import os import gradio as gr import time from langchain.document_loaders import PDFMinerLoader,CSVLoader ,UnstructuredWordDocumentLoader,TextLoader,OnlinePDFLoader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import SentenceTransformerEmbeddings from langchain.vectorstores import FAISS from langchain import HuggingFaceHub from langchain.chains import RetrievalQA from langchain.prompts import PromptTemplate DEVICE = 'cpu' FILE_EXT = ['pdf','text','csv','word','wav'] def loading_file(): return "Loading..." def get_openai_chat_model(API_key): try: from langchain.llms import OpenAI except ImportError as err: raise "{}, unable to load openAI. Please install openai and add OPENAIAPI_KEY" os.environ["OPENAI_API_KEY"] = API_key llm = OpenAI() return llm def process_documents(documents,data_chunk=2000,chunk_overlap=50): text_splitter = CharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap,separator='\n') texts = text_splitter.split_documents(documents) return texts def get_hugging_face_model(model_id,API_key,temperature=0.1): chat_llm = HuggingFaceHub(huggingfacehub_api_token=API_key, repo_id=model_id, model_kwargs={"temperature": temperature, "max_new_tokens": 2048}) return chat_llm def chat_application(llm_service,key): if llm_service == 'HuggingFace': llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key) else: llm = get_openai_chat_model(API_key=key) return llm def summarize_contents(): question = "Generate a summary of the contents. Do not return the response in json format" return qa.run(question) def document_loader(file_path,api_key,doc_type='pdf',llm='Huggingface'): document = None if doc_type == 'pdf': document = process_pdf_document(document_file=file_path) elif doc_type == 'text': document = process_text_document(document_file=file_path) elif doc_type == 'csv': document = process_csv_document(document_file=file_path) elif doc_type == 'word': document = process_word_document(document_file=file_path) print("Document :",document) embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-base',model_kwargs={"device": DEVICE}) global qa try: texts = process_documents(documents=document) vector_db = FAISS.from_documents(documents=texts, embedding= embedding_model) qa = RetrievalQA.from_chain_type(llm=chat_application(llm_service=llm,key=api_key), chain_type='stuff', retriever=vector_db.as_retriever(), # chain_type_kwargs=chain_type_kwargs, return_source_documents=True ) except: return "Error in loading Documents " return "Document Processing completed ..." def process_text_document(document_file): loader = TextLoader(document_file.name) document = loader.load() return document def process_csv_document(document_file): loader = CSVLoader(file_path=document_file.name) document = loader.load() return document def process_word_document(document_file): loader = UnstructuredWordDocumentLoader(file_path=document_file.name) document = loader.load() return document def process_pdf_document(document_file): print("Document File Name :",document_file.name) loader = PDFMinerLoader(document_file.name) document = loader.load() return document def infer(question, history): res = [] for human, ai in history[:-1]: pair = (human, ai) res.append(pair) chat_history = res result = qa({"query": question}) return result["answer"] def bot(history): response = infer(history[-1][0], history) history[-1][1] = "" for character in response: history[-1][1] += character time.sleep(0.05) yield history def add_text(history, text): history = history + [(text, None)] return history, "" css=""" #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} """ title = """

Chat with Data • OpenAI/HuggingFace

Upload a file from your computer, click the "Load data to LangChain" button,
when everything is ready, you can start asking questions about the data you uploaded ;)
This version is just for QA retrival so it will not use chat history, and uses Hugging face as LLM, so you don't need any key

""" with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML(title) with gr.Column(): with gr.Box(): gr.Row() LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Large Language Model Selection',info='LLM Service') file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!") API_key = gr.Textbox(label="Add API key", type="password") with gr.Column(): with gr.Box(): pdf_doc = gr.File(label="Upload File to start QA", file_types=FILE_EXT, type="file") with gr.Row(): langchain_status = gr.Textbox(label="Status", placeholder="", interactive = False) load_pdf = gr.Button("Upload File & Generate Embeddings",).style(full_width = False) # chatbot = gr.Chatbot()l̥ # question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter") # submit_button = gr.Button("Send Message") load_pdf.click(loading_file, None, langchain_status, queue=False) load_pdf.click(document_loader, inputs=[pdf_doc,API_key,file_extension,LLM_option], outputs=[langchain_status], queue=False) with gr.Column(): with gr.Row(): chatbot = gr.Chatbot(height=300) sources = gr.HTML(value = "Source paragraphs where I looked for answers will appear here", height=300) with gr.Row(): question = gr.Textbox(label="Type your question?",lines=1).style(full_width=False) submit_btn = gr.Button(value="Send message", variant="secondary", scale = 1) question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot) submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot) demo.launch()