import gradio as gr import openai import requests import csv import os import langchain import chromadb import glob from PyPDF2 import PdfReader from PyPDF2 import PdfWriter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import TokenTextSplitter #from langchain.llms import OpenAI from langchain import OpenAI from langchain.chat_models import ChatOpenAI from langchain.chains import ChatVectorDBChain from langchain.chains import RetrievalQA from langchain.document_loaders import PyPDFLoader from langchain.chains.question_answering import load_qa_chain # Use Chroma in Colab to create vector embeddings, I then saved them to HuggingFace so now I have to set it use them here. #from chromadb.config import Settings #client = chromadb.Client(Settings( ## chroma_db_impl="duckdb+parquet", # persist_directory="./embeddings" # Optional, defaults to .chromadb/ in the current directory #)) def get_empty_state(): return {"total_tokens": 0, "messages": []} #Initial prompt template, others added below from TXT file prompt_templates = {"All Needs Experts": "I want you to act as a needs assessment expert."} def download_prompt_templates(): url = "https://huggingface.co/spaces/ryanrwatkins/needs/raw/main/gurus.txt" try: response = requests.get(url) reader = csv.reader(response.text.splitlines()) next(reader) # skip the header row for row in reader: if len(row) >= 2: act = row[0].strip('"') prompt = row[1].strip('"') # description = row[2].strip('"') prompt_templates[act] = prompt except requests.exceptions.RequestException as e: print(f"An error occurred while downloading prompt templates: {e}") return choices = list(prompt_templates.keys()) choices = choices[:1] + sorted(choices[1:]) return gr.update(value=choices[0], choices=choices) def on_prompt_template_change(prompt_template): if not isinstance(prompt_template, str): return return prompt_templates[prompt_template] def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state): openai.api_key = os.environ['openai_key'] os.environ["OPENAI_API_KEY"] = os.environ['openai_key'] # load in all the files #path = './files' #pdf_files = glob.glob(os.path.join(path, "*.pdf")) #pdf_files = glob.glob(os.path.join(path, "*.pdf")) #for file in pdf_files: # loader = PyPDFLoader(file) # pages = loader.load_and_split() # text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0) # split_pages = text_splitter.split_documents(pages) #persist_directory = "./embeddings" #embeddings = OpenAIEmbeddings() #vectordb = Chroma.from_documents(split_pages, embeddings, persist_directory=persist_directory) #vectordb.persist() path = './files' pdf_files = glob.glob(os.path.join(path, "*.pdf")) merger = PdfWriter() # add all file in the list to the merger object for pdf in pdf_files: merger.append(pdf) merger.write("merged-pdf.pdf") merger.close() reader = PdfReader("merged-pdf.pdf") raw_text = '' for i, page in enumerate(reader.pages): text = page.extract_text() if text: raw_text += text text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 200, length_function = len, ) texts = text_splitter.split_text(raw_text) len(texts) embeddings = OpenAIEmbeddings() history = state['question'] if not prompt: return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state prompt_template = prompt_templates[prompt_template] system_prompt = [] if prompt_template: system_prompt = [{ "role": "system", "content": prompt_template }] prompt_msg = { "role": "user", "content": prompt } try: #completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" ) #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever()) #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) #completion = completion.run(query) # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8 #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" ) #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever(), return_source_documents=False) #completion = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff", retriever=vectordb.as_retriever(), return_source_documents=True) #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) #completion = completion({"query": query}) #completion = completion.run(query) # completion = completion({"question": query, "chat_history": history[-context_length*2:]}) #with open("foo.pkl", 'rb') as f: # new_docsearch = pickle.load(f) docsearch = FAISS.from_texts(texts, embeddings) #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) query = str(system_prompt + history[0] + [prompt_msg]) docs = docsearch.similarity_search(query) #print(docs[0].page_content) chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff") completion = chain.run(input_documents=docs, question=query) completion = { "content": completion } # VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=docsearch, return_source_documents=True) # https://colab.research.google.com/drive/1dzdNDZyofRB0f2KIB4gHXmIza7ehMX30?usp=sharing#scrollTo=b-ejDn_JfpWW history.append(prompt_msg.copy()) history.append(completion.copy()) #history.append(completion.choices[0].message.to_dict()) #history.append(completion["result"].choices[0].message.to_dict()) state['total_tokens'] += completion['usage']['total_tokens'] except Exception as e: history.append(prompt_msg.copy()) error = { "role": "system", "content": f"Error: {e}" } history.append(error.copy()) total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" chat_messages = [(prompt_msg['content'], completion['content'])] #chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] #chat_messages = [(history[-2]['content'], history[-1]['content'])] return '', chat_messages, total_tokens_used_msg, state def clear_conversation(): return gr.update(value=None, visible=True), None, "", get_empty_state() css = """ #col-container {max-width: 80%; margin-left: auto; margin-right: auto;} #chatbox {min-height: 400px;} #header {text-align: center;} #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;} #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;} #label {font-size: 0.8em; padding: 0.5em; margin: 0;} .message { font-size: 1.2em; } """ with gr.Blocks(css=css) as demo: state = gr.State(get_empty_state()) with gr.Column(elem_id="col-container"): gr.Markdown("""# Chat with Needs Assessment Experts (Past and Present) ## Ask questions of experts on needs assessments, get responses from *needs assessment* version of ChatGPT. Ask questions of all of them, or pick your expert.""", elem_id="header") with gr.Row(): with gr.Column(): chatbot = gr.Chatbot(elem_id="chatbox") input_message = gr.Textbox(show_label=False, placeholder="Enter your needs assessment question and press enter", visible=True).style(container=False) btn_submit = gr.Button("Submit") total_tokens_str = gr.Markdown(elem_id="total_tokens_str") btn_clear_conversation = gr.Button("Start New Conversation") with gr.Column(): prompt_template = gr.Dropdown(label="Choose a expert:", choices=list(prompt_templates.keys())) prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview") with gr.Accordion("Advanced parameters", open=False): temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Flexibility", info="Higher = more creative/chaotic, Lower = just the guru") max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Max tokens per response") context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context length", info="Number of previous questions you have asked. Be careful with high values, it can blow up the token budget quickly.") btn_submit.click(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) input_message.submit(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state]) prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False) demo.queue(concurrency_count=10) demo.launch(height='800px')