Update app.py

app.py

@@ -92,7 +92,18 @@ def generate_response(query, history, model, temperature, max_tokens, top_p, see
     retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
     llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
     custom_rag_prompt = PromptTemplate.from_template(template)
-   
+
+    
+    # Step 1: Prepare inputs manually
+    docs = retriever.invoke(query)
+    context = format_docs(docs)
+    inputs = {"context": context, "question": query}
+
+    # Step 2: Get the final prompt string
+    prompt_value = custom_rag_prompt.invoke(inputs)
+    final_prompt = prompt_value.to_string()
+    print("Final Prompt Sent to LLM:\n", final_prompt)
+
     rag_chain = (
         {"context": retriever | format_docs, "question": RunnablePassthrough()}
         | custom_rag_prompt