import initialize from langchain_openai import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from langchain.chains import VectorDBQA from langchain_community.llms import OpenAI from langchain_core.prompts import PromptTemplate from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline from langchain.chains import LLMChain from langchain_google_genai import GoogleGenerativeAI from langchain_google_genai import ChatGoogleGenerativeAI import google.generativeai as genai import gradio as gr import requests import os import sys sys.path.append('../..') # For Google Colab ''' from google.colab import userdata OPENAI_API_KEY = userdata.get('OPENAI_API_KEY') hf_token = userdata.get('hf_token') GEMINI_API_KEY = userdata.get('GEMINI_API_KEY') # For Desktop from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) # Read local .env file OPENAI_API_KEY = os.environ['OPENAI_API_KEY'] hf_token = os.environ['hf_token'] GEMINI_API_KEY = os.environ['GEMINI_API_KEY'] ''' # For Hugging Face OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY') hf_token = os.environ.get('hf_token') GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY') fs_token = os.environ.get('fs_token') llm_name = "gpt-3.5-turbo-0301" vectordb = initialize.initialize() chat_history = [] # For getting source documents def get_file(source_documents): files = set() for doc in source_documents: file = os.path.basename(doc.metadata['source']) files.add(file) # Print unique filenames return list(set(files)) def chat_query_doc(question, history): history = [] query_old = f"""As an experienced Electrical Engineer, please provide an elaborate, precise, and answer politely pointwise to the question: {question}. Also, Please consider the provided chat history: {history}. Ensure that your current response is detailed, accurate, and addresses each aspect of the question thoroughly. If the context of the question doesn't align with your last reply, please provide your response in a fresh manner. If don't get the answer, feel free to reply from your own knowledge.""" query = f"""As an experienced Electrical Engineer, please provide an detailed, accurate and point-wise answer to the question: """ #llm = ChatOpenAI(model = llm_name, temperature = 0.1, api_key = OPENAI_API_KEY) llm = GoogleGenerativeAI(model = "gemini-pro", google_api_key = GEMINI_API_KEY) #llm = ChatGoogleGenerativeAI(model = "gemini-1.0-pro", google_api_key = GEMINI_API_KEY, temperature = 0.1, top_k = 1, top_p = 0.95) retriever=vectordb.as_retriever(search_type="mmr") def get_relevant_passage(query, retriever): passage = (retriever.invoke(query)[0]).page_content return passage # Perform embedding search passage = get_relevant_passage(question, retriever) def make_prompt(query, relevant_passage): escaped = relevant_passage.replace("'", "").replace('"', "").replace("\n", " ") prompt = ("""You are a helpful and informative bot that answers questions only using the text from the reference passage included below. \ Be sure to respond in a complete sentence, being elaborate and comprehensive, including all relevant background information. \ However, you are talking to a technical audience, so be sure to break down complicated concepts and \ strike a friendly and converstional tone. \ QUESTION: '{query}' PASSAGE: '{relevant_passage}' ANSWER: """).format(query=query, relevant_passage=escaped) return prompt prompt = make_prompt(question, passage) genai.configure(api_key=GEMINI_API_KEY) model = genai.GenerativeModel('gemini-pro') answer = model.generate_content(prompt) # Conversation Retrival Chain with Memory # memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, return_source_documents=True) # # Replace input() with question variable for Gradio result = qa({"question": query+question, "chat_history" : history}) source_docs = result["source_documents"] file_names = get_file(source_docs) #file_name = os.path.basename(source_docs[0].metadata['source']) file_name = ', '.join([f"{x}" for x in file_names[:3]]) # print("History : ", history) # print("\n Chat_his : ", chat_history) return answer.text + "\n\nSources : " + file_name def chat_query_IS(question, history): llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=GEMINI_API_KEY) system_old = f""" Provide an elaborate, detailed and precise reply about the Topic as an experienced Electrical Engineer, as per relevant IS/IEEE/BIS Standard. Also, at the end of your reply, quote the Relevant Standard Referred. Topic : """ system = f""" Provide a reply poetically precise as william shakespeare for the Topic : """ result = llm.invoke(system_old + question) return result.content iface_doc = gr.ChatInterface( fn=chat_query_doc, title="""Standard TS of POWERGRID""", concurrency_limit = None, examples = ["What should be the GIB height outside the GIS hall ?" , "Explain about STATCOM Station Ratings" , "Specifications of XLPE POWER Cables."], # "Specification for Ethernet Switches in SAS."] , theme=gr.themes.Base(), fill_height = True, delete_cache = (300,360), css = "CSS/chat_style.css", ) iface_IS = gr.ChatInterface( fn = chat_query_IS, title = """Indian / International Standards""", concurrency_limit = None, examples = ["Type Tests for HV Switchgears." , "Measurement of acoustic noise level of Transformers & Reactors" , "Technical Requirement for 765kV class Transformer", "Specification of Distance Relays"] , theme=gr.themes.Base(), fill_height = True, delete_cache = (300,360), css = "CSS/chat_style.css", ) Title= "# Conversational BOT for Model-TS & Indian / International Standards" Description = """ ### Welcome to the Language Model (SS-Engg-Dept.)! 👋 This model is trained on **Model Technical Specifications** of the SS-Engg. Dept. and leverages the power of **Google Gemini-Pro** to answer your queries based on: * Relevant TS, GTR & Specific Requirements 📑 * International/Indian Standards 🌎🇮🇳 **Tips for Effective Use:** * Use elaborate questions for more accurate responses. 🤔 * Clear the chat if you don't receive a reply. 🔄 * Include **Specific Keywords** in your query for precise results. 🎯 """ with gr.Blocks(css="CSS/style.css", fill_height=True) as demo: with gr.Column(): with gr.Row(): with gr.Column(scale=1): gr.Image("Images/Chatbot.png", width = 110, show_download_button = False, show_label = False, show_share_button = False, elem_id = "Logo") with gr.Column(scale=3): gr.Markdown(Title) with gr.Column(scale=1): gr.Image("Images/PG Logo.png", width = 200, show_download_button = False, show_label = False, show_share_button = False, elem_id = "PG_Logo") with gr.Row(): gr.Markdown(Description) with gr.Row(equal_height=True): with gr.Column(elem_classes = ["chat_container"]): iface_doc.render() with gr.Column(elem_classes = ["chat_container"]): iface_IS.render() if __name__ == "__main__": demo.launch(debug=True, share=True)