2cae941bae
* Initial plan * Fix incomplete Gemini streaming responses in chat history Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> Co-authored-by: Raven95676 <Raven95676@gmail.com>
680 lines
26 KiB
Python
680 lines
26 KiB
Python
import asyncio
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import base64
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import json
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import logging
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import random
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from typing import Optional
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from collections.abc import AsyncGenerator
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from google import genai
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from google.genai import types
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from google.genai.errors import APIError
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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.api.provider import Provider
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.core.provider.entities import LLMResponse
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from astrbot.core.provider.func_tool_manager import FuncCall
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from astrbot.core.utils.io import download_image_by_url
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from ..register import register_provider_adapter
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class SuppressNonTextPartsWarning(logging.Filter):
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"""过滤 Gemini SDK 中的非文本部分警告"""
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def filter(self, record):
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return "there are non-text parts in the response" not in record.getMessage()
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logging.getLogger("google_genai.types").addFilter(SuppressNonTextPartsWarning())
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@register_provider_adapter(
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"googlegenai_chat_completion", "Google Gemini Chat Completion 提供商适配器"
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)
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class ProviderGoogleGenAI(Provider):
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CATEGORY_MAPPING = {
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"harassment": types.HarmCategory.HARM_CATEGORY_HARASSMENT,
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"hate_speech": types.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
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"sexually_explicit": types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
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"dangerous_content": types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
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}
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THRESHOLD_MAPPING = {
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"BLOCK_NONE": types.HarmBlockThreshold.BLOCK_NONE,
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"BLOCK_ONLY_HIGH": types.HarmBlockThreshold.BLOCK_ONLY_HIGH,
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"BLOCK_MEDIUM_AND_ABOVE": types.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
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"BLOCK_LOW_AND_ABOVE": types.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
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}
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def __init__(
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self,
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provider_config,
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provider_settings,
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default_persona=None,
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) -> None:
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super().__init__(
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provider_config,
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provider_settings,
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default_persona,
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)
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self.api_keys: list = provider_config.get("key", [])
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self.chosen_api_key: str = self.api_keys[0] if len(self.api_keys) > 0 else None
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self.timeout: int = int(provider_config.get("timeout", 180))
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self.api_base: Optional[str] = provider_config.get("api_base", None)
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if self.api_base and self.api_base.endswith("/"):
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self.api_base = self.api_base[:-1]
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self._init_client()
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self.set_model(provider_config["model_config"]["model"])
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self._init_safety_settings()
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def _init_client(self) -> None:
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"""初始化Gemini客户端"""
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self.client = genai.Client(
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api_key=self.chosen_api_key,
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http_options=types.HttpOptions(
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base_url=self.api_base,
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timeout=self.timeout * 1000, # 毫秒
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),
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).aio
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def _init_safety_settings(self) -> None:
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"""初始化安全设置"""
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user_safety_config = self.provider_config.get("gm_safety_settings", {})
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self.safety_settings = [
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types.SafetySetting(
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category=harm_category, threshold=self.THRESHOLD_MAPPING[threshold_str]
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)
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for config_key, harm_category in self.CATEGORY_MAPPING.items()
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if (threshold_str := user_safety_config.get(config_key))
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and threshold_str in self.THRESHOLD_MAPPING
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]
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async def _handle_api_error(self, e: APIError, keys: list[str]) -> bool:
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"""处理API错误，返回是否需要重试"""
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if e.code == 429 or "API key not valid" in e.message:
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keys.remove(self.chosen_api_key)
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if len(keys) > 0:
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self.set_key(random.choice(keys))
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logger.info(
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f"检测到 Key 异常({e.message})，正在尝试更换 API Key 重试... 当前 Key: {self.chosen_api_key[:12]}..."
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)
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await asyncio.sleep(1)
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return True
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else:
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logger.error(
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f"检测到 Key 异常({e.message})，且已没有可用的 Key。 当前 Key: {self.chosen_api_key[:12]}..."
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)
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raise Exception("达到了 Gemini 速率限制, 请稍后再试...")
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else:
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logger.error(
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f"发生了错误(gemini_source)。Provider 配置如下: {self.provider_config}"
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)
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raise e
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async def _prepare_query_config(
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self,
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payloads: dict,
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tools: Optional[FuncCall] = None,
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system_instruction: Optional[str] = None,
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modalities: Optional[list[str]] = None,
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temperature: float = 0.7,
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) -> types.GenerateContentConfig:
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"""准备查询配置"""
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if not modalities:
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modalities = ["Text"]
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# 流式输出不支持图片模态
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if (
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self.provider_settings.get("streaming_response", False)
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and "Image" in modalities
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):
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logger.warning("流式输出不支持图片模态，已自动降级为文本模态")
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modalities = ["Text"]
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tool_list = []
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model_name = self.get_model()
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native_coderunner = self.provider_config.get("gm_native_coderunner", False)
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native_search = self.provider_config.get("gm_native_search", False)
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url_context = self.provider_config.get("gm_url_context", False)
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if "gemini-2.5" in model_name:
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if native_coderunner:
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tool_list.append(types.Tool(code_execution=types.ToolCodeExecution()))
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if native_search:
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logger.warning("代码执行工具与搜索工具互斥，已忽略搜索工具")
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if url_context:
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logger.warning(
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"代码执行工具与URL上下文工具互斥，已忽略URL上下文工具"
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)
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else:
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if native_search:
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tool_list.append(types.Tool(google_search=types.GoogleSearch()))
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if url_context:
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if hasattr(types, "UrlContext"):
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tool_list.append(types.Tool(url_context=types.UrlContext()))
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else:
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logger.warning(
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"当前 SDK 版本不支持 URL 上下文工具，已忽略该设置，请升级 google-genai 包"
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)
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elif "gemini-2.0-lite" in model_name:
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if native_coderunner or native_search or url_context:
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logger.warning(
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"gemini-2.0-lite 不支持代码执行、搜索工具和URL上下文，将忽略这些设置"
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)
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tool_list = None
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else:
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if native_coderunner:
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tool_list.append(types.Tool(code_execution=types.ToolCodeExecution()))
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if native_search:
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logger.warning("代码执行工具与搜索工具互斥，已忽略搜索工具")
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elif native_search:
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tool_list.append(types.Tool(google_search=types.GoogleSearch()))
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if url_context and not native_coderunner:
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if hasattr(types, "UrlContext"):
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tool_list.append(types.Tool(url_context=types.UrlContext()))
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else:
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logger.warning(
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"当前 SDK 版本不支持 URL 上下文工具，已忽略该设置，请升级 google-genai 包"
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)
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if not tool_list:
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tool_list = None
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if tools and tool_list:
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logger.warning("已启用原生工具，函数工具将被忽略")
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elif tools and (func_desc := tools.get_func_desc_google_genai_style()):
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tool_list = [
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types.Tool(function_declarations=func_desc["function_declarations"])
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]
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return types.GenerateContentConfig(
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system_instruction=system_instruction,
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temperature=temperature,
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max_output_tokens=payloads.get("max_tokens")
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or payloads.get("maxOutputTokens"),
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top_p=payloads.get("top_p") or payloads.get("topP"),
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top_k=payloads.get("top_k") or payloads.get("topK"),
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frequency_penalty=payloads.get("frequency_penalty")
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or payloads.get("frequencyPenalty"),
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presence_penalty=payloads.get("presence_penalty")
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or payloads.get("presencePenalty"),
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stop_sequences=payloads.get("stop") or payloads.get("stopSequences"),
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response_logprobs=payloads.get("response_logprobs")
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or payloads.get("responseLogprobs"),
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logprobs=payloads.get("logprobs"),
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seed=payloads.get("seed"),
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response_modalities=modalities,
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tools=tool_list,
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safety_settings=self.safety_settings if self.safety_settings else None,
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thinking_config=types.ThinkingConfig(
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thinking_budget=min(
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int(
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self.provider_config.get("gm_thinking_config", {}).get(
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"budget", 0
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)
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),
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24576,
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),
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)
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if "gemini-2.5-flash" in self.get_model()
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and hasattr(types.ThinkingConfig, "thinking_budget")
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else None,
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automatic_function_calling=types.AutomaticFunctionCallingConfig(
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disable=True
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),
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)
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def _prepare_conversation(self, payloads: dict) -> list[types.Content]:
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"""准备 Gemini SDK 的 Content 列表"""
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def create_text_part(text: str) -> types.Part:
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content_a = text if text else " "
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if not text:
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logger.warning("文本内容为空，已添加空格占位")
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return types.Part.from_text(text=content_a)
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def process_image_url(image_url_dict: dict) -> types.Part:
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url = image_url_dict["url"]
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mime_type = url.split(":")[1].split(";")[0]
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image_bytes = base64.b64decode(url.split(",", 1)[1])
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return types.Part.from_bytes(data=image_bytes, mime_type=mime_type)
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def append_or_extend(
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contents: list[types.Content],
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part: list[types.Part],
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content_cls: type[types.Content],
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) -> None:
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if contents and isinstance(contents[-1], content_cls):
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contents[-1].parts.extend(part)
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else:
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contents.append(content_cls(parts=part))
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gemini_contents: list[types.Content] = []
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native_tool_enabled = any(
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[
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self.provider_config.get("gm_native_coderunner", False),
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self.provider_config.get("gm_native_search", False),
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]
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)
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for message in payloads["messages"]:
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role, content = message["role"], message.get("content")
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if role == "user":
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if isinstance(content, list):
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parts = [
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types.Part.from_text(text=item["text"] or " ")
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if item["type"] == "text"
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else process_image_url(item["image_url"])
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for item in content
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]
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else:
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parts = [create_text_part(content)]
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append_or_extend(gemini_contents, parts, types.UserContent)
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elif role == "assistant":
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if content:
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parts = [types.Part.from_text(text=content)]
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append_or_extend(gemini_contents, parts, types.ModelContent)
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elif not native_tool_enabled and "tool_calls" in message:
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parts = [
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types.Part.from_function_call(
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name=tool["function"]["name"],
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args=json.loads(tool["function"]["arguments"]),
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)
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for tool in message["tool_calls"]
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]
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append_or_extend(gemini_contents, parts, types.ModelContent)
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else:
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logger.warning("assistant 角色的消息内容为空，已添加空格占位")
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if native_tool_enabled and "tool_calls" in message:
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logger.warning(
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"检测到启用Gemini原生工具，且上下文中存在函数调用，建议使用 /reset 重置上下文"
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)
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parts = [types.Part.from_text(text=" ")]
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append_or_extend(gemini_contents, parts, types.ModelContent)
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elif role == "tool" and not native_tool_enabled:
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parts = [
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types.Part.from_function_response(
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name=message["tool_call_id"],
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response={
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"name": message["tool_call_id"],
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"content": message["content"],
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},
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)
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]
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append_or_extend(gemini_contents, parts, types.UserContent)
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if gemini_contents and isinstance(gemini_contents[0], types.ModelContent):
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gemini_contents.pop()
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return gemini_contents
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@staticmethod
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def _process_content_parts(
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result: types.GenerateContentResponse, llm_response: LLMResponse
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) -> MessageChain:
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"""处理内容部分并构建消息链"""
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finish_reason = result.candidates[0].finish_reason
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result_parts: Optional[types.Part] = result.candidates[0].content.parts
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if finish_reason == types.FinishReason.SAFETY:
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raise Exception("模型生成内容未通过 Gemini 平台的安全检查")
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if finish_reason in {
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types.FinishReason.PROHIBITED_CONTENT,
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types.FinishReason.SPII,
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types.FinishReason.BLOCKLIST,
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}:
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raise Exception("模型生成内容违反 Gemini 平台政策")
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# 防止旧版本SDK不存在IMAGE_SAFETY
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if hasattr(types.FinishReason, "IMAGE_SAFETY"):
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if finish_reason == types.FinishReason.IMAGE_SAFETY:
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raise Exception("模型生成内容违反 Gemini 平台政策")
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if not result_parts:
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logger.debug(result.candidates)
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raise Exception("API 返回的内容为空。")
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chain = []
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part: types.Part
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# 暂时这样Fallback
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if all(
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part.inline_data and part.inline_data.mime_type.startswith("image/")
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for part in result_parts
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):
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chain.append(Comp.Plain("这是图片"))
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for part in result_parts:
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if part.text:
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chain.append(Comp.Plain(part.text))
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elif part.function_call:
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llm_response.role = "tool"
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llm_response.tools_call_name.append(part.function_call.name)
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llm_response.tools_call_args.append(part.function_call.args)
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# gemini 返回的 function_call.id 可能为 None
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llm_response.tools_call_ids.append(
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part.function_call.id or part.function_call.name
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)
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elif part.inline_data and part.inline_data.mime_type.startswith("image/"):
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chain.append(Comp.Image.fromBytes(part.inline_data.data))
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return MessageChain(chain=chain)
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async def _query(self, payloads: dict, tools: FuncCall) -> LLMResponse:
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"""非流式请求 Gemini API"""
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system_instruction = next(
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(msg["content"] for msg in payloads["messages"] if msg["role"] == "system"),
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None,
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)
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modalities = ["Text"]
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if self.provider_config.get("gm_resp_image_modal", False):
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modalities.append("Image")
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conversation = self._prepare_conversation(payloads)
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temperature = payloads.get("temperature", 0.7)
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result: Optional[types.GenerateContentResponse] = None
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while True:
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try:
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config = await self._prepare_query_config(
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payloads, tools, system_instruction, modalities, temperature
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)
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result = await self.client.models.generate_content(
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model=self.get_model(),
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contents=conversation,
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config=config,
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)
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if result.candidates[0].finish_reason == types.FinishReason.RECITATION:
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if temperature > 2:
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raise Exception("温度参数已超过最大值2，仍然发生recitation")
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temperature += 0.2
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logger.warning(
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f"发生了recitation，正在提高温度至{temperature:.1f}重试..."
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)
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continue
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break
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except APIError as e:
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if "Developer instruction is not enabled" in e.message:
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logger.warning(
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f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)"
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)
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system_instruction = None
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elif "Function calling is not enabled" in e.message:
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logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
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tools = None
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elif (
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"Multi-modal output is not supported" in e.message
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or "Model does not support the requested response modalities"
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in e.message
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or "only supports text output" in e.message
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):
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logger.warning(
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f"{self.get_model()} 不支持多模态输出，降级为文本模态"
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)
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modalities = ["Text"]
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else:
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raise
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continue
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llm_response = LLMResponse("assistant")
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llm_response.result_chain = self._process_content_parts(result, llm_response)
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return llm_response
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async def _query_stream(
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self, payloads: dict, tools: FuncCall
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) -> AsyncGenerator[LLMResponse, None]:
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"""流式请求 Gemini API"""
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system_instruction = next(
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(msg["content"] for msg in payloads["messages"] if msg["role"] == "system"),
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None,
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)
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conversation = self._prepare_conversation(payloads)
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result = None
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while True:
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try:
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config = await self._prepare_query_config(
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payloads, tools, system_instruction
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)
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result = await self.client.models.generate_content_stream(
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model=self.get_model(),
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contents=conversation,
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config=config,
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)
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break
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except APIError as e:
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if "Developer instruction is not enabled" in e.message:
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logger.warning(
|
||
f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)"
|
||
)
|
||
system_instruction = None
|
||
elif "Function calling is not enabled" in e.message:
|
||
logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
|
||
tools = None
|
||
else:
|
||
raise
|
||
continue
|
||
|
||
# Accumulate the complete response text for the final response
|
||
accumulated_text = ""
|
||
final_response = None
|
||
|
||
async for chunk in result:
|
||
llm_response = LLMResponse("assistant", is_chunk=True)
|
||
|
||
if chunk.candidates[0].content.parts and any(
|
||
part.function_call for part in chunk.candidates[0].content.parts
|
||
):
|
||
llm_response = LLMResponse("assistant", is_chunk=False)
|
||
llm_response.result_chain = self._process_content_parts(
|
||
chunk, llm_response
|
||
)
|
||
yield llm_response
|
||
return
|
||
|
||
if chunk.text:
|
||
accumulated_text += chunk.text
|
||
llm_response.result_chain = MessageChain(chain=[Comp.Plain(chunk.text)])
|
||
yield llm_response
|
||
|
||
if chunk.candidates[0].finish_reason:
|
||
# Process the final chunk for potential tool calls or other content
|
||
if chunk.candidates[0].content.parts:
|
||
final_response = LLMResponse("assistant", is_chunk=False)
|
||
final_response.result_chain = self._process_content_parts(
|
||
chunk, final_response
|
||
)
|
||
break
|
||
|
||
# Yield final complete response with accumulated text
|
||
if not final_response:
|
||
final_response = LLMResponse("assistant", is_chunk=False)
|
||
|
||
# Set the complete accumulated text in the final response
|
||
if accumulated_text:
|
||
final_response.result_chain = MessageChain(
|
||
chain=[Comp.Plain(accumulated_text)]
|
||
)
|
||
elif not final_response.result_chain:
|
||
# If no text was accumulated and no final response was set, provide empty space
|
||
final_response.result_chain = MessageChain(chain=[Comp.Plain(" ")])
|
||
|
||
yield final_response
|
||
|
||
async def text_chat(
|
||
self,
|
||
prompt: str,
|
||
session_id=None,
|
||
image_urls=None,
|
||
func_tool=None,
|
||
contexts=None,
|
||
system_prompt=None,
|
||
tool_calls_result=None,
|
||
model=None,
|
||
**kwargs,
|
||
) -> LLMResponse:
|
||
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:
|
||
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"] = model or self.get_model()
|
||
|
||
payloads = {"messages": context_query, **model_config}
|
||
|
||
retry = 10
|
||
keys = self.api_keys.copy()
|
||
|
||
for _ in range(retry):
|
||
try:
|
||
return await self._query(payloads, func_tool)
|
||
except APIError as e:
|
||
if await self._handle_api_error(e, keys):
|
||
continue
|
||
break
|
||
|
||
async def text_chat_stream(
|
||
self,
|
||
prompt,
|
||
session_id=None,
|
||
image_urls=None,
|
||
func_tool=None,
|
||
contexts=None,
|
||
system_prompt=None,
|
||
tool_calls_result=None,
|
||
model=None,
|
||
**kwargs,
|
||
) -> AsyncGenerator[LLMResponse, None]:
|
||
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:
|
||
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"] = model or self.get_model()
|
||
|
||
payloads = {"messages": context_query, **model_config}
|
||
|
||
retry = 10
|
||
keys = self.api_keys.copy()
|
||
|
||
for _ in range(retry):
|
||
try:
|
||
async for response in self._query_stream(payloads, func_tool):
|
||
yield response
|
||
break
|
||
except APIError as e:
|
||
if await self._handle_api_error(e, keys):
|
||
continue
|
||
break
|
||
|
||
async def get_models(self):
|
||
try:
|
||
models = await self.client.models.list()
|
||
return [
|
||
m.name.replace("models/", "")
|
||
for m in models
|
||
if "generateContent" in m.supported_actions
|
||
]
|
||
except APIError as e:
|
||
raise Exception(f"获取模型列表失败: {e.message}")
|
||
|
||
def get_current_key(self) -> str:
|
||
return self.chosen_api_key
|
||
|
||
def get_keys(self) -> list[str]:
|
||
return self.api_keys
|
||
|
||
def set_key(self, key):
|
||
self.chosen_api_key = key
|
||
self._init_client()
|
||
|
||
async def assemble_context(self, text: str, image_urls: list[str] = None):
|
||
"""
|
||
组装上下文。
|
||
"""
|
||
if image_urls:
|
||
user_content = {
|
||
"role": "user",
|
||
"content": [{"type": "text", "text": text if text else "[图片]"}],
|
||
}
|
||
for image_url in image_urls:
|
||
if image_url.startswith("http"):
|
||
image_path = await download_image_by_url(image_url)
|
||
image_data = await self.encode_image_bs64(image_path)
|
||
elif image_url.startswith("file:///"):
|
||
image_path = image_url.replace("file:///", "")
|
||
image_data = await self.encode_image_bs64(image_path)
|
||
else:
|
||
image_data = await self.encode_image_bs64(image_url)
|
||
if not image_data:
|
||
logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
|
||
continue
|
||
user_content["content"].append(
|
||
{
|
||
"type": "image_url",
|
||
"image_url": {"url": image_data},
|
||
}
|
||
)
|
||
return user_content
|
||
else:
|
||
return {"role": "user", "content": text}
|
||
|
||
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 ""
|
||
|
||
async def terminate(self):
|
||
logger.info("Google GenAI 适配器已终止。")
|