202 lines
7.1 KiB
Python
202 lines
7.1 KiB
Python
import re
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import asyncio
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import functools
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from typing import List
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from .. import Provider, Personality
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from ..entities import LLMResponse
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from ..func_tool_manager import FuncCall
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from ..register import register_provider_adapter
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from astrbot.core.message.message_event_result import MessageChain
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from .openai_source import ProviderOpenAIOfficial
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from astrbot.core import logger, sp
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from dashscope import Application
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@register_provider_adapter("dashscope", "Dashscope APP 适配器。")
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class ProviderDashscope(ProviderOpenAIOfficial):
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def __init__(
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self,
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provider_config: dict,
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provider_settings: dict,
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default_persona: Personality | None = None,
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) -> None:
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Provider.__init__(
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self,
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provider_config,
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provider_settings,
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default_persona,
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)
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self.api_key = provider_config.get("dashscope_api_key", "")
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if not self.api_key:
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raise Exception("阿里云百炼 API Key 不能为空。")
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self.app_id = provider_config.get("dashscope_app_id", "")
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if not self.app_id:
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raise Exception("阿里云百炼 APP ID 不能为空。")
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self.dashscope_app_type = provider_config.get("dashscope_app_type", "")
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if not self.dashscope_app_type:
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raise Exception("阿里云百炼 APP 类型不能为空。")
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self.model_name = "dashscope"
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self.variables: dict = provider_config.get("variables", {})
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self.rag_options: dict = provider_config.get("rag_options", {})
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self.output_reference = self.rag_options.get("output_reference", False)
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self.rag_options = self.rag_options.copy()
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self.rag_options.pop("output_reference", None)
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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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def has_rag_options(self):
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"""判断是否有 RAG 选项
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Returns:
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bool: 是否有 RAG 选项
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"""
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if self.rag_options and (
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len(self.rag_options.get("pipeline_ids", [])) > 0
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or len(self.rag_options.get("file_ids", [])) > 0
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):
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return True
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return False
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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: str = None,
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image_urls: List[str] = [],
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func_tool: FuncCall = None,
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contexts: List = None,
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system_prompt: str = None,
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model=None,
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**kwargs,
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) -> LLMResponse:
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if contexts is None:
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contexts = []
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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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if (
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self.dashscope_app_type in ["agent", "dialog-workflow"]
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and not self.has_rag_options()
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):
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# 支持多轮对话的
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new_record = {"role": "user", "content": prompt}
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if image_urls:
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logger.warning("阿里云百炼暂不支持图片输入，将自动忽略图片内容。")
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contexts_no_img = await self._remove_image_from_context(contexts)
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context_query = [*contexts_no_img, new_record]
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if system_prompt:
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context_query.insert(0, {"role": "system", "content": system_prompt})
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for part in context_query:
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if "_no_save" in part:
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del part["_no_save"]
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# 调用阿里云百炼 API
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payload = {
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"app_id": self.app_id,
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"api_key": self.api_key,
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"messages": context_query,
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"biz_params": payload_vars or None,
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}
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partial = functools.partial(
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Application.call,
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**payload,
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)
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response = await asyncio.get_event_loop().run_in_executor(None, partial)
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else:
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# 不支持多轮对话的
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# 调用阿里云百炼 API
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payload = {
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"app_id": self.app_id,
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"prompt": prompt,
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"api_key": self.api_key,
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"biz_params": payload_vars or None,
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}
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if self.rag_options:
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payload["rag_options"] = self.rag_options
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partial = functools.partial(
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Application.call,
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**payload,
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)
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response = await asyncio.get_event_loop().run_in_executor(None, partial)
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logger.debug(f"dashscope resp: {response}")
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if response.status_code != 200:
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logger.error(
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f"阿里云百炼请求失败: request_id={response.request_id}, code={response.status_code}, message={response.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code"
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)
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return LLMResponse(
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role="err",
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result_chain=MessageChain().message(
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f"阿里云百炼请求失败: message={response.message} code={response.status_code}"
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),
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)
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output_text = response.output.get("text", "")
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# RAG 引用脚标格式化
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output_text = re.sub(r"<ref>\[(\d+)\]</ref>", r"[\1]", output_text)
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if self.output_reference and response.output.get("doc_references", None):
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ref_str = ""
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for ref in response.output.get("doc_references", []):
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ref_title = (
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ref.get("title", "")
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if ref.get("title")
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else ref.get("doc_name", "")
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)
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ref_str += f"{ref['index_id']}. {ref_title}\n"
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output_text += f"\n\n回答来源:\n{ref_str}"
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llm_response = LLMResponse("assistant")
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llm_response.result_chain = MessageChain().message(output_text)
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return llm_response
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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 forget(self, 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("阿里云百炼 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 阿里云百炼 的历史消息记录。")
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async def terminate(self):
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pass
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