feat(agent-core): 补齐提示词编排与结构化解析

agent_core/llm_provider.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass
 import json
+import os
 from urllib.error import URLError
 from urllib.request import Request, urlopen
 
@@ -25,13 +26,22 @@ class MockLLMProvider:
         self.model_name = model_name or "mock-model"
 
     def generate(self, messages: list[dict], response_format: dict | None = None) -> LLMResponse:
+        # Mock Provider 的职责是让页面和测试在未接入真实模型时也能闭环。
+        # 因此这里直接返回稳定 JSON，方便后续统一走结构化解析逻辑。
         user_content = ""
         for message in reversed(messages):
             if message.get("role") == "user":
                 user_content = message.get("content", "")
                 break
         return LLMResponse(
-            content=f"模拟模型回答：{user_content}",
+            content=json.dumps(
+                {
+                    "answer": f"模拟回答：{user_content}",
+                    "confidence": "medium",
+                    "references": [],
+                },
+                ensure_ascii=False,
+            ),
             model_name=self.model_name,
             success=True,
         )
@@ -112,7 +122,9 @@ def _post_json(base_url: str, endpoint: str, api_key: str, payload: dict) -> dic
 
 def create_llm_provider(config: dict | None = None):
     config = config or {}
-    provider_name = config.get("LLM_PROVIDER", "mock")
+    provider_name = config.get("LLM_PROVIDER")
+    if not provider_name:
+        provider_name = "openai_compatible" if config.get("LLM_API_KEY") else "mock"
     model_name = config.get("LLM_MODEL", "mock-model")
     if provider_name == "mock":
         return MockLLMProvider(model_name=model_name)
@@ -130,3 +142,21 @@ def create_embedding_provider(config: dict | None = None):
         base_url=config.get("EMBEDDING_BASE_URL", config.get("LLM_BASE_URL", "https://api.openai.com/v1")),
         model_name=config.get("EMBEDDING_MODEL", "text-embedding-3-small"),
     )
+
+
+def get_runtime_llm_config(overrides: dict | None = None) -> dict:
+    """
+    从环境变量读取运行时配置。
+
+    Agent Core 通过这层读取模型配置，避免直接依赖 Django settings，
+    这样本模块在独立脚本、测试和 Django 中都能复用。
+    """
+    config = {
+        "LLM_PROVIDER": os.environ.get("LLM_PROVIDER", ""),
+        "LLM_API_KEY": os.environ.get("LLM_API_KEY", ""),
+        "LLM_BASE_URL": os.environ.get("LLM_BASE_URL", "https://api.openai.com/v1"),
+        "LLM_MODEL": os.environ.get("LLM_MODEL", "mock-model"),
+    }
+    if overrides:
+        config.update(overrides)
+    return config