Refactor API structure: move chat functionality to v1 router, implement dependency injection for OpenAI client, and set up application state management

api/v1/chat.py

@@ -0,0 +1,70 @@
+from fastapi import APIRouter, Body, Depends
+from openai import OpenAI
+from pydantic import BaseModel
+
+from api.dependencies import get_llm_client
+
+router = APIRouter()
+
+
+class ChatRequest(BaseModel):
+    message: str
+    session_id: str | None = None
+
+
+def run_agent(client: OpenAI, message: str, session_id: str | None = None) -> str:
+    response = client.chat.completions.create(
+        model="deepseek-chat",
+        messages=[
+            {"role": "system", "content": "You are a helpful agent."},
+            {"role": "user", "content": message},
+        ],
+    )
+    return response.choices[0].message.content
+
+
+@router.get("/")
+def root():
+    return {"status": "ok"}
+
+
+@router.post("/chat")
+def chat(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
+    response = run_agent(client, req.message, req.session_id)
+    return {"response": response, "session_id": req.session_id}
+
+
+@router.get("/models")
+def list_models():
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": "agent-model",
+                "object": "model",
+                "created": 0,
+                "owned_by": "local-agent",
+            }
+        ],
+    }
+
+
+@router.post("/chat/completions")
+def chat_completions(
+    payload: dict = Body(...),
+    client: OpenAI = Depends(get_llm_client),
+):
+    messages = payload["messages"]
+    user_message = messages[-1]["content"]
+    response = run_agent(client, user_message)
+
+    return {
+        "id": "chatcmpl-local",
+        "object": "chat.completion",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": response},
+            }
+        ],
+    }