mirror of
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style: introduce ruff as linter and formatter (#1356)
* style: remove necessary imports * style: fix F841 * style: fix F401 * style: fix F811 * style: fix E402 * style: fix E721 * style: fix E722 * style: fix E722 * style: fix F541 * style: ruff format * style: all passed * style: add ruff in deps * style: more ignores in ruff.toml * style: add pre-commit
This commit is contained in:
Junyan Qin (Chin)
committed by
GitHub
GitHub
parent
09e70d70e9
commit
209f16af76
@@ -1,8 +1,6 @@
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from __future__ import annotations
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import typing
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import json
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import base64
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import re
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import dashscope
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@@ -10,7 +8,7 @@ import dashscope
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from .. import runner
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from ...core import app, entities as core_entities
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from .. import entities as llm_entities
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from ...utils import image
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class DashscopeAPIError(Exception):
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"""Dashscope API 请求失败"""
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@@ -20,49 +18,49 @@ class DashscopeAPIError(Exception):
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super().__init__(self.message)
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@runner.runner_class("dashscope-app-api")
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@runner.runner_class('dashscope-app-api')
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class DashScopeAPIRunner(runner.RequestRunner):
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"阿里云百炼DashsscopeAPI对话请求器"
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# 运行器内部使用的配置
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app_type: str # 应用类型
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app_id: str # 应用ID
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api_key: str # API Key
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references_quote: str # 引用资料提示（当展示回答来源功能开启时，这个变量会作为引用资料名前的提示，可在provider.json中配置）
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app_type: str # 应用类型
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app_id: str # 应用ID
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api_key: str # API Key
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references_quote: str # 引用资料提示（当展示回答来源功能开启时，这个变量会作为引用资料名前的提示，可在provider.json中配置）
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def __init__(self, ap: app.Application, pipeline_config: dict):
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"""初始化"""
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self.ap = ap
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self.pipeline_config = pipeline_config
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valid_app_types = ["agent", "workflow"]
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self.app_type = self.pipeline_config["ai"]["dashscope-app-api"]["app-type"]
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#检查配置文件中使用的应用类型是否支持
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if (self.app_type not in valid_app_types):
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raise DashscopeAPIError(
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f"不支持的 Dashscope 应用类型: {self.app_type}"
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)
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#初始化Dashscope 参数配置
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self.app_id = self.pipeline_config["ai"]["dashscope-app-api"]["app-id"]
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self.api_key = self.pipeline_config["ai"]["dashscope-app-api"]["api-key"]
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self.references_quote = self.pipeline_config["ai"]["dashscope-app-api"]["references_quote"]
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valid_app_types = ['agent', 'workflow']
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self.app_type = self.pipeline_config['ai']['dashscope-app-api']['app-type']
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# 检查配置文件中使用的应用类型是否支持
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if self.app_type not in valid_app_types:
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raise DashscopeAPIError(f'不支持的 Dashscope 应用类型: {self.app_type}')
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# 初始化Dashscope 参数配置
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self.app_id = self.pipeline_config['ai']['dashscope-app-api']['app-id']
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self.api_key = self.pipeline_config['ai']['dashscope-app-api']['api-key']
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self.references_quote = self.pipeline_config['ai']['dashscope-app-api'][
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'references_quote'
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]
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def _replace_references(self, text, references_dict):
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"""阿里云百炼平台的自定义应用支持资料引用，此函数可以将引用标签替换为参考资料"""
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# 匹配 <ref>[index_id]</ref> 形式的字符串
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pattern = re.compile(r'<ref>\[(.*?)\]</ref>')
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def replacement(match):
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# 获取引用编号
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ref_key = match.group(1)
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ref_key = match.group(1)
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if ref_key in references_dict:
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# 如果有对应的参考资料按照provider.json中的reference_quote返回提示，来自哪个参考资料文件
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return f"({self.references_quote} {references_dict[ref_key]})"
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return f'({self.references_quote} {references_dict[ref_key]})'
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else:
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# 如果没有对应的参考资料，保留原样
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return match.group(0)
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return match.group(0)
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# 使用 re.sub() 进行替换
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return pattern.sub(replacement, text)
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@@ -71,14 +69,14 @@ class DashScopeAPIRunner(runner.RequestRunner):
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self, query: core_entities.Query
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) -> tuple[str, list[str]]:
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"""预处理用户消息，提取纯文本，阿里云提供的上传文件方法过于复杂，暂不支持上传文件（包括图片）"""
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plain_text = ""
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plain_text = ''
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image_ids = []
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if isinstance(query.user_message.content, list):
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for ce in query.user_message.content:
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if ce.type == "text":
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if ce.type == 'text':
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plain_text += ce.text
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# 暂时不支持上传图片，保留代码以便后续扩展
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# elif ce.type == "image_base64":
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# elif ce.type == "image_base64":
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# image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
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# file_bytes = base64.b64decode(image_b64)
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# file = ("img.png", file_bytes, f"image/{image_format}")
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@@ -92,147 +90,141 @@ class DashScopeAPIRunner(runner.RequestRunner):
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plain_text = query.user_message.content
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return plain_text, image_ids
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async def _agent_messages(
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self, query: core_entities.Query
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""Dashscope 智能体对话请求"""
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#局部变量
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chunk = None # 流式传输的块
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pending_content = "" # 待处理的Agent输出内容
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references_dict = {} # 用于存储引用编号和对应的参考资料
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plain_text = "" # 用户输入的纯文本信息
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image_ids = [] # 用户输入的图片ID列表 （暂不支持）
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# 局部变量
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chunk = None # 流式传输的块
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pending_content = '' # 待处理的Agent输出内容
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references_dict = {} # 用于存储引用编号和对应的参考资料
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plain_text = '' # 用户输入的纯文本信息
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image_ids = [] # 用户输入的图片ID列表 （暂不支持）
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plain_text, image_ids = await self._preprocess_user_message(query)
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#发送对话请求
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# 发送对话请求
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response = dashscope.Application.call(
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api_key=self.api_key, # 智能体应用的API Key
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app_id=self.app_id, # 智能体应用的ID
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prompt=plain_text, # 用户输入的文本信息
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stream=True, # 流式输出
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incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
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session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
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api_key=self.api_key, # 智能体应用的API Key
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app_id=self.app_id, # 智能体应用的ID
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prompt=plain_text, # 用户输入的文本信息
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stream=True, # 流式输出
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incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
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session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
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# rag_options={ # 主要用于文件交互，暂不支持
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# "session_file_ids": ["FILE_ID1"], # FILE_ID1 替换为实际的临时文件ID,逗号隔开多个
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# }
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)
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for chunk in response:
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if chunk.get("status_code") != 200:
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if chunk.get('status_code') != 200:
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raise DashscopeAPIError(
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f"Dashscope API 请求失败: status_code={chunk.get('status_code')} message={chunk.get('message')} request_id={chunk.get('request_id')} "
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f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
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)
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if not chunk:
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continue
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#获取流式传输的output
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stream_output = chunk.get("output", {})
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if stream_output.get("text") is not None:
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pending_content += stream_output.get("text")
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#保存当前会话的session_id用于下次对话的语境
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query.session.using_conversation.uuid = stream_output.get("session_id")
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#获取模型传出的参考资料列表
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references_dict_list = stream_output.get("doc_references", [])
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#从模型传出的参考资料信息中提取用于替换的字典
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# 获取流式传输的output
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stream_output = chunk.get('output', {})
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if stream_output.get('text') is not None:
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pending_content += stream_output.get('text')
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# 保存当前会话的session_id用于下次对话的语境
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query.session.using_conversation.uuid = stream_output.get('session_id')
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# 获取模型传出的参考资料列表
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references_dict_list = stream_output.get('doc_references', [])
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# 从模型传出的参考资料信息中提取用于替换的字典
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if references_dict_list is not None:
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for doc in references_dict_list:
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if doc.get("index_id") is not None:
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references_dict[doc.get("index_id")] = doc.get("doc_name")
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#将参考资料替换到文本中
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if doc.get('index_id') is not None:
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references_dict[doc.get('index_id')] = doc.get('doc_name')
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# 将参考资料替换到文本中
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pending_content = self._replace_references(pending_content, references_dict)
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yield llm_entities.Message(
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role="assistant",
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role='assistant',
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content=pending_content,
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)
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async def _workflow_messages(
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self, query: core_entities.Query
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""Dashscope 工作流对话请求"""
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#局部变量
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chunk = None # 流式传输的块
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pending_content = "" # 待处理的Agent输出内容
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references_dict = {} # 用于存储引用编号和对应的参考资料
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plain_text = "" # 用户输入的纯文本信息
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image_ids = [] # 用户输入的图片ID列表 （暂不支持）
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# 局部变量
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chunk = None # 流式传输的块
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pending_content = '' # 待处理的Agent输出内容
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references_dict = {} # 用于存储引用编号和对应的参考资料
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plain_text = '' # 用户输入的纯文本信息
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image_ids = [] # 用户输入的图片ID列表 （暂不支持）
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plain_text, image_ids = await self._preprocess_user_message(query)
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biz_params = {}
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biz_params.update(query.variables)
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#发送对话请求
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# 发送对话请求
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response = dashscope.Application.call(
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api_key=self.api_key, # 智能体应用的API Key
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app_id=self.app_id, # 智能体应用的ID
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prompt=plain_text, # 用户输入的文本信息
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stream=True, # 流式输出
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incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
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session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
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biz_params=biz_params, # 工作流应用的自定义输入参数传递
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api_key=self.api_key, # 智能体应用的API Key
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app_id=self.app_id, # 智能体应用的ID
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prompt=plain_text, # 用户输入的文本信息
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stream=True, # 流式输出
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incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
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session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
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biz_params=biz_params, # 工作流应用的自定义输入参数传递
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# rag_options={ # 主要用于文件交互，暂不支持
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# "session_file_ids": ["FILE_ID1"], # FILE_ID1 替换为实际的临时文件ID,逗号隔开多个
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# }
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)
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#处理API返回的流式输出
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# 处理API返回的流式输出
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for chunk in response:
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if chunk.get("status_code") != 200:
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if chunk.get('status_code') != 200:
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raise DashscopeAPIError(
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f"Dashscope API 请求失败: status_code={chunk.get('status_code')} message={chunk.get('message')} request_id={chunk.get('request_id')} "
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f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
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)
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if not chunk:
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continue
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#获取流式传输的output
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stream_output = chunk.get("output", {})
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if stream_output.get("text") is not None:
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pending_content += stream_output.get("text")
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#保存当前会话的session_id用于下次对话的语境
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query.session.using_conversation.uuid = stream_output.get("session_id")
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#获取模型传出的参考资料列表
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references_dict_list = stream_output.get("doc_references", [])
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#从模型传出的参考资料信息中提取用于替换的字典
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# 获取流式传输的output
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stream_output = chunk.get('output', {})
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if stream_output.get('text') is not None:
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pending_content += stream_output.get('text')
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# 保存当前会话的session_id用于下次对话的语境
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query.session.using_conversation.uuid = stream_output.get('session_id')
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# 获取模型传出的参考资料列表
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references_dict_list = stream_output.get('doc_references', [])
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# 从模型传出的参考资料信息中提取用于替换的字典
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if references_dict_list is not None:
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for doc in references_dict_list:
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if doc.get("index_id") is not None:
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references_dict[doc.get("index_id")] = doc.get("doc_name")
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#将参考资料替换到文本中
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if doc.get('index_id') is not None:
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references_dict[doc.get('index_id')] = doc.get('doc_name')
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# 将参考资料替换到文本中
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pending_content = self._replace_references(pending_content, references_dict)
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yield llm_entities.Message(
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role="assistant",
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role='assistant',
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content=pending_content,
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)
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async def run(
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self, query: core_entities.Query
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""运行"""
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if self.app_type == "agent":
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if self.app_type == 'agent':
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async for msg in self._agent_messages(query):
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yield msg
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elif self.app_type == "workflow":
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elif self.app_type == 'workflow':
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async for msg in self._workflow_messages(query):
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yield msg
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else:
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raise DashscopeAPIError(
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f"不支持的 Dashscope 应用类型: {self.app_type}"
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)
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raise DashscopeAPIError(f'不支持的 Dashscope 应用类型: {self.app_type}')
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@@ -5,9 +5,7 @@ import json
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import uuid
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import re
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import base64
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import datetime
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import aiohttp
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from .. import runner
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from ...core import app, entities as core_entities
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@@ -17,7 +15,7 @@ from ...utils import image
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from libs.dify_service_api.v1 import client, errors
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@runner.runner_class("dify-service-api")
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@runner.runner_class('dify-service-api')
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class DifyServiceAPIRunner(runner.RequestRunner):
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"""Dify Service API 对话请求器"""
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@@ -27,38 +25,54 @@ class DifyServiceAPIRunner(runner.RequestRunner):
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self.ap = ap
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self.pipeline_config = pipeline_config
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valid_app_types = ["chat", "agent", "workflow"]
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valid_app_types = ['chat', 'agent', 'workflow']
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if (
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self.pipeline_config["ai"]["dify-service-api"]["app-type"]
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self.pipeline_config['ai']['dify-service-api']['app-type']
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not in valid_app_types
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):
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raise errors.DifyAPIError(
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f"不支持的 Dify 应用类型: {self.pipeline_config['ai']['dify-service-api']['app-type']}"
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f'不支持的 Dify 应用类型: {self.pipeline_config["ai"]["dify-service-api"]["app-type"]}'
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)
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api_key = self.pipeline_config["ai"]["dify-service-api"]["api-key"]
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api_key = self.pipeline_config['ai']['dify-service-api']['api-key']
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self.dify_client = client.AsyncDifyServiceClient(
|
||||
api_key=api_key,
|
||||
base_url=self.pipeline_config["ai"]["dify-service-api"]["base-url"],
|
||||
base_url=self.pipeline_config['ai']['dify-service-api']['base-url'],
|
||||
)
|
||||
|
||||
def _try_convert_thinking(self, resp_text: str) -> str:
|
||||
"""尝试转换 Dify 的思考提示"""
|
||||
if not resp_text.startswith("<details style=\"color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;\" open> <summary> Thinking... </summary>"):
|
||||
if not resp_text.startswith(
|
||||
'<details style="color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;" open> <summary> Thinking... </summary>'
|
||||
):
|
||||
return resp_text
|
||||
|
||||
if self.pipeline_config["ai"]["dify-service-api"]["thinking-convert"] == "original":
|
||||
if (
|
||||
self.pipeline_config['ai']['dify-service-api']['thinking-convert']
|
||||
== 'original'
|
||||
):
|
||||
return resp_text
|
||||
|
||||
if self.pipeline_config["ai"]["dify-service-api"]["thinking-convert"] == "remove":
|
||||
return re.sub(r'<details style="color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;" open> <summary> Thinking... </summary>.*?</details>', '', resp_text, flags=re.DOTALL)
|
||||
|
||||
if self.pipeline_config["ai"]["dify-service-api"]["thinking-convert"] == "plain":
|
||||
|
||||
if (
|
||||
self.pipeline_config['ai']['dify-service-api']['thinking-convert']
|
||||
== 'remove'
|
||||
):
|
||||
return re.sub(
|
||||
r'<details style="color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;" open> <summary> Thinking... </summary>.*?</details>',
|
||||
'',
|
||||
resp_text,
|
||||
flags=re.DOTALL,
|
||||
)
|
||||
|
||||
if (
|
||||
self.pipeline_config['ai']['dify-service-api']['thinking-convert']
|
||||
== 'plain'
|
||||
):
|
||||
pattern = r'<details style="color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;" open> <summary> Thinking... </summary>(.*?)</details>'
|
||||
thinking_text = re.search(pattern, resp_text, flags=re.DOTALL)
|
||||
content_text = re.sub(pattern, '', resp_text, flags=re.DOTALL)
|
||||
return f"<think>{thinking_text.group(1)}</think>\n{content_text}"
|
||||
return f'<think>{thinking_text.group(1)}</think>\n{content_text}'
|
||||
|
||||
async def _preprocess_user_message(
|
||||
self, query: core_entities.Query
|
||||
@@ -68,22 +82,24 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
Returns:
|
||||
tuple[str, list[str]]: 纯文本和图片的 Dify 服务图片 ID
|
||||
"""
|
||||
plain_text = ""
|
||||
plain_text = ''
|
||||
image_ids = []
|
||||
|
||||
if isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == "text":
|
||||
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)
|
||||
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 = ('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}",
|
||||
f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
)
|
||||
image_id = file_upload_resp["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
|
||||
@@ -94,116 +110,119 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
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 ""
|
||||
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,
|
||||
'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
|
||||
mode = 'basic' # 标记是基础编排还是工作流编排
|
||||
|
||||
basic_mode_pending_chunk = ''
|
||||
|
||||
inputs = {}
|
||||
|
||||
|
||||
inputs.update(query.variables)
|
||||
|
||||
async for chunk in self.dify_client.chat_messages(
|
||||
inputs=inputs,
|
||||
query=plain_text,
|
||||
user=f"{query.session.launcher_type.value}_{query.session.launcher_id}",
|
||||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
conversation_id=cov_id,
|
||||
files=files,
|
||||
timeout=self.pipeline_config["ai"]["dify-service-api"]["timeout"],
|
||||
timeout=self.pipeline_config['ai']['dify-service-api']['timeout'],
|
||||
):
|
||||
self.ap.logger.debug("dify-chat-chunk: " + str(chunk))
|
||||
self.ap.logger.debug('dify-chat-chunk: ' + str(chunk))
|
||||
|
||||
if chunk['event'] == 'workflow_started':
|
||||
mode = "workflow"
|
||||
mode = 'workflow'
|
||||
|
||||
if mode == "workflow":
|
||||
if mode == 'workflow':
|
||||
if chunk['event'] == 'node_finished':
|
||||
if chunk['data']['node_type'] == 'answer':
|
||||
yield llm_entities.Message(
|
||||
role="assistant",
|
||||
content=self._try_convert_thinking(chunk['data']['outputs']['answer']),
|
||||
role='assistant',
|
||||
content=self._try_convert_thinking(
|
||||
chunk['data']['outputs']['answer']
|
||||
),
|
||||
)
|
||||
elif mode == "basic":
|
||||
elif mode == 'basic':
|
||||
if chunk['event'] == 'message':
|
||||
basic_mode_pending_chunk += chunk['answer']
|
||||
elif chunk['event'] == 'message_end':
|
||||
yield llm_entities.Message(
|
||||
role="assistant",
|
||||
role='assistant',
|
||||
content=self._try_convert_thinking(basic_mode_pending_chunk),
|
||||
)
|
||||
basic_mode_pending_chunk = ''
|
||||
|
||||
query.session.using_conversation.uuid = chunk["conversation_id"]
|
||||
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 ""
|
||||
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,
|
||||
'type': 'image',
|
||||
'transfer_method': 'local_file',
|
||||
'upload_file_id': image_id,
|
||||
}
|
||||
for image_id in image_ids
|
||||
]
|
||||
|
||||
ignored_events = ["agent_message"]
|
||||
ignored_events = ['agent_message']
|
||||
|
||||
inputs = {}
|
||||
|
||||
|
||||
inputs.update(query.variables)
|
||||
|
||||
async for chunk in self.dify_client.chat_messages(
|
||||
inputs=inputs,
|
||||
query=plain_text,
|
||||
user=f"{query.session.launcher_type.value}_{query.session.launcher_id}",
|
||||
response_mode="streaming",
|
||||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
response_mode='streaming',
|
||||
conversation_id=cov_id,
|
||||
files=files,
|
||||
timeout=self.pipeline_config["ai"]["dify-service-api"]["timeout"],
|
||||
timeout=self.pipeline_config['ai']['dify-service-api']['timeout'],
|
||||
):
|
||||
self.ap.logger.debug("dify-agent-chunk: " + str(chunk))
|
||||
self.ap.logger.debug('dify-agent-chunk: ' + str(chunk))
|
||||
|
||||
if chunk["event"] in ignored_events:
|
||||
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
|
||||
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"],
|
||||
role='assistant',
|
||||
content=chunk['thought'],
|
||||
)
|
||||
yield msg
|
||||
|
||||
if chunk['tool']:
|
||||
msg = llm_entities.Message(
|
||||
role="assistant",
|
||||
role='assistant',
|
||||
tool_calls=[
|
||||
llm_entities.ToolCall(
|
||||
id=chunk['id'],
|
||||
type="function",
|
||||
type='function',
|
||||
function=llm_entities.FunctionCall(
|
||||
name=chunk["tool"],
|
||||
name=chunk['tool'],
|
||||
arguments=json.dumps({}),
|
||||
),
|
||||
)
|
||||
@@ -211,9 +230,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
)
|
||||
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'):
|
||||
@@ -222,11 +239,11 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
image_url = base_url + chunk['url']
|
||||
|
||||
yield llm_entities.Message(
|
||||
role="assistant",
|
||||
role='assistant',
|
||||
content=[llm_entities.ContentElement.from_image_url(image_url)],
|
||||
)
|
||||
|
||||
query.session.using_conversation.uuid = chunk["conversation_id"]
|
||||
query.session.using_conversation.uuid = chunk['conversation_id']
|
||||
|
||||
async def _workflow_messages(
|
||||
self, query: core_entities.Query
|
||||
@@ -235,58 +252,57 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
|
||||
if not query.session.using_conversation.uuid:
|
||||
query.session.using_conversation.uuid = str(uuid.uuid4())
|
||||
|
||||
query.variables["conversation_id"] = query.session.using_conversation.uuid
|
||||
|
||||
query.variables['conversation_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,
|
||||
'type': 'image',
|
||||
'transfer_method': 'local_file',
|
||||
'upload_file_id': image_id,
|
||||
}
|
||||
for image_id in image_ids
|
||||
]
|
||||
|
||||
ignored_events = ["text_chunk", "workflow_started"]
|
||||
ignored_events = ['text_chunk', 'workflow_started']
|
||||
|
||||
inputs = { # these variables are legacy variables, we need to keep them for compatibility
|
||||
"langbot_user_message_text": plain_text,
|
||||
"langbot_session_id": query.variables["session_id"],
|
||||
"langbot_conversation_id": query.variables["conversation_id"],
|
||||
"langbot_msg_create_time": query.variables["msg_create_time"],
|
||||
'langbot_user_message_text': plain_text,
|
||||
'langbot_session_id': query.variables['session_id'],
|
||||
'langbot_conversation_id': query.variables['conversation_id'],
|
||||
'langbot_msg_create_time': query.variables['msg_create_time'],
|
||||
}
|
||||
|
||||
|
||||
inputs.update(query.variables)
|
||||
|
||||
async for chunk in self.dify_client.workflow_run(
|
||||
inputs=inputs,
|
||||
user=f"{query.session.launcher_type.value}_{query.session.launcher_id}",
|
||||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
files=files,
|
||||
timeout=self.pipeline_config["ai"]["dify-service-api"]["timeout"],
|
||||
timeout=self.pipeline_config['ai']['dify-service-api']['timeout'],
|
||||
):
|
||||
self.ap.logger.debug("dify-workflow-chunk: " + str(chunk))
|
||||
if chunk["event"] in ignored_events:
|
||||
self.ap.logger.debug('dify-workflow-chunk: ' + str(chunk))
|
||||
if chunk['event'] in ignored_events:
|
||||
continue
|
||||
|
||||
if chunk["event"] == "node_started":
|
||||
|
||||
if chunk['event'] == 'node_started':
|
||||
if (
|
||||
chunk["data"]["node_type"] == "start"
|
||||
or chunk["data"]["node_type"] == "end"
|
||||
chunk['data']['node_type'] == 'start'
|
||||
or chunk['data']['node_type'] == 'end'
|
||||
):
|
||||
continue
|
||||
|
||||
msg = llm_entities.Message(
|
||||
role="assistant",
|
||||
role='assistant',
|
||||
content=None,
|
||||
tool_calls=[
|
||||
llm_entities.ToolCall(
|
||||
id=chunk["data"]["node_id"],
|
||||
type="function",
|
||||
id=chunk['data']['node_id'],
|
||||
type='function',
|
||||
function=llm_entities.FunctionCall(
|
||||
name=chunk["data"]["title"],
|
||||
name=chunk['data']['title'],
|
||||
arguments=json.dumps({}),
|
||||
),
|
||||
)
|
||||
@@ -295,13 +311,13 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
|
||||
yield msg
|
||||
|
||||
elif chunk["event"] == "workflow_finished":
|
||||
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"]["summary"],
|
||||
role='assistant',
|
||||
content=chunk['data']['outputs']['summary'],
|
||||
)
|
||||
|
||||
yield msg
|
||||
@@ -310,16 +326,16 @@ class DifyServiceAPIRunner(runner.RequestRunner):
|
||||
self, query: core_entities.Query
|
||||
) -> typing.AsyncGenerator[llm_entities.Message, None]:
|
||||
"""运行请求"""
|
||||
if self.pipeline_config["ai"]["dify-service-api"]["app-type"] == "chat":
|
||||
if self.pipeline_config['ai']['dify-service-api']['app-type'] == 'chat':
|
||||
async for msg in self._chat_messages(query):
|
||||
yield msg
|
||||
elif self.pipeline_config["ai"]["dify-service-api"]["app-type"] == "agent":
|
||||
elif self.pipeline_config['ai']['dify-service-api']['app-type'] == 'agent':
|
||||
async for msg in self._agent_chat_messages(query):
|
||||
yield msg
|
||||
elif self.pipeline_config["ai"]["dify-service-api"]["app-type"] == "workflow":
|
||||
elif self.pipeline_config['ai']['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.pipeline_config['ai']['dify-service-api']['app-type']}"
|
||||
f'不支持的 Dify 应用类型: {self.pipeline_config["ai"]["dify-service-api"]["app-type"]}'
|
||||
)
|
||||
|
||||
@@ -4,24 +4,28 @@ import json
|
||||
import typing
|
||||
|
||||
from .. import runner
|
||||
from ...core import app, entities as core_entities
|
||||
from ...core import entities as core_entities
|
||||
from .. import entities as llm_entities
|
||||
|
||||
|
||||
@runner.runner_class("local-agent")
|
||||
@runner.runner_class('local-agent')
|
||||
class LocalAgentRunner(runner.RequestRunner):
|
||||
"""本地Agent请求运行器
|
||||
"""
|
||||
"""本地Agent请求运行器"""
|
||||
|
||||
async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
|
||||
"""运行请求
|
||||
"""
|
||||
async def run(
|
||||
self, query: core_entities.Query
|
||||
) -> typing.AsyncGenerator[llm_entities.Message, None]:
|
||||
"""运行请求"""
|
||||
pending_tool_calls = []
|
||||
|
||||
req_messages = query.prompt.messages.copy() + query.messages.copy() + [query.user_message]
|
||||
req_messages = (
|
||||
query.prompt.messages.copy() + query.messages.copy() + [query.user_message]
|
||||
)
|
||||
|
||||
# 首次请求
|
||||
msg = await query.use_llm_model.requester.invoke_llm(query, query.use_llm_model, req_messages, query.use_funcs)
|
||||
msg = await query.use_llm_model.requester.invoke_llm(
|
||||
query, query.use_llm_model, req_messages, query.use_funcs
|
||||
)
|
||||
|
||||
yield msg
|
||||
|
||||
@@ -34,7 +38,7 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
for tool_call in pending_tool_calls:
|
||||
try:
|
||||
func = tool_call.function
|
||||
|
||||
|
||||
parameters = json.loads(func.arguments)
|
||||
|
||||
func_ret = await self.ap.tool_mgr.execute_func_call(
|
||||
@@ -42,7 +46,9 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
)
|
||||
|
||||
msg = llm_entities.Message(
|
||||
role="tool", content=json.dumps(func_ret, ensure_ascii=False), tool_call_id=tool_call.id
|
||||
role='tool',
|
||||
content=json.dumps(func_ret, ensure_ascii=False),
|
||||
tool_call_id=tool_call.id,
|
||||
)
|
||||
|
||||
yield msg
|
||||
@@ -51,7 +57,7 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
except Exception as e:
|
||||
# 工具调用出错，添加一个报错信息到 req_messages
|
||||
err_msg = llm_entities.Message(
|
||||
role="tool", content=f"err: {e}", tool_call_id=tool_call.id
|
||||
role='tool', content=f'err: {e}', tool_call_id=tool_call.id
|
||||
)
|
||||
|
||||
yield err_msg
|
||||
@@ -59,7 +65,9 @@ class LocalAgentRunner(runner.RequestRunner):
|
||||
req_messages.append(err_msg)
|
||||
|
||||
# 处理完所有调用，再次请求
|
||||
msg = await query.use_llm_model.requester.invoke_llm(query, query.use_llm_model, req_messages, query.use_funcs)
|
||||
msg = await query.use_llm_model.requester.invoke_llm(
|
||||
query, query.use_llm_model, req_messages, query.use_funcs
|
||||
)
|
||||
|
||||
yield msg
|
||||
|
||||
|
||||
Reference in New Issue
Block a user