ChatAnthropic

.env

@@ -19,9 +19,13 @@ DB_NAME=offerpie
 VOLCENGINE_API_KEY=fd065993-bee2-4f31-8bf2-56d5d3012c02
 VOLCENGINE_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
 
+# Claude（Anthropic 风格）
+ANTHROPIC_API_KEY=sk-43ccdb29caa7e9ebe0db8ac0958c63f6d3a2d62e59064d3d26d94332055a9bc9
+ANTHROPIC_BASE_URL=https://code.warpdevloper.cloud
+
 # 岗位清洗参数
-CLEAN_BATCH_SIZE=100
+CLEAN_BATCH_SIZE=10
-CLEAN_CONCURRENCY=50
+CLEAN_CONCURRENCY=10
 CLEAN_INTERVAL_SECONDS=180
 
 # 公司补充参数

app/ai/model_config.py

@@ -9,14 +9,14 @@ from app.ai.models import LLM
 class JobCleanModel:
     """岗位清洗模块"""
     # 第一次AI：结构化提取岗位信息
-    STRUCTURE = LLM.DOUBAO_SEED_LITE.create(temperature=0)
+    STRUCTURE = LLM.CLAUDE_OPUS.create(temperature=0)
     # 第二次AI：专业匹配
-    MAJOR_MATCH = LLM.DOUBAO_SEED_LITE.create(temperature=0)
+    MAJOR_MATCH = LLM.CLAUDE_OPUS.create(temperature=0)
     # 第三次AI：技能提取
-    SKILL_EXTRACT = LLM.DOUBAO_SEED_LITE.create(temperature=0)
+    SKILL_EXTRACT = LLM.CLAUDE_OPUS.create(temperature=0)
 
 
 class CompanyCleanModel:
     """公司补充模块"""
     # 公司信息补充
-    ENRICH = LLM.DOUBAO_SEED_LITE.create(temperature=0)
+    ENRICH = LLM.CLAUDE_OPUS.create(temperature=0)

app/ai/models.py

@@ -4,36 +4,48 @@ Usage:
     from app.ai.models import LLM
 
     llm = LLM.DOUBAO_SEED_LITE.create(temperature=0)
+    llm = LLM.CLAUDE_OPUS.create(temperature=0)
 """
 
 from enum import Enum
 
+from langchain_anthropic import ChatAnthropic
+from langchain_core.language_models import BaseChatModel
 from langchain_openai import ChatOpenAI
 
 from app.config import settings
 
-# 供应商连接配置
+# 供应商连接配置 = (api_key函数, base_url函数)
 _VOLCENGINE = (lambda: settings.volcengine_api_key, lambda: settings.volcengine_base_url)
+_ANTHROPIC = (lambda: settings.anthropic_api_key, lambda: settings.anthropic_base_url)
 
 
 class LLM(Enum):
-    """所有可用模型，每个枚举值 = (模型名, api_key函数, base_url函数)"""
+    """所有可用模型，每个枚举值 = (模型名, 封装类, api_key函数, base_url函数)"""
 
-    # 火山引擎
+    # 火山引擎（OpenAI 兼容）
-    DOUBAO_PRO_32K = ("doubao-1-5-pro-32k-250115", *_VOLCENGINE)
+    DOUBAO_PRO_32K = ("doubao-1-5-pro-32k-250115", ChatOpenAI, *_VOLCENGINE)
-    DOUBAO_LITE_32K = ("doubao-1-5-lite-32k-250115", *_VOLCENGINE)
+    DOUBAO_LITE_32K = ("doubao-1-5-lite-32k-250115", ChatOpenAI, *_VOLCENGINE)
-    DOUBAO_SEED_LITE = ("doubao-seed-2-0-lite-260215", *_VOLCENGINE)
+    DOUBAO_SEED_LITE = ("doubao-seed-2-0-lite-260215", ChatOpenAI, *_VOLCENGINE)
-    DOUBAO_SEED_PRO = ("doubao-seed-2-0-pro-260215", *_VOLCENGINE)
+    DOUBAO_SEED_PRO = ("doubao-seed-2-0-pro-260215", ChatOpenAI, *_VOLCENGINE)
-    DEEPSEEK_V4_FLASH = ("deepseek-v4-flash-260425", *_VOLCENGINE)
+    DEEPSEEK_V4_FLASH = ("deepseek-v4-flash-260425", ChatOpenAI, *_VOLCENGINE)
 
-    def __init__(self, model_name: str, api_key_fn, base_url_fn):
+    # Claude（Anthropic 风格）
+    CLAUDE_OPUS = ("claude-opus-4-6", ChatAnthropic, *_ANTHROPIC)
+
+    def __init__(self, model_name: str, cls, api_key_fn, base_url_fn):
         self.model_name = model_name
+        self._cls = cls
         self._api_key_fn = api_key_fn
         self._base_url_fn = base_url_fn
 
-    def create(self, **kwargs) -> ChatOpenAI:
+    def create(self, **kwargs) -> BaseChatModel:
-        """创建 LLM 实例，kwargs 透传给 ChatOpenAI（temperature, max_tokens 等）"""
+        """创建 LLM 实例，kwargs 透传给底层封装（temperature, max_tokens 等）
-        return ChatOpenAI(
+
+        封装类（ChatOpenAI / ChatAnthropic）均实现 langchain BaseChatModel 接口，
+        对上层调用方完全透明。
+        """
+        return self._cls(
             model=self.model_name,
             api_key=self._api_key_fn(),
             base_url=self._base_url_fn(),

app/config/settings.py

@@ -26,9 +26,14 @@ class Settings(BaseSettings):
     mysql_max_overflow: int = 20
 
     # ──────────── AI 供应商 ────────────
+    # 火山引擎（OpenAI 兼容风格）
     volcengine_api_key: str =  "fd065993-bee2-4f31-8bf2-56d5d3012c02"
     volcengine_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
 
+    # Claude（Anthropic 风格）
+    anthropic_api_key: str = "sk-43ccdb29caa7e9ebe0db8ac0958c63f6d3a2d62e59064d3d26d94332055a9bc9"
+    anthropic_base_url: str = "https://code.warpdevloper.cloud"
+
     # ──────────── 岗位清洗参数 ────────────
     clean_batch_size: int = 100
     clean_concurrency: int = 80

app/services/ai_tool.py

@@ -4,7 +4,7 @@ import re
 from typing import Any
 
 from json_repair import repair_json
-from langchain_openai import ChatOpenAI
+from langchain_core.language_models import BaseChatModel
 from langchain_core.messages import SystemMessage, HumanMessage
 
 from app.core.logger import log
@@ -28,7 +28,7 @@ def parse_llm_json(text: str) -> Any:
     return repair_json(cleaned, return_objects=True)
 
 
-async def ai_chat(llm: ChatOpenAI, system_prompt: str, user_message: str) -> str:
+async def ai_chat(llm: BaseChatModel, system_prompt: str, user_message: str) -> str:
     """异步调用 LLM，返回原始文本"""
     messages = [
         SystemMessage(content=system_prompt),
@@ -38,7 +38,7 @@ async def ai_chat(llm: ChatOpenAI, system_prompt: str, user_message: str) -> str
     return response.content
 
 
-async def ai_chat_json(llm: ChatOpenAI, system_prompt: str, user_message: str) -> Any:
+async def ai_chat_json(llm: BaseChatModel, system_prompt: str, user_message: str) -> Any:
     """异步调用 LLM，返回解析后的 JSON 对象"""
     raw = await ai_chat(llm, system_prompt, user_message)
     if not raw or not raw.strip():

requirements.txt

@@ -7,6 +7,7 @@ apscheduler>=3.10
 
 # AI
 langchain-openai>=0.3
+langchain-anthropic>=0.3
 langchain-core>=0.3
 
 # 工具