UI élégante pour ChatGPT avec fichiers PDF

Table of Contents

1. Introduction
2. The User Interface
3. Setting Up the Node Server
4. Loading and Storing PDF Files
5. Interacting with the PDF File
6. The Front-End Implementation
7. Handling User Inputs
8. Displaying Responses
9. Limitations and Future Improvements
10. Conclusion

Introduction

Welcome to part two of our series on building a custom chatbot for your PDF files. In the first part of this series, we covered setting up a node server to load and Chat with PDF files. However, the user experience was not optimal. In this article, we will focus on creating a fully functional user interface for the chatbot. With this interface, you can easily Interact with your PDF files and retain chat history, resulting in a more pleasant user experience.

The User Interface

In this section, we will discuss the user interface of the chatbot. It includes an input field where You can enter the link of any PDF file available on the internet. The file is then downloaded, stored in a local database, and added to the chatbot's vector store. We will also explore the conversation history feature, which allows you to have contextually rich conversations with your PDF files.

Setting Up the Node Server

To enable the chatbot's functionality, we will explore the setup of a node server. It will handle downloading the PDF file, checking its validity, and storing it in the vector store. We will walk through the necessary code adjustments and refinements made to the server file, app.js. The entire code will be available on GitHub, and we welcome contributions from users.

Loading and Storing PDF Files

In this section, we dive into the process of loading and storing PDF files. The vector store serves as a repository for the PDF files and allows the chatbot to maintain Context and carry out conversations. We will discuss the index and doc store, which respectively represent the vector store and the JSON representation of the vector store. Additionally, we will explain how the PDF files are downloaded, stored, and sent to the vector store.

Interacting with the PDF File

Here, we explore the interaction process with the PDF file. We will demonstrate various conversational queries and examine the responses. We discuss the summary of the PDF file, explore specific sections such as the introduction and literature review, and highlight the evolution of the llm's performance. We acknowledge that the llm may occasionally provide inaccurate responses or hallucinate, but with continuous refinement, we can have a more precise and reliable conversational experience.

The Front-End Implementation

In this section, we Delve into the implementation of the front-end of the chatbot. The front-end code, written in React and Next.js, facilitates the user interface and its functionalities. We will analyze the loading screens, error handling, and the form for submitting PDF files. By examining the index.js file, we understand how the front-end communicates with the server and manages user interactions.

Handling User Inputs

One of the crucial aspects of the chatbot's functionality is handling user inputs. We explain the process of submitting a PDF file link and how it is sent to the server through a post request. We discuss the various states and functions involved, including error handling, link validation, and the clearing of the input box. Additionally, we examine the loading bar that appears while the llm processes the user's queries.

Displaying Responses

In this section, we focus on displaying the llm's responses to the user's queries. We explore the function responsible for submitting a question to the llm through another post request to the server. We discuss how the llm retrieves context from the vector store and generates a response. The response is then displayed in the user interface, featuring emojis denoting the individual and the llm.

Limitations and Future Improvements

While our chatbot offers a valuable and functional user interface, we acknowledge its limitations. We discuss the challenges faced when structuring PDF files, which may affect the llm's performance. We express our commitment to continuously refining the code and addressing these limitations. We invite users to contribute to the project on GitHub and help improve the chatbot's capabilities.

Conclusion

In conclusion, this article has provided an in-depth understanding of building a custom chatbot for PDF files. We have covered the development of a user-friendly interface, the setup of the node server, loading and storing PDF files, interaction with the PDF content, front-end implementation, handling user inputs, and displaying responses. We have also discussed the chatbot's limitations and future improvements. With this knowledge, you can now Create your own chatbot to engage in Meaningful conversations with PDF files.