TypeError Traceback (most recent call last) <ipython-input-74-15688820a1ef> in <cell line: 3>() 2 file_path="vector_index.pkl" 3 with open(file_path, "wb") as f: ----> 4 pickle.dump(vectorindex_openai, f) TypeError: cannot pickle '_thread.RLock' object

[UsmanAmeeer]

TypeError Traceback (most recent call last)
in <cell line: 3>()
2 file_path="vector_index.pkl"
3 with open(file_path, "wb") as f:
----> 4 pickle.dump(vectorindex_openai, f)

TypeError: cannot pickle '_thread.RLock' object

-- coding: utf-8 --

"""Langchain_LLM.ipynb

Automatically generated by Colaboratory.

Original file is located at
https://colab.research.google.com/drive/1DWToK3XFOM0v5bl7-LwT0GBfKyYVulnb
"""

!pip install python-magic langchain unstructured streamlit openai tiktoken faiss-gpu

import os
import streamlit as st
import pickle
import time
from langchain import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

os.environ['OPENAI_API_KEY'] = "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"

llm = OpenAI(temperature = 0.9, max_tokens=500)

loader = UnstructuredURLLoader(
urls = [
"https://www.moneycontrol.com/news/business/banks/hdfc-bank-re-appoints-sanmoy-chakrabarti-as-chief-risk-officer-11259771.html",
"https://www.moneycontrol.com/news/business/markets/market-corrects-post-rbi-ups-inflation-forecast-icrr-bet-on-these-top-10-rate-sensitive-stocks-ideas-11142611.html"
]
)
data = loader.load()
len(data)

data[0].metadata

text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 1000, # size of each chunk created
chunk_overlap = 200, # size of overlap between chunks in order to maintain the context
)
docs = text_splitter.split_documents(data)
len(docs)

docs[2]

Create the embeddings of the chunks using openAIEmbeddings

embeddings = OpenAIEmbeddings()

Pass the documents and embeddings inorder to create FAISS vector index

vectorindex_openai = FAISS.from_documents(docs, embeddings)

Storing vector index create in local

file_path="vector_index.pkl"
with open(file_path, "wb") as f:
pickle.dump(vectorindex_openai, f)