These vector representations capture the essence of words, images, or items, transforming complex data into a format that machines can efficiently process and understand. By mapping data into a vector space, embeddings allow similar items to cluster together, facilitating tasks like semantic search, recommendation systems, and clustering with high accuracy. This capability makes embeddings crucial for delivering precise and personalized experiences across various AI applications. In this article, we'll walk through the steps of creating text embeddings and saving them into a vector database.

Saving Embeddings into a Vector Database

The process of saving embeddings into a vector database involves a few important steps. First, we start with raw data, such as text or images. When documents are too large or the embedding model has a limited context window, we break this data into smaller pieces—a process known as chunking. While chunking is not always required, it generally helps improve retrieval accuracy by allowing the model to focus on smaller, more relevant sections of the data. After chunking, we use an embedding model to transform each piece into a vector, which is a numerical representation that captures the semantic information of the original input. Finally, we store these vectors into a database, along with any extra information, such as Pinecone. The vector database allows us to quickly find and retrieve semantically relevant information, making it a powerful tool for various applications like search and recommendation systems.

Chunking

So, what specifically is chunking? Chunking is the process of breaking big pieces of data, like long texts or images, into smaller, easier-to-handle parts. This makes it simpler to understand work with the information presented. For example, when dealing with a long article, we can split it into sentences or paragraphs.The purpose of chunking is for the search to return more relevant information rather than big bulks of data where target information is buried by irrelevant text.

Embedding Models to Use

Let’s explore text embedding models in a simple way:

Text embedding models are special tools that turn words into numbers that computers can understand. Different companies and organizations have created their own versions of these models.

OpenAI offers several models:

• The “text-embedding-3-small” is the most cost-effective option among OpenAI’s embedding models. IT creates embeddings with 1536 dimensions and performs better than its earlier model while being 5 times cheaper. This text embedding is mostly used for when you need a good balance between performance and cost, for general-purpose vector search applications, and when working with a limited budget but still need good accuracy.

• The “text-embedding-3-large” is OpenAI’s best performing embedding model. It created embeddings with up to 3072 dimensions and significantly outperforms the previous models. This embedding is chosen when you need the highest accuracy possible, for complex tasks that require capturing subtle nuances in text, and when working with multilingual content.

Pinecone offers a singular model:

• “multilingual-e5-large” model is designed to work with many different languages. It is particularly good at understanding the meaning of text across various languages. One would choose this model when working with text in multiple languages, for tasks that need to compare or analyze text from several languages, and if one is already using Pinecone’s vector database and want a compatible embedding model.

Saving into Pinecone

Storing vectors in a database such as Pinecone is an important step in managing your data. Each vector is given a unique ID, which helps you keep track of it, and you can also add extra information about the vector if needed. Pinecone organizes these vectors so that you can easily search for similar ones later.

Ingesting Embeddings with Fleak

If you’re looking to ingest data embeddings into a vector database and you only need to do this once with a small dataset, a Python notebook like this one is the way to go. It is perfect for those one-off data imports or for smaller projects where the data volume isn’t overwhelming.

On the other hand, if you’re planning to build a repeatable, continuous, or real-time data ingestion pipeline, you’ll want to use the Fleak API. This is especially true if your application needs to handle ongoing and frequent data updates. The API is designed for production environments and can manage load balancing and parallel processing, ensuring that your system can scale and remain efficient as data volumes grow. It’s like setting up a robust, automated workflow that keeps your vector database up-to-date without constant manual intervention.

So, if your needs are more about one-time, small-scale imports, stick with the Python notebook. But for anything that requires regular, high-volume, or real-time data ingestion, go with the Fleak API for a more scalable and reliable solution. Check out this template to easily embed and store data in Pinecone using Fleak.