Create an AI Chatbot with ChatGPT and PDF Knowledge Base

Create an AI Chatbot with ChatGPT and PDF Knowledge Base

Table of Contents:

1. Introduction
2. Testing the Application
3. Uploading and Processing the PDF
4. Overview of the Code
5. Saving the Uploaded PDF
6. Learning the PDF
7. Storing Information in the Knowledge Base
8. Retrieving User Input
9. Generating Response Using Cosine Similarity
10. Using GPT 3.5 API
11. Conclusion

Introduction

Hey everyone, welcome to this tutorial on building a simple application using Streamlit. In today's video, we will be creating a GPT 3.5 powered chatbot that can answer questions Based on uploaded documents, specifically PDF files. This tutorial will walk You through the process of testing the application, uploading and processing the PDF, the code overview, and the implementation details.

Testing the Application

To start, we will test the functionality of the application. We will upload a PDF file and ask questions to see if the chatbot can provide accurate answers. Let's begin by asking a question about Sohail, the creator of the document. Unfortunately, the chatbot doesn't have any information about Sohail at the moment. Let's upload Sohail's CV and see if the chatbot can learn from it.

Uploading and Processing the PDF

By clicking the "Read PDF" button and selecting Sohail's CV, we initiate the process of uploading and processing the PDF. The application Prompts us to ask a question after the PDF has been processed. Let's ask the same question again to see if the chatbot has learned from the document. This time, the chatbot provides accurate information about Sohail's degree in computer system engineering.

Overview of the Code

Now that we have tested the application, let's take a closer look at the code behind it. We start by importing the necessary libraries and setting up the OpenAI API key. The code handles the Streamlit application and runs it with the command "streamlit run app.py". We then have a function responsible for uploading and processing the PDF, followed by a function that saves the uploaded file.

Saving the Uploaded PDF

The function responsible for saving the uploaded PDF file simply saves the file in the home directory so that it can be read and processed.

Learning the PDF

The "learn_pdf" function reads the uploaded PDF file and generates embeddings for each page using the OpenAI "get_embeddings" function. It stores the ID, text content, and generated embeddings for each page in a list called "content_chunks". The function then appends this list to a JSON file called "knowledgebase.json" for later retrieval.

Storing Information in the Knowledge Base

The "knowledgebase.json" file stores the information from the processed PDF. It includes randomly generated IDs, the text content of each page, and the corresponding embeddings generated by the "get_embeddings" function.

Retrieving User Input

Upon entering user input on the front-end side, the "answer_from_document" function is called. This function generates embeddings for the user input and retrieves the knowledge base from the JSON file. It compares the embeddings of the user input with the embeddings stored in the knowledge base using cosine similarity. The function then returns the top-ranking responses based on similarity.

Generating Response Using Cosine Similarity

The code iterates through the sorted data based on cosine similarity and generates a Context for the GPT 3.5 API call. The GPT 3.5 API is used to obtain the response to the user query based on the generated context and user input. The response is then returned and displayed on the front-end.

Using GPT 3.5 API

This section explains how to use the GPT 3.5 API to generate a response. The code passes the context and user query to the API and receives the response.

Conclusion

In conclusion, this tutorial covered the process of building a chatbot using Streamlit and GPT 3.5. We tested the application, discussed the code, and explored the steps involved in uploading, processing, and retrieving information from PDF files. This application has the potential to be expanded and customized for various use cases.

FAQ

Q: Can the chatbot answer questions from any PDF file? A: Yes, as long as the PDF file is uploaded and processed by the application, the chatbot can provide answers based on the document's content.

Q: How accurate are the responses generated by the chatbot? A: The accuracy of the responses depends on the quality of embeddings and the similarity measure used. The cosine similarity between user input and the knowledge base determines the relevance of the responses.

Q: Can I use this application with other file formats? A: Currently, the application is designed to handle PDF files. However, with modifications, it can be adapted to work with other file formats as well.

Q: Is the GPT 3.5 API the only option for generating responses? A: No, there are other options available for generating responses, such as other versions of GPT or other language models. The choice of API depends on the specific requirements of the project.

Q: How can I improve the performance of the chatbot? A: Improving the performance of the chatbot can be achieved by fine-tuning the language model, optimizing the embeddings generation process, and refining the cosine similarity calculation.

Q: Can I extend the functionality of the chatbot? A: Yes, the chatbot's functionality can be extended by adding more features, such as handling multiple file uploads, supporting different languages, or integrating additional APIs for advanced natural language processing tasks.

Highlights:

• Building a chatbot using Streamlit and GPT 3.5
• Testing the application with PDF files
• Uploading and processing PDF documents
• Storing and retrieving information from a knowledge base
• Generating responses using cosine similarity
• Utilizing the GPT 3.5 API for natural language processing