="assistant", content=basic_mode_pending_chunk, ) basic_mode_pending_chunk = '' query.session.using_conversation.uuid = chunk["conversation_id"] async def _agent_chat_messages( self, query: core_entities.Query ) -> typing.AsyncGenerator[llm_entities.Message, None]: """调用聊天助手""" cov_id = query.session.using_conversation.uuid or "" plain_text, image_ids = await self._preprocess_user_message(query) files = [ { "type": "image", "transfer_method": "local_file", "upload_file_id": image_id, } for image_id in image_ids ] ignored_events = ["agent_message"] async for chunk in self.dify_client.chat_messages( inputs={}, query=plain_text, user=f"{query.session.launcher_type.value}_{query.session.launcher_id}", response_mode="streaming", conversation_id=cov_id, files=files, timeout=self.ap.provider_cfg.data["dify-service-api"]["chat"]["timeout"], ): self.ap.logger.debug("dify-agent-chunk: " + str(chunk)) if chunk["event"] in ignored_events: continue if chunk["event"] == "agent_thought": if chunk['tool'] != '' and chunk['observation'] != '': # 工具调用结果，跳过 continue if chunk['thought'].strip() != '': # 文字回复内容 msg = llm_entities.Message( role="assistant", content=chunk["thought"], ) yield msg if chunk['tool']: msg = llm_entities.Message( role="assistant", tool_calls=[ llm_entities.ToolCall( id=chunk['id'], type="function", function=llm_entities.FunctionCall( name=chunk["tool"], arguments=json.dumps({}), ), ) ], ) yield msg if chunk['event'] == 'message_file': if chunk['type'] == 'image' and chunk['belongs_to'] == 'assistant': base_url = self.dify_client.base_url if base_url.endswith('/v1'): base_url = base_url[:-3] image_url = base_url + chunk['url'] yield llm_entities.Message( role="assistant", content=[llm_entities.ContentElement.from_image_url(image_url)], ) query.session.using_conversation.uuid = chunk["conversation_id"] async def _workflow_messages( self, query: core_entities.Query ) -> typing.AsyncGenerator[llm_entities.Message, None]: """调用工作流""" if not query.session.using_conversation.uuid: query.session.using_conversation.uuid = str(uuid.uuid4()) cov_id = query.session.using_conversation.uuid plain_text, image_ids = await self._preprocess_user_message(query) files = [ { "type": "image", "transfer_method": "local_file", "upload_file_id": image_id, } for image_id in image_ids ] ignored_events = ["text_chunk", "workflow_started"] async for chunk in self.dify_client.workflow_run( inputs={ "langbot_user_message_text": plain_text, "langbot_session_id": f"{query.session.launcher_type.value}_{query.session.launcher_id}", "langbot_conversation_id": cov_id, }, user=f"{query.session.launcher_type.value}_{query.session.launcher_id}", files=files, timeout=self.ap.provider_cfg.data["dify-service-api"]["workflow"]["timeout"], ): self.ap.logger.debug("dify-workflow-chunk: " + str(chunk)) if chunk["event"] in ignored_events: continue if chunk["event"] == "node_started": if ( chunk["data"]["node_type"] == "start" or chunk["data"]["node_type"] == "end" ): continue msg = llm_entities.Message( role="assistant", content=None, tool_calls=[ llm_entities.ToolCall( id=chunk["data"]["node_id"], type="function", function=llm_entities.FunctionCall( name=chunk["data"]["title"], arguments=json.dumps({}), ), ) ], ) yield msg elif chunk["event"] == "workflow_finished": if chunk['data']['error']: raise errors.DifyAPIError(chunk['data']['error']) msg = llm_entities.Message( role="assistant", content=chunk["data"]["outputs"][ self.ap.provider_cfg.data["dify-service-api"]["workflow"][ "output-key" ] ], ) yield msg async def run( self, query: core_entities.Query ) -> typing.AsyncGenerator[llm_entities.Message, None]: """运行请求""" if self.ap.provider_cfg.data["dify-service-api"]["app-type"] == "chat": async for msg in self._chat_messages(query): yield msg elif self.ap.provider_cfg.data["dify-service-api"]["app-type"] == "agent": async for msg in self._agent_chat_messages(query): yield msg elif self.ap.provider_cfg.data["dify-service-api"]["app-type"] == "workflow": async for msg in self._workflow_messages(query): yield msg else: raise errors.DifyAPIError( f"不支持的 Dify 应用类型: {self.ap.provider_cfg.data['dify-service-api']['app-type']}" )