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VecML DB SDK & Documentation 🚀

Welcome to the VecML Database SDK Documentation. This guide provides all the necessary information for setting up, integrating, and using VecML's powerful database solutions across different platforms, including Windows, Android, MacOS, C++, and Python.

🚀 Why VecML DB?

VecML provides:
✔ Fast indexing & retrieval – Optimized for high-speed queries.
✔ Memory-efficient storage – Advanced offloading & compression.
✔ Versatile functionality – Integrate your document, vector, and graph data into a unified VecML Database.
✔ Built-in powerful machine learning – train AutoML models in the database, with the best efficiency and accuracy.
✔ Cross-platform support – Works on Windows, Android, MacOS, C++, and Python.
✔ Scalability – Suitable for both resource constrained applications and large-scale deployments.

📖 Table of Contents

🔹 Installation & Setup

🔹 Databases

🔹 User Interfaces

🔥 Getting Started

Step 1: Choose Your Platform

VecML DB supports multiple platforms:

Step 2: Construct the Database

For vector query and storage, see Vector Database.

For document search, refer to Document Database.

🎯 Next Steps

🔹 Install the SDK – Set up VecML on your platform.
🔹 Learn about Document Storage – Store and query documents efficiently.
🔹 Explore the Vector Search Engine – Perform similarity search at scale.
🔹 Try the Web UI – Manage VecML DB with a user-friendly web interface.
🔹 Use the CLI – Work with VecML from the command line.

For further assistance, contact VecML Support.