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# -*- coding: utf-8 -*-
"""wiki_chat_3_hack.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1chXsWeq1LzbvYIs6H73gibYmNDRbIgkD
"""
#!pip install gradio
#!pip install -U sentence-transformers
#!pip install datasets
#!pip install langchain
#!pip install openai
#!pip install faiss-cpu
#import numpy as np
import gradio as gr
#import random
from sentence_transformers import SentenceTransformer, CrossEncoder, util
from torch import tensor as torch_tensor
from datasets import load_dataset
"""# import models"""
bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
bi_encoder.max_seq_length = 256 #Truncate long passages to 256 tokens
#The bi-encoder will retrieve top_k documents. We use a cross-encoder, to re-rank the results list to improve the quality
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
"""# import datasets"""
dataset = load_dataset("gfhayworth/hack_policy", split='train')
mypassages = list(dataset.to_pandas()['psg'])
dataset_embed = load_dataset("gfhayworth/hack_policy_embed", split='train')
dataset_embed_pd = dataset_embed.to_pandas()
mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)
def search(query, passages = mypassages, doc_embedding = mycorpus_embeddings, top_k=20, top_n = 1):
question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
question_embedding = question_embedding #.cuda()
hits = util.semantic_search(question_embedding, doc_embedding, top_k=top_k)
hits = hits[0] # Get the hits for the first query
##### Re-Ranking #####
cross_inp = [[query, passages[hit['corpus_id']]] for hit in hits]
cross_scores = cross_encoder.predict(cross_inp)
# Sort results by the cross-encoder scores
for idx in range(len(cross_scores)):
hits[idx]['cross-score'] = cross_scores[idx]
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
predictions = hits[:top_n]
return predictions
# for hit in hits[0:3]:
# print("\t{:.3f}\t{}".format(hit['cross-score'], mypassages[hit['corpus_id']].replace("\n", " ")))
def get_text(qry):
predictions = search(qry)
prediction_text = []
for hit in predictions:
prediction_text.append("{}".format(mypassages[hit['corpus_id']]))
return prediction_text
# def prt_rslt(qry):
# rslt = get_text(qry)
# for r in rslt:
# print(r)
# prt_rslt("What is the name of the plan described by this summary of benefits?")
"""# new LLM based functions"""
import os
from langchain.llms import OpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
#from langchain.vectorstores.faiss import FAISS
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import VectorDBQAWithSourcesChain
chain_qa = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
def get_text_fmt(qry, passages = mypassages, doc_embedding=mycorpus_embeddings):
predictions = search(qry, passages = passages, doc_embedding = doc_embedding, top_n=5, )
prediction_text = []
for hit in predictions:
page_content = passages[hit['corpus_id']]
metadata = {"source": hit['corpus_id']}
result = Document(page_content=page_content, metadata=metadata)
prediction_text.append(result)
return prediction_text
#mypassages[0]
#mycorpus_embeddings[0][:5]
# query = "What is the name of the plan described by this summary of benefits?"
# mydocs = get_text_fmt(query)
# print(len(mydocs))
# for d in mydocs:
# print(d)
# chain_qa.run(input_documents=mydocs, question=query)
def get_llm_response(message):
mydocs = get_text_fmt(message)
responses = chain_qa.run(input_documents=mydocs, question=message)
return responses
"""# chat example"""
def chat(message, history):
history = history or []
message = message.lower()
response = get_llm_response(message)
history.append((message, response))
return history, history
css=".gradio-container {background-color: lightgray}"
with gr.Blocks(css=css) as demo:
history_state = gr.State()
gr.Markdown('# Hack QA')
title='Benefit Chatbot'
description='chatbot with search on Health Benefits'
with gr.Row():
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(label='Input your question here:',
placeholder='What is the name of the plan described by this summary of benefits?',
lines=1)
submit = gr.Button(value='Send',
variant='secondary').style(full_width=False)
submit.click(chat,
inputs=[message, history_state],
outputs=[chatbot, history_state])
gr.Examples(
examples=["What is the name of the plan described by this summary of benefits?",
"How much is the monthly premium?",
"How much do I have to pay if I am admitted to the hospital?"],
inputs=message
)
demo.launch()
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