import os import gradio as gr from langchain.document_loaders import PDFMinerLoader,CSVLoader ,UnstructuredWordDocumentLoader,TextLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import SentenceTransformerEmbeddings from langchain.vectorstores import FAISS from langchain import HuggingFaceHub DEVICE = 'cpu ' FILE_EXT = ['pdf','text','csv','word','wav'] def loading_pdf(): 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=1000,chunk_overlap=50): text_splitter = RecursiveCharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap) texts = text_splitter.split_documents(documents[0]) 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_api(file_data,doc_type='pdf',key=None,llm_model='HuggingFace'): embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE}) document = None if doc_type == 'pdf': document = process_pdf_document(document_file_name=file_data) elif doc_type == 'text': document = process_text_document(document_file_name=file_data) elif doc_type == 'csv': document = process_csv_document(document_file_name=file_data) elif doc_type == 'word': document = process_word_document(document_file_name=file_data) texts = process_documents(documents=document) vectordb = FAISS.from_documents(documents=texts, embedding= embedding_model) if llm_model == 'HuggingFace': llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key) else: llm_model = get_openai_chat_model(API_key=key) def process_text_document(document_file_name): loader = TextLoader(document_file_name) document = loader.load() return document def process_csv_document(document_file_name): loader = CSVLoader(file_path=document_file_name) document = loader.load() return document def process_word_document(document_file_name): loader = UnstructuredWordDocumentLoader(file_path=document_file_name) document = loader.load() return document def process_pdf_document(document_file_name): loader = PDFMinerLoader(document_file_name) document = loader.load()[0] return document 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(): LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='LLM',info='select the LLM to be used') API_key = gr.Textbox(label="You OpenAI/Huggingface API key", type="password") with gr.Column(): file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!") pdf_doc = gr.File(label="Load a File", file_types=FILE_EXT, type="file") with gr.Row(): langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) load_pdf = gr.Button("Load file to langchain") chatbot = gr.Chatbot() question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter") submit_button = gr.Button("Send Message") load_pdf.click(loading_pdf, None, langchain_status, queue=False) load_pdf.click(chat_api, inputs=[pdf_doc,file_extension,API_key,LLM_option], outputs=[langchain_status], queue=False)