feat: 支持多轮次工具调用并且存储到数据库
移除了 llm tuner 适配器
This commit is contained in:
Soulter
@@ -774,17 +774,6 @@ CONFIG_METADATA_2 = {
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"model": "deepseek/deepseek-r1",
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},
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},
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"LLMTuner": {
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"id": "llmtuner_default",
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"type": "llm_tuner",
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"provider_type": "chat_completion",
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"enable": True,
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"base_model_path": "",
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"adapter_model_path": "",
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"llmtuner_template": "",
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"finetuning_type": "lora",
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"quantization_bit": 4,
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},
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"Dify": {
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"id": "dify_app_default",
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"type": "dify",
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@@ -98,6 +98,15 @@ class MessageChain:
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self.chain.append(Image.fromFileSystem(path))
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return self
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def base64_image(self, base64_str: str):
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"""添加一条图片消息（base64 编码字符串）到消息链 `chain` 中。
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Example:
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CommandResult().base64_image("iVBORw0KGgoAAAANSUhEUgAAAAUA...")
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"""
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self.chain.append(Image.fromBase64(base64_str))
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return self
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def use_t2i(self, use_t2i: bool):
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"""设置是否使用文本转图片服务。
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@@ -157,7 +166,7 @@ class ResultContentType(enum.Enum):
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"""普通的消息结果"""
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STREAMING_RESULT = enum.auto()
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"""调用 LLM 产生的流式结果"""
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STREAMING_FINISH= enum.auto()
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STREAMING_FINISH = enum.auto()
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"""流式输出完成"""
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@@ -1,6 +1,14 @@
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import inspect
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import traceback
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import typing as T
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from dataclasses import dataclass
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from astrbot.core.config.astrbot_config import AstrBotConfig
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from astrbot.core.platform.astr_message_event import AstrMessageEvent
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from astrbot.core.star import PluginManager
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from astrbot.api import logger
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from astrbot.core.star.star_handler import star_handlers_registry, EventType
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from astrbot.core.star.star import star_map
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from astrbot.core.message.message_event_result import MessageEventResult, CommandResult
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@dataclass
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@@ -9,3 +17,91 @@ class PipelineContext:
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astrbot_config: AstrBotConfig # AstrBot 配置对象
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plugin_manager: PluginManager # 插件管理器对象
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async def call_event_hook(
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self,
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event: AstrMessageEvent,
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hook_type: EventType,
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*args,
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):
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platform_id = event.get_platform_id()
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handlers = star_handlers_registry.get_handlers_by_event_type(
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hook_type, platform_id=platform_id
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)
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for handler in handlers:
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try:
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logger.debug(
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f"hook(on_llm_request) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
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)
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await handler.handler(event, *args)
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except BaseException:
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logger.error(traceback.format_exc())
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if event.is_stopped():
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logger.info(
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f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
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)
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return
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async def call_handler(
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self,
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event: AstrMessageEvent,
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handler: T.Awaitable,
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*args,
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**kwargs,
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) -> T.AsyncGenerator[None, None]:
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"""执行事件处理函数并处理其返回结果
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该方法负责调用处理函数并处理不同类型的返回值。它支持两种类型的处理函数:
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1. 异步生成器: 实现洋葱模型，每次 yield 都会将控制权交回上层
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2. 协程: 执行一次并处理返回值
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Args:
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ctx (PipelineContext): 消息管道上下文对象
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event (AstrMessageEvent): 事件对象
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handler (Awaitable): 事件处理函数
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Returns:
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AsyncGenerator[None, None]: 异步生成器，用于在管道中传递控制流
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"""
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ready_to_call = None # 一个协程或者异步生成器
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trace_ = None
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try:
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ready_to_call = handler(event, *args, **kwargs)
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except TypeError as _:
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# 向下兼容
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trace_ = traceback.format_exc()
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# 以前的 handler 会额外传入一个参数, 但是 context 对象实际上在插件实例中有一份
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ready_to_call = handler(event, self.plugin_manager.context, *args, **kwargs)
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if isinstance(ready_to_call, T.AsyncGenerator):
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_has_yielded = False
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try:
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async for ret in ready_to_call:
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# 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
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# 返回值只能是 MessageEventResult 或者 None（无返回值）
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_has_yielded = True
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if isinstance(ret, (MessageEventResult, CommandResult)):
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# 如果返回值是 MessageEventResult, 设置结果并继续
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event.set_result(ret)
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yield
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else:
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# 如果返回值是 None, 则不设置结果并继续
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# 继续执行后续阶段
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yield ret
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if not _has_yielded:
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# 如果这个异步生成器没有执行到 yield 分支
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yield
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except Exception as e:
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logger.error(f"Previous Error: {trace_}")
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raise e
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elif inspect.iscoroutine(ready_to_call):
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# 如果只是一个协程, 直接执行
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ret = await ready_to_call
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if isinstance(ret, (MessageEventResult, CommandResult)):
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event.set_result(ret)
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yield
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else:
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yield ret
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@@ -0,0 +1,43 @@
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import abc
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import typing as T
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from dataclasses import dataclass
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from astrbot.core.provider.entities import LLMResponse
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@dataclass
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class AgentResponse:
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type: str
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data: dict
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class BaseAgentRunner:
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@abc.abstractmethod
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async def reset(self) -> None:
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"""
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Reset the agent to its initial state.
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This method should be called before starting a new run.
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"""
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...
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@abc.abstractmethod
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async def step(self) -> T.AsyncGenerator[AgentResponse, None]:
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"""
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Process a single step of the agent.
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"""
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...
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@abc.abstractmethod
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def done(self) -> bool:
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"""
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Check if the agent has completed its task.
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Returns True if the agent is done, False otherwise.
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"""
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...
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@abc.abstractmethod
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def get_final_llm_resp(self) -> LLMResponse | None:
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"""
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Get the final observation from the agent.
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This method should be called after the agent is done.
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"""
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...
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@@ -0,0 +1,276 @@
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import sys
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import traceback
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import typing as T
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from .base import BaseAgentRunner, AgentResponse
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from ...context import PipelineContext
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from astrbot.core.provider.provider import Provider
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from astrbot.core.platform.astr_message_event import AstrMessageEvent
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from astrbot.core.message.message_event_result import (
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MessageChain,
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)
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from astrbot.core.provider.entities import (
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ProviderRequest,
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LLMResponse,
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ToolCallMessageSegment,
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AssistantMessageSegment,
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ToolCallsResult,
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)
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from mcp.types import (
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TextContent,
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ImageContent,
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EmbeddedResource,
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TextResourceContents,
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BlobResourceContents,
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)
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from astrbot.core.star.star_handler import EventType
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from astrbot import logger
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if sys.version_info >= (3, 12):
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from typing import override
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else:
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from typing_extensions import override
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# TODO:
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# 1. 处理平台不兼容的处理器
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class ToolLoopAgent(BaseAgentRunner):
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def __init__(
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self, provider: Provider, event: AstrMessageEvent, pipeline_ctx: PipelineContext
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) -> None:
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self.provider = provider
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self.req = None
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self.event = event
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self.pipeline_ctx = pipeline_ctx
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@override
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async def reset(self, req: ProviderRequest, streaming: bool) -> None:
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self.req = req
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self.streaming = streaming
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self.final_llm_resp = None
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self.is_done = False
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@override
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async def step(self):
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"""
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Process a single step of the agent.
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This method should return the result of the step.
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"""
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if not self.req:
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raise ValueError("Request is not set. Please call reset() first.")
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# 执行 LLM 请求
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llm_resp_result = None
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if self.streaming:
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stream = self.provider.text_chat_stream(**self.req.__dict__)
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async for llm_response in stream: # type: ignore
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assert isinstance(llm_response, LLMResponse)
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if llm_response.is_chunk:
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if llm_response.result_chain:
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yield AgentResponse(
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type="streaming_delta",
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data={
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"chain": llm_response.result_chain.chain,
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},
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)
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else:
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yield AgentResponse(
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type="streaming_delta",
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data={
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"chain": MessageChain().message(
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llm_response.completion_text
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),
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},
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)
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else:
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llm_resp_result = llm_response
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else:
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llm_resp_result = await self.provider.text_chat(**self.req.__dict__)
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if not llm_resp_result:
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return
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# 执行事件钩子
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await self.pipeline_ctx.call_event_hook(
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self.event, EventType.OnLLMResponseEvent, self.final_llm_resp
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)
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# 处理 LLM 响应
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llm_resp = llm_resp_result
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logger.info(f"LLMResp: {llm_resp}")
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if llm_resp.role == "err":
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# 如果 LLM 响应错误，直接返回错误信息
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self.final_llm_resp = llm_resp
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self.is_done = True
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yield AgentResponse(
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type="err",
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data={
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"chain": MessageChain().message(
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f"LLM 响应错误: {llm_resp.completion_text or '未知错误'}"
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),
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},
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)
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if not llm_resp.tools_call_name:
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# 如果没有工具调用，结束 Agent Loop
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self.final_llm_resp = llm_resp
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self.is_done = True
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# 返回 LLM 结果
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if llm_resp.result_chain:
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yield AgentResponse(
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type="llm_result",
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data={
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"chain": llm_resp.result_chain.chain,
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},
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)
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elif llm_resp.completion_text:
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yield AgentResponse(
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type="llm_result",
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data={
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"chain": MessageChain().message(llm_resp.completion_text),
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},
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)
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# 如果有工具调用，还需处理工具调用
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if llm_resp.tools_call_name:
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tool_call_result_blocks = []
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async for result in self._handle_function_tools(self.req, llm_resp):
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if isinstance(result, list):
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tool_call_result_blocks = result
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elif isinstance(result, MessageChain):
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yield AgentResponse(
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type="tool_call_result",
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data={
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"chain": result.chain,
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},
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)
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# 将结果添加到上下文中
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tool_calls_result = ToolCallsResult(
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tool_calls_info=AssistantMessageSegment(
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role="assistant", tool_calls=llm_resp.to_openai_tool_calls()
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),
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tool_calls_result=tool_call_result_blocks,
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)
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self.req.append_tool_calls_result(tool_calls_result)
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logger.info("done: %s", self.is_done)
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async def _handle_function_tools(
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self,
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req: ProviderRequest,
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llm_response: LLMResponse,
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) -> T.AsyncGenerator[MessageChain | list[ToolCallMessageSegment], None]:
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"""处理函数工具调用。"""
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tool_call_result_blocks: list[ToolCallMessageSegment] = []
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logger.info(f"Agent 使用工具: {llm_response.tools_call_name}")
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# 执行函数调用
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for func_tool_name, func_tool_args, func_tool_id in zip(
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llm_response.tools_call_name,
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llm_response.tools_call_args,
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llm_response.tools_call_ids,
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):
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try:
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func_tool = req.func_tool.get_func(func_tool_name)
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if func_tool.origin == "mcp":
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logger.info(
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f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
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)
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client = req.func_tool.mcp_client_dict[func_tool.mcp_server_name]
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res = await client.session.call_tool(func_tool.name, func_tool_args)
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if not res:
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continue
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if isinstance(res.content[0], TextContent):
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tool_call_result_blocks.append(
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ToolCallMessageSegment(
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role="tool",
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tool_call_id=func_tool_id,
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content=res.content[0].text,
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)
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)
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elif isinstance(res.content[0], ImageContent):
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tool_call_result_blocks.append(
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ToolCallMessageSegment(
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role="tool",
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tool_call_id=func_tool_id,
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content="返回了图片(已直接发送给用户)",
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)
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)
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yield MessageChain().base64_image(res.content[0].data)
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elif isinstance(res.content[0], EmbeddedResource):
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resource = res.content[0].resource
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if isinstance(resource, TextResourceContents):
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tool_call_result_blocks.append(
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ToolCallMessageSegment(
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role="tool",
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tool_call_id=func_tool_id,
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content=resource.text,
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)
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)
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elif (
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isinstance(resource, BlobResourceContents)
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and resource.mimeType
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and resource.mimeType.startswith("image/")
|
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):
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tool_call_result_blocks.append(
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ToolCallMessageSegment(
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role="tool",
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tool_call_id=func_tool_id,
|
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content="返回了图片(已直接发送给用户)",
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)
|
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)
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yield MessageChain().base64_image(res.content[0].data)
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else:
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tool_call_result_blocks.append(
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ToolCallMessageSegment(
|
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role="tool",
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tool_call_id=func_tool_id,
|
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content="返回的数据类型不受支持",
|
||||
)
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||||
)
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else:
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logger.info(f"使用工具：{func_tool_name}，参数：{func_tool_args}")
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# 尝试调用工具函数
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wrapper = self.pipeline_ctx.call_handler(
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self.event, func_tool.handler, **func_tool_args
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)
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async for resp in wrapper:
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if resp is not None:
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# Tool 返回结果
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tool_call_result_blocks.append(
|
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ToolCallMessageSegment(
|
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role="tool",
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tool_call_id=func_tool_id,
|
||||
content=resp,
|
||||
)
|
||||
)
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else:
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# Tool 直接请求发送消息给用户
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# 这里我们将直接结束 Agent Loop。
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self.is_done = True
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if res := self.event.get_result():
|
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if res.chain:
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yield MessageChain(chain=res.chain)
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|
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self.event.clear_result()
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||||
except BaseException as e:
|
||||
logger.warning(traceback.format_exc())
|
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tool_call_result_blocks.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=f"error: {str(e)}",
|
||||
)
|
||||
)
|
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|
||||
# 处理函数调用响应
|
||||
if tool_call_result_blocks:
|
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yield tool_call_result_blocks
|
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|
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def done(self) -> bool:
|
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return self.is_done
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|
||||
def get_final_llm_resp(self) -> LLMResponse | None:
|
||||
return self.final_llm_resp
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||||
@@ -3,6 +3,7 @@
|
||||
"""
|
||||
|
||||
import traceback
|
||||
import copy
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Union, AsyncGenerator
|
||||
@@ -20,39 +21,25 @@ from astrbot.core.utils.metrics import Metric
|
||||
from astrbot.core.provider.entities import (
|
||||
ProviderRequest,
|
||||
LLMResponse,
|
||||
ToolCallMessageSegment,
|
||||
AssistantMessageSegment,
|
||||
ToolCallsResult,
|
||||
)
|
||||
from astrbot.core.star.star_handler import star_handlers_registry, EventType
|
||||
from astrbot.core.star.star import star_map
|
||||
from mcp.types import (
|
||||
TextContent,
|
||||
ImageContent,
|
||||
EmbeddedResource,
|
||||
TextResourceContents,
|
||||
BlobResourceContents,
|
||||
)
|
||||
from astrbot.core.star.star_handler import EventType
|
||||
from astrbot.core import web_chat_back_queue
|
||||
from ..agent_runner.tool_loop_agent import ToolLoopAgent
|
||||
|
||||
|
||||
class LLMRequestSubStage(Stage):
|
||||
async def initialize(self, ctx: PipelineContext) -> None:
|
||||
self.ctx = ctx
|
||||
self.bot_wake_prefixs = ctx.astrbot_config["wake_prefix"] # list
|
||||
self.provider_wake_prefix = ctx.astrbot_config["provider_settings"][
|
||||
"wake_prefix"
|
||||
] # str
|
||||
self.max_context_length = ctx.astrbot_config["provider_settings"][
|
||||
"max_context_length"
|
||||
] # int
|
||||
self.dequeue_context_length = min(
|
||||
max(1, ctx.astrbot_config["provider_settings"]["dequeue_context_length"]),
|
||||
conf = ctx.astrbot_config
|
||||
self.bot_wake_prefixs: list[str] = conf["wake_prefix"] # list
|
||||
self.provider_wake_prefix: str = conf["provider_settings"]["wake_prefix"] # str
|
||||
self.max_context_length = conf["provider_settings"]["max_context_length"] # int
|
||||
self.dequeue_context_length: int = min(
|
||||
max(1, conf["provider_settings"]["dequeue_context_length"]),
|
||||
self.max_context_length - 1,
|
||||
) # int
|
||||
self.streaming_response = ctx.astrbot_config["provider_settings"][
|
||||
"streaming_response"
|
||||
] # bool
|
||||
)
|
||||
self.streaming_response: bool = conf["provider_settings"]["streaming_response"]
|
||||
self.max_step: int = conf["provider_settings"].get("max_agent_step", 10)
|
||||
|
||||
for bwp in self.bot_wake_prefixs:
|
||||
if self.provider_wake_prefix.startswith(bwp):
|
||||
@@ -83,10 +70,7 @@ class LLMRequestSubStage(Stage):
|
||||
)
|
||||
|
||||
if req.conversation:
|
||||
all_contexts = json.loads(req.conversation.history)
|
||||
req.contexts = self._process_tool_message_pairs(
|
||||
all_contexts, remove_tags=True
|
||||
)
|
||||
req.contexts = json.loads(req.conversation.history)
|
||||
|
||||
else:
|
||||
req = ProviderRequest(prompt="", image_urls=[])
|
||||
@@ -127,26 +111,7 @@ class LLMRequestSubStage(Stage):
|
||||
return
|
||||
|
||||
# 执行请求 LLM 前事件钩子。
|
||||
# 装饰 system_prompt 等功能
|
||||
# 获取当前平台ID
|
||||
platform_id = event.get_platform_id()
|
||||
handlers = star_handlers_registry.get_handlers_by_event_type(
|
||||
EventType.OnLLMRequestEvent, platform_id=platform_id
|
||||
)
|
||||
for handler in handlers:
|
||||
try:
|
||||
logger.debug(
|
||||
f"hook(on_llm_request) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
|
||||
)
|
||||
await handler.handler(event, req)
|
||||
except BaseException:
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
if event.is_stopped():
|
||||
logger.info(
|
||||
f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
|
||||
)
|
||||
return
|
||||
await self.ctx.call_event_hook(event, EventType.OnLLMRequestEvent, req)
|
||||
|
||||
if isinstance(req.contexts, str):
|
||||
req.contexts = json.loads(req.contexts)
|
||||
@@ -176,77 +141,46 @@ class LLMRequestSubStage(Stage):
|
||||
if not req.session_id:
|
||||
req.session_id = event.unified_msg_origin
|
||||
|
||||
async def requesting(req: ProviderRequest):
|
||||
try:
|
||||
need_loop = True
|
||||
while need_loop:
|
||||
need_loop = False
|
||||
logger.debug(f"提供商请求 Payload: {req}")
|
||||
# Call Agent
|
||||
tool_loop_agent = ToolLoopAgent(
|
||||
provider=provider,
|
||||
event=event,
|
||||
pipeline_ctx=self.ctx,
|
||||
)
|
||||
await tool_loop_agent.reset(req=req, streaming=self.streaming_response)
|
||||
|
||||
final_llm_response = None
|
||||
|
||||
if self.streaming_response:
|
||||
stream = provider.text_chat_stream(**req.__dict__)
|
||||
async for llm_response in stream:
|
||||
if llm_response.is_chunk:
|
||||
if llm_response.result_chain:
|
||||
yield llm_response.result_chain # MessageChain
|
||||
else:
|
||||
yield MessageChain().message(
|
||||
llm_response.completion_text
|
||||
)
|
||||
else:
|
||||
final_llm_response = llm_response
|
||||
else:
|
||||
final_llm_response = await provider.text_chat(
|
||||
**req.__dict__
|
||||
) # 请求 LLM
|
||||
|
||||
if not final_llm_response:
|
||||
raise Exception("LLM response is None.")
|
||||
|
||||
# 执行 LLM 响应后的事件钩子。
|
||||
handlers = star_handlers_registry.get_handlers_by_event_type(
|
||||
EventType.OnLLMResponseEvent
|
||||
async def requesting():
|
||||
step_idx = 0
|
||||
while step_idx < self.max_step:
|
||||
step_idx += 1
|
||||
try:
|
||||
async for resp in tool_loop_agent.step():
|
||||
if not self.streaming_response:
|
||||
content_typ = (
|
||||
ResultContentType.LLM_RESULT
|
||||
if resp.type == "llm_resp"
|
||||
else ResultContentType.GENERAL_RESULT
|
||||
)
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=resp.data["chain"],
|
||||
result_content_type=content_typ,
|
||||
)
|
||||
)
|
||||
yield
|
||||
event.clear_result()
|
||||
else:
|
||||
yield resp.data["chain"]
|
||||
if tool_loop_agent.done():
|
||||
break
|
||||
except Exception as e:
|
||||
logger.error(traceback.format_exc())
|
||||
event.set_result(
|
||||
MessageEventResult().message(
|
||||
f"AstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {str(e)}"
|
||||
)
|
||||
)
|
||||
for handler in handlers:
|
||||
try:
|
||||
logger.debug(
|
||||
f"hook(on_llm_response) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
|
||||
)
|
||||
await handler.handler(event, final_llm_response)
|
||||
except BaseException:
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
if event.is_stopped():
|
||||
logger.info(
|
||||
f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
|
||||
)
|
||||
return
|
||||
|
||||
if self.streaming_response:
|
||||
# 流式输出的处理
|
||||
async for result in self._handle_llm_stream_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
if isinstance(result, ProviderRequest):
|
||||
# 有函数工具调用并且返回了结果，我们需要再次请求 LLM
|
||||
req = result
|
||||
need_loop = True
|
||||
else:
|
||||
yield
|
||||
else:
|
||||
# 非流式输出的处理
|
||||
async for result in self._handle_llm_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
if isinstance(result, ProviderRequest):
|
||||
# 有函数工具调用并且返回了结果，我们需要再次请求 LLM
|
||||
req = result
|
||||
need_loop = True
|
||||
else:
|
||||
yield
|
||||
|
||||
return
|
||||
asyncio.create_task(
|
||||
Metric.upload(
|
||||
llm_tick=1,
|
||||
@@ -255,44 +189,38 @@ class LLMRequestSubStage(Stage):
|
||||
)
|
||||
)
|
||||
|
||||
# 保存到历史记录
|
||||
await self._save_to_history(event, req, final_llm_response)
|
||||
|
||||
except BaseException as e:
|
||||
logger.error(traceback.format_exc())
|
||||
event.set_result(
|
||||
MessageEventResult().message(
|
||||
f"AstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {str(e)}"
|
||||
)
|
||||
)
|
||||
|
||||
if not self.streaming_response:
|
||||
event.set_extra("tool_call_result", None)
|
||||
async for _ in requesting(req):
|
||||
yield
|
||||
else:
|
||||
if self.streaming_response:
|
||||
# 流式响应
|
||||
event.set_result(
|
||||
MessageEventResult()
|
||||
.set_result_content_type(ResultContentType.STREAMING_RESULT)
|
||||
.set_async_stream(requesting(req))
|
||||
.set_async_stream(requesting())
|
||||
)
|
||||
# 这里使用yield来暂停当前阶段，等待流式输出完成后继续处理
|
||||
yield
|
||||
|
||||
if event.get_extra("tool_call_result"):
|
||||
event.set_result(event.get_extra("tool_call_result"))
|
||||
event.set_extra("tool_call_result", None)
|
||||
if tool_loop_agent.done():
|
||||
if final_llm_resp := tool_loop_agent.get_final_llm_resp():
|
||||
if final_llm_resp.completion_text:
|
||||
chain = (
|
||||
MessageChain().message(final_llm_resp.completion_text).chain
|
||||
)
|
||||
else:
|
||||
chain = final_llm_resp.result_chain.chain
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=chain,
|
||||
result_content_type=ResultContentType.STREAMING_FINISH,
|
||||
)
|
||||
)
|
||||
else:
|
||||
async for _ in requesting():
|
||||
yield
|
||||
|
||||
# 暂时直接发出去
|
||||
if img_b64 := event.get_extra("tool_call_img_respond"):
|
||||
await event.send(MessageChain(chain=[Image.fromBase64(img_b64)]))
|
||||
event.set_extra("tool_call_img_respond", None)
|
||||
|
||||
# 异步处理 WebChat 特殊情况
|
||||
if event.get_platform_name() == "webchat":
|
||||
# 异步处理 WebChat 特殊情况
|
||||
asyncio.create_task(self._handle_webchat(event, req))
|
||||
|
||||
await self._save_to_history(event, req, tool_loop_agent.get_final_llm_resp())
|
||||
|
||||
async def _handle_webchat(self, event: AstrMessageEvent, req: ProviderRequest):
|
||||
"""处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
|
||||
conversation = await self.conv_manager.get_conversation(
|
||||
@@ -305,10 +233,6 @@ class LLMRequestSubStage(Stage):
|
||||
return
|
||||
provider = self.ctx.plugin_manager.context.get_using_provider()
|
||||
cleaned_text = "User: " + latest_pair[0].get("content", "").strip()
|
||||
# if len(latest_pair) > 1:
|
||||
# cleaned_text += (
|
||||
# "\nAssistant: " + latest_pair[1].get("content", "").strip()
|
||||
# )
|
||||
logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
|
||||
llm_resp = await provider.text_chat(
|
||||
system_prompt="You are expert in summarizing user's query.",
|
||||
@@ -349,322 +273,34 @@ class LLMRequestSubStage(Stage):
|
||||
}
|
||||
)
|
||||
|
||||
async def _handle_llm_response(
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理非流式 LLM 响应。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
|
||||
Yields:
|
||||
Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
|
||||
"""
|
||||
if llm_response.role == "assistant":
|
||||
# text completion
|
||||
if llm_response.result_chain:
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=llm_response.result_chain.chain
|
||||
).set_result_content_type(ResultContentType.LLM_RESULT)
|
||||
)
|
||||
else:
|
||||
event.set_result(
|
||||
MessageEventResult()
|
||||
.message(llm_response.completion_text)
|
||||
.set_result_content_type(ResultContentType.LLM_RESULT)
|
||||
)
|
||||
elif llm_response.role == "err":
|
||||
event.set_result(
|
||||
MessageEventResult().message(
|
||||
f"AstrBot 请求失败。\n错误信息: {llm_response.completion_text}"
|
||||
)
|
||||
)
|
||||
elif llm_response.role == "tool":
|
||||
# 处理函数工具调用
|
||||
async for result in self._handle_function_tools(event, req, llm_response):
|
||||
yield result
|
||||
|
||||
async def _handle_llm_stream_response(
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理流式 LLM 响应。
|
||||
|
||||
专门用于处理流式输出完成后的响应，与非流式响应处理分离。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
|
||||
Yields:
|
||||
Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
|
||||
"""
|
||||
if llm_response.role == "assistant":
|
||||
# text completion
|
||||
if llm_response.result_chain:
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=llm_response.result_chain.chain
|
||||
).set_result_content_type(ResultContentType.STREAMING_FINISH)
|
||||
)
|
||||
else:
|
||||
event.set_result(
|
||||
MessageEventResult()
|
||||
.message(llm_response.completion_text)
|
||||
.set_result_content_type(ResultContentType.STREAMING_FINISH)
|
||||
)
|
||||
elif llm_response.role == "err":
|
||||
event.set_result(
|
||||
MessageEventResult().message(
|
||||
f"AstrBot 请求失败。\n错误信息: {llm_response.completion_text}"
|
||||
)
|
||||
)
|
||||
elif llm_response.role == "tool":
|
||||
# 处理函数工具调用
|
||||
async for result in self._handle_function_tools(event, req, llm_response):
|
||||
yield result
|
||||
|
||||
async def _handle_function_tools(
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理函数工具调用。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
"""
|
||||
# function calling
|
||||
tool_call_result: list[ToolCallMessageSegment] = []
|
||||
logger.info(
|
||||
f"触发 {len(llm_response.tools_call_name)} 个函数调用: {llm_response.tools_call_name}"
|
||||
)
|
||||
for func_tool_name, func_tool_args, func_tool_id in zip(
|
||||
llm_response.tools_call_name,
|
||||
llm_response.tools_call_args,
|
||||
llm_response.tools_call_ids,
|
||||
):
|
||||
try:
|
||||
func_tool = req.func_tool.get_func(func_tool_name)
|
||||
if func_tool.origin == "mcp":
|
||||
logger.info(
|
||||
f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
|
||||
)
|
||||
client = req.func_tool.mcp_client_dict[func_tool.mcp_server_name]
|
||||
res = await client.session.call_tool(func_tool.name, func_tool_args)
|
||||
if res:
|
||||
# TODO 仅对ImageContent | EmbeddedResource进行了简单的Fallback
|
||||
if isinstance(res.content[0], TextContent):
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=res.content[0].text,
|
||||
)
|
||||
)
|
||||
elif isinstance(res.content[0], ImageContent):
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content="返回了图片(已直接发送给用户)",
|
||||
)
|
||||
)
|
||||
event.set_extra(
|
||||
"tool_call_img_respond",
|
||||
res.content[0].data,
|
||||
)
|
||||
elif isinstance(res.content[0], EmbeddedResource):
|
||||
resource = res.content[0].resource
|
||||
if isinstance(resource, TextResourceContents):
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=resource.text,
|
||||
)
|
||||
)
|
||||
elif (
|
||||
isinstance(resource, BlobResourceContents)
|
||||
and resource.mimeType
|
||||
and resource.mimeType.startswith("image/")
|
||||
):
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content="返回了图片(已直接发送给用户)",
|
||||
)
|
||||
)
|
||||
event.set_extra(
|
||||
"tool_call_img_respond",
|
||||
res.content[0].data,
|
||||
)
|
||||
else:
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content="返回的数据类型不受支持",
|
||||
)
|
||||
)
|
||||
else:
|
||||
# 获取处理器，过滤掉平台不兼容的处理器
|
||||
platform_id = event.get_platform_id()
|
||||
star_md = star_map.get(func_tool.handler_module_path)
|
||||
if (
|
||||
star_md
|
||||
and platform_id in star_md.supported_platforms
|
||||
and not star_md.supported_platforms[platform_id]
|
||||
):
|
||||
logger.debug(
|
||||
f"处理器 {func_tool_name}({star_md.name}) 在当前平台不兼容或者被禁用，跳过执行"
|
||||
)
|
||||
# 直接跳过，不添加任何消息到tool_call_result
|
||||
continue
|
||||
|
||||
logger.info(
|
||||
f"调用工具函数：{func_tool_name}，参数：{func_tool_args}"
|
||||
)
|
||||
# 尝试调用工具函数
|
||||
wrapper = self._call_handler(
|
||||
self.ctx, event, func_tool.handler, **func_tool_args
|
||||
)
|
||||
async for resp in wrapper:
|
||||
if resp is not None: # 有 return 返回
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=resp,
|
||||
)
|
||||
)
|
||||
else:
|
||||
res = event.get_result()
|
||||
if res and res.chain:
|
||||
event.set_extra("tool_call_result", res)
|
||||
yield # 有生成器返回
|
||||
event.clear_result() # 清除上一个 handler 的结果
|
||||
except BaseException as e:
|
||||
logger.warning(traceback.format_exc())
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=f"error: {str(e)}",
|
||||
)
|
||||
)
|
||||
if tool_call_result:
|
||||
# 函数调用结果
|
||||
req.func_tool = None # 暂时不支持递归工具调用
|
||||
assistant_msg_seg = AssistantMessageSegment(
|
||||
role="assistant", tool_calls=llm_response.to_openai_tool_calls()
|
||||
)
|
||||
# 在多轮 Tool 调用的情况下，这里始终保持最新的 Tool 调用结果，减少上下文长度。
|
||||
req.tool_calls_result = ToolCallsResult(
|
||||
tool_calls_info=assistant_msg_seg,
|
||||
tool_calls_result=tool_call_result,
|
||||
)
|
||||
yield req # 再次执行 LLM 请求
|
||||
else:
|
||||
if llm_response.completion_text:
|
||||
event.set_result(
|
||||
MessageEventResult().message(llm_response.completion_text)
|
||||
)
|
||||
|
||||
async def _save_to_history(
|
||||
self, event: AstrMessageEvent, req: ProviderRequest, llm_response: LLMResponse
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse | None,
|
||||
):
|
||||
if not req or not req.conversation or not llm_response:
|
||||
if (
|
||||
not req
|
||||
or not req.conversation
|
||||
or not llm_response
|
||||
or llm_response.role != "assistant"
|
||||
):
|
||||
return
|
||||
|
||||
if llm_response.role == "assistant":
|
||||
# 文本回复
|
||||
contexts = req.contexts.copy()
|
||||
contexts.append(await req.assemble_context())
|
||||
|
||||
# 记录并标记函数调用结果
|
||||
if req.tool_calls_result:
|
||||
tool_calls_messages = req.tool_calls_result.to_openai_messages()
|
||||
|
||||
# 添加标记
|
||||
for message in tool_calls_messages:
|
||||
message["_tool_call_history"] = True
|
||||
|
||||
processed_tool_messages = self._process_tool_message_pairs(
|
||||
tool_calls_messages, remove_tags=False
|
||||
)
|
||||
|
||||
contexts.extend(processed_tool_messages)
|
||||
|
||||
contexts.append(
|
||||
{"role": "assistant", "content": llm_response.completion_text}
|
||||
)
|
||||
contexts_to_save = list(
|
||||
filter(lambda item: "_no_save" not in item, contexts)
|
||||
)
|
||||
await self.conv_manager.update_conversation(
|
||||
event.unified_msg_origin, req.conversation.cid, history=contexts_to_save
|
||||
)
|
||||
|
||||
def _process_tool_message_pairs(self, messages, remove_tags=True):
|
||||
"""处理工具调用消息，确保assistant和tool消息成对出现
|
||||
|
||||
Args:
|
||||
messages (list): 消息列表
|
||||
remove_tags (bool): 是否移除_tool_call_history标记
|
||||
|
||||
Returns:
|
||||
list: 处理后的消息列表，保证了assistant和对应tool消息的成对出现
|
||||
"""
|
||||
result = []
|
||||
i = 0
|
||||
|
||||
while i < len(messages):
|
||||
current_msg = messages[i]
|
||||
|
||||
# 普通消息直接添加
|
||||
if "_tool_call_history" not in current_msg:
|
||||
result.append(current_msg.copy() if remove_tags else current_msg)
|
||||
i += 1
|
||||
continue
|
||||
|
||||
# 工具调用消息成对处理
|
||||
if current_msg.get("role") == "assistant" and "tool_calls" in current_msg:
|
||||
assistant_msg = current_msg.copy()
|
||||
|
||||
if remove_tags and "_tool_call_history" in assistant_msg:
|
||||
del assistant_msg["_tool_call_history"]
|
||||
|
||||
related_tools = []
|
||||
j = i + 1
|
||||
while (
|
||||
j < len(messages)
|
||||
and messages[j].get("role") == "tool"
|
||||
and "_tool_call_history" in messages[j]
|
||||
):
|
||||
tool_msg = messages[j].copy()
|
||||
|
||||
if remove_tags:
|
||||
del tool_msg["_tool_call_history"]
|
||||
|
||||
related_tools.append(tool_msg)
|
||||
j += 1
|
||||
|
||||
# 成对的时候添加到结果
|
||||
if related_tools:
|
||||
result.append(assistant_msg)
|
||||
result.extend(related_tools)
|
||||
|
||||
i = j # 跳过已处理
|
||||
else:
|
||||
# 单独的tool消息
|
||||
i += 1
|
||||
|
||||
return result
|
||||
# 历史上下文
|
||||
messages = copy.deepcopy(req.contexts)
|
||||
# 这一轮对话请求的用户输入
|
||||
messages.append(await req.assemble_context())
|
||||
# 这一轮对话的 LLM 响应
|
||||
if req.tool_calls_result:
|
||||
if not isinstance(req.tool_calls_result, list):
|
||||
messages.extend(req.tool_calls_result.to_openai_messages())
|
||||
elif isinstance(req.tool_calls_result, list):
|
||||
for tcr in req.tool_calls_result:
|
||||
messages.extend(tcr.to_openai_messages())
|
||||
messages.append({"role": "assistant", "content": llm_response.completion_text})
|
||||
messages = list(filter(lambda item: "_no_save" not in item, messages))
|
||||
await self.conv_manager.update_conversation(
|
||||
event.unified_msg_origin, req.conversation.cid, history=messages
|
||||
)
|
||||
logger.debug(f"messages persisted: {messages}")
|
||||
|
||||
@@ -50,7 +50,7 @@ class StarRequestSubStage(Stage):
|
||||
logger.debug(
|
||||
f"plugin -> {star_map.get(handler.handler_module_path).name} - {handler.handler_name}"
|
||||
)
|
||||
wrapper = self._call_handler(self.ctx, event, handler.handler, **params)
|
||||
wrapper = self.ctx.call_handler(event, handler.handler, **params)
|
||||
async for ret in wrapper:
|
||||
yield ret
|
||||
event.clear_result() # 清除上一个 handler 的结果
|
||||
|
||||
@@ -1,12 +1,8 @@
|
||||
from __future__ import annotations
|
||||
import abc
|
||||
import inspect
|
||||
import traceback
|
||||
from astrbot.api import logger
|
||||
from typing import List, AsyncGenerator, Union, Awaitable
|
||||
from typing import List, AsyncGenerator, Union
|
||||
from astrbot.core.platform.astr_message_event import AstrMessageEvent
|
||||
from .context import PipelineContext
|
||||
from astrbot.core.message.message_event_result import MessageEventResult, CommandResult
|
||||
|
||||
registered_stages: List[Stage] = [] # 维护了所有已注册的 Stage 实现类
|
||||
|
||||
@@ -41,70 +37,3 @@ class Stage(abc.ABC):
|
||||
Union[None, AsyncGenerator[None, None]]: 处理结果，可能是 None 或者异步生成器, 如果为 None 则表示不需要继续处理, 如果为异步生成器则表示需要继续处理(进入下一个阶段)
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
async def _call_handler(
|
||||
self,
|
||||
ctx: PipelineContext,
|
||||
event: AstrMessageEvent,
|
||||
handler: Awaitable,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> AsyncGenerator[None, None]:
|
||||
"""执行事件处理函数并处理其返回结果
|
||||
|
||||
该方法负责调用处理函数并处理不同类型的返回值。它支持两种类型的处理函数:
|
||||
1. 异步生成器: 实现洋葱模型，每次yield都会将控制权交回上层
|
||||
2. 协程: 执行一次并处理返回值
|
||||
|
||||
Args:
|
||||
ctx (PipelineContext): 消息管道上下文对象
|
||||
event (AstrMessageEvent): 待处理的事件对象
|
||||
handler (Awaitable): 事件处理函数
|
||||
*args: 传递给handler的位置参数
|
||||
**kwargs: 传递给handler的关键字参数
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[None, None]: 异步生成器，用于在管道中传递控制流
|
||||
"""
|
||||
ready_to_call = None # 一个协程或者异步生成器(async def)
|
||||
|
||||
trace_ = None
|
||||
|
||||
try:
|
||||
ready_to_call = handler(event, *args, **kwargs)
|
||||
except TypeError as _:
|
||||
# 向下兼容
|
||||
trace_ = traceback.format_exc()
|
||||
# 以前的handler会额外传入一个参数, 但是context对象实际上在插件实例中有一份
|
||||
ready_to_call = handler(event, ctx.plugin_manager.context, *args, **kwargs)
|
||||
|
||||
if isinstance(ready_to_call, AsyncGenerator):
|
||||
# 如果是一个异步生成器, 进入洋葱模型
|
||||
_has_yielded = False # 是否返回过值
|
||||
try:
|
||||
async for ret in ready_to_call:
|
||||
# 这里逐步执行异步生成器, 对于每个yield返回的ret, 执行下面的代码
|
||||
# 返回值只能是 MessageEventResult 或者 None（无返回值）
|
||||
_has_yielded = True
|
||||
if isinstance(ret, (MessageEventResult, CommandResult)):
|
||||
# 如果返回值是 MessageEventResult, 设置结果并继续
|
||||
event.set_result(ret)
|
||||
yield # 传递控制权给上一层的process函数
|
||||
else:
|
||||
# 如果返回值是 None, 则不设置结果并继续
|
||||
# 继续执行后续阶段
|
||||
yield ret # 传递控制权给上一层的process函数
|
||||
if not _has_yielded:
|
||||
# 如果这个异步生成器没有执行到yield分支
|
||||
yield
|
||||
except Exception as e:
|
||||
logger.error(f"Previous Error: {trace_}")
|
||||
raise e
|
||||
elif inspect.iscoroutine(ready_to_call):
|
||||
# 如果只是一个协程, 直接执行
|
||||
ret = await ready_to_call
|
||||
if isinstance(ret, (MessageEventResult, CommandResult)):
|
||||
event.set_result(ret)
|
||||
yield # 传递控制权给上一层的process函数
|
||||
else:
|
||||
yield ret # 传递控制权给上一层的process函数
|
||||
|
||||
@@ -95,19 +95,19 @@ class ProviderRequest:
|
||||
"""提示词"""
|
||||
session_id: str = ""
|
||||
"""会话 ID"""
|
||||
image_urls: List[str] = None
|
||||
image_urls: list[str] = field(default_factory=list)
|
||||
"""图片 URL 列表"""
|
||||
func_tool: FuncCall = None
|
||||
func_tool: FuncCall | None = None
|
||||
"""可用的函数工具"""
|
||||
contexts: List = None
|
||||
contexts: list[dict] = field(default_factory=list)
|
||||
"""上下文。格式与 openai 的上下文格式一致：
|
||||
参考 https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages
|
||||
"""
|
||||
system_prompt: str = ""
|
||||
"""系统提示词"""
|
||||
conversation: Conversation = None
|
||||
conversation: Conversation | None = None
|
||||
|
||||
tool_calls_result: ToolCallsResult = None
|
||||
tool_calls_result: list[ToolCallsResult] | ToolCallsResult | None = None
|
||||
"""附加的上次请求后工具调用的结果。参考: https://platform.openai.com/docs/guides/function-calling#handling-function-calls"""
|
||||
|
||||
def __repr__(self):
|
||||
@@ -116,6 +116,14 @@ class ProviderRequest:
|
||||
def __str__(self):
|
||||
return self.__repr__()
|
||||
|
||||
def append_tool_calls_result(self, tool_calls_result: ToolCallsResult):
|
||||
"""添加工具调用结果到请求中"""
|
||||
if not self.tool_calls_result:
|
||||
self.tool_calls_result = []
|
||||
if isinstance(self.tool_calls_result, ToolCallsResult):
|
||||
self.tool_calls_result = [self.tool_calls_result]
|
||||
self.tool_calls_result.append(tool_calls_result)
|
||||
|
||||
def _print_friendly_context(self):
|
||||
"""打印友好的消息上下文。将 image_url 的值替换为 <Image>"""
|
||||
if not self.contexts:
|
||||
|
||||
@@ -190,11 +190,6 @@ class ProviderManager:
|
||||
from .sources.anthropic_source import (
|
||||
ProviderAnthropic as ProviderAnthropic,
|
||||
)
|
||||
case "llm_tuner":
|
||||
logger.info("加载 LLM Tuner 工具 ...")
|
||||
from .sources.llmtuner_source import (
|
||||
LLMTunerModelLoader as LLMTunerModelLoader,
|
||||
)
|
||||
case "dify":
|
||||
from .sources.dify_source import ProviderDify as ProviderDify
|
||||
case "dashscope":
|
||||
|
||||
@@ -86,11 +86,11 @@ class Provider(AbstractProvider):
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: List[str] = None,
|
||||
image_urls: list[str] = None,
|
||||
func_tool: FuncCall = None,
|
||||
contexts: List = None,
|
||||
contexts: list = None,
|
||||
system_prompt: str = None,
|
||||
tool_calls_result: ToolCallsResult = None,
|
||||
tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
"""获得 LLM 的文本对话结果。会使用当前的模型进行对话。
|
||||
@@ -114,11 +114,11 @@ class Provider(AbstractProvider):
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: List[str] = None,
|
||||
image_urls: list[str] = None,
|
||||
func_tool: FuncCall = None,
|
||||
contexts: List = None,
|
||||
contexts: list = None,
|
||||
system_prompt: str = None,
|
||||
tool_calls_result: ToolCallsResult = None,
|
||||
tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
|
||||
**kwargs,
|
||||
) -> AsyncGenerator[LLMResponse, None]:
|
||||
"""获得 LLM 的流式文本对话结果。会使用当前的模型进行对话。在生成的最后会返回一次完整的结果。
|
||||
|
||||
@@ -1,3 +1,6 @@
|
||||
import json
|
||||
import anthropic
|
||||
import base64
|
||||
from typing import List
|
||||
from mimetypes import guess_type
|
||||
|
||||
@@ -10,15 +13,14 @@ from astrbot.api.provider import Provider, Personality
|
||||
from astrbot import logger
|
||||
from astrbot.core.provider.func_tool_manager import FuncCall
|
||||
from ..register import register_provider_adapter
|
||||
from astrbot.core.message.message_event_result import MessageChain
|
||||
from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
|
||||
from .openai_source import ProviderOpenAIOfficial
|
||||
from astrbot.core.provider.entities import LLMResponse
|
||||
from typing import AsyncGenerator
|
||||
|
||||
|
||||
@register_provider_adapter(
|
||||
"anthropic_chat_completion", "Anthropic Claude API 提供商适配器"
|
||||
)
|
||||
class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
class ProviderAnthropic(Provider):
|
||||
def __init__(
|
||||
self,
|
||||
provider_config: dict,
|
||||
@@ -27,9 +29,7 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
persistant_history=True,
|
||||
default_persona: Personality = None,
|
||||
) -> None:
|
||||
# Skip OpenAI's __init__ and call Provider's __init__ directly
|
||||
Provider.__init__(
|
||||
self,
|
||||
super().__init__(
|
||||
provider_config,
|
||||
provider_settings,
|
||||
persistant_history,
|
||||
@@ -51,10 +51,63 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
|
||||
self.set_model(provider_config["model_config"]["model"])
|
||||
|
||||
def _prepare_payload(self, messages: list[dict]):
|
||||
"""准备 Anthropic API 的请求 payload
|
||||
|
||||
Args:
|
||||
messages: OpenAI 格式的消息列表，包含用户输入和系统提示等信息
|
||||
Returns:
|
||||
system_prompt: 系统提示内容
|
||||
new_messages: 处理后的消息列表，去除系统提示
|
||||
"""
|
||||
system_prompt = ""
|
||||
new_messages = []
|
||||
for message in messages:
|
||||
if message["role"] == "system":
|
||||
system_prompt = message["content"]
|
||||
elif message["role"] == "assistant":
|
||||
blocks = []
|
||||
if isinstance(message["content"], str):
|
||||
blocks.append({"type": "text", "text": message["content"]})
|
||||
if "tool_calls" in message:
|
||||
for tool_call in message["tool_calls"]:
|
||||
blocks.append( # noqa: PERF401
|
||||
{
|
||||
"type": "tool_use",
|
||||
"name": tool_call["function"]["name"],
|
||||
"input": json.loads(tool_call["function"]["arguments"])
|
||||
if isinstance(tool_call["function"]["arguments"], str)
|
||||
else tool_call["function"]["arguments"],
|
||||
"id": tool_call["id"],
|
||||
}
|
||||
)
|
||||
new_messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": blocks,
|
||||
}
|
||||
)
|
||||
elif message["role"] == "tool":
|
||||
new_messages.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": message["tool_call_id"],
|
||||
"content": message["content"],
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
else:
|
||||
new_messages.append(message)
|
||||
|
||||
return system_prompt, new_messages
|
||||
|
||||
async def _query(self, payloads: dict, tools: FuncCall) -> LLMResponse:
|
||||
if tools:
|
||||
tool_list = tools.get_func_desc_anthropic_style()
|
||||
if tool_list:
|
||||
if tool_list := tools.get_func_desc_anthropic_style():
|
||||
payloads["tools"] = tool_list
|
||||
|
||||
completion = await self.client.messages.create(**payloads, stream=False)
|
||||
@@ -64,70 +117,157 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
|
||||
if len(completion.content) == 0:
|
||||
raise Exception("API 返回的 completion 为空。")
|
||||
# TODO: 如果进行函数调用，思维链被截断，用户可能需要思维链的内容
|
||||
# 选最后一条消息，如果要进行函数调用，anthropic会先返回文本消息的思维链，然后再返回函数调用请求
|
||||
content = completion.content[-1]
|
||||
|
||||
llm_response = LLMResponse("assistant")
|
||||
llm_response = LLMResponse(role="assistant")
|
||||
|
||||
if content.type == "text":
|
||||
# text completion
|
||||
completion_text = str(content.text).strip()
|
||||
# llm_response.completion_text = completion_text
|
||||
llm_response.result_chain = MessageChain().message(completion_text)
|
||||
|
||||
# Anthropic每次只返回一个函数调用
|
||||
if completion.stop_reason == "tool_use":
|
||||
# tools call (function calling)
|
||||
args_ls = []
|
||||
func_name_ls = []
|
||||
tool_use_ids = []
|
||||
func_name_ls.append(content.name)
|
||||
args_ls.append(content.input)
|
||||
tool_use_ids.append(content.id)
|
||||
llm_response.role = "tool"
|
||||
llm_response.tools_call_args = args_ls
|
||||
llm_response.tools_call_name = func_name_ls
|
||||
llm_response.tools_call_ids = tool_use_ids
|
||||
for content_block in completion.content:
|
||||
if content_block.type == "text":
|
||||
completion_text = str(content_block.text).strip()
|
||||
llm_response.completion_text = completion_text
|
||||
|
||||
if content_block.type == "tool_use":
|
||||
llm_response.tools_call_args.append(content_block.input)
|
||||
llm_response.tools_call_name.append(content_block.name)
|
||||
llm_response.tools_call_ids.append(content_block.id)
|
||||
# TODO(Soulter): 处理 end_turn 情况
|
||||
if not llm_response.completion_text and not llm_response.tools_call_args:
|
||||
logger.error(f"API 返回的 completion 无法解析：{completion}。")
|
||||
raise Exception(f"API 返回的 completion 无法解析：{completion}。")
|
||||
|
||||
llm_response.raw_completion = completion
|
||||
raise Exception(f"Anthropic API 返回的 completion 无法解析：{completion}。")
|
||||
|
||||
return llm_response
|
||||
|
||||
async def _query_stream(
|
||||
self, payloads: dict, tools: FuncCall
|
||||
) -> AsyncGenerator[LLMResponse, None]:
|
||||
if tools:
|
||||
if tool_list := tools.get_func_desc_anthropic_style():
|
||||
payloads["tools"] = tool_list
|
||||
|
||||
# 用于累积工具调用信息
|
||||
tool_use_buffer = {}
|
||||
# 用于累积最终结果
|
||||
final_text = ""
|
||||
final_tool_calls = []
|
||||
|
||||
async with self.client.messages.stream(**payloads) as stream:
|
||||
assert isinstance(stream, anthropic.AsyncMessageStream)
|
||||
async for event in stream:
|
||||
if event.type == "content_block_start":
|
||||
if event.content_block.type == "text":
|
||||
# 文本块开始
|
||||
yield LLMResponse(
|
||||
role="assistant", completion_text="", is_chunk=True
|
||||
)
|
||||
elif event.content_block.type == "tool_use":
|
||||
# 工具使用块开始，初始化缓冲区
|
||||
tool_use_buffer[event.index] = {
|
||||
"id": event.content_block.id,
|
||||
"name": event.content_block.name,
|
||||
"input": {},
|
||||
}
|
||||
|
||||
elif event.type == "content_block_delta":
|
||||
if event.delta.type == "text_delta":
|
||||
# 文本增量
|
||||
final_text += event.delta.text
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
completion_text=event.delta.text,
|
||||
is_chunk=True,
|
||||
)
|
||||
elif event.delta.type == "input_json_delta":
|
||||
# 工具调用参数增量
|
||||
if event.index in tool_use_buffer:
|
||||
# 累积 JSON 输入
|
||||
if "input_json" not in tool_use_buffer[event.index]:
|
||||
tool_use_buffer[event.index]["input_json"] = ""
|
||||
tool_use_buffer[event.index]["input_json"] += (
|
||||
event.delta.partial_json
|
||||
)
|
||||
|
||||
elif event.type == "content_block_stop":
|
||||
# 内容块结束
|
||||
if event.index in tool_use_buffer:
|
||||
# 解析完整的工具调用
|
||||
tool_info = tool_use_buffer[event.index]
|
||||
try:
|
||||
if "input_json" in tool_info:
|
||||
tool_info["input"] = json.loads(tool_info["input_json"])
|
||||
|
||||
# 添加到最终结果
|
||||
final_tool_calls.append(
|
||||
{
|
||||
"id": tool_info["id"],
|
||||
"name": tool_info["name"],
|
||||
"input": tool_info["input"],
|
||||
}
|
||||
)
|
||||
|
||||
yield LLMResponse(
|
||||
role="tool",
|
||||
completion_text="",
|
||||
tools_call_args=[tool_info["input"]],
|
||||
tools_call_name=[tool_info["name"]],
|
||||
tools_call_ids=[tool_info["id"]],
|
||||
is_chunk=True,
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
# JSON 解析失败，跳过这个工具调用
|
||||
logger.warning(f"工具调用参数 JSON 解析失败: {tool_info}")
|
||||
|
||||
# 清理缓冲区
|
||||
del tool_use_buffer[event.index]
|
||||
|
||||
# 返回最终的完整结果
|
||||
final_response = LLMResponse(
|
||||
role="assistant", completion_text=final_text, is_chunk=False
|
||||
)
|
||||
|
||||
if final_tool_calls:
|
||||
final_response.tools_call_args = [
|
||||
call["input"] for call in final_tool_calls
|
||||
]
|
||||
final_response.tools_call_name = [call["name"] for call in final_tool_calls]
|
||||
final_response.tools_call_ids = [call["id"] for call in final_tool_calls]
|
||||
|
||||
yield final_response
|
||||
|
||||
async def text_chat(
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: List[str] = [],
|
||||
func_tool: FuncCall = None,
|
||||
prompt,
|
||||
session_id = None,
|
||||
image_urls = [],
|
||||
func_tool = None,
|
||||
contexts=None,
|
||||
system_prompt=None,
|
||||
tool_calls_result: ToolCallsResult = None,
|
||||
tool_calls_result = None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
if contexts is None:
|
||||
contexts = []
|
||||
if not prompt:
|
||||
prompt = "<image>"
|
||||
|
||||
new_record = await self.assemble_context(prompt, image_urls)
|
||||
context_query = [*contexts, new_record]
|
||||
if system_prompt:
|
||||
context_query.insert(0, {"role": "system", "content": system_prompt})
|
||||
|
||||
for part in context_query:
|
||||
if "_no_save" in part:
|
||||
del part["_no_save"]
|
||||
|
||||
# tool calls result
|
||||
if tool_calls_result:
|
||||
# 暂时这样写。
|
||||
prompt += f"Here are the related results via using tools: {str(tool_calls_result.tool_calls_result)}"
|
||||
if not isinstance(tool_calls_result, list):
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
else:
|
||||
for tcr in tool_calls_result:
|
||||
context_query.extend(tcr.to_openai_messages())
|
||||
|
||||
system_prompt, new_messages = self._prepare_payload(context_query)
|
||||
|
||||
model_config = self.provider_config.get("model_config", {})
|
||||
model_config["model"] = self.get_model()
|
||||
|
||||
payloads = {"messages": new_messages, **model_config}
|
||||
|
||||
payloads = {"messages": context_query, **model_config}
|
||||
# Anthropic has a different way of handling system prompts
|
||||
if system_prompt:
|
||||
payloads["system"] = system_prompt
|
||||
@@ -135,32 +275,9 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
llm_response = None
|
||||
try:
|
||||
llm_response = await self._query(payloads, func_tool)
|
||||
|
||||
except Exception as e:
|
||||
if "maximum context length" in str(e):
|
||||
retry_cnt = 20
|
||||
while retry_cnt > 0:
|
||||
logger.warning(
|
||||
f"上下文长度超过限制。尝试弹出最早的记录然后重试。当前记录条数: {len(context_query)}"
|
||||
)
|
||||
try:
|
||||
await self.pop_record(context_query)
|
||||
response = await self.client.messages.create(
|
||||
messages=context_query, **model_config
|
||||
)
|
||||
llm_response = LLMResponse("assistant")
|
||||
llm_response.result_chain = MessageChain().message(response.content[0].text)
|
||||
llm_response.raw_completion = response
|
||||
return llm_response
|
||||
except Exception as e:
|
||||
if "maximum context length" in str(e):
|
||||
retry_cnt -= 1
|
||||
else:
|
||||
raise e
|
||||
return LLMResponse("err", "err: 请尝试 /reset 清除会话记录。")
|
||||
else:
|
||||
logger.error(f"发生了错误。Provider 配置如下: {model_config}")
|
||||
raise e
|
||||
logger.error(f"发生了错误。Provider 配置如下: {model_config}")
|
||||
raise e
|
||||
|
||||
return llm_response
|
||||
|
||||
@@ -175,21 +292,34 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
tool_calls_result=None,
|
||||
**kwargs,
|
||||
):
|
||||
# raise NotImplementedError("This method is not implemented yet.")
|
||||
# 调用 text_chat 模拟流式
|
||||
llm_response = await self.text_chat(
|
||||
prompt=prompt,
|
||||
session_id=session_id,
|
||||
image_urls=image_urls,
|
||||
func_tool=func_tool,
|
||||
contexts=contexts,
|
||||
system_prompt=system_prompt,
|
||||
tool_calls_result=tool_calls_result,
|
||||
)
|
||||
llm_response.is_chunk = True
|
||||
yield llm_response
|
||||
llm_response.is_chunk = False
|
||||
yield llm_response
|
||||
if contexts is None:
|
||||
contexts = []
|
||||
new_record = await self.assemble_context(prompt, image_urls)
|
||||
context_query = [*contexts, new_record]
|
||||
if system_prompt:
|
||||
context_query.insert(0, {"role": "system", "content": system_prompt})
|
||||
|
||||
for part in context_query:
|
||||
if "_no_save" in part:
|
||||
del part["_no_save"]
|
||||
|
||||
# tool calls result
|
||||
if tool_calls_result:
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
|
||||
system_prompt, new_messages = self._prepare_payload(context_query)
|
||||
|
||||
model_config = self.provider_config.get("model_config", {})
|
||||
model_config["model"] = self.get_model()
|
||||
|
||||
payloads = {"messages": new_messages, **model_config}
|
||||
|
||||
# Anthropic has a different way of handling system prompts
|
||||
if system_prompt:
|
||||
payloads["system"] = system_prompt
|
||||
|
||||
async for llm_response in self._query_stream(payloads, func_tool):
|
||||
yield llm_response
|
||||
|
||||
async def assemble_context(self, text: str, image_urls: List[str] = None):
|
||||
"""组装上下文，支持文本和图片"""
|
||||
@@ -232,3 +362,14 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
|
||||
)
|
||||
|
||||
return {"role": "user", "content": content}
|
||||
|
||||
async def encode_image_bs64(self, image_url: str) -> str:
|
||||
"""
|
||||
将图片转换为 base64
|
||||
"""
|
||||
if image_url.startswith("base64://"):
|
||||
return image_url.replace("base64://", "data:image/jpeg;base64,")
|
||||
with open(image_url, "rb") as f:
|
||||
image_bs64 = base64.b64encode(f.read()).decode("utf-8")
|
||||
return "data:image/jpeg;base64," + image_bs64
|
||||
return ""
|
||||
|
||||
@@ -506,12 +506,12 @@ class ProviderGoogleGenAI(Provider):
|
||||
async def text_chat(
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: list[str] = None,
|
||||
func_tool: FuncCall = None,
|
||||
contexts: list = None,
|
||||
system_prompt: str = None,
|
||||
tool_calls_result: ToolCallsResult = None,
|
||||
session_id = None,
|
||||
image_urls = None,
|
||||
func_tool = None,
|
||||
contexts = None,
|
||||
system_prompt = None,
|
||||
tool_calls_result = None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
if contexts is None:
|
||||
@@ -527,7 +527,11 @@ class ProviderGoogleGenAI(Provider):
|
||||
|
||||
# tool calls result
|
||||
if tool_calls_result:
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
if not isinstance(tool_calls_result, list):
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
else:
|
||||
for tcr in tool_calls_result:
|
||||
context_query.extend(tcr.to_openai_messages())
|
||||
|
||||
model_config = self.provider_config.get("model_config", {})
|
||||
model_config["model"] = self.get_model()
|
||||
|
||||
@@ -1,134 +0,0 @@
|
||||
import os
|
||||
from llmtuner.chat import ChatModel
|
||||
from typing import List
|
||||
from .. import Provider
|
||||
from ..entities import LLMResponse
|
||||
from ..func_tool_manager import FuncCall
|
||||
from astrbot.core.db import BaseDatabase
|
||||
from ..register import register_provider_adapter
|
||||
|
||||
|
||||
@register_provider_adapter(
|
||||
"llm_tuner", "LLMTuner 适配器, 用于装载使用 LlamaFactory 微调后的模型"
|
||||
)
|
||||
class LLMTunerModelLoader(Provider):
|
||||
def __init__(
|
||||
self,
|
||||
provider_config: dict,
|
||||
provider_settings: dict,
|
||||
db_helper: BaseDatabase,
|
||||
persistant_history=True,
|
||||
default_persona=None,
|
||||
) -> None:
|
||||
super().__init__(
|
||||
provider_config,
|
||||
provider_settings,
|
||||
persistant_history,
|
||||
db_helper,
|
||||
default_persona,
|
||||
)
|
||||
if not os.path.exists(provider_config["base_model_path"]) or not os.path.exists(
|
||||
provider_config["adapter_model_path"]
|
||||
):
|
||||
raise FileNotFoundError("模型文件路径不存在。")
|
||||
self.base_model_path = provider_config["base_model_path"]
|
||||
self.adapter_model_path = provider_config["adapter_model_path"]
|
||||
self.model = ChatModel(
|
||||
{
|
||||
"model_name_or_path": self.base_model_path,
|
||||
"adapter_name_or_path": self.adapter_model_path,
|
||||
"template": provider_config["llmtuner_template"],
|
||||
"finetuning_type": provider_config["finetuning_type"],
|
||||
"quantization_bit": provider_config["quantization_bit"],
|
||||
}
|
||||
)
|
||||
self.set_model(
|
||||
os.path.basename(self.base_model_path)
|
||||
+ "_"
|
||||
+ os.path.basename(self.adapter_model_path)
|
||||
)
|
||||
|
||||
async def assemble_context(self, text: str, image_urls: List[str] = None):
|
||||
"""
|
||||
组装上下文。
|
||||
"""
|
||||
return {"role": "user", "content": text}
|
||||
|
||||
async def text_chat(
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: List[str] = None,
|
||||
func_tool: FuncCall = None,
|
||||
contexts: List = None,
|
||||
system_prompt: str = None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
if contexts is None:
|
||||
contexts = []
|
||||
system_prompt = ""
|
||||
new_record = {"role": "user", "content": prompt}
|
||||
query_context = [*contexts, new_record]
|
||||
|
||||
# 提取出系统提示
|
||||
system_idxs = []
|
||||
for idx, context in enumerate(query_context):
|
||||
if context["role"] == "system":
|
||||
system_idxs.append(idx)
|
||||
|
||||
if "_no_save" in context:
|
||||
del context["_no_save"]
|
||||
|
||||
for idx in reversed(system_idxs):
|
||||
system_prompt += " " + query_context.pop(idx)["content"]
|
||||
|
||||
conf = {
|
||||
"messages": query_context,
|
||||
"system": system_prompt,
|
||||
}
|
||||
if func_tool:
|
||||
tool_list = func_tool.get_func_desc_openai_style()
|
||||
if tool_list:
|
||||
conf["tools"] = tool_list
|
||||
|
||||
responses = await self.model.achat(**conf)
|
||||
|
||||
llm_response = LLMResponse("assistant", responses[-1].response_text)
|
||||
|
||||
return llm_response
|
||||
|
||||
async def text_chat_stream(
|
||||
self,
|
||||
prompt,
|
||||
session_id=None,
|
||||
image_urls=...,
|
||||
func_tool=None,
|
||||
contexts=...,
|
||||
system_prompt=None,
|
||||
tool_calls_result=None,
|
||||
**kwargs,
|
||||
):
|
||||
# raise NotImplementedError("This method is not implemented yet.")
|
||||
# 调用 text_chat 模拟流式
|
||||
llm_response = await self.text_chat(
|
||||
prompt=prompt,
|
||||
session_id=session_id,
|
||||
image_urls=image_urls,
|
||||
func_tool=func_tool,
|
||||
contexts=contexts,
|
||||
system_prompt=system_prompt,
|
||||
tool_calls_result=tool_calls_result,
|
||||
)
|
||||
llm_response.is_chunk = True
|
||||
yield llm_response
|
||||
llm_response.is_chunk = False
|
||||
yield llm_response
|
||||
|
||||
async def get_current_key(self):
|
||||
return "none"
|
||||
|
||||
async def set_key(self, key):
|
||||
pass
|
||||
|
||||
async def get_models(self):
|
||||
return [self.get_model()]
|
||||
@@ -9,7 +9,6 @@ import astrbot.core.message.components as Comp
|
||||
from openai import AsyncOpenAI, AsyncAzureOpenAI
|
||||
from openai.types.chat.chat_completion import ChatCompletion
|
||||
|
||||
# from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
|
||||
from openai._exceptions import NotFoundError, UnprocessableEntityError
|
||||
from openai.lib.streaming.chat._completions import ChatCompletionStreamState
|
||||
from astrbot.core.utils.io import download_image_by_url
|
||||
@@ -224,12 +223,10 @@ class ProviderOpenAIOfficial(Provider):
|
||||
async def _prepare_chat_payload(
|
||||
self,
|
||||
prompt: str,
|
||||
session_id: str = None,
|
||||
image_urls: list[str] = None,
|
||||
func_tool: FuncCall = None,
|
||||
contexts: list = None,
|
||||
system_prompt: str = None,
|
||||
tool_calls_result: ToolCallsResult = None,
|
||||
image_urls: list[str] | None = None,
|
||||
contexts: list | None = None,
|
||||
system_prompt: str | None = None,
|
||||
tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
|
||||
**kwargs,
|
||||
) -> tuple:
|
||||
"""准备聊天所需的有效载荷和上下文"""
|
||||
@@ -246,14 +243,20 @@ class ProviderOpenAIOfficial(Provider):
|
||||
|
||||
# tool calls result
|
||||
if tool_calls_result:
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
if isinstance(tool_calls_result, ToolCallsResult):
|
||||
context_query.extend(tool_calls_result.to_openai_messages())
|
||||
else:
|
||||
for tcr in tool_calls_result:
|
||||
context_query.extend(tcr.to_openai_messages())
|
||||
|
||||
model_config = self.provider_config.get("model_config", {})
|
||||
model_config["model"] = self.get_model()
|
||||
|
||||
payloads = {"messages": context_query, **model_config}
|
||||
|
||||
return payloads, context_query, func_tool
|
||||
logger.debug(f"payloads: {payloads}")
|
||||
|
||||
return payloads, context_query
|
||||
|
||||
async def _handle_api_error(
|
||||
self,
|
||||
@@ -352,11 +355,9 @@ class ProviderOpenAIOfficial(Provider):
|
||||
tool_calls_result=None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
payloads, context_query, func_tool = await self._prepare_chat_payload(
|
||||
payloads, context_query = await self._prepare_chat_payload(
|
||||
prompt,
|
||||
session_id,
|
||||
image_urls,
|
||||
func_tool,
|
||||
contexts,
|
||||
system_prompt,
|
||||
tool_calls_result,
|
||||
@@ -422,11 +423,9 @@ class ProviderOpenAIOfficial(Provider):
|
||||
**kwargs,
|
||||
) -> AsyncGenerator[LLMResponse, None]:
|
||||
"""流式对话，与服务商交互并逐步返回结果"""
|
||||
payloads, context_query, func_tool = await self._prepare_chat_payload(
|
||||
payloads, context_query = await self._prepare_chat_payload(
|
||||
prompt,
|
||||
session_id,
|
||||
image_urls,
|
||||
func_tool,
|
||||
contexts,
|
||||
system_prompt,
|
||||
tool_calls_result,
|
||||
|
||||
@@ -414,7 +414,6 @@ export default {
|
||||
"anthropic_chat_completion": "chat_completion",
|
||||
"googlegenai_chat_completion": "chat_completion",
|
||||
"zhipu_chat_completion": "chat_completion",
|
||||
"llm_tuner": "chat_completion",
|
||||
"dify": "chat_completion",
|
||||
"dashscope": "chat_completion",
|
||||
"openai_whisper_api": "speech_to_text",
|
||||
|
||||
@@ -0,0 +1,82 @@
|
||||
import urllib.request
|
||||
import json
|
||||
|
||||
# --- 配置 ---
|
||||
REPO_OWNER = "AstrBotDevs"
|
||||
REPO_NAME = "AstrBot"
|
||||
START_ISSUE_NUMBER = 970
|
||||
LABEL_TO_FIND = "plugin-publish"
|
||||
OUTPUT_FILENAME = "plugin_publish_issues.txt"
|
||||
ISSUE_STATE = "closed" # 只筛选状态为 closed 的 Issue
|
||||
|
||||
|
||||
def fetch_and_format_issues(
|
||||
repo_owner, repo_name, start_issue_number, label_name, output_file, issue_state
|
||||
):
|
||||
api_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/issues?state={issue_state}&labels={label_name}&sort=created&direction=asc&per_page=100"
|
||||
headers = {"Accept": "application/vnd.github+json"}
|
||||
found_issues = []
|
||||
page = 1
|
||||
|
||||
while True:
|
||||
request_url = f"{api_url}&page={page}"
|
||||
request = urllib.request.Request(request_url, headers=headers)
|
||||
try:
|
||||
with urllib.request.urlopen(request) as response:
|
||||
data = json.loads(response.read().decode("utf-8"))
|
||||
if not data:
|
||||
break # 没有更多 Issues 了
|
||||
|
||||
for issue in data:
|
||||
issue_number = issue.get("number")
|
||||
if issue_number is not None and issue_number >= start_issue_number:
|
||||
title = issue.get("title", "No Title")
|
||||
author = issue.get("user", {}).get("login", "Unknown")
|
||||
found_issues.append(f"{title} by @{author} in #{issue_number}")
|
||||
|
||||
# 检查是否有下一页
|
||||
if "Link" in response.headers:
|
||||
links = response.headers["Link"].split(",")
|
||||
next_page_exists = False
|
||||
for link in links:
|
||||
if 'rel="next"' in link:
|
||||
next_page_exists = True
|
||||
break
|
||||
if not next_page_exists:
|
||||
break
|
||||
page += 1
|
||||
else:
|
||||
break # 没有 Link header，假设没有更多页了
|
||||
|
||||
except urllib.error.HTTPError as e:
|
||||
print(f"HTTP Error: {e.code} - {e.reason}")
|
||||
return
|
||||
except urllib.error.URLError as e:
|
||||
print(f"URL Error: {e.reason}")
|
||||
return
|
||||
except json.JSONDecodeError:
|
||||
print("Error decoding JSON response.")
|
||||
return
|
||||
|
||||
if found_issues:
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
for line in found_issues:
|
||||
f.write(line + "\n")
|
||||
print(
|
||||
f"已找到 {len(found_issues)} 个状态为 '{issue_state}'，带有 '{label_name}' 标签且 Issue Number 大于等于 {start_issue_number} 的 Issues，并已保存到 '{output_file}'。"
|
||||
)
|
||||
else:
|
||||
print(
|
||||
f"未找到任何状态为 '{issue_state}'，带有 '{label_name}' 标签且 Issue Number 大于等于 {start_issue_number} 的 Issues。"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fetch_and_format_issues(
|
||||
REPO_OWNER,
|
||||
REPO_NAME,
|
||||
START_ISSUE_NUMBER,
|
||||
LABEL_TO_FIND,
|
||||
OUTPUT_FILENAME,
|
||||
ISSUE_STATE,
|
||||
)
|
||||
Reference in New Issue
Block a user