seekdb-client is an extension toolkit built _for personal developers_, delivering powerful database management and development workflows. Deeply integrated with the **seekdb** database system—and fully compatible with **MySQL**—it unifies database operations under one intuitive interface.
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
seekdb Client is a VS Code extension toolkit built for personal developers, delivering powerful database management and development workflows. Deeply integrated with the seekdb database system—and fully compatible with MySQL—it unifies database operations under one intuitive interface.
Key Features
Feature
Description
Multi‑Database Support
Connect to and manage both seekdb and MySQL instances seamlessly.
Visual UI
Modern React‑based WebView interface with intuitive navigation and actions.
Smart Code Completion
Hover and jump-to-definition for getOrCreateCollection() in your editor.
Vector Search
Built‑in AI vectorization enables semantic similarity search—no raw embeddings required.
SQL Execution
Run ad‑hoc SQL queries, inspect results, and benchmark execution time.
Collection Browser
Browse, search, and inspect Collections without writing a single query.
Functionality
🗄️ Database Connection Management
Create connections with host, port, username, password, etc.
Test connections before saving.
Multi‑connection support: manage & switch between numerous databases.
Persistent config: connections auto‑saved across sessions.
Default presets: host (127.0.0.1), port (2881), tenant (sys), DB (test), user (root)—configurable per project.
📊 Collection Browser
Tree view of databases and Collections.
One‑click preview: click a Collection to see its data instantly.
SQL pane: write and run queries inline; results rendered in a sortable/filterable table.
Execution stats: query latency + row count for performance profiling.
🔍 Smart Vector Search
Semantic queries: search with natural language, not just keywords.
Cosine similarity: accurate matching powered by vector embeddings.