If your primary database is hosted elsewhere, for example AWS RDS, or Azure Postgres, you can get your data replicated to PostgresML in real time using logical replication.

Having access to your data immediately is very useful to

accelerate your machine learning use cases and removes the need for moving data multiple times between microservices. Latency-sensitive applications should consider using this approach.

Foreign data wrappers are a set of PostgreSQL extensions that allow making direct connections from inside the database directly to other databases, even if they aren't running on Postgres. For example, Postgres has foreign data wrappers for MySQL, S3, Snowflake and many others.

FDWs are useful when data access is infrequent and not latency-sensitive. For many use cases, like offline batch workloads and not very busy websites, this approach is suitable and easy to get started with.

Foreign data wrappers are a set of Postgres extensions that allow making direct connections to other databases from inside your PostgresML database. Other databases can be your production Postgres database on RDS or Azure, or another database engine like MySQL, Snowflake, or even an S3 bucket.

Logical replication allows your PostgresML database to copy data from your primary database to PostgresML in real time. As soon as your customers make changes to their data on your website, those changes will become available in PostgresML.

text -> tokens -> LLM -> probability distribution -> predicted token -> text

{% hint style="info" %}

We have simplified the tokenization process. Words do not always map directly to tokens. For instance, the word "Baldur's" may actually map to multiple tokens. For more information on tokenization checkout [HuggingFace's summary](https://huggingface.co/docs/transformers/tokenizer\_summary).

embedding_1 = embed("King") # embed returns something like [0.11, -0.32, 0.46, ...]

`Embeddings` aren't limited to words, we have models that can embed entire sentences.

Why do we care about `embeddings`? `Embeddings` have a very interesting property. Words and sentences that have close [semantic similarity](https://en.wikipedia.org/wiki/Semantic\_similarity) sit closer to one another in vector space than words and sentences that do not have close semantic similarity.

There is a lot going on with this, let's check out this diagram and step through it.

Step 1: We take the document and split it into chunks. Chunks are typically a paragraph or two in size. There are many ways to split documents into chunks, for more information check out [this guide](https://www.pinecone.io/learn/chunking-strategies/).