Cannot pickle '_thread.RLock' object

API
1

image
image1919×868 62.5 KB

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

from dotenv import load_dotenv
load_dotenv() # take environment variables from .env (especially openai api key)

st.title(“RockyBot: News Research Tool :chart_with_upwards_trend:”)
st.sidebar.title(“News Article URLs”)

urls =
for i in range(3):
url = st.sidebar.text_input(f"URL {i+1}")
urls.append(url)

process_url_clicked = st.sidebar.button(“Process URLs”)
file_path = “faiss_store_openai.pkl”

main_placeholder = st.empty()
llm = OpenAI(temperature=0.9, max_tokens=500)

if process_url_clicked:
# load data
loader = UnstructuredURLLoader(urls=urls)
main_placeholder.text(“Data Loading…Started…:white_check_mark::white_check_mark::white_check_mark:”)
data = loader.load()
# split data
text_splitter = RecursiveCharacterTextSplitter(
separators=[‘\n\n’, ‘\n’, ‘.’, ‘,’],
chunk_size=1000
)
main_placeholder.text(“Text Splitter…Started…:white_check_mark::white_check_mark::white_check_mark:”)
docs = text_splitter.split_documents(data)
# create embeddings and save it to FAISS index
embeddings = OpenAIEmbeddings()
vectorstore_openai = FAISS.from_documents(docs, embeddings)
main_placeholder.text(“Embedding Vector Started Building…:white_check_mark::white_check_mark::white_check_mark:”)
time.sleep(2)

# Save the FAISS index to a pickle file
with open(file_path, "wb") as f:
    pickle.dump(vectorstore_openai, f)

query = st.text_input("Question: ")
if query:
if os.path.exists(file_path):
with open(file_path, “rb”) as f: # corrected mode to “rb”
vectorstore = pickle.load(f)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
result = chain({“question”: query}, return_only_outputs=True)
# result will be a dictionary of this format → {“answer”: “”, “sources”: }
st.header(“Answer”)
st.write(result[“answer”])

        # Display sources, if available
        sources = result.get("sources", "")
        if sources:
            st.subheader("Sources:")
            sources_list = sources.split("\n")  # Split the sources by newline
            for source in sources_list:
                st.write(source)