import datetime import openai import uuid import gradio as gr from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter from langchain.chains import ConversationalRetrievalChain from langchain.chains import RetrievalQA import os from langchain.chat_models import ChatOpenAI from langchain import OpenAI from langchain.document_loaders import WebBaseLoader, TextLoader, Docx2txtLoader, PyMuPDFLoader from whatsapp_chat_custom import WhatsAppChatLoader # use this instead of from langchain.document_loaders import WhatsAppChatLoader from collections import deque import re from bs4 import BeautifulSoup import requests from urllib.parse import urlparse import mimetypes from pathlib import Path import tiktoken from ttyd_functions import * from ttyd_consts import * ############################################################################################### # select the mode at runtime when starting container - modes options are in ttyd_consts.py if os.getenv("TTYD_MODE")=='arslan': mode = mode_arslan elif os.getenv("TTYD_MODE")=='nustian': mode = mode_nustian else: mode = mode_general if mode.type!='userInputDocs': # local vector store as opposed to gradio state vector store vsDict_hard = localData_vecStore(os.getenv("OPENAI_API_KEY"), inputDir=mode.inputDir, file_list=mode.file_list, url_list=mode.url_list) ############################################################################################### # Gradio ############################################################################################### def generateExamples(api_key_st, vsDict_st): qa_chain = RetrievalQA.from_llm(llm=ChatOpenAI(openai_api_key=api_key_st, temperature=0), retriever=vsDict_st['chromaClient'].as_retriever(search_type="similarity", search_kwargs={"k": 4})) result = qa_chain({'query': exp_query}) answer = result['result'].strip('\n') grSamples = [[]] if answer.startswith('1. '): lines = answer.split("\n") # split the answers into individual lines list_items = [line.split(". ")[1] for line in lines] # extract each answer after the numbering grSamples = [[x] for x in list_items] # gr takes list of each item as a list return grSamples # initialize chatbot function sets the QA Chain, and also sets/updates any other components to start chatting. updateQaChain function only updates QA chain and will be called whenever Adv Settings are updated. def initializeChatbot(temp, k, modelName, stdlQs, api_key_st, vsDict_st, progress=gr.Progress()): progress(0.1, waitText_initialize) qa_chain_st = updateQaChain(temp, k, modelName, stdlQs, api_key_st, vsDict_st) progress(0.5, waitText_initialize) #generate welcome message if mode.welcomeMsg: welMsg = mode.welcomeMsg else: welMsg = qa_chain_st({'question': initialize_prompt, 'chat_history':[]})['answer'] # exSamples = generateExamples(api_key_st, vsDict_st) # exSamples_vis = True if exSamples[0] else False return qa_chain_st, btn.update(interactive=True), initChatbot_btn.update('Chatbot ready. Now visit the chatbot Tab.', interactive=False)\ , aKey_tb.update(), gr.Tabs.update(selected='cb'), chatbot.update(value=[('', welMsg)]) def setApiKey(api_key): api_key = transformApi(api_key) try: openai.Model.list(api_key=api_key) # test the API key api_key_st = api_key return aKey_tb.update('API Key accepted', interactive=False, type='text'), aKey_btn.update(interactive=False), api_key_st except Exception as e: return aKey_tb.update(str(e), type='text'), *[x.update() for x in [aKey_btn, api_key_state]] # convert user uploaded data to vectorstore def uiData_vecStore(userFiles, userUrls, api_key_st, vsDict_st={}, progress=gr.Progress()): opComponents = [data_ingest_btn, upload_fb, urls_tb] # parse user data file_paths = [] documents = [] if userFiles is not None: if not isinstance(userFiles, list): userFiles = [userFiles] file_paths = [file.name for file in userFiles] userUrls = [x.strip() for x in userUrls.split(",")] if userUrls else [] #create documents documents = data_ingestion(file_list=file_paths, url_list=userUrls, prog=progress) if documents: for file in file_paths: os.remove(file) else: return {}, '', *[x.update() for x in opComponents] # Splitting and Chunks docs = split_docs(documents) # Embeddings try: api_key_st='Null' if api_key_st is None or api_key_st=='' else api_key_st openai.Model.list(api_key=api_key_st) # test the API key embeddings = OpenAIEmbeddings(openai_api_key=api_key_st) except Exception as e: return {}, str(e), *[x.update() for x in opComponents] progress(0.5, 'Creating Vector Database') vsDict_st = getVsDict(embeddings, docs, vsDict_st) # get sources from metadata src_str = getSourcesFromMetadata(vsDict_st['chromaClient'].get()['metadatas']) src_str = str(src_str[1]) + ' source document(s) successfully loaded in vector store.'+'\n\n' + src_str[0] progress(1, 'Data loaded') return vsDict_st, src_str, *[x.update(interactive=False) for x in [data_ingest_btn, upload_fb]], urls_tb.update(interactive=False, placeholder='') # just update the QA Chain, no updates to any UI def updateQaChain(temp, k, modelName, stdlQs, api_key_st, vsDict_st): # if we are not adding data from ui, then use vsDict_hard as vectorstore if vsDict_st=={} and mode.type!='userInputDocs': vsDict_st=vsDict_hard modelName = modelName.split('(')[0].strip() # so we can provide any info in brackets # check if the input model is chat model or legacy model try: ChatOpenAI(openai_api_key=api_key_st, temperature=0,model_name=modelName,max_tokens=1).predict('') llm = ChatOpenAI(openai_api_key=api_key_st, temperature=float(temp),model_name=modelName) except: OpenAI(openai_api_key=api_key_st, temperature=0,model_name=modelName,max_tokens=1).predict('') llm = OpenAI(openai_api_key=api_key_st, temperature=float(temp),model_name=modelName) # settingsUpdated = 'Settings updated:'+ ' Model=' + modelName + ', Temp=' + str(temp)+ ', k=' + str(k) # gr.Info(settingsUpdated) # Now create QA Chain using the LLM if stdlQs==0: # 0th index i.e. first option qa_chain_st = RetrievalQA.from_llm( llm=llm, retriever=vsDict_st['chromaClient'].as_retriever(search_type="similarity", search_kwargs={"k": int(k)}), return_source_documents=True, input_key = 'question', output_key='answer' # to align with ConversationalRetrievalChain for downstream functions ) else: rephQs = False if stdlQs==1 else True qa_chain_st = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vsDict_st['chromaClient'].as_retriever(search_type="similarity", search_kwargs={"k": int(k)}), rephrase_question=rephQs, return_source_documents=True, return_generated_question=True ) return qa_chain_st def respond(message, chat_history, qa_chain): result = qa_chain({'question': message, "chat_history": [tuple(x) for x in chat_history]}) src_docs = getSourcesFromMetadata([x.metadata for x in result["source_documents"]], sourceOnly=False)[0] # streaming streaming_answer = "" for ele in "".join(result['answer']): streaming_answer += ele yield "", chat_history + [(message, streaming_answer)], src_docs, btn.update('Please wait...', interactive=False) chat_history.extend([(message, result['answer'])]) yield "", chat_history, src_docs, btn.update('Send Message', interactive=True) ##################################################################################################### with gr.Blocks(theme=gr.themes.Default(primary_hue='orange', secondary_hue='gray', neutral_hue='blue'), css="footer {visibility: hidden}") as demo: # Initialize state variables - stored in this browser session - these can only be used within input or output of .click/.submit etc, not as a python var coz they are not stored in backend, only as a frontend gradio component # but if you initialize it with a default value, that value will be stored in backend and accessible across all users. You can also change it with statear.value='newValue' qa_state = gr.State() api_key_state = gr.State(os.getenv("OPENAI_API_KEY") if mode.type=='personalBot' else 'Null') chromaVS_state = gr.State({}) # Setup the Gradio Layout gr.Markdown(mode.title) with gr.Tabs() as tabs: with gr.Tab('Initialization', id='init'): with gr.Row(): with gr.Column(): aKey_tb = gr.Textbox(label="OpenAI API Key", type='password'\ , info='You can find OpenAI API key at https://platform.openai.com/account/api-keys'\ , placeholder='Enter your API key here and hit enter to begin chatting') aKey_btn = gr.Button("Submit API Key") with gr.Row(visible=mode.uiAddDataVis): upload_fb = gr.Files(scale=5, label="Upload (multiple) Files - pdf/txt/docx supported", file_types=['.doc', '.docx', 'text', '.pdf', '.csv']) urls_tb = gr.Textbox(scale=5, label="Enter URLs starting with https (comma separated)"\ , info=url_tb_info\ , placeholder=url_tb_ph) data_ingest_btn = gr.Button("Load Data") status_tb = gr.TextArea(label='Status bar', show_label=False, visible=mode.uiAddDataVis) initChatbot_btn = gr.Button("Initialize Chatbot", variant="primary") with gr.Tab('Chatbot', id='cb'): with gr.Row(): chatbot = gr.Chatbot(label="Chat History", scale=2) srcDocs = gr.TextArea(label="References") msg = gr.Textbox(label="User Input",placeholder="Type your questions here") with gr.Row(): btn = gr.Button("Send Message", interactive=False, variant="primary") clear = gr.ClearButton(components=[msg, chatbot, srcDocs], value="Clear chat history") # exp_comp = gr.Dataset(scale=0.7, samples=[['123'],['456'], ['123'],['456'],['456']], components=[msg], label='Examples (auto generated by LLM)', visible=False) # gr.Examples(examples=exps, inputs=msg) with gr.Accordion("Advance Settings - click to expand", open=False): with gr.Row(): with gr.Column(): temp_sld = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="Temperature", info='Sampling temperature to use when calling LLM. Defaults to 0.7') k_sld = gr.Slider(minimum=1, maximum=10, step=1, value=mode.k, label="K", info='Number of relavant documents to return from Vector Store. Defaults to 4') model_dd = gr.Dropdown(label='Model Name'\ , choices=model_dd_choices\ , value=model_dd_choices[0], allow_custom_value=True\ , info=model_dd_info) stdlQs_rb = gr.Radio(label='Standalone Question', info=stdlQs_rb_info\ , type='index', value=stdlQs_rb_choices[1]\ , choices=stdlQs_rb_choices) ### Setup the Gradio Event Listeners # API button aKey_btn_args = {'fn':setApiKey, 'inputs':[aKey_tb], 'outputs':[aKey_tb, aKey_btn, api_key_state]} aKey_btn.click(**aKey_btn_args) aKey_tb.submit(**aKey_btn_args) # Data Ingest Button data_ingest_event = data_ingest_btn.click(uiData_vecStore, [upload_fb, urls_tb, api_key_state, chromaVS_state], [chromaVS_state, status_tb, data_ingest_btn, upload_fb, urls_tb]) # Adv Settings advSet_args = {'fn':updateQaChain, 'inputs':[temp_sld, k_sld, model_dd, stdlQs_rb, api_key_state, chromaVS_state], 'outputs':[qa_state]} temp_sld.release(**advSet_args) k_sld.release(**advSet_args) model_dd.change(**advSet_args) stdlQs_rb.change(**advSet_args) # Initialize button initCb_args = {'fn':initializeChatbot, 'inputs':[temp_sld, k_sld, model_dd, stdlQs_rb, api_key_state, chromaVS_state], 'outputs':[qa_state, btn, initChatbot_btn, aKey_tb, tabs, chatbot]} if mode.type=='personalBot': demo.load(**initCb_args) # load Chatbot UI directly on startup initChatbot_btn.click(**initCb_args) # Chatbot submit button chat_btn_args = {'fn':respond, 'inputs':[msg, chatbot, qa_state], 'outputs':[msg, chatbot, srcDocs, btn]} btn.click(**chat_btn_args) msg.submit(**chat_btn_args) demo.queue(concurrency_count=10) demo.launch(show_error=True)