Home > Workload Solutions > Data Analytics > White Papers > Multimodal RAG Chatbot Powered by Dell Data Lakehouse > 10. Response Generation
The LLM synthesizes a final response using the context provided by the retrieved data, including both the results from the DDAE and the information from the vector database.
# Function to synthesize final response using LLM
def synthesize_final_response(query, sql_description, non_sql_components):
# Construct input prompt for LLM
input_prompt = (f"Based on the following context, synthesize a comprehensive response for the user query.\n\n"
f"User Query: {query}\n\n"
f"SQL Query Results Description: {sql_description}\n\n"
f"Additional Context from Vector Database: {non_sql_components}\n\n"
f"Final Response:")
# Tokenize and generate the final response
inputs = tokenizer(input_prompt, return_tensors="pt")
outputs = model.generate(**inputs)
final_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the final response (usually found after the prompt)
final_response = final_response.split("Final Response:")[1].strip()
return final_response
# Synthesize the final response
final_response = synthesize_final_response(user_query, sql_description, non_sql_components)
# Output the synthesized final response
print("Final Synthesized Response:", final_response)
The generated response is presented to the user, providing accurate, contextually informed answers to their query. This step ensures that the user receives a comprehensive and relevant response based on the available data.