# install pinecone-client
 pip install pinecone-client 
# 导入 pinecone 并使用您的 API 密钥和环境名称进行初始化
import pinecone 
pinecone.init(api_key= "YOUR_API_KEY" ,environment= "YOUR_ENVIRONMENT" ) 
# 创建您的第一个索引以开始存储Vectors
 pinecone.create_index( "first_index" ,Dimension= 8 , metric= "cosine" ) 
# 更新插入样本数据（5个8维向量）
 index.upsert([ 
    ( "A" , [ 0.1 , 0.1 , 0.1 , 0.1 , 0.1 ) , 0.1 , 0.1 , 0.1 ]), 
    ( "B" , [ 0.2 , 0.2 , 0.2 , 0.2 , 0.2 , 0.2 , 0.2 , 0.2 ]), 
    ( "C" , [ 0.3 , 0.3 , 0.3 , 0.3 , 0.3 , 0.3 , 0.3 , 0.3 ]), 
    ( "D" , [ 0.4 , 0.4 , 0.4 , 0.4 , 0.4 , 0.4 , 0.4 , 0.4 ]), 
    ( "E" , [ 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ]) 
]) 
# 使用 list_indexes() 方法调用 db 中可用的多个索引
pinecone.list_indexes() 
[Output]>>> [ 'first_index' ]

# install langchain and openai with other dependencies
!pip install --upgrade langchain openai -q
!pip install pillow==6.2.2
!pip install unstructured -q
!pip install unstructured[local-inference] -q
!pip install detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2 -q
!apt-get install poppler-utils
!pip install pinecone-client -q
!pip install tiktoken -q
# setup openai environment
import os
os.environ["OPENAI_API_KEY"] = "YOUR-API-KEY"
# importing libraries
import os
import openai
import pinecone
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain

#load the documents from content/data dir
directory = '/content/data'
# load_docs functions to load documents using langchain function
def load_docs(directory):
  loader = DirectoryLoader(directory)
  documents = loader.load()
  return documents
documents = load_docs(directory)
len(documents)
[Output]>>> 5

# split the docs using recursive text splitter
def split_docs(documents, chunk_size=200, chunk_overlap=20):
  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
  docs = text_splitter.split_documents(documents)
  return docs
# split the docs
docs = split_docs(documents)
print(len(docs))
[Output]>>>12

# embedding example on random word
embeddings = OpenAIEmbeddings()
# initiate pinecondb
pinecone.init(
    api_key="YOUR-API-KEY",
    environment="YOUR-ENV"
)
# define index name
index_name = "langchain-project"
# store the data and embeddings into pinecone index
index = Pinecone.from_documents(docs, embeddings, index_name=index_name)

# An example query to our database
query = "What are the different types of pet animals are there?"
# do a similarity search and store the documents in result variable 
result = index.similarity_search(
    query,  # our search query
    k=3  # return 3 most relevant docs
)
-
--------------------------------[Output]--------------------------------------
result
[Document(page_content='Small mammals like hamsters, guinea pigs, 
and rabbits are often chosen for their
low maintenance needs. Birds offer beauty and song,
and reptiles like turtles and lizards can make intriguing pets.', 
metadata={'source': '/content/data/Different Types of Pet Animals.txt'}),
 Document(page_content='Pet animals come in all shapes and sizes, each suited 
to different lifestyles and home environments. Dogs and cats are the most 
common, known for their companionship and unique personalities. Small', 
metadata={'source': '/content/data/Different Types of Pet Animals.txt'}),
 Document(page_content='intriguing pets. Even fish, with their calming presence
, can be wonderful pets.', 
metadata={'source': '/content/data/Different Types of Pet Animals.txt'})]

# import necessary libraries
import streamlit as st
import openai
import qanda
from vector_search import *
from utils import *
from io  import StringIO
# take openai api key in
api_key = st.sidebar.text_input("Enter your OpenAI API key:", type='password')
# open ai key
openai.api_key = str(api_key)
# header of the app
_ , col2,_ = st.columns([1,7,1])
with col2:
    col2 = st.header("Simplchat: Chat with your data")
    url = False
    query = False
    pdf = False
    data = False
    # select option based on user need
    options = st.selectbox("Select the type of data source",
                            options=['Web URL','PDF','Existing data source'])
    #ask a query based on options of data sources
    if options == 'Web URL':
        url = st.text_input("Enter the URL of the data source")
        query = st.text_input("Enter your query")
        button = st.button("Submit")
    elif options == 'PDF':
        pdf = st.text_input("Enter your PDF link here") 
        query = st.text_input("Enter your query")
        button = st.button("Submit")
    elif options == 'Existing data source':
        data= True
        query = st.text_input("Enter your query")
        button = st.button("Submit") 
# write code to get the output based on given query and data sources   
if button and url:
    with st.spinner("Updating the database..."):
        corpusData = scrape_text(url)
        encodeaddData(corpusData,url=url,pdf=False)
        st.success("Database Updated")
    with st.spinner("Finding an answer..."):
        title, res = find_k_best_match(query,2)
        context = "\n\n".join(res)
        st.expander("Context").write(context)
        prompt = qanda.prompt(context,query)
        answer = qanda.get_answer(prompt)
        st.success("Answer: "+ answer)
# write a code to get output on given query and data sources
if button and pdf:
    with st.spinner("Updating the database..."):
        corpusData = pdf_text(pdf=pdf)
        encodeaddData(corpusData,pdf=pdf,url=False)
        st.success("Database Updated")
    with st.spinner("Finding an answer..."):
        title, res = find_k_best_match(query,2)
        context = "\n\n".join(res)
        st.expander("Context").write(context)
        prompt = qanda.prompt(context,query)
        answer = qanda.get_answer(prompt)
        st.success("Answer: "+ answer)
        
if button and data:
    with st.spinner("Finding an answer..."):
        title, res = find_k_best_match(query,2)
        context = "\n\n".join(res)
        st.expander("Context").write(context)
        prompt = qanda.prompt(context,query)
        answer = qanda.get_answer(prompt)
        st.success("Answer: "+ answer)
        
        
# delete the vectors from the database
st.expander("Delete the indexes from the database")
button1 = st.button("Delete the current vectors")
if button1 == True:
    index.delete(deleteAll='true')