Spaces:
Sleeping
Sleeping
File size: 4,354 Bytes
45f1f60 7165161 45f1f60 7165161 45f1f60 7165161 45f1f60 7165161 45f1f60 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
# gradio
import gradio as gr
import random
import time
#boto3 for S3 access
import boto3
from botocore import UNSIGNED
from botocore.client import Config
# access .env file
import os
from dotenv import load_dotenv
#from bs4 import BeautifulSoup
# HF libraries
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceHubEmbeddings
# vectorestore
from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
# retrieval chain
from langchain.chains import RetrievalQA
# prompt template
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
# logging
#import logging
import zipfile
# load .env variables
config = load_dotenv(".env")
HUGGINGFACEHUB_API_TOKEN=os.getenv('HUGGINGFACEHUB_API_TOKEN')
AWS_S3_LOCATION=os.getenv('AWS_S3_LOCATION')
AWS_S3_FILE=os.getenv('AWS_S3_FILE')
VS_DESTINATION=os.getenv('VS_DESTINATION')
model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={
"temperature":0.1,
"max_new_tokens":1024,
"repetition_penalty":1.2,
"streaming": True,
"return_full_text":True
})
model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
embeddings = HuggingFaceHubEmbeddings(repo_id=model_name)
s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
## Chroma DB
s3.download_file(AWS_S3_LOCATION, AWS_S3_FILE, VS_DESTINATION)
db = Chroma(persist_directory="./vectorstore", embedding_function=embeddings)
db.get()
## FAISS DB
# s3.download_file('rad-rag-demos', 'vectorstores/faiss_db_ray.zip', './chroma_db/faiss_db_ray.zip')
# with zipfile.ZipFile('./chroma_db/faiss_db_ray.zip', 'r') as zip_ref:
# zip_ref.extractall('./chroma_db/')
# FAISS_INDEX_PATH='./chroma_db/faiss_db_ray'
# db = FAISS.load_local(FAISS_INDEX_PATH, embeddings)
retriever = db.as_retriever(search_type = "mmr")#, search_kwargs={'k': 5, 'fetch_k': 25})
global qa
template = """
You are the friendly documentation buddy Arti, who helps the Human in using RAY, the open-source unified framework for scaling AI and Python applications.\
Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question :
------
<ctx>
{context}
</ctx>
------
<hs>
{history}
</hs>
------
{question}
Answer:
"""
prompt = PromptTemplate(
input_variables=["history", "context", "question"],
template=template,
)
memory = ConversationBufferMemory(memory_key="history", input_key="question")
qa = RetrievalQA.from_chain_type(llm=model_id, chain_type="stuff", retriever=retriever, verbose=True, return_source_documents=True, chain_type_kwargs={
"verbose": True,
"memory": memory,
"prompt": prompt
}
)
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0], history)
print(*memory)
sources = [doc.metadata.get("source") for doc in response['source_documents']]
src_list = '\n'.join(sources)
print_this = response['result']+"\n\n\n Sources: \n\n\n"+src_list
#history[-1][1] = ""
#for character in response['result']: #print_this:
# history[-1][1] += character
# time.sleep(0.05)
# yield history
history[-1][1] = print_this #response['result']
return history
def infer(question, history):
query = question
result = qa({"query": query, "history": history, "question": question})
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 your Documentation</h1>
<p style="text-align: center;">Chat with Documentation, <br />
when everything is ready, you can start asking questions about the docu ;)</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")
clear = gr.Button("Clear")
with gr.Row():
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch() |