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| from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes | |
| exp_query = 'Generate top 5 questions that I can ask about this data. Questions should be very precise and short, ideally less than 10 words.' | |
| waitText_initialize = 'Preparing the documents, please wait...' | |
| initialize_prompt = """Write a short welcome message to the user. Describe the data with a comprehensive overview including short summary.\ | |
| If this data is about a person, mention his name instead of using pronouns. After describing the overview, you should mention top 3 example questions that the user can ask about this data.\ | |
| \n\nYour response should be short and precise. Format of your response should be Summary:\n{Description and Summary} \n\n Example Questions:\n{Example Questions}""" | |
| user_avatar = 'https://cdn-icons-png.flaticon.com/512/6861/6861326.png' | |
| # user_avatar = None | |
| bot_avatar = 'https://cdn-icons-png.flaticon.com/512/1782/1782384.png' | |
| nustian_exps = ['Tell me about NUSTIAN', | |
| 'Who is the NUSTIAN regional lead for Silicon Valley?', | |
| 'Tell me details about NUSTIAN coaching program.', | |
| 'How can we donate to NUSTIAN fundraiser?', | |
| 'Who is the president of NUSTIAN?', | |
| "What are top five missions of NUSTIAN?", | |
| ] | |
| stdlQs_rb_info = 'Standalone question is a new rephrased question generated based on your original question and chat history' | |
| stdlQs_rb_choices = ['Retrieve relavant docs using original question, send original question to LLM (Chat history not considered)'\ | |
| , 'Retrieve relavant docs using standalone question, send original question to LLM'\ | |
| , 'Retrieve relavant docs using standalone question, send standalone question to LLM'] | |
| bam_models = sorted(['bigscience/bloom', | |
| 'salesforce/codegen2-16b', | |
| 'codellama/codellama-34b-instruct', | |
| 'tiiuae/falcon-40b', | |
| 'ibm/falcon-40b-8lang-instruct', | |
| 'google/flan-t5-xl', | |
| 'google/flan-t5-xxl', | |
| 'google/flan-ul2', | |
| 'eleutherai/gpt-neox-20b', | |
| 'togethercomputer/gpt-neoxt-chat-base-20b', | |
| 'ibm/granite-13b-sft', | |
| 'ibm/granite-13b-sft-cft', | |
| 'ibm/granite-3b-code-v1', | |
| 'meta-llama/llama-2-13b', | |
| 'meta-llama/llama-2-13b-chat', | |
| 'meta-llama/llama-2-13b-chat-beam', | |
| 'meta-llama/llama-2-70b', | |
| 'meta-llama/llama-2-70b-chat', | |
| 'meta-llama/llama-2-7b', | |
| 'meta-llama/llama-2-7b-chat', | |
| 'mosaicml/mpt-30b', | |
| 'ibm/mpt-7b-instruct', | |
| 'bigscience/mt0-xxl', | |
| 'bigcode/starcoder', | |
| 'google/ul2']) | |
| model_dd_info = 'You can also input any OpenAI model name or BAM model ID.' | |
| model_dd_choices = ['gpt-3.5-turbo (openai)', 'gpt-3.5-turbo-16k (openai)', 'gpt-4 (openai)', 'text-davinci-003 (Legacy - openai)', 'text-curie-001 (Legacy - openai)', 'babbage-002 (openai)'] + [model.value+' (watsonx)' for model in ModelTypes] + [model + ' (bam)' for model in bam_models] | |
| url_tb_info = 'Upto 100 domain webpages will be crawled for each URL. You can also enter online PDF files.' | |
| url_tb_ph = 'https://example.com, https://another.com, https://anyremotedocument.pdf' | |
| md_title_general = """ | |
| ## Chat with your documents and websites<br> | |
| Step 1) Enter your OpenAI API or Watsonx Credentials, and click Submit.<br> | |
| Step 2) Upload your documents and/or enter URLs, then click Load Data.<br> | |
| Step 3) Once data is loaded, click Initialize Chatbot (at the bottom of the page) to start talking to your data.<br> | |
| Your documents should be semantically similar (covering related topics or having the similar meaning) in order to get the best results. | |
| You may also play around with Advanced Settings, like changing the model name and parameters. | |
| """ | |
| md_title_nustian = """ | |
| ## Chat with NUSTIAN website<br> | |
| Step 1) Submit your OpenAI API Key.<br> | |
| Step 2) Click Initialize Chatbot to start sending messages.<br> | |
| You may also play around with Advanced Settings, like changing the model name and parameters. | |
| """ | |
| md_title_arslan = """ | |
| ## Talk to Arslan<br> | |
| Welcome to Arslan Ahmed's Chatbot!<br> | |
| This is LLM-based question-answer application built using Retrieval Augmented Generation (RAG) approach with Langchain, implementing Generative AI technology.\ | |
| He has developed this application to help people get quick answers on frequently asked questions and topics, rather than waiting for his personal reply.\ | |
| Currently, this chatbot is trained on Arslan's resume and LinkedIn profile, with plans to incorporate additional data in the future.<br><br> | |
| By default, this chatbot is powered by OpenAI's Large Language Model gpt-3.5-turbo. For those interested to explore, there are options under Advanced Settings to change the model and its parameters. | |
| """ | |
| welcomeMsgArslan = """Summary: The document provides a comprehensive overview of Arslan Ahmed\'s professional background and expertise as a data scientist.\ | |
| It highlights his experience in various industries and his proficiency in a wide range of data analysis tools and techniques.\ | |
| The document also mentions his involvement in research projects, publications, and academic achievements.\ | |
| \n\nExample Questions: | |
| 1. What are some of the key projects that Arslan has worked on as a data scientist? | |
| 2. What tools and technologies did Arslan Ahmed utilize in his data science work at IBM? | |
| 3. Tell me about Arslan's educational background. | |
| """ | |
| welcomeMsgDefault = """Hello and welcome! I'm your personal data assistant. Ask me anything about your data and I'll try my best to answer.""" | |
| class TtydMode(): | |
| def __init__(self, name='', title='', type='', dir=None, files=[], urls=[], vis=False, welMsg='', def_k=4): | |
| self.name = name | |
| self.title = title # markdown title for the top display | |
| self.type = type # userInputDocs, fixedDocs, personalBot | |
| self.inputDir=dir | |
| self.file_list=files | |
| self.url_list=urls | |
| self.uiAddDataVis = vis # load data from user - this will be true for type = userInputDocs | |
| self.welcomeMsg = welMsg #welcome msg constant - if not provided LLM will generate it | |
| self.k = def_k # default k docs to retrieve | |
| mode_general = TtydMode(name='general', title=md_title_general, type='userInputDocs', vis=True) | |
| mode_nustian = TtydMode(name='nustian', title=md_title_nustian, type='fixedDocs', urls=['https://nustian.ca']) | |
| mode_arslan = TtydMode(name='arslan', title=md_title_arslan, type='personalBot', dir='./documents/', welMsg=welcomeMsgArslan, def_k=8) |