476 lines
17 KiB
Python
476 lines
17 KiB
Python
"""
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智谱 GLM 适配器
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基于 utils/glm_adapter.py 重构
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使用zai-sdk。因为已经完成这一部分的整套逻辑，如果更换OpenAI-SDK会花很多时间调试。
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"""
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import json
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import os
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from typing import Dict, List, Optional
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from fastapi.responses import JSONResponse, StreamingResponse
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from .base import BaseAdapter, ChatCompletionRequest, ModelInfo
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from .plugins import get_web_search_mode, build_glm_search_tool
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from core import get_logger
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logger = get_logger()
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# GLM 模型配置
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GLM_MODELS = [
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ModelInfo(
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id="glm-5",
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name="GLM-5",
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description="Coding与长程Agent能力SOTA",
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max_tokens=128000,
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provider="ZhipuAI",
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supports_thinking=True,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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ModelInfo(
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id="glm-4.6v",
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name="GLM-4.6V（推荐）",
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description="最新旗舰模型，支持文本/图像/文档/深度思考",
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max_tokens=128000,
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provider="ZhipuAI",
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supports_thinking=True,
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supports_web_search=False,
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supports_vision=True,
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supports_files=True,
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),
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ModelInfo(
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id="glm-4-flash",
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name="GLM-4 Flash",
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description="高性价比文本模型",
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max_tokens=128000,
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provider="ZhipuAI",
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supports_thinking=False,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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ModelInfo(
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id="glm-4v-plus-0111",
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name="GLM-4V Plus",
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description="图像 + PDF/DOCX 原生多模态",
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max_tokens=128000,
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provider="ZhipuAI",
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supports_thinking=False,
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supports_web_search=False,
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supports_vision=True,
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supports_files=True,
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),
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ModelInfo(
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id="glm-z1-flash",
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name="GLM-Z1 Flash",
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description="深度思考推理模型，默认开启深度思考",
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max_tokens=128000,
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provider="ZhipuAI",
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supports_thinking=True,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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]
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# 视觉模型列表（用于自动切换）
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VISION_MODELS = {"glm-4v", "glm-4v-plus", "glm-4v-plus-0111", "glm-4.6v"}
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# 支持深度思考的模型
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THINKING_MODELS = {"glm-z1-flash", "glm-z1-air", "glm-4.6v", "glm-4.6"}
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class GLMAdapter(BaseAdapter):
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"""智谱 GLM 平台适配器"""
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_client = None
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@property
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def provider_name(self) -> str:
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return "glm"
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def is_available(self) -> bool:
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"""检查 API Key 是否配置"""
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return bool(os.getenv("ZHIPU_API_KEY") or os.getenv("GLM_API_KEY"))
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def _get_client(self):
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"""获取 GLM 客户端（懒加载）"""
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if self._client is None:
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from zhipuai import ZhipuAI
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api_key = os.getenv("ZHIPU_API_KEY") or os.getenv("GLM_API_KEY")
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self._client = ZhipuAI(api_key=api_key)
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return self._client
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def list_models(self) -> List[ModelInfo]:
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return GLM_MODELS
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async def chat(self, request: ChatCompletionRequest):
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"""
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处理 GLM 聊天请求
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支持流式/非流式、图像、文档、联网搜索、深度思考
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"""
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client = self._get_client()
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# 构建消息
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glm_messages, has_vision, has_files = self._build_messages(request)
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actual_model = self._resolve_model(request.model, has_vision, has_files)
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# 调试：打印原始请求参数
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logger.info(f"[GLM] 原始请求参数:")
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logger.info(
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f" - request.deep_thinking: {request.deep_thinking} (type: {type(request.deep_thinking)})"
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)
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logger.info(f" - request.web_search: {request.web_search}")
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logger.info(f" - request.deep_search: {request.deep_search}")
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logger.info(f" - actual_model: {actual_model}")
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logger.info(f" - supports_thinking: {self._supports_thinking(actual_model)}")
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# 构建额外参数
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extra_kwargs = {}
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web_search_mode = get_web_search_mode(request)
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if web_search_mode:
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extra_kwargs["tools"] = [build_glm_search_tool(web_search_mode)]
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extra_kwargs["tool_choice"] = "auto"
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# 深度思考：正向选择（True 时启用，False 时禁用）
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# 注意：只有特定模型支持深度思考（如 glm-z1-flash）
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thinking_enabled = request.deep_thinking and self._supports_thinking(
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actual_model
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)
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logger.info(
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f"[GLM] 深度思考判断: {request.deep_thinking} and {self._supports_thinking(actual_model)} = {thinking_enabled}"
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)
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if thinking_enabled:
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extra_kwargs["thinking"] = {"type": "enabled"}
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logger.info(
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f"[GLM] 深度思考已启用: extra_kwargs['thinking'] = {extra_kwargs['thinking']}"
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)
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else:
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extra_kwargs["thinking"] = {"type": "disabled"}
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logger.info(
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f"[GLM] 深度思考已禁用: extra_kwargs['thinking'] = {extra_kwargs['thinking']}"
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)
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if extra_kwargs:
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logger.info(
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f"[GLM] 最终 extra_kwargs: {json.dumps(extra_kwargs, ensure_ascii=False)}"
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)
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if request.stream:
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return self._stream_chat(
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client, glm_messages, actual_model, request, extra_kwargs
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)
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else:
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return self._sync_chat(
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client, glm_messages, actual_model, request, extra_kwargs
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)
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def _build_messages(
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self, request: ChatCompletionRequest
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) -> tuple[List[Dict], bool, bool]:
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"""
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构建 GLM 格式的消息
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返回：(消息列表, 是否包含图片, 是否包含文件附件)
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"""
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messages = []
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has_vision = False
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has_files = bool(request.files) # 检查是否有文件附件
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for msg in request.messages:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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if isinstance(content, str):
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# 纯文本
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if content.strip():
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messages.append({"role": role, "content": content})
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elif isinstance(content, list):
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# 多模态内容
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glm_content = []
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for item in content:
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if isinstance(item, dict):
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item_type = item.get("type", "")
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if item_type == "text":
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text = item.get("text", "")
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if text:
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glm_content.append({"type": "text", "text": text})
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elif item_type == "image_url":
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img_url = self._extract_image_url(item)
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if img_url:
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glm_content.append(
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{"type": "image_url", "image_url": {"url": img_url}}
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)
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has_vision = True
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if glm_content:
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messages.append({"role": role, "content": glm_content})
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# 处理文件附件
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if request.files:
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file_content = self._build_file_content(request.files)
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if messages and messages[-1]["role"] == "user":
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# 追加到最后一个用户消息
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if isinstance(messages[-1]["content"], list):
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messages[-1]["content"].extend(file_content)
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else:
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messages[-1]["content"] = [
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{"type": "text", "text": messages[-1]["content"]},
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*file_content,
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]
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else:
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messages.append({"role": "user", "content": file_content})
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return messages, has_vision, has_files
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def _extract_image_url(self, item: Dict) -> Optional[str]:
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"""提取图片 URL"""
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img_val = item.get("image_url", "")
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if isinstance(img_val, str):
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return img_val
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elif isinstance(img_val, dict):
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return img_val.get("url", "")
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return None
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def _build_file_content(self, files: List[str]) -> List[Dict]:
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"""构建文件内容"""
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content = []
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for file_url in files:
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if file_url.startswith(("http://", "https://")):
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content.append({"type": "file_url", "file_url": {"url": file_url}})
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return content
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def _resolve_model(
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self, model: str, has_vision: bool, has_files: bool = False
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) -> str:
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"""解析实际使用的模型"""
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model_lower = model.lower()
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# 如果有图片或文件附件，强制使用 glm-4.6v（支持多模态）
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if (has_vision or has_files) and model_lower not in VISION_MODELS:
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logger.info(
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f"[GLM] 检测到图片或文件附件，强制切换模型: {model} -> glm-4.6v"
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)
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return "glm-4.6v"
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return model
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def _supports_thinking(self, model: str) -> bool:
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"""检查模型是否支持深度思考"""
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return model.lower() in THINKING_MODELS
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def _stream_chat(
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self, client, messages, model, request, extra_kwargs
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) -> StreamingResponse:
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"""流式聊天"""
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logger.info(f"[GLM] 开始流式响应...")
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# 提取深度思考配置
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thinking_config = extra_kwargs.get("thinking")
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tools_config = extra_kwargs.get("tools")
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def generator():
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from utils.helpers import generate_unique_id, get_current_timestamp
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full_content = ""
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# 构建 API 调用参数
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api_params = {
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"model": model,
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"messages": messages,
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"stream": True,
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"temperature": request.temperature,
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"max_tokens": request.max_tokens,
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}
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# 深度思考：使用 extra_body 传递
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if thinking_config:
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api_params["extra_body"] = {"thinking": thinking_config}
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# 联网搜索：使用 tools 参数
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if tools_config:
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api_params["tools"] = tools_config
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api_params["tool_choice"] = "auto"
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# 打印请求参数
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logger.info(f"[GLM] API 调用参数:")
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logger.info(f" - model: {model}")
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logger.info(f" - stream: True")
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logger.info(f" - temperature: {request.temperature}")
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logger.info(f" - max_tokens: {request.max_tokens}")
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if thinking_config:
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logger.info(f" - extra_body: {{'thinking': {thinking_config}}}")
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if tools_config:
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logger.info(
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f" - tools: {json.dumps(tools_config, ensure_ascii=False)}"
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)
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logger.info(f" - tool_choice: auto")
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logger.info(
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f" - messages: {json.dumps(messages, ensure_ascii=False, indent=2)}"
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)
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chunk_count = 0
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resp = client.chat.completions.create(**api_params)
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for chunk in resp:
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chunk_count += 1
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# 检查 delta 是否存在
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if not hasattr(chunk.choices[0], "delta"):
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continue
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delta = chunk.choices[0].delta
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# 处理深度思考内容（reasoning_content）
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reasoning_content = getattr(delta, "reasoning_content", None)
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if reasoning_content:
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data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"reasoning_content": reasoning_content},
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"finish_reason": None,
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}
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],
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}
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yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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continue
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# 处理普通内容
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content = getattr(delta, "content", None)
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if content:
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full_content += content
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data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": content},
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"finish_reason": None,
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}
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],
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}
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yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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# 结束标记
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finish = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
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}
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yield f"data: {json.dumps(finish, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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# 打印流式响应结果
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logger.info(f"[GLM] 流式响应完成:")
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logger.info(f" - chunks: {chunk_count}")
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logger.info(f" - content_length: {len(full_content)} 字符")
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logger.info(
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f" - content_preview: {full_content[:200]}..."
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if len(full_content) > 200
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else f" - content: {full_content}"
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)
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return StreamingResponse(generator(), media_type="text/event-stream")
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def _sync_chat(
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self, client, messages, model, request, extra_kwargs
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) -> JSONResponse:
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"""非流式聊天"""
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from utils.helpers import generate_unique_id, get_current_timestamp
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# 提取深度思考配置
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thinking_config = extra_kwargs.get("thinking")
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tools_config = extra_kwargs.get("tools")
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# 构建 API 调用参数
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api_params = {
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"model": model,
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"messages": messages,
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"stream": False,
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"temperature": request.temperature,
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"max_tokens": request.max_tokens,
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}
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# 深度思考：使用 extra_body 传递
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if thinking_config:
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api_params["extra_body"] = {"thinking": thinking_config}
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# 联网搜索：使用 tools 参数
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if tools_config:
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api_params["tools"] = tools_config
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api_params["tool_choice"] = "auto"
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# 打印请求参数
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logger.info(f"[GLM] API 调用参数:")
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logger.info(f" - model: {model}")
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logger.info(f" - stream: {request.stream}")
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logger.info(f" - temperature: {request.temperature}")
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logger.info(f" - max_tokens: {request.max_tokens}")
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if thinking_config:
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logger.info(f" - extra_body: {{'thinking': {thinking_config}}}")
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if tools_config:
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logger.info(f" - tools: {json.dumps(tools_config, ensure_ascii=False)}")
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logger.info(f" - tool_choice: auto")
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logger.info(
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f" - messages: {json.dumps(messages, ensure_ascii=False, indent=2)}"
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)
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resp = client.chat.completions.create(**api_params)
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message = resp.choices[0].message
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content = message.content or ""
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# 构建响应
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response_message = {"role": "assistant", "content": content}
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# 处理深度思考内容
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reasoning_content = getattr(message, "reasoning_content", None)
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if reasoning_content:
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response_message["reasoning_content"] = reasoning_content
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response = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"message": response_message,
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"finish_reason": "stop",
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}
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],
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}
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if hasattr(resp, "usage") and resp.usage:
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response["usage"] = {
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"prompt_tokens": resp.usage.prompt_tokens,
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"completion_tokens": resp.usage.completion_tokens,
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"total_tokens": resp.usage.total_tokens,
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}
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# 打印响应结果
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logger.info(f"[GLM] 响应结果:")
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logger.info(f" - content_length: {len(content)} 字符")
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logger.info(
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f" - content_preview: {content[:200]}..."
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if len(content) > 200
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else f" - content: {content}"
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)
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if hasattr(resp, "usage") and resp.usage:
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logger.info(f" - usage: {response['usage']}")
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return JSONResponse(content=response)
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