search.py

import os
import openai
import pinecone
import langchain
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma, Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import streamlit as st
from langchain.llms import OpenAIChat
from langchain import PromptTemplate
from embedchain import App

if 'response' not in st.session_state:
    st.session_state.response = ''

st.set_page_config(page_title="Knowledge Base Search", page_icon=':rocket:', layout='wide')
st.title("👋 Enterprise Search - HR Documents")
sidebar_placeholder = st.sidebar.container()
sidebar_placeholder.header('Knowledge base documents:')
sidebar_placeholder.subheader('[Employee Handbook](https://www.foundation.cpp.edu/content/es/d/nh/employee-handbook.pdf)')
# sidebar_placeholder.subheader('[Gartner Cloud HCM Vendor Comparison](https://www.gartner.com/reviews/market/cloud-hcm-suites-for-1000-employees)')
# sidebar_placeholder.subheader('[Benefits Guide](https://selecthealth.org/-/media/providerdevelopment/pdfs/manuals/masterprm_120121.ashx)')
# sidebar_placeholder.subheader('[HR RFP](http://fresnoeoc.org/files/pdf/180215-hris-rfp.pdf)')


#sidebar_placeholder.write('A subset of first 30 pages of the employee handbook')

os.environ['OPENAI_API_KEY'] = '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'
index_name = 'demo-index'

# initialize connection (get API key at app.pinecone.io)
pinecone.init(
    api_key="5e6a8cb6-f036-4a23-9b34-c95aec8e317f",
    environment="us-west1-gcp-free"  # find next to API key
)

# connect to index
#print(pinecone)
index = pinecone.Index(index_name)
# view index stats
#print(index.describe_index_stats())

#Load PDFS
#loader = PyPDFLoader("data/employee-handbook.pdf")
#documents = loader.load()
#print(documents[:5])
#text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
#texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
#docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)
docsearch = Pinecone.from_existing_index(index_name, embeddings)

llm = OpenAIChat(temperature=0, openai_api_key=os.environ['OPENAI_API_KEY'],model_name='gpt-3.5-turbo', model_kwargs={'max_tokens':4000})

# prompt_template = "Answer based on context:nn{context}nn{question}"
# prompt = PromptTemplate(template=prompt_template, input_variables=["context”, “question”])

chain = load_qa_chain(llm,  chain_type="stuff")

#query = "Tell me meal periods for employees working less than five hours a day"

def send_click():
    with st.spinner("Fetching response..."):
        query = "You are a friendly knowledge bot that provides factual responses. Always end with a line that cites references and paragraphs used for your results." + st.session_state.prompt
 # docs = docsearch.similarity_search(query, include_metadata=True)
 #        print(docs)
#    response = chain.run(input_documents=docs, question=query)
        chat_bot = App()
        # chat_bot.add("web_page",'https://www.constellationr.com/blog-news/c3-ai-ceo-tom-siebel-generative-ai-enterprise-search-will-have-wide-impact')
        # chat_bot.add("pdf_file",'https://www.foundation.cpp.edu/content/es/d/nh/employee-handbook.pdf')
        # print(query)
        # from_db = (chat_bot.retrieve_from_database(query))
        # try:
        response = chat_bot.query(query)
        # except Exception as e:
        #     response = f'caught {type(e)}: e'
        # # total_tokens = response.get("total_tokens")
        # # pricing logic: https://openai.com/pricing#language-models
        # if st.session_state.model == "gpt-3.5-turbo":
        #     cost = total_tokens * 0.002 / 1000
        print(response)
        st.session_state.response = response
        # st.session_state.from_db = from_db
        # st.session_state.response.append(cost)

c1, c2, c3,c4,c5 = st.columns(5)

with c1:
    st.info("Religious, Medical and Disability Accommodations",icon='⚛️')
with c2:
    st.info("Explain by way of example, confidential or proprietary information ?",icon='🤖')
with c3:
    st.info("Summarize the key takeaway on getting paid and company policy.",icon='🧠')
with c4:
    st.info(
        "Tell me about performance evaluations",
        icon='🧠')
with c5:
    st.info("Tell me meal periods for employees working more than five hours and those working more than ten hours a workday", icon='☮️')

st.text_input("Ask something: ", key='prompt', value='Explain the trade secrets and confidentiality in the employee handbook' )
st.button("Send", on_click=send_click)
if st.session_state.response:
     st.subheader("Response: ")
     st.success(st.session_state.response, icon= "🤖")
     # st.warning("Document sections from the Knowledge base used to generate response....", icon="🤖")
     # st.markdown(st.session_state.from_db)