Langchain + ChatGPT + Pinecone: A Question Answering Streamlit App

In this exciting video tutorial, I walk you through creating a Streamlit application that allows you to search and query PDF documents effortlessly. Using cutting-edge technologies such as Pinecone and LLM (OpenAI's ChatGPT), I guide you step-by-step in harnessing the potential of these tools.

By leveraging Pinecone as a vector database and search engine, we enable lightning-fast search capabilities for PDF documents. Additionally, we employ LLM to enhance the search functionality with question-answering capabilities, making your app even more versatile and intelligent.

To ensure smooth data preprocessing, chains, and other essential tasks, we utilize the incredible Langchain framework. With its powerful features, Langchain simplifies and streamlines the development process, enabling you to focus on building an exceptional PDF query search app.

Whether a beginner or an experienced developer, this tutorial provides a comprehensive guide to building your own Streamlit app with Pinecone, LLM, and Langchain. Join me as we dive deep into natural language processing and create a game-changing application together!

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Langchain + ChatGPT + Pinecone: A Question Answering Streamlit App

[GiangNguyen-efbr]

Thank you!!! Great resource. Pinecone has moved into a serverless model, and apparently there have been quite a bit of movements with the langchain packages. Would it be possible for us to have an updated script as of Apr 2024? Otherwise, I would be very interested in a private meet to discuss this. Would greatly appreciate it!

[michael]

thanks so much for posting this - it's been very helpful!

Just wanted to ask about the doc_preprocessing fx - I sometimes get "ValueError: zero-size array to reduction operation maximum which has no identity" when trying to run streamlit

I first got the error when I downloaded a google sheet file (containing text) as a pdf. So I deleted that file and then retried with downloading a google word file as pdf and streamlit loaded and worked fine.

But if I have both of the above mentioned files, then the error recurs again. I'm assuming it must have something to do with the data type of the google sheet based pdf messing with the directoryloader module. But it's interesting how it ends up being a zero-size array.

Just wondering if you had any insights into the issue?

[thomashietkamp]

Yo bro, great video! However, I got an error 'batch size exceeds maximum'... Does that mean I use too many documents? And can I fix that?

[FCrobot]

In the scenario of conversational robots, how to limit the token consumption of the entire conversation?

For example, once the consumption reaches 1, 000, it will prompt that the tokens for this conversation have been used up.

[hernandocastroarana]

Great. Thanks for the video. Do you know how I can make it show the sources from where it was consulted? or for example to show the links from where the information was extracted (for the case when doing web scrap)?

[jorgerios]

Great video, thanks! Would it work if we replace openAI with LaMini-LLM? in order to run it on a cpu.

[akshay_raut]

This one is good, but i have one question if pdf have information in table format so it will still able to retrieve data from it?

[abduljaweed]

so we only use openai for generating embedding and using pinecone store embedding and querying result ?

[ShivamKumar-ivrk]

can you make a video on langchain streaming response using RetrievalQA and pinecone

[binitkunal]

Hi
i am getting this error in your code
can please check this
, in partition
elements = partition_pdf(
NameError: name 'partition_pdf' is not defined. Did you mean: 'partition_xml'?

[NISRAL]

Thank you for this video, would this work to generate q/a from pdf ?

[bongomango]

thanks heaps for this tutorial. Are you able to add from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
and
from langchain.chains import ConversationChain
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.memory import
import streamlit as st
from streamlit_chat import message

and get this app you make in this tutorial have buffer memory and retrieve answers only from the /data folder (corpus of PDFs?) or is this not posssible. I can't find a video that explains how to QA my own corpus and use the buffer memory.