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import logging
import time
from generator.generate_response import generate_response
from retriever.retrieve_documents import retrieve_top_k_documents
from generator.compute_metrics import get_metrics
from generator.extract_attributes import extract_attributes
def retrieve_and_generate_response(gen_llm, vector_store, query):
logging.info(f"Query: {query}")
# Step 1: Retrieve relevant documents for given query
relevant_docs = retrieve_top_k_documents(vector_store, query, top_k=5)
#logging.info(f"Relevant documents retrieved :{len(relevant_docs)}")
# Log each retrieved document individually
#for i, doc in enumerate(relevant_docs):
#logging.info(f"Relevant document {i+1}: {doc} \n")
# Step 2: Generate a response using LLM
response, source_docs = generate_response(gen_llm, vector_store, query, relevant_docs)
logging.info(f"Response from LLM ({gen_llm.name}): {response}")
return response, source_docs
def generate_metrics(val_llm, response, source_docs, query, time_to_wait):
# Add a sleep interval to avoid hitting the rate limit
time.sleep(time_to_wait) # Adjust the sleep time as needed
# Step 3: Extract attributes and total sentences for each query
logging.info(f"Extracting attributes through validation LLM")
attributes, total_sentences = extract_attributes(val_llm, query, source_docs, response)
logging.info(f"Extracted attributes successfully")
# Step 4 : Call the get metrics calculate metrics
metrics = get_metrics(attributes, total_sentences)
return attributes, metrics |