Supercharge Your Vector Database: Storing OpenAI Embeddings with Bubble.io

Table of Contents

1. Introduction
2. Setting up the OpenAI Embeddings Model with Pinecones Database
   1. Converting Data into Pinecones Database
   2. Using Similarity Search
3. Ingesting Data into Pinecones Database
   1. API Calls for Ingestion
   2. Creating a New Index
   3. Initializing API Calls
   4. Upsert Method a. Input Values
   5. Querying Pinecones Database a. Input Values b. Metadata and Query Result
4. Conclusion
5. FAQs

Setting up the OpenAI Embeddings Model with Pinecones Database

In this tutorial, we will guide You through the process of setting up an OpenAI Embeddings Model with Pinecones Database. This will allow you to ingest your own data and convert it into embeddings, which can later be used as Context in Prompts. Please note that this tutorial focuses on the data ingestion process and similarity search, rather than leveraging GPT models for contextual prompts.

To begin, we have created a simple UI where you can input data. For example, you can enter "My favorite NFL team is the Kansas City Chiefs." This data is then ingested into Pinecones Database as embeddings. Subsequently, a specific question can be asked, such as "What is my favorite NFL team?", and the metadata, which is the original input, will be returned.

The key process involves two API calls. The first call is the OpenAI Embeddings API, which converts the input data into embeddings. The Second call is the Pinecones API, which ingests the embeddings and stores them in the database. By querying the database, you can retrieve the metadata that matches the input query.

Ingesting Data into Pinecones Database

To set up the API calls for data ingestion, follow these steps:

1. Make sure you have the API connector plugin installed in Bubble.
2. Copy the curl request from the OpenAI documentation and import it into Bubble's API connector.
3. Add your own API key from OpenAI and set the input value as a dynamic value.
4. Reinitialize the call and save it. Ensure that the response is properly initialized.
5. Copy the curl request from the Pinecones documentation and import it into Bubble's API connector.
6. Sign up for a Pinecones account and Create a new index.
7. Enter a unique ID STRING and specify the Dimensions as 1536.
8. Set the metric as Cosine and choose the Pod Type.
9. Reinitialize the call and save it.

Once the API calls are set up, you can proceed with the ingestion process. The upsert method is used to ingest data into Pinecones. You will need to specify the text and values of the metadata. The text is the metadata from the input, while the values are the embeddings returned by OpenAI. Make sure to set the dynamic values accordingly.

After ingesting the data, you can query the Pinecones database by using the query API call. Specify the namespace and the embeddings generated from the input. The query will return the metadata that matches the input query. Update the state of the metadata field to display the result.

Conclusion

In this tutorial, we have covered the process of setting up the OpenAI Embeddings Model with Pinecones Database. You have learned how to ingest data into the database and perform similarity searches using the embeddings. This process allows you to convert your data into embeddings and retrieve Relevant metadata Based on user queries. With further development, you can expand the application to handle larger documents and employ GPT models for more robust responses.

FAQs

Q: What is the purpose of converting data into embeddings? A: Converting data into embeddings allows computers to understand and compare the context of text, enabling similarity searches and semantic analysis.

Q: Can I upload large documents into the Pinecones database? A: Yes, you can upload large documents by chunking them into smaller portions. This helps optimize token usage and minimizes resource consumption.

Q: How can I integrate GPT models with Pinecones for more complex applications? A: By leveraging the metadata returned from Pinecones, you can use it as context in GPT models like GPT-4 to generate responses based on user queries. This enables more advanced applications of natural language processing.