Communiquez avec des fichiers PDF: ChatGPT pour les PDF avec Langchain, HF et Chainlit || Tutoriel pas à pas

Table of Contents

1. Introduction
2. Requirements
3. Document Loader
4. Chunking
5. Embedding
6. Vector Store
7. Large Language Model
8. Document Retriever
9. Integration and Interface
10. Conclusion

Introduction

In this article, we will explore the process of building a PDF-Based chatbot using Lang chain and hugging face Hub. We will go through the steps required to Create a chatbot that accepts a PDF from the user and provides Relevant answers based on that PDF. This tutorial assumes that You have already built a basic chatbot using Lang chain, but if you haven't, don't worry - we will provide a brief overview of the required libraries and concepts. So let's dive in and learn how to build our very own PDF chatbot!

Requirements

Before we begin, let's take a moment to discuss the requirements for building our PDF chatbot. First, we need to install Lang chain and hugging face Hub, as we will be integrating with hugging face models. Additionally, we will be using a vector store to store vector representations of our text data. We will also need to install Pi PDF, as we will be converting our PDF into text format. Lastly, we will need a large language model (LLM) for question answering, and a document retriever to combine all the components together. In the following sections, we will explore each of these requirements in Detail and provide step-by-step instructions on how to install and use them.

Document Loader

The first step in building our PDF chatbot is to load the document or PDF file that will serve as the basis for our chatbot. We will use the Pi PDF loader to Read the PDF file and convert it into text format. This will allow us to process the text and extract relevant information. Once the document is loaded, we can proceed to the next step.

Chunking

In many cases, PDF files can be large in size or contain a large number of tokens. This can pose limitations when working with language models. To overcome this limitation, we will perform chunking on our text data. Chunking involves breaking down the text into smaller chunks or segments based on specified separators. We will use the recursive character text splitter to perform this operation. By chunking our text, we can ensure that it is suitable for processing by our language model.

Embedding

After we have generated the chunks of text, the next step is to create embeddings or vector representations of our text. This step is important as it allows us to perform semantic matching and find the most relevant chunks for a given query. We will use hugging face embeddings, which utilize the sentence transformer to create embeddings for our text data. These embeddings will be stored in a vector store for indexing and retrieval.

Vector Store

The vector store is an integral component of our PDF chatbot. It serves as a memory mapping that allows us to map queries to relevant chunks from the PDF file. By indexing our vectors in the vector store, we can quickly retrieve the most relevant information based on user queries. The vector store will be created using Chroma Vector Store, a powerful tool that integrates with Lang chain. We will provide instructions on how to install and use Chroma Vector Store to create our vector store.

Large Language Model

A large language model (LLM) is essential for performing question answering in our chatbot. The LLM will be responsible for understanding user queries and providing relevant answers based on the PDF data. We will be using the Thai UA Falcon 7B model, but feel free to experiment with other models to achieve better results. We will provide instructions on how to set up and use the LLM in our chatbot.

Document Retriever

The document retriever is the component that brings everything together. It connects our LLM, vector store, and user queries to provide a seamless user experience. The document retriever performs semantic matching to identify the most relevant chunks of text from the PDF file based on user queries. By combining the LLM, vector store, and document retriever, our chatbot will be able to answer user queries accurately and efficiently.

Integration and Interface

Once we have completed the setup and configuration of all the components, it's time to integrate them and create an interface for our chatbot. We will be using Chain Lit, a powerful library that simplifies the creation of interactive interfaces for language models. By using Chain Lit, we can provide a user-friendly interface for users to input their queries and receive answers based on the PDF data. We will provide step-by-step instructions on how to integrate and create the interface for our chatbot.

Conclusion

In conclusion, building a PDF-based chatbot using Lang chain and hugging face Hub allows us to create a powerful tool for extracting information and answering user queries from PDF files. By following the steps outlined in this article, you will be able to build your very own PDF chatbot and provide a seamless user experience. So let's get started and unlock the potential of PDF-based chatbots!

Note: The above Table of Contents is a representation of the headings and subheadings that will be included in the article. The actual content may differ based on the structure and flow of the article.