Using Hologres as a vector database for OpenAI embeddings

This tutorial will guide you through the process of using Hologres as a vector database for storing and querying OpenAI embeddings. Here are the steps we'll follow:

1. Precompute Embeddings with OpenAI API: We'll start by generating embeddings from text data using the OpenAI API. These embeddings are numerical representations of the text data that can be used for various machine learning tasks, including similarity searches.
2. Store Embeddings in Hologres: Next, we'll store the precomputed embeddings in a Hologres instance. This involves setting up a Hologres database and creating a table designed to hold vector data.
3. Convert Raw Text Query to Embedding: When a user makes a query, we'll convert that query text into an embedding using the OpenAI API. This embedding will then be used to search for similar items in the Hologres database.
4. Nearest Neighbour Search in Hologres: We'll use Hologres's built-in vector search capabilities to find the nearest neighbors to the query embedding. This involves executing a SQL query that leverages Hologres's vector search functions.
5. Use Results in Prompt Engineering: Finally, we'll take the results of the nearest neighbor search and use them to provide context to a large language model, helping to refine its responses.