mirror of
https://github.com/AstrBotDevs/AstrBot
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402 lines
15 KiB
Python
402 lines
15 KiB
Python
from __future__ import annotations
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import base64
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import enum
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import json
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from dataclasses import dataclass, field
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from typing import Any
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from anthropic.types import Message as AnthropicMessage
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from google.genai.types import GenerateContentResponse
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from openai.types.chat.chat_completion import ChatCompletion
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import astrbot.core.message.components as Comp
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from astrbot import logger
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from astrbot.core.agent.message import (
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AssistantMessageSegment,
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ContentPart,
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ToolCall,
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ToolCallMessageSegment,
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)
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from astrbot.core.agent.tool import ToolSet
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from astrbot.core.db.po import Conversation
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.core.utils.io import download_image_by_url
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class ProviderType(enum.Enum):
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CHAT_COMPLETION = "chat_completion"
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SPEECH_TO_TEXT = "speech_to_text"
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TEXT_TO_SPEECH = "text_to_speech"
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EMBEDDING = "embedding"
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RERANK = "rerank"
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@dataclass
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class ProviderMeta:
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"""The basic metadata of a provider instance."""
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id: str
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"""the unique id of the provider instance that user configured"""
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model: str | None
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"""the model name of the provider instance currently used"""
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type: str
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"""the name of the provider adapter, such as openai, ollama"""
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provider_type: ProviderType = ProviderType.CHAT_COMPLETION
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"""the capability type of the provider adapter"""
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@dataclass
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class ProviderMetaData(ProviderMeta):
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"""The metadata of a provider adapter for registration."""
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desc: str = ""
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"""the short description of the provider adapter"""
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cls_type: Any = None
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"""the class type of the provider adapter"""
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default_config_tmpl: dict | None = None
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"""the default configuration template of the provider adapter"""
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provider_display_name: str | None = None
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"""the display name of the provider shown in the WebUI configuration page; if empty, the type is used"""
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@dataclass
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class ToolCallsResult:
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"""工具调用结果"""
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tool_calls_info: AssistantMessageSegment
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"""函数调用的信息"""
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tool_calls_result: list[ToolCallMessageSegment]
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"""函数调用的结果"""
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def to_openai_messages(self) -> list[dict]:
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ret = [
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self.tool_calls_info.model_dump(),
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*[item.model_dump() for item in self.tool_calls_result],
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]
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return ret
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def to_openai_messages_model(
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self,
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) -> list[AssistantMessageSegment | ToolCallMessageSegment]:
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return [
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self.tool_calls_info,
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*self.tool_calls_result,
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]
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@dataclass
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class ProviderRequest:
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prompt: str | None = None
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"""提示词"""
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session_id: str | None = ""
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"""会话 ID"""
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image_urls: list[str] = field(default_factory=list)
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"""图片 URL 列表"""
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extra_user_content_parts: list[ContentPart] = field(default_factory=list)
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"""额外的用户消息内容部分列表，用于在用户消息后添加额外的内容块（如系统提醒、指令等）。"""
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func_tool: ToolSet | None = None
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"""可用的函数工具"""
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contexts: list[dict] = field(default_factory=list)
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"""
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OpenAI 格式上下文列表。
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参考 https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages
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"""
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system_prompt: str = ""
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"""系统提示词"""
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conversation: Conversation | None = None
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"""关联的对话对象"""
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tool_calls_result: list[ToolCallsResult] | ToolCallsResult | None = None
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"""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"""
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model: str | None = None
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"""模型名称，为 None 时使用提供商的默认模型"""
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def __repr__(self):
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return (
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f"ProviderRequest(prompt={self.prompt}, session_id={self.session_id}, "
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f"image_count={len(self.image_urls or [])}, "
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f"func_tool={self.func_tool}, "
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f"contexts={self._print_friendly_context()}, "
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f"system_prompt={self.system_prompt}, "
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f"conversation_id={self.conversation.cid if self.conversation else 'N/A'}, "
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)
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def __str__(self):
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return self.__repr__()
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def append_tool_calls_result(self, tool_calls_result: ToolCallsResult):
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"""添加工具调用结果到请求中"""
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if not self.tool_calls_result:
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self.tool_calls_result = []
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if isinstance(self.tool_calls_result, ToolCallsResult):
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self.tool_calls_result = [self.tool_calls_result]
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self.tool_calls_result.append(tool_calls_result)
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def _print_friendly_context(self):
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"""打印友好的消息上下文。将 image_url 的值替换为 <Image>"""
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if not self.contexts:
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return f"prompt: {self.prompt}, image_count: {len(self.image_urls or [])}"
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result_parts = []
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for ctx in self.contexts:
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role = ctx.get("role", "unknown")
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content = ctx.get("content", "")
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if isinstance(content, str):
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result_parts.append(f"{role}: {content}")
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elif isinstance(content, list):
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msg_parts = []
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image_count = 0
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for item in content:
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item_type = item.get("type", "")
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if item_type == "text":
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msg_parts.append(item.get("text", ""))
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elif item_type == "image_url":
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image_count += 1
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if image_count > 0:
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if msg_parts:
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msg_parts.append(f"[+{image_count} images]")
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else:
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msg_parts.append(f"[{image_count} images]")
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result_parts.append(f"{role}: {''.join(msg_parts)}")
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return result_parts
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async def assemble_context(self) -> dict:
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"""将请求(prompt 和 image_urls)包装成 OpenAI 的消息格式。"""
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# 构建内容块列表
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content_blocks = []
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# 1. 用户原始发言（OpenAI 建议：用户发言在前）
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if self.prompt and self.prompt.strip():
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content_blocks.append({"type": "text", "text": self.prompt})
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elif self.image_urls:
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# 如果没有文本但有图片，添加占位文本
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content_blocks.append({"type": "text", "text": "[图片]"})
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# 2. 额外的内容块（系统提醒、指令等）
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if self.extra_user_content_parts:
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for part in self.extra_user_content_parts:
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content_blocks.append(part.model_dump())
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# 3. 图片内容
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if self.image_urls:
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for image_url in self.image_urls:
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if image_url.startswith("http"):
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image_path = await download_image_by_url(image_url)
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image_data = await self._encode_image_bs64(image_path)
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elif image_url.startswith("file:///"):
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image_path = image_url.replace("file:///", "")
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image_data = await self._encode_image_bs64(image_path)
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else:
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image_data = await self._encode_image_bs64(image_url)
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if not image_data:
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logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
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continue
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content_blocks.append(
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{"type": "image_url", "image_url": {"url": image_data}},
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)
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# 只有当只有一个来自 prompt 的文本块且没有额外内容块时，才降级为简单格式以保持向后兼容
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if (
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len(content_blocks) == 1
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and content_blocks[0]["type"] == "text"
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and not self.extra_user_content_parts
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and not self.image_urls
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):
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return {"role": "user", "content": content_blocks[0]["text"]}
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# 否则返回多模态格式
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return {"role": "user", "content": content_blocks}
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async def _encode_image_bs64(self, image_url: str) -> str:
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"""将图片转换为 base64"""
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if image_url.startswith("base64://"):
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return image_url.replace("base64://", "data:image/jpeg;base64,")
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with open(image_url, "rb") as f:
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image_bs64 = base64.b64encode(f.read()).decode("utf-8")
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return "data:image/jpeg;base64," + image_bs64
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return ""
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@dataclass
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class TokenUsage:
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input_other: int = 0
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"""The number of input tokens, excluding cached tokens."""
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input_cached: int = 0
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"""The number of input cached tokens."""
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output: int = 0
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"""The number of output tokens."""
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@property
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def total(self) -> int:
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return self.input_other + self.input_cached + self.output
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@property
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def input(self) -> int:
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return self.input_other + self.input_cached
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def __add__(self, other: TokenUsage) -> TokenUsage:
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return TokenUsage(
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input_other=self.input_other + other.input_other,
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input_cached=self.input_cached + other.input_cached,
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output=self.output + other.output,
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)
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def __sub__(self, other: TokenUsage) -> TokenUsage:
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return TokenUsage(
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input_other=self.input_other - other.input_other,
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input_cached=self.input_cached - other.input_cached,
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output=self.output - other.output,
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)
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@dataclass
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class LLMResponse:
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role: str
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"""The role of the message, e.g., assistant, tool, err"""
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result_chain: MessageChain | None = None
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"""A chain of message components representing the text completion from LLM."""
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tools_call_args: list[dict[str, Any]] = field(default_factory=list)
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"""Tool call arguments."""
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tools_call_name: list[str] = field(default_factory=list)
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"""Tool call names."""
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tools_call_ids: list[str] = field(default_factory=list)
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"""Tool call IDs."""
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tools_call_extra_content: dict[str, dict[str, Any]] = field(default_factory=dict)
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"""Tool call extra content. tool_call_id -> extra_content dict"""
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reasoning_content: str = ""
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"""The reasoning content extracted from the LLM, if any."""
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raw_completion: (
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ChatCompletion | GenerateContentResponse | AnthropicMessage | None
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) = None
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"""The raw completion response from the LLM provider."""
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_completion_text: str = ""
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"""The plain text of the completion."""
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is_chunk: bool = False
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"""Indicates if the response is a chunked response."""
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id: str | None = None
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"""The ID of the response. For chunked responses, it's the ID of the chunk; for non-chunked responses, it's the ID of the response."""
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usage: TokenUsage | None = None
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"""The usage of the response. For chunked responses, it's the usage of the chunk; for non-chunked responses, it's the usage of the response."""
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def __init__(
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self,
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role: str,
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completion_text: str = "",
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result_chain: MessageChain | None = None,
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tools_call_args: list[dict[str, Any]] | None = None,
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tools_call_name: list[str] | None = None,
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tools_call_ids: list[str] | None = None,
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tools_call_extra_content: dict[str, dict[str, Any]] | None = None,
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raw_completion: ChatCompletion
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| GenerateContentResponse
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| AnthropicMessage
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| None = None,
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is_chunk: bool = False,
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id: str | None = None,
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usage: TokenUsage | None = None,
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):
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"""初始化 LLMResponse
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Args:
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role (str): 角色, assistant, tool, err
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completion_text (str, optional): 返回的结果文本，已经过时，推荐使用 result_chain. Defaults to "".
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result_chain (MessageChain, optional): 返回的消息链. Defaults to None.
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tools_call_args (List[Dict[str, any]], optional): 工具调用参数. Defaults to None.
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tools_call_name (List[str], optional): 工具调用名称. Defaults to None.
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raw_completion (ChatCompletion, optional): 原始响应, OpenAI 格式. Defaults to None.
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"""
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if tools_call_args is None:
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tools_call_args = []
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if tools_call_name is None:
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tools_call_name = []
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if tools_call_ids is None:
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tools_call_ids = []
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if tools_call_extra_content is None:
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tools_call_extra_content = {}
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self.role = role
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self.completion_text = completion_text
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self.result_chain = result_chain
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self.tools_call_args = tools_call_args
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self.tools_call_name = tools_call_name
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self.tools_call_ids = tools_call_ids
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self.tools_call_extra_content = tools_call_extra_content
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self.raw_completion = raw_completion
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self.is_chunk = is_chunk
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@property
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def completion_text(self):
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if self.result_chain:
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return self.result_chain.get_plain_text()
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return self._completion_text
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@completion_text.setter
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def completion_text(self, value):
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if self.result_chain:
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self.result_chain.chain = [
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comp
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for comp in self.result_chain.chain
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if not isinstance(comp, Comp.Plain)
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] # 清空 Plain 组件
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self.result_chain.chain.insert(0, Comp.Plain(value))
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else:
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self._completion_text = value
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def to_openai_tool_calls(self) -> list[dict]:
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"""Convert to OpenAI tool calls format. Deprecated, use to_openai_to_calls_model instead."""
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ret = []
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for idx, tool_call_arg in enumerate(self.tools_call_args):
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payload = {
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"id": self.tools_call_ids[idx],
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"function": {
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"name": self.tools_call_name[idx],
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"arguments": json.dumps(tool_call_arg),
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},
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"type": "function",
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}
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if self.tools_call_extra_content.get(self.tools_call_ids[idx]):
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payload["extra_content"] = self.tools_call_extra_content[
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self.tools_call_ids[idx]
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]
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ret.append(payload)
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return ret
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def to_openai_to_calls_model(self) -> list[ToolCall]:
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"""The same as to_openai_tool_calls but return pydantic model."""
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ret = []
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for idx, tool_call_arg in enumerate(self.tools_call_args):
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ret.append(
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ToolCall(
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id=self.tools_call_ids[idx],
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function=ToolCall.FunctionBody(
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name=self.tools_call_name[idx],
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arguments=json.dumps(tool_call_arg),
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),
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# the extra_content will not serialize if it's None when calling ToolCall.model_dump()
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extra_content=self.tools_call_extra_content.get(
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self.tools_call_ids[idx]
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),
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),
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)
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return ret
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@dataclass
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class RerankResult:
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index: int
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"""在候选列表中的索引位置"""
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relevance_score: float
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"""相关性分数"""
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