216 lines
7.5 KiB
Python
216 lines
7.5 KiB
Python
"""
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智谱 GLM 适配器
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使用 OpenAI SDK 调用智谱 OpenAI 兼容 API
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"""
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import json
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import os
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from typing import Any, Dict, List, Optional
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from .base import ChatCompletionRequest, ModelInfo
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from .unified_adapter import UnifiedOpenAIAdapter
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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",
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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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# 从 GLM_MODELS 自动计算
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VISION_MODELS = {m.id.lower() for m in GLM_MODELS if m.supports_vision}
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THINKING_MODELS = {m.id.lower() for m in GLM_MODELS if m.supports_thinking}
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class GLMAdapter(UnifiedOpenAIAdapter):
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"""智谱 GLM 平台适配器"""
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_provider_type = "zhipu"
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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 list_models(self) -> List[ModelInfo]:
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return GLM_MODELS
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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 _build_messages(self, request: ChatCompletionRequest) -> List[Dict]:
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"""
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构建 GLM 格式的消息
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处理文件附件和多模态内容
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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)
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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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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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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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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
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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(self, model: str, has_vision: bool, has_files: bool = False) -> 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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if (has_vision or has_files) and model_lower not in VISION_MODELS:
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logger.info(f"[GLM] 检测到图片或文件附件，切换模型: {model} -> glm-4.6v")
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return "glm-4.6v"
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return model
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def _get_extra_params(self, request: ChatCompletionRequest) -> Dict[str, Any]:
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"""
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获取 GLM 特殊参数
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- 深度思考: extra_body={"thinking": {"type": "enabled/disabled"}}
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- 联网搜索: tools=[{"type": "web_search", ...}]
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"""
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extra_params = {}
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# 检测是否有多模态内容，决定最终使用的模型
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messages = self._build_messages(request)
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has_vision = any(
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isinstance(m.get("content"), list) and
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any(c.get("type") == "image_url" for c in m.get("content", []))
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for m in messages
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)
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has_files = bool(request.files)
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actual_model = self._resolve_model(request.model, has_vision, has_files)
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# 更新请求中的模型（如果有变化）
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if actual_model != request.model:
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extra_params["model"] = actual_model
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# 联网搜索
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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_params["tools"] = [build_glm_search_tool(web_search_mode)]
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extra_params["tool_choice"] = "auto"
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logger.info(f"[GLM] 联网搜索已启用: mode={web_search_mode}")
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# 深度思考 - 始终传递，明确启用或禁用
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logger.info(f"[GLM] 深度思考请求: deep_thinking={request.deep_thinking}, actual_model={actual_model}")
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# 判断是否支持深度思考
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supports_thinking = self._supports_thinking(actual_model)
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logger.info(f"[GLM] 模型 {actual_model} 支持深度思考: {supports_thinking}")
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# 只有前端请求启用 且 模型支持时才启用
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thinking_enabled = request.deep_thinking and supports_thinking
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thinking_type = "enabled" if thinking_enabled else "disabled"
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extra_params["extra_body"] = {"thinking": {"type": thinking_type}}
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logger.info(f"[GLM] 深度思考最终状态: {thinking_type}")
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return extra_params |