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copilot/ad
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copilot/ad
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9d8dd68b59 | ||
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e99258a276 |
@@ -205,21 +205,165 @@ class ProviderDify(Provider):
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model=None,
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**kwargs,
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):
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# raise NotImplementedError("This method is not implemented yet.")
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# 调用 text_chat 模拟流式
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llm_response = await self.text_chat(
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prompt=prompt,
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session_id=session_id,
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image_urls=image_urls,
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func_tool=func_tool,
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contexts=contexts,
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system_prompt=system_prompt,
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tool_calls_result=tool_calls_result,
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)
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llm_response.is_chunk = True
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yield llm_response
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llm_response.is_chunk = False
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yield llm_response
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if image_urls is None:
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image_urls = []
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session_id = session_id or kwargs.get("user") or "unknown"
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conversation_id = self.conversation_ids.get(session_id, "")
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files_payload = []
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for image_url in image_urls:
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image_path = (
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await download_image_by_url(image_url)
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if image_url.startswith("http")
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else image_url
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)
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file_response = await self.api_client.file_upload(
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image_path,
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user=session_id,
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)
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logger.debug(f"Dify 上传图片响应：{file_response}")
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if "id" not in file_response:
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logger.warning(
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f"上传图片后得到未知的 Dify 响应：{file_response}，图片将忽略。",
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)
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continue
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files_payload.append(
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{
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"type": "image",
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"transfer_method": "local_file",
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"upload_file_id": file_response["id"],
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},
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)
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# 获得会话变量
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payload_vars = self.variables.copy()
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# 动态变量
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session_var = await sp.session_get(session_id, "session_variables", default={})
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payload_vars.update(session_var)
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payload_vars["system_prompt"] = system_prompt
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try:
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match self.api_type:
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case "chat" | "agent" | "chatflow":
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if not prompt:
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prompt = "请描述这张图片。"
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accumulated_text = ""
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async for chunk in self.api_client.chat_messages(
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inputs={
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**payload_vars,
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},
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query=prompt,
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user=session_id,
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conversation_id=conversation_id,
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files=files_payload,
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timeout=self.timeout,
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):
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logger.debug(f"dify resp chunk: {chunk}")
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if (
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chunk["event"] == "message"
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or chunk["event"] == "agent_message"
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):
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accumulated_text += chunk["answer"]
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if not conversation_id:
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self.conversation_ids[session_id] = chunk[
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"conversation_id"
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]
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conversation_id = chunk["conversation_id"]
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# Yield streaming chunk
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llm_response = LLMResponse(
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role="assistant",
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result_chain=MessageChain(
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chain=[Comp.Plain(chunk["answer"])]
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),
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is_chunk=True,
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)
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yield llm_response
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elif chunk["event"] == "message_end":
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logger.debug("Dify message end")
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break
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elif chunk["event"] == "error":
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logger.error(f"Dify 出现错误：{chunk}")
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yield LLMResponse(
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role="err",
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completion_text=f"Dify 出现错误 status: {chunk['status']} message: {chunk['message']}",
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)
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return
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# Yield final complete result
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chain = MessageChain(chain=[Comp.Plain(accumulated_text)])
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yield LLMResponse(
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role="assistant", result_chain=chain, is_chunk=False
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)
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case "workflow":
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workflow_result = None
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async for chunk in self.api_client.workflow_run(
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inputs={
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self.dify_query_input_key: prompt,
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"astrbot_session_id": session_id,
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**payload_vars,
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},
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user=session_id,
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files=files_payload,
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timeout=self.timeout,
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):
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match chunk["event"]:
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case "workflow_started":
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logger.info(
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f"Dify 工作流(ID: {chunk['workflow_run_id']})开始运行。",
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)
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case "node_finished":
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logger.debug(
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f"Dify 工作流节点(ID: {chunk['data']['node_id']} Title: {chunk['data'].get('title', '')})运行结束。",
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)
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case "workflow_finished":
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logger.info(
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f"Dify 工作流(ID: {chunk['workflow_run_id']})运行结束",
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)
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logger.debug(f"Dify 工作流结果：{chunk}")
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if chunk["data"]["error"]:
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logger.error(
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f"Dify 工作流出现错误：{chunk['data']['error']}",
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)
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yield LLMResponse(
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role="err",
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completion_text=f"Dify 工作流出现错误：{chunk['data']['error']}",
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)
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return
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if (
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self.workflow_output_key
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not in chunk["data"]["outputs"]
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):
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yield LLMResponse(
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role="err",
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completion_text=f"Dify 工作流的输出不包含指定的键名：{self.workflow_output_key}",
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)
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return
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workflow_result = chunk
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if workflow_result:
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chain = await self.parse_dify_result(workflow_result)
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yield LLMResponse(
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role="assistant", result_chain=chain, is_chunk=False
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)
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else:
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logger.warning("Dify 工作流请求结果为空，请查看 Debug 日志。")
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yield LLMResponse(
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role="err", completion_text="Dify 工作流请求结果为空"
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)
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case _:
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yield LLMResponse(
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role="err",
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completion_text=f"未知的 Dify API 类型：{self.api_type}",
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)
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except Exception as e:
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logger.error(f"Dify 请求失败：{e!s}")
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yield LLMResponse(role="err", completion_text=f"Dify 请求失败：{e!s}")
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async def parse_dify_result(self, chunk: dict | str) -> MessageChain:
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if isinstance(chunk, str):
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