6849af2bad
* refactor: LLM response handling with reasoning content - Added a `show_reasoning` parameter to `run_agent` to control the display of reasoning content. - Updated `LLMResponse` to include a `reasoning_content` field for storing reasoning text. - Modified `WebChatMessageEvent` to handle and send reasoning content in streaming responses. - Implemented reasoning extraction in various provider sources (e.g., OpenAI, Gemini). - Updated the chat interface to display reasoning content in a collapsible format. - Removed the deprecated `thinking_filter` package and its associated logic. - Updated localization files to include new reasoning-related strings. * feat: add Groq chat completion provider and associated configurations * Update astrbot/core/provider/sources/gemini_source.py Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> --------- Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
286 lines
11 KiB
Python
286 lines
11 KiB
Python
import os
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import astrbot.core.message.components as Comp
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from astrbot.core import logger, sp
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.core.utils.astrbot_path import get_astrbot_data_path
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from astrbot.core.utils.dify_api_client import DifyAPIClient
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from astrbot.core.utils.io import download_file, download_image_by_url
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from .. import Provider
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from ..entities import LLMResponse
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from ..register import register_provider_adapter
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@register_provider_adapter("dify", "Dify APP 适配器。")
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class ProviderDify(Provider):
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def __init__(
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self,
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provider_config,
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provider_settings,
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) -> None:
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super().__init__(
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provider_config,
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provider_settings,
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)
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self.api_key = provider_config.get("dify_api_key", "")
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if not self.api_key:
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raise Exception("Dify API Key 不能为空。")
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api_base = provider_config.get("dify_api_base", "https://api.dify.ai/v1")
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self.api_type = provider_config.get("dify_api_type", "")
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if not self.api_type:
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raise Exception("Dify API 类型不能为空。")
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self.model_name = "dify"
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self.workflow_output_key = provider_config.get(
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"dify_workflow_output_key",
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"astrbot_wf_output",
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)
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self.dify_query_input_key = provider_config.get(
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"dify_query_input_key",
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"astrbot_text_query",
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)
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if not self.dify_query_input_key:
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self.dify_query_input_key = "astrbot_text_query"
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if not self.workflow_output_key:
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self.workflow_output_key = "astrbot_wf_output"
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self.variables: dict = provider_config.get("variables", {})
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self.timeout = provider_config.get("timeout", 120)
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if isinstance(self.timeout, str):
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self.timeout = int(self.timeout)
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self.conversation_ids = {}
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"""记录当前 session id 的对话 ID"""
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self.api_client = DifyAPIClient(self.api_key, api_base)
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async def text_chat(
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self,
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prompt: str,
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session_id=None,
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image_urls=None,
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func_tool=None,
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contexts=None,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> LLMResponse:
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if image_urls is None:
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image_urls = []
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result = ""
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session_id = session_id or kwargs.get("user") or "unknown" # 1734
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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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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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result += 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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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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raise Exception(
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f"Dify 出现错误 status: {chunk['status']} message: {chunk['message']}",
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)
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case "workflow":
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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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raise Exception(
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f"Dify 工作流出现错误：{chunk['data']['error']}",
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)
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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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raise Exception(
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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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result = chunk
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case _:
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raise Exception(f"未知的 Dify API 类型：{self.api_type}")
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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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return LLMResponse(role="err", completion_text=f"Dify 请求失败：{e!s}")
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if not result:
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logger.warning("Dify 请求结果为空，请查看 Debug 日志。")
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chain = await self.parse_dify_result(result)
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return LLMResponse(role="assistant", result_chain=chain)
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async def text_chat_stream(
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self,
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prompt,
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session_id=None,
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image_urls=...,
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func_tool=None,
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contexts=...,
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system_prompt=None,
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tool_calls_result=None,
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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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async def parse_dify_result(self, chunk: dict | str) -> MessageChain:
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if isinstance(chunk, str):
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# Chat
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return MessageChain(chain=[Comp.Plain(chunk)])
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async def parse_file(item: dict):
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match item["type"]:
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case "image":
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return Comp.Image(file=item["url"], url=item["url"])
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case "audio":
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# 仅支持 wav
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temp_dir = os.path.join(get_astrbot_data_path(), "temp")
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path = os.path.join(temp_dir, f"{item['filename']}.wav")
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await download_file(item["url"], path)
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return Comp.Image(file=item["url"], url=item["url"])
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case "video":
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return Comp.Video(file=item["url"])
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case _:
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return Comp.File(name=item["filename"], file=item["url"])
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output = chunk["data"]["outputs"][self.workflow_output_key]
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chains = []
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if isinstance(output, str):
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# 纯文本输出
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chains.append(Comp.Plain(output))
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elif isinstance(output, list):
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# 主要适配 Dify 的 HTTP 请求结点的多模态输出
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for item in output:
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# handle Array[File]
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if (
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not isinstance(item, dict)
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or item.get("dify_model_identity", "") != "__dify__file__"
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):
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chains.append(Comp.Plain(str(output)))
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break
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else:
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chains.append(Comp.Plain(str(output)))
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# scan file
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files = chunk["data"].get("files", [])
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for item in files:
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comp = await parse_file(item)
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chains.append(comp)
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return MessageChain(chain=chains)
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async def forget(self, session_id):
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self.conversation_ids[session_id] = ""
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return True
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async def get_current_key(self):
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return self.api_key
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async def set_key(self, key):
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raise Exception("Dify 适配器不支持设置 API Key。")
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async def get_models(self):
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return [self.get_model()]
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async def get_human_readable_context(self, session_id, page, page_size):
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raise Exception("暂不支持获得 Dify 的历史消息记录。")
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async def terminate(self):
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await self.api_client.close()
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