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The retrieved data, along with the original user query, is fed into the LLM. This step involves passing both the contextually relevant data and the user’s request to the model.
# Integration with LLM
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the LLM
model_name = "gpt-3.5-turbo"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Function to prepare input for LLM
def prepare_llm_input(query, context_data):
# Combine the user query with the retrieved context data
input_text = f"User Query: {query}\nContextual Data: {context_data}"
return input_text
# Function to generate a response from LLM
def generate_llm_response(input_text):
# Tokenize and generate a response from the LLM
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Prepare the input for the LLM
llm_input = prepare_llm_input(user_query, search_results)
# Generate a response from the LLM
llm_response = generate_llm_response(llm_input)
# Output the response from the LLM
print("LLM Response:", response)
The LLM processes the input to understand the context and relevance of the retrieved data, preparing it for further analysis and response generation.