Effortlessly Manage Documents with Bubble, OpenAI, and Pinecone

Table of Contents

1. Introduction
2. Building the Document Search Application
3. Setting Up the API Connector
4. Querying Pinecone by Vector
5. Constructing the Prompt for OpenAI
6. Sending the Prompt to OpenAI
7. Displaying the Response
8. Conclusion

Introduction

Welcome to part two of our video on building a document search application with Bubble, OpenAI, and Pinecone. In this video, we will focus on the process of asking questions and retrieving similar documents Based on those questions.

Building the Document Search Application

To get started, we need to set up the API connectors for both OpenAI and Pinecone. These connectors will allow us to Interact with the respective platforms and retrieve the necessary data.

Setting Up the API Connector

First, let's go into the Bubble editor and access the API connector. We will need to set up two API calls, one for OpenAI and one for Pinecone. In the Pinecone API call, we will query by vector to find matching vectors in the database. We will also include metadata to retrieve the document ID and other Relevant information.

Querying Pinecone by Vector

Once the API connectors are set up, we can perform a query by vector in Pinecone. This will involve inputting the vector and document ID we want to search for. Pinecone will then return a list of matching documents based on the vector.

Constructing the Prompt for OpenAI

Next, we need to construct a prompt using the Pinecone results. The prompt will contain the Context of the matched responses along with the question we want to ask. This prompt will be sent to OpenAI for further processing.

Sending the Prompt to OpenAI

We will use the OpenAI completions API route to send the prompt to OpenAI. We will specify the GPT-3.5 turbo model and include additional parameters such as temperature and max tokens. This will ensure that we receive an appropriate response from OpenAI based on our prompt.

Displaying the Response

Once we receive a response from OpenAI, we will display it in the application. We will use a group box to contain the response and use the display data action to populate the group box with the response content.

Conclusion

In conclusion, we have successfully built a document search application using Bubble, OpenAI, and Pinecone. By embedding questions in vectors and querying the database, we were able to retrieve relevant documents and construct Prompts for OpenAI. The application can now provide accurate responses to user queries.

Now, let's move on to the actual article.

Building a Document Search Application with Bubble, OpenAI, and Pinecone

Introduction: Welcome to part two of our video series on building a document search application. In the previous video, we discussed the initial setup and integration of Bubble, OpenAI, and Pinecone. In this video, we will focus on the actual process of asking questions and retrieving similar documents based on those questions. By the end of this tutorial, you will have a fully functional document search application that leverages the power of machine learning and natural language processing.

Building the Document Search Application: To start building our document search application, we first need to set up the API connectors for both OpenAI and Pinecone. These connectors will allow us to interact with the respective platforms and retrieve the necessary data. In the Pinecone API call, we will query by vector to find matching vectors in the database. We will also include metadata to retrieve the document ID and other relevant information.

Once the API connectors are set up, we can perform a query by vector in Pinecone. This involves inputting the vector and document ID we want to search for. Pinecone will then return a list of matching documents based on the vector.

Now that we have the matching documents, we can construct a prompt using the Pinecone results. The prompt will contain the context of the matched responses and the question we want to ask. This prompt will be sent to OpenAI for further processing using the completions API route. We will specify the GPT-3.5 turbo model and include additional parameters such as temperature and max tokens to ensure an appropriate response from OpenAI.

Once we receive a response from OpenAI, we can display it in our application. We will use a group box to contain the response and use the display data action to populate the group box with the response content. This will allow users to view the answers to their questions within the application.

In conclusion, by leveraging the power of Bubble, OpenAI, and Pinecone, we have successfully built a document search application that is capable of answering user queries with relevant information. This application combines the strengths of natural language processing and machine learning to provide accurate and comprehensive search results. With further enhancements and iterations, this application has the potential to revolutionize the way we search and retrieve information from documents.

Pros:

• Accurate and relevant search results
• Integration with powerful AI models from OpenAI
• Efficient handling of large amounts of data using Pinecone
• User-friendly interface for easy querying and document retrieval

Cons:

• Dependence on external APIs for processing and retrieval
• Potential limitations in the response generation by OpenAI's models
• Complexity in setting up and configuring the API connectors

Overall, the pros of building a document search application with Bubble, OpenAI, and Pinecone outweigh the cons. This powerful combination of technologies opens up new possibilities for search and retrieval applications, making it easier for users to find the information they need.

Highlights

• Building a document search application with Bubble, OpenAI, and Pinecone
• Leveraging the power of machine learning and natural language processing
• Querying Pinecone by vector to find matching documents
• Constructing prompts for OpenAI based on the Pinecone results
• Sending prompts to OpenAI using the completions API route
• Displaying the responses in the application
• Pros and cons of the document search application

FAQ

1. How accurate are the search results in the document search application?

   • The search results in the document search application are highly accurate and relevant. The application leverages the power of machine learning and natural language processing to ensure precise retrieval of matching documents.

2. Can the document search application handle large amounts of data?

   • Yes, the application is designed to efficiently handle large amounts of data using the Pinecone platform. Pinecone's vector database enables fast and scalable search operations, making it suitable for applications with large document collections.

3. Are there any limitations in the response generation by OpenAI's models?

   • While OpenAI's models are powerful in generating responses, there may be limitations in terms of context understanding and generating coherent answers. It is important to carefully design prompts and evaluate the generated responses to ensure accuracy and relevance.

4. Is the setup process for the API connectors complex?

   • The setup process for the API connectors requires some technical knowledge and configuration. However, with proper documentation and guidance, it can be easily accomplished. Bubble provides user-friendly tools for integrating APIs, simplifying the setup process.

5. Can the document search application be customized for different use cases?

   • Yes, the document search application can be customized and extended to meet specific use cases and requirements. Bubble's flexibility and Pinecone's scalability allow for easy adaptation and expansion of the application's functionality.