添加AI 优化接口

app/ai/resume_diagnoser/diagnoser.py

@@ -9,7 +9,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
 from app.ai.models import LLM
-from app.ai.resume_diagnoser.prompts import DIAGNOSE_MODULE_PROMPT, SUMMARY_PROMPT
+from app.ai.resume_diagnoser.prompts import DIAGNOSE_MODULE_PROMPT, SUMMARY_PROMPT, POLISH_PROMPT
 from app.core.logger import log
 
 
@@ -62,6 +62,45 @@ async def generate_summary(grade: str, urgent_total: int, important_total: int,
         return "简历诊断已完成，请查看各模块的详细诊断结果。"
 
 
+_polish_chain = (
+    ChatPromptTemplate.from_messages([("system", POLISH_PROMPT), ("human", "请开始优化。")])
+    | LLM.CLAUDE_SONNET_4.create(temperature=0.3)
+    | StrOutputParser()
+)
+
+
+async def polish_content(module_type: str, reference_content: list[dict] | str | None,
+                         user_content: list[str], is_summary: bool) -> list[str]:
+    """润色用户编辑后的文本"""
+    ref_text = ""
+    if reference_content:
+        if isinstance(reference_content, list):
+            ref_text = "\n".join(
+                item.get("text", "") if isinstance(item, dict) else str(item)
+                for item in reference_content
+            )
+        else:
+            ref_text = str(reference_content)
+    if not ref_text:
+        ref_text = "无"
+
+    inp = {
+        "module_type": module_type,
+        "reference_content": ref_text,
+        "user_content": "\n".join(user_content),
+        "summary_constraint": "- 注意：此模块只能输出一个段落，数组只能有一个元素" if is_summary else "",
+    }
+    try:
+        raw = await _polish_chain.ainvoke(inp)
+        result = _parse_json(raw)
+        if isinstance(result, list):
+            return [str(item) for item in result]
+        return [str(result)]
+    except Exception as e:
+        log.warning(f"AI润色失败: {e}")
+        return user_content
+
+
 async def _safe_invoke(task: dict) -> dict:
     """单条记录诊断，失败返回空结果"""
     raw = ""