Update main.py

main.py

@@ -1,58 +1,60 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from langchain_community.llms import Ollama  # Correct Import
-import logging
-import time  # Import time module
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-
-app = FastAPI()
-
-# OpenAI-compatible request format
-class OpenAIRequest(BaseModel):
-    model: str
-    messages: list
-    stream: bool = False  # Default to non-streaming
-
-# Initialize LangChain LLM with Ollama
-def get_llm(model_name: str):
-    return Ollama(model=model_name)
-
-@app.post("/v1/chat/completions")
-def generate_text(request: OpenAIRequest):
-    try:
-        llm = get_llm(request.model)
-
-        # Extract last user message from messages
-        user_message = next((msg["content"] for msg in reversed(request.messages) if msg["role"] == "user"), None)
-        if not user_message:
-            raise HTTPException(status_code=400, detail="User message is required")
-
-        response_text = llm.invoke(user_message)
-
-        # OpenAI-like response format
-        response = {
-            "id": "chatcmpl-123",
-            "object": "chat.completion",
-            "created": int(time.time()),
-            "model": request.model,
-            "choices": [
-                {
-                    "index": 0,
-                    "message": {"role": "assistant", "content": response_text},
-                    "finish_reason": "stop",
-                }
-            ],
-            "usage": {
-                "prompt_tokens": len(user_message.split()),
-                "completion_tokens": len(response_text.split()),
-                "total_tokens": len(user_message.split()) + len(response_text.split()),
-            }
-        }
-
-        return response
-
-    except Exception as e:
-        logging.error(f"Error generating response: {e}")
-        raise HTTPException(status_code=500, detail="Internal server error")
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from langchain_community.llms import Ollama  # Correct Import
+import logging
+import time  # Import time module
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+
+app = FastAPI()
+
+# OpenAI-compatible request format
+class OpenAIRequest(BaseModel):
+    model: str
+    messages: list
+    stream: bool = False  # Default to non-streaming
+
+# Initialize LangChain LLM with Ollama
+def get_llm(model_name: str):
+    return Ollama(model=model_name)
+@app.get("/")
+def home():
+    return {"message": "OpenAI-compatible LangChain + Ollama API is running"}
+@app.post("/v1/chat/completions")
+def generate_text(request: OpenAIRequest):
+    try:
+        llm = get_llm(request.model)
+
+        # Extract last user message from messages
+        user_message = next((msg["content"] for msg in reversed(request.messages) if msg["role"] == "user"), None)
+        if not user_message:
+            raise HTTPException(status_code=400, detail="User message is required")
+
+        response_text = llm.invoke(user_message)
+
+        # OpenAI-like response format
+        response = {
+            "id": "chatcmpl-123",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": request.model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": response_text},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {
+                "prompt_tokens": len(user_message.split()),
+                "completion_tokens": len(response_text.split()),
+                "total_tokens": len(user_message.split()) + len(response_text.split()),
+            }
+        }
+
+        return response
+
+    except Exception as e:
+        logging.error(f"Error generating response: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error")