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| import os | |
| import gradio as gr | |
| from langchain.chains import RetrievalQA | |
| from langchain_community.document_loaders import TextLoader | |
| from langchain_community.document_loaders import UnstructuredExcelLoader | |
| from langchain.indexes import VectorstoreIndexCreator | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from dotenv import find_dotenv, load_dotenv | |
| from langchain.chains import create_retrieval_chain, RetrievalQA | |
| from langchain_community.vectorstores import FAISS | |
| _=load_dotenv(find_dotenv()) | |
| hf_api = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| def indexdocs (file_path, progress=gr.Progress()): | |
| progress(0.1, desc="Loading documents...") | |
| loaders = [UnstructuredExcelLoader(file, mode="elements") for file in file_path] | |
| documents=[] | |
| for loader in loaders: | |
| documents.extend(loader.load()) | |
| progress(0.3, desc="Splitting documents...") | |
| text_splitter = RecursiveCharacterTextSplitter (chunk_size=1500, chunk_overlap=300) | |
| pages=text_splitter.split_documents(documents) | |
| embedding = HuggingFaceEmbeddings() | |
| progress(0.5, desc="Creating vectorstore...") | |
| vector=FAISS.from_documents (documents=pages,embedding=embedding) | |
| retriever = vector.as_retriever() | |
| progress(0.8, desc="Setting up language model...") | |
| memory = ConversationBufferMemory( | |
| memory_key="chat_history", | |
| output_key='answer', | |
| return_messages=True | |
| ) | |
| llm = HuggingFaceEndpoint( | |
| repo_id="Mistralai/Mistral-7B-Instruct-v0.2", | |
| temperature = 0.1, | |
| max_new_tokens = 200, | |
| top_k = 1 #top_k, | |
| ) | |
| qa_chain = ConversationalRetrievalChain.from_llm( | |
| llm, | |
| retriever=retriever, | |
| chain_type="stuff", | |
| memory=memory, | |
| return_source_documents=True, | |
| verbose=False, | |
| ) | |
| return qa_chain, None | |
| def format_chat_history(chat_history): | |
| formatted_chat_history = [] | |
| for user_message, bot_message in chat_history: | |
| formatted_chat_history.append(f"User: {user_message}") | |
| formatted_chat_history.append(f"Assistant: {bot_message}") | |
| return formatted_chat_history | |
| def chat(qa_chain,msg,history): | |
| formatted_chat_history = format_chat_history(history) | |
| response = qa_chain.invoke({"question": msg, "chat_history": formatted_chat_history}) | |
| response_answer = response["answer"] | |
| response_sources=response["source_documents"] | |
| response_source1= response_sources[0].metadata["filename"] | |
| response_source_sheet= response_sources[0].metadata["page_name"] | |
| new_history = history + [(msg, response_answer)] | |
| return qa_chain, gr.update(value=""), new_history, response_source1, response_source_sheet | |
| with gr.Blocks() as demo: | |
| qa_chain=gr.State() | |
| gr.Markdown( | |
| """ | |
| # MS Excel Knowledge Base QA using RAG | |
| """ | |
| ) | |
| with gr.Column(): | |
| file_list = gr.File(label='Upload your MS Excel files...', file_count='multiple', file_types=['.xls,.xlsx']) | |
| fileuploadbtn= gr.Button ("Index Documents and Start Chatting") | |
| with gr.Row(): | |
| chatbot=gr.Chatbot(height=300) | |
| with gr.Row(): | |
| source=gr.Textbox(info="Source",container=False,scale=4) | |
| source_page=gr.Textbox(info="Sheet",container=False,scale=1) | |
| with gr.Row(): | |
| prompt=gr.Textbox(placeholder="Please enter your prompt...",container=False, scale=4, visible=True, interactive=False) | |
| promptsubmit=gr.Button("Submit", scale=1, visible=True, interactive=False) | |
| gr.Markdown( | |
| """ | |
| # Responsible AI Usage | |
| Your documents uploaded to the system or interactions with the chatbot are not saved. | |
| """ | |
| ) | |
| fileuploadbtn.click(fn=indexdocs, inputs = [file_list], outputs=[qa_chain,chatbot]).then(lambda:[gr.Textbox(interactive=True), gr.Button (interactive=True)], \ | |
| inputs=None, outputs=[prompt,promptsubmit], queue=False) | |
| promptsubmit.click(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False) | |
| prompt.submit(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False) | |
| if __name__ == "__main__": | |
| demo.launch() |