feat: Initialize FastAPI AI Gateway project structure with authentication, module management, and LLM API routing.

app/api/endpoints/gemini.py

@@ -0,0 +1,54 @@
+from fastapi import APIRouter, Depends, Request
+from app.api.deps import get_api_key
+from app.core.limiter import limiter
+from app.core.config import settings
+from pydantic import BaseModel
+from google import genai
+import asyncio
+
+router = APIRouter()
+
+class LLMRequest(BaseModel):
+    prompt: str
+    context: str = ""
+
+# Shared client instance (global)
+_client = None
+
+def get_gemini_client():
+    global _client
+    if _client is None and settings.GOOGLE_API_KEY and settings.GOOGLE_API_KEY != "your-google-api-key":
+        _client = genai.Client(api_key=settings.GOOGLE_API_KEY, http_options={'api_version': 'v1alpha'})
+    return _client
+
+@router.post("/chat")
+@limiter.limit(settings.RATE_LIMIT)
+async def gemini_chat(
+    request: Request,
+    chat_data: LLMRequest,
+    api_key: str = Depends(get_api_key)
+):
+    client = get_gemini_client()
+    
+    try:
+        if not client:
+             return {
+                "status": "mock",
+                "model": "gemini",
+                "response": f"MOCK: Gemini response to '{chat_data.prompt}'"
+            }
+            
+        # Using the async generation method provided by the new google-genai library
+        # We use await to ensure we don't block the event loop
+        response = await client.aio.models.generate_content(
+            model="gemini-2.0-flash", 
+            contents=chat_data.prompt
+        )
+        
+        return {
+            "status": "success",
+            "model": "gemini",
+            "response": response.text
+        }
+    except Exception as e:
+        return {"status": "error", "detail": str(e)}