Chat GPT之外的方法 | Chat with PDF | 如何做同样的事情？

Table of Contents:

1. Introduction
2. Converting Prototype into Application
3. Setting Up the Main File
4. Importing Necessary Libraries
5. Reading PDF with Pi PDF and Streamlit
6. Extracting Text from PDF Pages
7. Splitting Text into Segments
8. Storing PDF Data in a Database
9. Optimizing Code with Pickle
10. Handling User Queries and Semantic Search
11. Conclusion

Introduction

In this video, we will be converting the prototype discussed in our previous video into a full-fledged end-to-end application. We will explore the use of the Pi PDF and Streamlit libraries for creating a user interface where PDF files can be processed. The application will be able to extract text from PDF pages, perform semantic search, and provide Relevant answers to user queries. By the end of this video, You will have a better understanding of how to develop a PDF-Based chatbot using Python.

Converting Prototype into Application

To convert the prototype into an application, we need to set up the main file and import the necessary libraries. The main file, main.py, will serve as the entry point for our application. We will also Create a requirement.txt file to store the library dependencies.

Setting Up the Main File

In the main.py file, all the required libraries will be imported for the development of the application. We will be using Pi PDF for PDF processing and Streamlit for creating the user interface. Additionally, we will import the Phase, Facebook AI, and OpenAI libraries for semantic search functionality. The load_env function will be used to Read data from the environment file, which stores important keys and connections. The base64 library will be used for encoding images, and the pickle library will be used to load the model file.

Importing Necessary Libraries

The necessary libraries for developing the application are Pi PDF, Streamlit, Phase, Facebook AI, OpenAI, base64, and pickle. Pi PDF is used for PDF processing, Streamlit for creating the user interface, and Phase for semantic search. Facebook AI and OpenAI libraries are also used for semantic search functionality. The base64 library is used for encoding and decoding images, while the pickle library is used to load and save the model file.

Reading PDF with Pi PDF and Streamlit

In order to extract text from PDF pages, we will use the Pi PDF library. After uploading a PDF file, the application will call the PDF reader function to extract text from the pages. The extracted text will be concatenated into a single STRING for further processing. We will also use the Streamlit library to create a user-friendly interface for uploading and processing PDF files.

Extracting Text from PDF Pages

Once the PDF file is uploaded and read using the Pi PDF library, we will iterate over the pages and extract the text from each page. The extracted text will be stored in a string variable for further processing. By extracting text from each page, we will have the complete content of the PDF at our disposal.

Splitting Text into Segments

To facilitate efficient searching and retrieval of information, we will split the extracted text into segments using a text splitter API. The text splitter can be customized to split the text based on characters, lines, or any other criteria. Splitting the text into segments will allow us to perform semantic search and retrieve relevant answers to user queries more effectively.

Storing PDF Data in a Database

To manage the PDF data efficiently, we will store it in a database. Each PDF will be associated with its respective name, and the text segments will be stored as entries in the database. Storing the PDF data in a database will facilitate quicker retrieval and processing of information. Additionally, whenever a PDF is uploaded, the application will check if it already exists in the database to avoid redundant processing.

Optimizing Code with Pickle

To optimize the code and reduce processing time, we will use the pickle library. The PDF data will be converted into a pickle file and stored, which will allow us to directly retrieve the vectorized data without performing redundant processing. This optimization technique will help improve the performance of the application, especially when dealing with frequently accessed PDFs.

Handling User Queries and Semantic Search

Once the PDF data is stored and the application is ready to accept user queries, the user can ask questions related to the uploaded PDF. The application will use semantic search to find the most relevant answers to the user's queries. The semantic search functionality will match the queries with the stored PDF data and provide a personalized and accurate response.

Conclusion

In this video, we learned how to convert a prototype into a full-fledged end-to-end application for PDF-based chatbot development. We explored the use of Pi PDF and Streamlit libraries for PDF processing and user interface creation. We also implemented functionalities such as text extraction, segmentation, database storage, and semantic search. The application allows users to upload PDF files, ask questions related to the content, and receive accurate answers. With the code provided in the GitHub repository, you can try it out yourself and explore different use cases for PDF-based chatbots.

Highlights

• Converting a prototype into a full-fledged end-to-end application
• Using Pi PDF and Streamlit libraries for PDF processing and user interface creation
• Extracting text from PDF pages and splitting into segments for efficient processing
• Storing PDF data in a database for quicker retrieval and processing
• Optimizing code with the pickle library for faster performance
• Implementing semantic search for accurate and personalized responses to user queries

FAQs

Q: Can I use any PDF file for the application? A: Yes, you can upload any PDF file to the application for processing and querying.

Q: How does the semantic search functionality work? A: The semantic search matches user queries with the segmented text data from the PDFs stored in the database. It retrieves the most relevant answers based on the semantic similarity between the query and the stored data.

Q: Can I modify the text splitter criteria? A: Yes, the text splitter criteria can be customized based on your specific requirements. You can choose to split the text based on characters, lines, or any other criteria that suit your needs.

Q: How accurate are the answers provided by the application? A: The application aims to provide accurate answers by leveraging semantic search and personalized responses. However, the accuracy may vary depending on the quality of the PDF data and the nature of the queries.

Q: Is the application scalable for handling large volumes of PDFs? A: The application is designed to handle a reasonable volume of PDFs. However, for handling large-scale PDF processing, additional optimizations and infrastructure may be required.