modified app.py

app.py

@@ -4,11 +4,10 @@ from rag_pipeline import full_rag_pipeline
 from langchain_google_genai import GoogleGenerativeAI
 import os
 from dotenv import load_dotenv
-import psutil
-import os
+
 # Load environment variables from .env file
 load_dotenv()
-import time
+
 
 app = FastAPI()
 
@@ -18,28 +17,8 @@ expanding_retriever = prepare_environment_and_retriever()
 
 @app.get("/ask")
 def ask_question(question: str, with_citations: bool = Query(False, description="Include citations in the response")):
-    process = psutil.Process(os.getpid())
-    start_time = time.time()
-    start_mem = process.memory_info().rss  # RAM in bytes
-
-    # Run RAG
-    response = full_rag_pipeline(question, expanding_retriever, clean_all_citations=with_citations)
-
-    end_time = time.time()
-    end_mem = process.memory_info().rss
-
-    # Metrics
-    latency = end_time - start_time
-    ram_used_mb = (end_mem - start_mem) / (1024 ** 2)  # Convert to MB
-
-    return {
-        "question": question,
-        "answer": response,
-        "metrics": {
-            "latency_seconds": round(latency, 3),
-            "ram_usage_delta_mb": round(ram_used_mb, 2)
-        }
-    }
+    response = full_rag_pipeline(question, expanding_retriever,clean_all_citations=with_citations)
+    return {"question": question, "answer": response}
 @app.get("/generate_title")
 def generate_title(first_question: str = Query(..., description="The first question to generate a title from")):
     # Initialize the LLM - using the same model as in prepare_env.py