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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() |