diff --git a/app/main.py b/app/main.py
index cac379f..156c43c 100644
--- a/app/main.py
+++ b/app/main.py
@@ -2,6 +2,7 @@ import os
 import sys
 import logging
 import time
+import json
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, Depends, Request
 from starlette.responses import StreamingResponse
@@ -58,31 +59,42 @@ async def startup_event():
 
 @app.post("/v1/chat/completions")
 async def chat_completions(
-    request: IncomingRequest,
+    request: Request,
     settings: Settings = Depends(get_settings)
 ):
     """
     This endpoint mimics the OpenAI Chat Completions API and supports both
     streaming and non-streaming responses, with detailed logging.
     """
-    log_id = log_request(client_request=request.model_dump())
+    # Read raw request body
+    raw_body = await request.body()
+    body_str = raw_body.decode('utf-8')
+
+    # Log the raw client request
+    client_request = json.loads(body_str)
+    log_id = log_request(client_request=client_request)
     logger.info(f"Request body logged with ID: {log_id}")
 
+    # Parse into IncomingRequest model for validation and type safety
+    try:
+        request_obj = IncomingRequest(**client_request)
+    except Exception as e:
+        logger.error(f"Failed to parse request: {e}")
+        raise HTTPException(status_code=400, detail=f"Invalid request format: {str(e)}")
+
     if not settings.REAL_LLM_API_KEY or not settings.REAL_LLM_API_URL:
         logger.error("REAL_LLM_API_KEY or REAL_LLM_API_URL is not configured.")
         raise HTTPException(status_code=500, detail="LLM API Key or URL is not configured.")
 
-    messages_to_llm = request.messages
-    if request.tools:
-        messages_to_llm = inject_tools_into_prompt(request.messages, request.tools)
+    messages_to_llm = request_obj.messages
+    if request_obj.tools:
+        messages_to_llm = inject_tools_into_prompt(request_obj.messages, request_obj.tools)
 
     # Handle streaming request
-    if request.stream:
+    if request_obj.stream:
         logger.info(f"Initiating streaming request for log ID: {log_id}")
 
         async def stream_and_log():
-            import json
-
             stream_content_buffer = []
             raw_chunks = []
 
diff --git a/app/models.py b/app/models.py
index 8f705b3..ffd6cf0 100644
--- a/app/models.py
+++ b/app/models.py
@@ -5,7 +5,9 @@ from typing import List, Dict, Any, Optional
 class ChatMessage(BaseModel):
     """Represents a single message in the chat history."""
     role: str
-    content: str
+    content: Optional[str] = None
+    name: Optional[str] = None  # For function role messages
+    tool_calls: Optional[List[Dict[str, Any]]] = None  # For assistant messages with tool calls
 
 class Function(BaseModel):
     """Represents the function definition within a tool."""