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CapybaraDB Is a High-Level Database

High-Level Database

A high-level database abstracts traditional systems by unifying diverse storage types (e.g., NoSQL, vector, object) into a single interface. This allows developers to manage varied data seamlessly without juggling multiple databases or complex pipelines, streamlining workflows for advanced applications like those using large language models (LLMs).

CapybaraDB

CapybaraDB

Low-Level Database

A low-level database requires developers to manage individual database types (e.g., NoSQL, vector, object storage) and build the necessary pipelines to connect them. This approach demands specialized knowledge and manual setup, making it more complex and time-consuming for handling diverse data types in LLM applications.

MySQL
MongoDB
PostgreSQL
Redis
Pinecone
Amazon S3
SQLight
Cassandra

Database development is
often complex and time-consuming

Choosing a Database Type

Setting Up the Database Server

Designing the Data Model

Building the Data Pipelines

Writing Queries and Operations

Scaling the Database

Database Abstraction

A Single Database That Handles It All

C
Python
Javascript
MongoDB
Pinecone
CapybaraDB

CapybaraDB

With CapybaraDB, all the complexities of managing multiple databases are eliminated.
Much like how Python and JavaScript simplify programming compared to low-level languages, CapybaraDB unifies the full capabilities of NoSQL, vector databases, and object storage into a single system.

Faster

Abstracted database interface make it much easier to save, index, and retrieve with your data.

Cheaper

Optimize database management and reduce operational costs by automating tasks and minimizing the need for manual intervention.

EmbJSON

CapybaraDB Extended JSON


{
  "name": "Alice",
  "profile": {
    "bio": EmbText(
      "Alice is a data scientist with expertise in AI and machine learning.",
      "text-embedding-3-small"
    ),
    "profile_picture": EmbImage(
      "<binary image data>",
      "image/jpeg",
      "gpt-4o-mini"
    )
  }
}

CapybaraDB Extended JSON is designed to simplify AI-driven applications by embedding and indexing complex data types like text and images directly in the database. With no need for external pipelines, it handles everything from storage to semantic search automatically, saving developers time and effort. This powerful format makes managing both structured and unstructured data seamless, ideal for LLM applications.

Why CapybaraDB is Advanced

Automated Data Processing

CapybaraDB Extended JSON automates complex tasks like image-to-text conversion, text chunking, embedding generation, and vector indexing, saving developers from manually handling these processes.

Customizable Embeddings

Developers can choose the embedding model for each JSON field, giving flexibility and control over how text and images are processed for semantic search.

Seamless Nested Fields

CapybaraDB Extended JSON can be placed in any nested field, allowing complex data structures to be stored and indexed efficiently without constraints.

Effortless Media Storage

Store large media files, such as images, directly within the database without needing to deal with separate object storage systems.

Signed URLs for Media

Every saved media file is automatically assigned a signed URL, making it easy to retrieve and securely share stored media.

Customizable Parameters

Easily adjust parameters like chunk size and embedding models to fine-tune how data is processed and indexed.

Asynchronous Processing

CapybaraDB processes data asynchronously, ensuring your users don’t have to wait while embedding and indexing happens in the background.

MongoDB Compatibility

Fully MongoDB-compatible, CapybaraDB Extended JSON benefits from MongoDB’s rich ecosystem, extensive documentation, and community support.