feat: AI聊天室多Agent协作讨论平台
- 实现Agent管理，支持AI辅助生成系统提示词 - 支持多个AI提供商（OpenRouter、智谱、MiniMax等） - 实现聊天室和讨论引擎 - WebSocket实时消息推送 - 前端使用React + Ant Design - 后端使用FastAPI + MongoDB Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Claude Code
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backend/adapters/gemini_adapter.py
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backend/adapters/gemini_adapter.py
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"""
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Gemini适配器
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支持Google Gemini大模型API
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"""
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import json
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from datetime import datetime
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from typing import List, Dict, Any, Optional, AsyncGenerator
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from loguru import logger
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from .base_adapter import BaseAdapter, ChatMessage, AdapterResponse
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from utils.proxy_handler import get_http_client
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class GeminiAdapter(BaseAdapter):
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"""
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Google Gemini API适配器
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使用Gemini的原生API格式
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"""
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DEFAULT_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
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def __init__(
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self,
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api_key: str,
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base_url: str = "",
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model: str = "gemini-1.5-pro",
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use_proxy: bool = True, # Gemini通常需要代理
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proxy_config: Optional[Dict[str, Any]] = None,
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timeout: int = 60,
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**kwargs
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):
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super().__init__(
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api_key=api_key,
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base_url=base_url or self.DEFAULT_BASE_URL,
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model=model,
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use_proxy=use_proxy,
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proxy_config=proxy_config,
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timeout=timeout,
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**kwargs
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)
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def _convert_messages_to_gemini(
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self,
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messages: List[ChatMessage]
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) -> tuple[str, List[Dict[str, Any]]]:
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"""
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将消息转换为Gemini格式
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Args:
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messages: 标准消息列表
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Returns:
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(system_instruction, contents)
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"""
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system_instruction = ""
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contents = []
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for msg in messages:
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if msg.role == "system":
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system_instruction += msg.content + "\n"
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else:
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role = "user" if msg.role == "user" else "model"
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contents.append({
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"role": role,
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"parts": [{"text": msg.content}]
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})
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return system_instruction.strip(), contents
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async def chat(
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self,
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messages: List[ChatMessage],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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**kwargs
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) -> AdapterResponse:
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"""发送聊天请求"""
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start_time = datetime.utcnow()
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try:
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=self.timeout
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) as client:
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system_instruction, contents = self._convert_messages_to_gemini(messages)
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payload = {
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"contents": contents,
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"generationConfig": {
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"temperature": temperature,
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"maxOutputTokens": max_tokens,
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"topP": kwargs.get("top_p", 0.95),
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"topK": kwargs.get("top_k", 40)
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}
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}
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# 添加系统指令
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if system_instruction:
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payload["systemInstruction"] = {
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"parts": [{"text": system_instruction}]
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}
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url = f"{self.base_url}/models/{self.model}:generateContent?key={self.api_key}"
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response = await client.post(
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url,
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json=payload
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)
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if response.status_code != 200:
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error_text = response.text
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logger.error(f"Gemini API错误: {response.status_code} - {error_text}")
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return AdapterResponse(
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success=False,
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error=f"API错误: {response.status_code} - {error_text}",
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latency_ms=self._calculate_latency(start_time)
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)
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data = response.json()
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# 检查是否有候选回复
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candidates = data.get("candidates", [])
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if not candidates:
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return AdapterResponse(
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success=False,
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error="没有生成回复",
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latency_ms=self._calculate_latency(start_time)
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)
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candidate = candidates[0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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text = "".join(part.get("text", "") for part in parts)
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# 获取token使用情况
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usage = data.get("usageMetadata", {})
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return AdapterResponse(
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success=True,
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content=text,
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model=self.model,
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finish_reason=candidate.get("finishReason", ""),
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prompt_tokens=usage.get("promptTokenCount", 0),
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completion_tokens=usage.get("candidatesTokenCount", 0),
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total_tokens=usage.get("totalTokenCount", 0),
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latency_ms=self._calculate_latency(start_time)
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)
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except Exception as e:
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logger.error(f"Gemini请求异常: {e}")
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return AdapterResponse(
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success=False,
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error=str(e),
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latency_ms=self._calculate_latency(start_time)
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)
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async def chat_stream(
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self,
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messages: List[ChatMessage],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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**kwargs
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) -> AsyncGenerator[str, None]:
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"""发送流式聊天请求"""
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try:
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=self.timeout
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) as client:
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system_instruction, contents = self._convert_messages_to_gemini(messages)
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payload = {
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"contents": contents,
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"generationConfig": {
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"temperature": temperature,
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"maxOutputTokens": max_tokens,
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"topP": kwargs.get("top_p", 0.95),
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"topK": kwargs.get("top_k", 40)
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}
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}
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if system_instruction:
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payload["systemInstruction"] = {
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"parts": [{"text": system_instruction}]
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}
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url = f"{self.base_url}/models/{self.model}:streamGenerateContent?key={self.api_key}&alt=sse"
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async with client.stream(
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"POST",
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url,
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json=payload
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) as response:
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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data_str = line[6:]
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try:
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data = json.loads(data_str)
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candidates = data.get("candidates", [])
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if candidates:
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content = candidates[0].get("content", {})
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parts = content.get("parts", [])
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for part in parts:
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text = part.get("text", "")
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if text:
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yield text
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except json.JSONDecodeError:
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continue
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except Exception as e:
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logger.error(f"Gemini流式请求异常: {e}")
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yield f"[错误: {str(e)}]"
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async def test_connection(self) -> Dict[str, Any]:
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"""测试API连接"""
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start_time = datetime.utcnow()
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try:
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test_messages = [
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ChatMessage(role="user", content="Hello, respond with 'OK'")
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]
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response = await self.chat(
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messages=test_messages,
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temperature=0,
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max_tokens=10
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)
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if response.success:
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return {
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"success": True,
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"message": "连接成功",
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"model": response.model,
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"latency_ms": response.latency_ms
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}
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else:
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return {
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"success": False,
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"message": response.error,
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"latency_ms": response.latency_ms
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}
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except Exception as e:
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return {
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"success": False,
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"message": str(e),
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"latency_ms": self._calculate_latency(start_time)
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}
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