GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document (w/Pinecone)

How to Chat with a Long PDF

In this video, Mayor from Chartered Data explains how to use Lang chain and GPT-4 to chat with a long PDF document. He provides an overview of the architecture and explains the two phases involved in the process.

PDF Chatbot Architecture

• The PDF document is converted to text and split into chunks using Lang chain.

• Each chunk is converted into embeddings, which are stored in a vector base.

• The user asks a question, which is combined with the chat history and sent to GPT-4 to create a standalone question.

• The standalone question is converted into embeddings and compared to the embeddings of relevant documents in the vector store.

• GPT-4 returns an answer based on the standalone question and relevant documents.

Ingesting Phase

• The ingestion phase involves converting the PDF document to text, splitting it into chunks, and converting each chunk into embeddings that are stored in a vector base.

Overall, Mayor provides an informative overview of how to use Lang chain and GPT-4 for chatting with long PDF documents. He explains the architecture involved in detail and provides insights on each step of the process.

Introduction to Pinecone

In this section, the speaker introduces Pinecone and explains how it works.

What is Pinecone?

• Pinecone is a service that helps you store and retrieve vectors quickly.

• It allows you to compare vectors and find the most relevant one to a given query.

How does Pinecone work?

• Pinecone loads raw documents from a PDF file, splits them into chunks of 1000 with overlap, and creates embeddings using an OpenUI embeddings function.

• The embeddings are stored in an index for Python called "Pinecone".

• The process of creating embeddings and storing them in the namespace is called "ingestion".

Using Pinecone

• To use Pinecone, run the ingest script in package.json.

• You can access your index on the Pinecone dashboard where you can set your environment, API keys, and view your vectors.

Exploring the Pinecone Dashboard

In this section, the speaker shows how to explore the Pinecone dashboard.

Setting up Environment

• Set up your environment closest to where it will be served.

Understanding Cosine Calculation

• Cosine calculation is used to find what's similar between two vectors.

Viewing Vectors on Dashboard

• On the dashboard, you can see all indexes created including their number of vectors.

• You can also view individual vectors by querying their ID.

Retrieving Vectors from Query

In this section, the speaker demonstrates how to retrieve vectors from a query.

Vector Structure

• A vector has an ID, values, and metadata which is text.

Comparing Vectors

• To compare vectors, Pinecone compares the embeddings to the question asked by the user and finds the most relevant one.

Setting up Pinecone

In this section, the speaker explains how to set up Pinecone.

Cloning Repository

• Clone the repository and create an environment variable folder where you put in examples.

Getting API Keys

• Get your API keys from OpenAI and Pinecone.

Introduction to Chat Vector DBQA Chain

In this section, the speaker introduces the Chat Vector DBQA Chain and explains how it retrieves similar documents in response to a question.

Chat Vector DBQA Chain

• The Chat Vector DBQA Chain takes a question and retrieves similar documents in response.

• It uses Pinecone as the vector store and has custom prompts for returning source documents.

• The chain is a series of actions that follow the flow shown in the diagram.

Front End Overview

In this section, the speaker provides an overview of the front end and how it manages queries.

Query Management

• The front end manages queries using states for messages, pending messages, and chat history.

• When a user submits a query, it is cleaned up by trimming spaces and added to the message state.

• Pending messages are defined as those that are being loaded or processed. They are set to an empty string once they have been processed.

API Call

• Once a query is submitted, an API call is made to retrieve similar documents using Pinecone's vector store.

• A callback function sends data back to the front end every time a token comes through from Pinecone's search results. This creates a streaming effect on the front end.

• Source documents are returned along with search results when "return source documents" is set to true in custom prompts.

Overview of the Source Code

In this section, the speaker discusses how the source documents are handled in the code and explains the use of memo to improve efficiency.

Handling Source Documents

• The message that comes in represents the source document.

• The data is passed if there are no source documents.

• The state is set with the source documents before being checked.

Use of Memo

• Memo is used to memorize a function that is called repeatedly.

• This improves efficiency on the front end where all data is captured and mapped over.

Comprehensive Workshop for Building a Chatbot

In this section, the speaker talks about potential workshops for building a chatbot for your document.

Potential Workshop

• There have been many requests for an in-depth step-by-step guide on building a chatbot.

• A comprehensive workshop may be offered in response to these requests.

• The workshop will cover building a chatbot for various types of documents such as PDFs, books, multiple PDFs, doc CX or Excel files.

• By attending this workshop, participants will learn how to build an application for themselves or their clients that allows back-and-forth interaction with their documents.

Conclusion and Contact Information

In this section, the speaker concludes by thanking viewers and inviting them to ask questions or provide feedback.

Conclusion

• Viewers can contact the speaker through comments if they have any questions or feedback.

• The speaker thanks viewers for watching.