Create a Powerful PDF AI Chat with Bubble - GPT-4

Table of Contents

1. Introduction
2. Building a Chat with PDF App without Code
   1. The concept of Retrieval Augmented Generation
   2. Tools and platforms required
3. Upserting a Document
   1. Splitting the PDF and converting to vectors
   2. Storing the vectors in a vector database
4. Querying the Database
   1. Turning a question into a vector
   2. Retrieving the most similar results
   3. Generating a response using GPT-3.4 or GPT-3.5
5. Creating the Front-end in Bubble
   1. Setting up the user interface
   2. Sending a question to the API and displaying the response
6. Streaming the Response
   1. Using the Chat GPT Toolkit plugin
   2. Implementing streaming functionality
7. Conclusion

Building a Chat with PDF App without Code

In today's age of AI, building AI-enabled products without writing a single line of code has become a reality. In this tutorial, we will explore how to build a chat application with PDF integration using the Bubble platform and the powerful language model CPT-3.5. This app will allow users to ask questions about a specific PDF document and receive intelligent answers generated by the language model.

Introduction

Before we dive into the technical details, let's first understand the concept of Retrieval Augmented Generation (RAG). RAG is a fancy way of saying that we retrieve data from a database and use it to generate a response. In our case, we will retrieve chunks of text from a PDF document stored in a vector database and feed them into the language model to generate intelligent answers.

To build our chat with PDF app, we need a few essential tools and platforms. Firstly, we will be using the Bubble platform, which allows us to build web applications without code. Additionally, we will need access to the OpenAI models, specifically the API keys for the language models. Lastly, we will use a vector database called Pinecone to store and retrieve the document chunks.

Upserting a Document

The first step in building our chat with PDF app is upserting the document. This involves splitting the PDF into chunks, converting the chunks into vectors using OpenAI's embeddings model, and storing them in the Pinecone vector database. By breaking the PDF into smaller chunks, we can work with them more dynamically within the Context window limitations of the language model.

Querying the Database

Once we have the document stored in the vector database, we can start querying it by turning user questions into vectors. We will use OpenAI's embeddings model again to vectorize the questions. By searching the vector database with the vectorized question, we can retrieve the most similar results Based on the user's query. These results will be used as a prompt to generate a response using the language model.

Creating the Front-end in Bubble

In the Bubble platform, we will Create the user interface for our chat with PDF app. This will include input fields for users to ask questions and a button to submit the question to the API. The response generated by the language model will be displayed on the front-end for the user to see. We will utilize the capabilities of the Bubble platform to design a visually appealing and user-friendly interface.

Streaming the Response

To enhance the real-time experience, we can implement streaming functionality using the Chat GPT Toolkit plugin. This plugin allows us to stream responses from the language model as the user interacts with the app. By using streaming, we can receive continuous answers without the need to wait for the full response. This creates a more interactive and engaging chat experience for the user.

Article

Building a Chat with PDF App without Code

In today's world, technology has advanced to an extent that building AI-enabled products without writing a single line of code is now possible. In this tutorial, we will explore how to build a chat application integrated with PDF support using the Bubble platform and the powerful open AI language model CPT-3.5. This app will enable users to ask questions related to a specific PDF document and receive intelligent answers generated by the language model.

Introduction

The concept of Retrieval Augmented Generation (RAG) plays a crucial role in the functionality of our chat with PDF app. RAG is essentially a method of retrieving specific data from a database and utilizing it to generate a suitable response. In our case, we will retrieve chunks of text from a PDF document stored in a vector database and leverage the retrieved data to generate intelligent answers.

Building a Chat with PDF App without Code entails utilizing several tools and platforms. Firstly, the Bubble platform acts as the foundation, providing a code-free environment for building web applications. Additionally, access to the OpenAI language models is required, including the acquisition of API keys for these models. Lastly, the Pinecone vector database will be utilized to store the document chunks and facilitate retrieval.

Upserting a Document

The initial step in constructing our chat with PDF app revolves around the process of upserting the document. This primarily encompasses splitting the PDF into smaller chunks, converting these chunks into vectors with the assistance of the OpenAI embeddings model, and subsequently storing them within the Pinecone vector database. By dividing the PDF into smaller sections, we can effectively work with them more dynamically and avoid any limitations that could arise due to the language model's context window.

Querying the Database

Once the document is successfully stored within the vector database, the subsequent step involves querying this database. This is achieved by transforming user questions into vectors using OpenAI's embeddings model. By utilizing the vectorized user question, we can perform a search within the vector database and retrieve the most similar results pertinent to the user's query. These retrieved results will then serve as a prompt for the language model to generate an appropriate response.

Creating the Front-end in Bubble

Within the Bubble platform, it is imperative to establish an intuitive and visually appealing user interface for our chat with PDF app. This entails incorporating input fields where users can pose their questions and a submission button to forward the question to the API. The response generated by the language model will subsequently be displayed on the front-end, allowing users to Visualize and comprehend the generated answer. The extensive capabilities of the Bubble platform will aid in creating an engaging and seamless user experience.

Streaming the Response

To further enhance the real-time interaction experience, an effective implementation of streaming functionality can be employed using the Chat GPT Toolkit plugin. This plugin empowers the app to receive continuous and immediate responses from the language model, facilitating a dynamic and interactive chat-like experience for users. By employing streaming, users can receive answers without having to wait for the full response, creating a seamless and engaging interaction.

Pros and Cons

Pros:

• Building a chat with PDF app without code allows for the swift creation of AI-enabled products.
• The use of the Bubble platform and language models simplifies the development process.
• Retrieval augmented generation enables accurate and contextually suitable responses.
• Streaming functionality enhances the real-time user experience.
• The flexibility of the platform and tools allows for further customization and expansion.

Cons:

• Reliance on external tools and platforms may lead to additional costs.
• The complexity of AI models may require a learning curve for beginners.
• Language models may produce biased or inaccurate responses without careful input and supervision.
• Limited control over the AI-generated responses may lead to unexpected or undesired outcomes.

Highlights

• Building a chat application integrated with PDF support without writing any code.
• Utilizing the Bubble platform and the powerful CPT-3.5 language model from OpenAI.
• Understanding the concept of Retrieval Augmented Generation (RAG) for generating intelligent responses.
• Upserting a PDF document by splitting it into chunks, converting them into vectors, and storing them in a vector database.
• Querying the vector database by transforming user questions into vectors and retrieving the most Relevant results.
• Creating a user-friendly front-end interface in Bubble to facilitate user interactions.
• Implementing streaming functionality with the Chat GPT Toolkit plugin for real-time responses.
• The pros and cons of building a chat with PDF app without code.
• The potential for further customization and expansion of the app's functionality.

FAQs

Q: Can this chat with PDF app be used for other types of documents, such as Word or Excel files? A: Yes, the app can be modified to handle other document formats. You would need to adjust the chunking process and vectorization methods accordingly to support different file types.

Q: Is the streaming functionality limited to text-based responses, or can it also handle media files, such as images or videos? A: In this tutorial, we focused on streaming text-based responses. However, with additional customization and integration of appropriate tools, it is possible to extend the streaming functionality to other forms of media as well.

Q: How accurate are the responses generated by the language model? A: The accuracy of the responses depends on various factors, including the quality of the training data and the specific instructions given to the model. While the language model is capable of generating high-quality answers, it's important to review and validate the responses to ensure accuracy.

Q: Can multiple users Interact with the chat with PDF app simultaneously? A: Yes, the app can support multiple users simultaneously. By implementing appropriate user management and session handling techniques, you can enable concurrent interactions with the chat interface.

Q: Can the app handle large PDF files with hundreds of pages? A: Yes, the app can handle large PDF files by splitting them into chunks and storing them in the vector database. The chunking process allows for efficient retrieval and analysis of specific sections within the document, regardless of its size.

Q: Can the app be extended to support multiple languages? A: Absolutely! By incorporating language-specific vectorization techniques and training the language model on diverse multilingual data, you can enable the app to support multiple languages.

Q: Are there any limitations or restrictions when using the Bubble platform for building AI-enabled applications? A: While the Bubble platform offers great flexibility and a wide range of features, there may be limitations in terms of scalability and advanced customization. It's important to evaluate your specific requirements and assess whether the platform aligns with your needs.