docs_qachat / app.py
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import gradio as gr
import random
import time
import boto3
from botocore import UNSIGNED
from botocore.client import Config
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
from langchain.llms import HuggingFaceHub
model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":1024})
from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
s3.download_file('rad-rag-demos', 'vectorstores/chroma.sqlite3', './chroma_db/chroma.sqlite3')
db = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
db.get()
retriever = db.as_retriever()
global qa
qa = RetrievalQA.from_chain_type(llm=model_id, chain_type="stuff", retriever=retriever)
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0])
history[-1][1] = ""
for character in response['result']:
history[-1][1] += character
time.sleep(0.05)
yield history
def infer(question):
query = question
result = qa({"query": query})
return result
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with the RAY Docs</h1>
<p style="text-align: center;">The AI bot is here to help you with the RAY Documentation, <br />
start asking questions about the open-source software </p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
chatbot = gr.Chatbot([], elem_id="chatbot")
with gr.Row():
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
question.submit(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
demo.launch()