In this lesson, we will explore the process of saving and using embeddings locally using Python. By focusing on practical implementation, you will gain hands-on experience in managing embeddings within your projects. By the end of this lesson, you will understand how to save embeddings for future use and load them back into your application, allowing you to streamline your workflows and improve the efficiency of your systems.

Saving embeddings is crucial as it allows you to reuse them without recalculating, thereby conserving computational resources and time. This is especially important when dealing with large datasets or when embeddings are generated using models that require significant processing power. In production environments and with very large databases, embeddings are typically stored in specialized databases known as vector databases. However, in this lesson, we will focus on saving embeddings locally, which is a practical approach for smaller projects or initial development stages. We will explore different vector databases in future courses.

First, let's define our sentences and calculate their embeddings using a pre-trained model.

After generating the embeddings, it's important to save them for future use. In this section, we'll demonstrate how to store embeddings in both JSON and Pickle formats. JSON is a widely-used, human-readable format that makes it easy to inspect and share data. In contrast, Pickle is a binary format that is optimized for quick storage and retrieval, making it particularly useful when working with large datasets.

In this code, we first convert the embeddings into a list of dictionaries, where each embedding is converted from a NumPy array to a Python list to ensure compatibility with JSON serialization. This allows for straightforward inspection and sharing. We also save the embeddings in Pickle format, which is efficient for both storage and retrieval.

To utilize the saved embeddings, we need to load them back into our application. Below are the steps to load embeddings from both JSON and Pickle files.

First, let's load the embeddings stored in the JSON file:

In this code snippet, we extract the embeddings from the loaded data, which can then be used for various tasks.

Next, let's load the embeddings from the Pickle file:

This code demonstrates how to load embeddings from a Pickle file into a variable.

Both methods enable you to reuse the embeddings for tasks such as calculating similarities or integrating them into machine learning models.

While storing embeddings locally in JSON or Pickle is useful for small projects, it becomes inefficient when dealing with large-scale applications that require fast retrieval and structured querying. Instead of manually managing embeddings, pgvector allows you to store and search embeddings directly within a PostgreSQL database.

pgvector brings vector search capabilities into PostgreSQL, enabling seamless integration with structured data and making it an excellent choice for applications that need both traditional SQL queries and AI-driven similarity search.

Why Store Embeddings in pgvector?

• Persistent and Queryable: Embeddings are stored within PostgreSQL tables, enabling SQL-based retrieval alongside metadata.
• Efficient Indexing for Similarity Search: Supports exact search (L2 distance, cosine similarity) and approximate search (HNSW, IVFFlat) for optimized performance.
• Scales with PostgreSQL: Works with existing PostgreSQL replication and indexing strategies, making it suitable for production workloads.

While this lesson focused on saving embeddings locally, in the next part of this learning path, we will explore how pgvector enables efficient embedding storage and retrieval, making it a powerful tool for AI applications that require both vector search and relational data capabilities.

In this lesson, we focused on the process of saving and using embeddings locally using Python. We explored how to store embeddings in both JSON and Pickle formats, allowing for efficient reuse in various applications. Additionally, we introduced the concept of using vector databases like pgvector for handling large-scale applications, which provides optimized storage and querying solutions for high-dimensional vector data. By storing embeddings, you can save time and computational resources, which is crucial for building scalable and efficient systems. As you move forward, consider practicing by saving embeddings for your own datasets and experimenting with different storage formats to find what best suits your needs. Good job on completing this lesson—you're almost there! Let's now tackle the final practice tasks.