IndexError: list index out of range

🎈 Using Streamlit
1

Hii All I have been using streamlit to build a pdf summarisation tool , I am encountering with this error of list out of index

The problem is that once the user select the index number the system crash and display the below error:

File "/opt/homebrew/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 534, in _run_script
    exec(code, module.__dict__)
File "/Users/sandesh/Coding /Python/AI SUMM PBL/jupyter nb/app2.py", line 105, in <module>
    main()
File "/Users/sandesh/Coding /Python/AI SUMM PBL/jupyter nb/app2.py", line 82, in main
    handle_userinput(user_question)
File "/Users/sandesh/Coding /Python/AI SUMM PBL/jupyter nb/app2.py", line 56, in handle_userinput
    response = st.session_state.conversation({'question': user_question})
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 166, in __call__
    raise e
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 160, in __call__
    self._call(inputs, run_manager=run_manager)
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/conversational_retrieval/base.py", line 114, in _call
    answer = self.combine_docs_chain.run(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 293, in run
    return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 166, in __call__
    raise e
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 160, in __call__
    self._call(inputs, run_manager=run_manager)
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/combine_documents/base.py", line 84, in _call
    output, extra_return_dict = self.combine_docs(
                                ^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/combine_documents/stuff.py", line 87, in combine_docs
    return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/llm.py", line 252, in predict
    return self(kwargs, callbacks=callbacks)[self.output_key]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 166, in __call__
    raise e
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 160, in __call__
    self._call(inputs, run_manager=run_manager)
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/llm.py", line 93, in _call
    return self.create_outputs(response)[0]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/llm.py", line 217, in create_outputs
    result = [
             ^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/llm.py", line 220, in <listcomp>
    self.output_key: self.output_parser.parse_result(generation),
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/schema.py", line 355, in parse_result
    return self.parse(result[0].text)
                      ~~~~~~^^^

this is my code


    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain


def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']

     for i, message in enumerate(st.session_state.chat_history):
            if i % 2 == 0:
                st.write(user_template.replace(
                    "{{MSG}}", message.content), unsafe_allow_html=True)
            else:
                st.write(bot_template.replace(
                    "{{MSG}}", message.content), unsafe_allow_html=True)


def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple PDFs",
                       page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with multiple PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # get the text chunks
                text_chunks = get_text_chunks(raw_text)

                # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # create conversation chain
                st.session_state.conversation = get_conversation_chain(
                    vectorstore)


if __name__ == '__main__':
    main()
1 Like
2

Hi @SANDESH_BUCHKUL

The provided code snippet seems to be incomplete, could you share a minimal working example of the app so that the community could reproduce the error.

Also you’ve mentioned about selecting the index number, could you elaborate more on this by pointing out which specific code blocks that you are referring to.

Thanks!

1 Like
3

Yes I have been getting the same error
’ ’ ’
Traceback (most recent call last):
File “J:\Projects\LLM\PDF.venv\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py”, line 534, in _run_script
exec(code, module.dict)
File “J:\Projects\LLM\PDF\index.py”, line 106, in
main()
File “J:\Projects\LLM\PDF\index.py”, line 98, in main
vectorstore = get_vectorstore(text_chunks)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “J:\Projects\LLM\PDF\index.py”, line 37, in get_vectorstore
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “J:\Projects\LLM\PDF.venv\Lib\site-packages\langchain_community\vectorstores\faiss.py”, line 915, in from_texts
return cls.__from(
^^^^^^^^^^^
File “J:\Projects\LLM\PDF.venv\Lib\site-packages\langchain_community\vectorstores\faiss.py”, line 874, in __from
index = faiss.IndexFlatL2(len(embeddings[0]))
~~~~~~~~~~^^^
IndexError: list index out of range
’ ’ ’

here is the code
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI, ChatGooglePalm
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from HTMLtemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
from langchain.embeddings.huggingface import HuggingFaceEmbeddings

def get_pdf_text(pdf_docs):
text = “”
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text

def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator=“\n”,
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks

def get_vectorstore(text_chunks):
# embeddings = OpenAIEmbeddings()
embeddings = HuggingFaceEmbeddings(model_name=“all-MiniLM-L6-v2”)
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore

def get_conversation_chain(vectorstore):

llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})

memory = ConversationBufferMemory(
    memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    memory=memory
)
return conversation_chain

def handle_userinput(user_question):
response = st.session_state.conversation({‘question’: user_question})
st.session_state.chat_history = response[‘chat_history’]

for i, message in enumerate(st.session_state.chat_history):
    if i % 2 == 0:
        st.write(user_template.replace(
            "{{MSG}}", message.content), unsafe_allow_html=True)
    else:
        st.write(bot_template.replace(
            "{{MSG}}", message.content), unsafe_allow_html=True)

def main():
load_dotenv()
st.set_page_config(page_title=“Chat with multiple PDFs”,
page_icon=“:books:”)
st.write(css, unsafe_allow_html=True)

if "conversation" not in st.session_state:
    st.session_state.conversation = None
if "chat_history" not in st.session_state:
    st.session_state.chat_history = None

st.header("Chat with multiple PDFs 🕮")
st.caption("Which you can't do with girls when you have a girlfriend 💀")
user_question = st.text_input("Ask any questions related to the PDF. PS: They don't get offended 😂")
if user_question:
    handle_userinput(user_question)

with st.sidebar:
    st.subheader("Your documents")
    pdf_docs = st.file_uploader(
        "Upload your PDFs here", accept_multiple_files=True)
    if st.button("Start Chat"):
        with st.spinner("Processing"):
            # get pdf text
            raw_text = get_pdf_text(pdf_docs)

            # get the text chunks
            text_chunks = get_text_chunks(raw_text)

            # create vector store
            vectorstore = get_vectorstore(text_chunks)

            # create conversation chain
            st.session_state.conversation = get_conversation_chain(
                vectorstore)

if name == ‘main’:
main()

1 Like
4

I also getting same

1 Like
5

Hi @raw_siddhesh @Joel_D_Joy

Just checking in as your queries remained unanswered - are you still experiencing the issue?

Best wishes,
Charly

1 Like