Baseplate is a backend optimized for LLM apps. Teams use our multimodal context database to build rich user experiences with LLMs. All from an interface as simple as a spreadsheet. That means you don’t have to maintain and manage separate databases for your vectors and regular data anymore!

Hybrid database for your regular data and your embeddings

Using Baseplate, a team can deploy a chat GPT app that responds with domain-specific information from documents, thumbnails, links, images, and more. (Who knows, maybe the next GPT model is multimodal too)

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Why would we need this?

In most applications, LLMs need to be connected to an ever changing set of data. It’s simple enough to parse and embed a few PDF’s. However, managing this data is tedious when you’re working with large, multimodal datasets that consistently need to be updated, re-indexed, and replaced.

Plus you’re likely managing multiple databases - one for vectors, and one for your other data. This gets painful really fast.

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Key Features:

  1. A flexible hybrid database. A single dataset in Baseplate can contain
    • Embeddings
    • Text
    • Code
    • Documents
    • Images
    • Links
  2. Database management
    • Sync datasets with your tools (Google Drive, S3, Box, Sharepoint, etc.)
    • Utilities for updating and managing vectors in bulk
    • Organize and segment your data intuitively
    • Work in the UI or programmatically via API
  3. Smart Search
    • Choose an embedding model for your use case, tailored for your documents
    • Multi-step Search. Retrieve context from multiple datasets within an app.
    • Use keyword, semantic, or hybrid search
    • Retreive whole documents or just relevant chunks
  4. App Builder
    • Choose between a number of popular LLMs
    • Build a custom prompt for your use case
    • Test and deploy your app without any code
  5. Endpoints
    • One-click Slackbots, Discord Bots, Teams Bots, and more!
    • Build, test, and deploy your Apps via API endpoints
    • Unlimited queries
    • Built-in utilities for human feedback, logging, & caching
📈 Use Cases