101/RAG/main.py

import time
import os
from dotenv import load_dotenv
from config import system_prompt
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from embeddings import load_embeddings
from rag_chain import simple_rag_prompt, get_context
import pandas as pd
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma

# Load environment variables
load_dotenv('environment.env')

# Set up cache
set_llm_cache(SQLiteCache(database_path=".langchain.db"))

# Load standard QA data
qa_df = pd.read_csv('log_record_rows.csv')

# Initialize embeddings and Chroma
embeddings_model = OpenAIEmbeddings()
vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=embeddings_model)

def search_similarity(query, SIMILARITY_THRESHOLD=0.82):
    docs_and_scores = vectorstore.similarity_search_with_relevance_scores(query, k=1)
    if docs_and_scores:
        doc, score = docs_and_scores[0]
        if score >= SIMILARITY_THRESHOLD:
            # Find the corresponding answer in the qa_df
            question = doc.page_content
            answer = qa_df[qa_df['question'] == question]['answer'].values
            if len(answer) > 0:
                return answer[0], score
    return None, 0

def main():
    # Load embeddings and index
    embeddings, docs, df, index = load_embeddings()
    
    # Define retrieval chain
    retrieval_chain = lambda q: get_context(q, index, docs)
    
    print("RAG system initialized. You can start asking questions.")
    print("Type 'quit' to exit the program.")
    
    while True:
        user_input = input("\nEnter your question: ")
        
        if user_input.lower() == 'quit':
            break
        
        start_time = time.time()
        
        try:
            # First, search in the standard QA
            standard_answer, similarity_score = search_similarity(user_input)
            
            if standard_answer is not None:
                print(f"\nAnswer (from standard QA): {standard_answer}")
                # print(f"Similarity Score: {similarity_score:.4f}")
            else:
                # If not found in standard QA, use RAG
                rag_answer, rag_similarity_score = simple_rag_prompt(retrieval_chain, user_input)
                print(f"\nAnswer (from RAG): {rag_answer}")
                # print(f"Retrieval Similarity Score: {rag_similarity_score:.4f}")
            
            end_time = time.time()
            response_time = end_time - start_time
            print(f"Response Time: {response_time:.2f} seconds")
        
        except Exception as e:
            print(f"Error processing question: {str(e)}")
        
        # Add a small delay to avoid rate limiting
        time.sleep(1)

if __name__ == "__main__":
    main()