Effortlessly Manage and Explore Your Documents with Bubble and Pinecone

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Effortlessly Manage and Explore Your Documents with Bubble and Pinecone

Table of Contents

  1. Introduction
  2. Uploading and Storing Documents 2.1. Creating a Document 2.2. Chunking the Document 2.3. Storing the Text Content
  3. Connecting with OpenAI 3.1. Setting up the OpenAI API 3.2. Embedding Text into Vectors
  4. Connecting with Pinecone 4.1. Creating a Pinecone Index 4.2. Upserting Vectors to Pinecone
  5. Building the Workflow 5.1. Chunking the Text 5.2. Embedding and Upserting 5.3. Scheduling the Workflow

How to Connect Bubble with OpenAI and Pinecone

In this tutorial, we will guide You through the process of connecting Bubble with OpenAI and Pinecone to enable you to upload your own documents and perform question and answer tasks Based on those documents. The tutorial will be divided into several parts, each covering a specific aspect of the integration.

Part 1: Uploading and Storing Documents

Before we can start asking questions based on our documents, we first need to upload and store the documents in Bubble. This process involves three key steps: creating a document, chunking the document into smaller pieces, and storing the text content of each chunk.

To Create a document, we will set up a database table in Bubble with a single field for the document name. This will allow us to easily identify and manage our documents.

Next, we will chunk the document into smaller pieces, specifically in increments of 100 words. This will help us process and analyze the text more efficiently.

Once the document is chunked, we will store the text content of each chunk in Bubble. We will create another table in the database to hold this information, with fields for the connected document and the text content of the chunk.

Part 2: Connecting with OpenAI

To leverage the power of OpenAI's language models, we need to set up the OpenAI API in our Bubble project. This involves installing the necessary plugin and configuring the API connector. Once set up, we can use the API to embed the text chunks into semantic vectors.

We will start by obtaining a curl command from the OpenAI documentation and importing it into the API connector. This command includes the necessary authorization and content Type headers, as well as the input text and the model to use for embedding.

After setting up the curl command, we will adjust the settings in the API connector to make the necessary fields private and dynamic. We will also insert our OpenAI API Key to enable the connection. This key can be obtained from the OpenAI developer tools.

Once the API connector is properly configured, we can initialize the call and ensure that the embeddings are generated successfully. The embeddings will represent the semantic meaning of the text chunks.

Part 3: Connecting with Pinecone

In order to store and query our vectors, we will connect Bubble with Pinecone, a vector database. We will set up a Pinecone index to store our vectors and define the Dimensions and metric. We will also create a namespace to group our vectors.

After creating the index and namespace, we will use the Pinecone upsert API to insert our vectors into Pinecone. This API call will include the embeddings from the OpenAI API, as well as additional information linking the vectors to Bubble's database.

By connecting Bubble with Pinecone, we can efficiently store and query our vectors to perform advanced search and retrieval tasks.

Part 4: Building the Workflow

To automate the process of uploading, chunking, embedding, and upserting, we will create a backend workflow in Bubble. This workflow will be triggered by a button click and will handle the processing of text and interaction with the APIs.

The workflow will start by creating a document and adding all the necessary fields, such as the document name and the full text. It will then iterate through the text, chunking it into smaller pieces and embedding each chunk using the OpenAI API.

After embedding the text chunks, the workflow will upsert the vectors into Pinecone, linking them to the corresponding documents in Bubble's database.

To ensure that all the chunks are processed, the workflow will schedule itself to run again, starting from the next chunk. This process will Continue until all the chunks have been processed, ensuring that the entire document is uploaded and stored in Pinecone.

Conclusion

By following this tutorial, you will be able to connect Bubble with OpenAI and Pinecone, allowing you to upload your own documents and perform powerful question and answer tasks based on those documents. With the ability to store and query vectors, you can unlock advanced search and retrieval capabilities for your applications. Stay tuned for the next part of the tutorial, where we will cover the querying and prompting process using the OpenAI Completions API.

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