Create a database for your LLM App
Instead of managing your data, a vector database, an embedding model, a search algorithm, a PostgreSQL database, and your LLM all in different places...
What if you could do it in one spot, with your teammates?
Import documents through the UI, or check our API docs to add content from your codebase

Create Columns

Add data, metadata, and embedding columns
A Baseplate database is as flexible as a spreadsheet. You can create any number of columns, and edit them directly from the UI.
  1. 1.
    Embedding Columns
    The content in these columns will be embedded using the embedding model you select
  2. 2.
    Data Columns
    Typically images, urls, code snippets, or links that you'd like to be returned with your search result
  3. 3.
    Metadata Columns
    Really useful for large datasets to make sure you're getting relevant vectors. Segment your data based on customer, datasource, version, date, or whatever your prefer!

Adding Documents

  1. 1.
    Add a whole document
    You can use Baseplate's built in parsing capabilities to break documents into chunks for embedding. Specify a chunk size and which column to add the chunks to, and we'll do the rest
  2. 2.
    Add context chunk-by-chunk
    For more nuanced indices, upload rows to Baseplate via API once you've chunked them

Updating Vectors

Edit your vectors from the interface or through the API. We'll automatically handle the embedding, updating of your text representation, and upsert of the new vector.

Manage Documents

Manage your Database
You can view, edit, replace, and delete your documents at once, instead of going row-by-row. See our API for a guide on replacing old data.