model相关参数和默认

.env

@@ -0,0 +1,16 @@
+# LLM provider API keys
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=
+OPENROUTER_API_KEY=
+OPENROUTER_SITE_URL=
+OPENROUTER_APP_NAME=
+GEMINI_API_KEY=
+QWEN_API_KEY=
+DEEPSEEK_API_KEY="sk-657f0752a1564563be7ce35b6a0a7b46"
+
+# Data import analysis defaults
+IMPORT_SUPPORTED_MODELS=openai:gpt-5,deepseek:deepseek-chat,openrouter:anthropic/claude-4.0-sonnet
+DEFAULT_IMPORT_MODEL=deepseek:deepseek-chat
+
+# Service configuration
+IMPORT_GATEWAY_BASE_URL=http://localhost:8000

app/models.py

@@ -5,6 +5,8 @@ from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel, Field, HttpUrl
 
+from app.settings import DEFAULT_IMPORT_MODEL
+
 
 class LLMRole(str, Enum):
     USER = "user"
@@ -117,8 +119,8 @@ class DataImportAnalysisJobRequest(BaseModel):
         description="URL to notify when the analysis completes. Receives JSON payload with status/results.",
     )
     llm_model: str = Field(
-        "gpt-4.1-mini",
+        DEFAULT_IMPORT_MODEL,
-        description="Target LLM model identifier. Defaults to gpt-4.1-mini.",
+        description="Target LLM model identifier. Defaults to DEFAULT_IMPORT_MODEL.",
     )
     temperature: Optional[float] = Field(
         None,

app/settings.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import os
+from functools import lru_cache
+from typing import Dict, Set
+
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+
+PROVIDER_KEY_ENV_MAP: Dict[str, str] = {
+    "openai": "OPENAI_API_KEY",
+    "anthropic": "ANTHROPIC_API_KEY",
+    "openrouter": "OPENROUTER_API_KEY",
+    "gemini": "GEMINI_API_KEY",
+    "qwen": "QWEN_API_KEY",
+    "deepseek": "DEEPSEEK_API_KEY",
+}
+
+
+DEFAULT_IMPORT_MODEL = os.getenv("DEFAULT_IMPORT_MODEL", "openai:gpt-4.1-mini")
+
+
+@lru_cache(maxsize=1)
+def get_supported_import_models() -> Set[str]:
+    raw = os.getenv("IMPORT_SUPPORTED_MODELS", "")
+    return {model.strip() for model in raw.split(",") if model.strip()}
+
+
+@lru_cache(maxsize=1)
+def get_available_provider_keys() -> Dict[str, str]:
+    keys: Dict[str, str] = {}
+    for provider, env_name in PROVIDER_KEY_ENV_MAP.items():
+        value = os.getenv(env_name)
+        if value:
+            keys[provider] = value
+    return keys