Vector Go FAQs

Question 1

What kind of code analysis capabilities does Vector Go offer?

Accepted Answer

Vector Go provides deep code analysis, automatically identifying symbols (Go, TS, JS), mapping relationships, and employing semantic chunking. This creates a holistic context for your codebase, aiding in tasks like dependency tracing and context retrieval.

Question 2

How does Vector Go ensure privacy and performance with embeddings?

Accepted Answer

It leverages local embeddings powered by `bge-m3` via ONNX Runtime. This means high-quality semantic understanding is achieved directly on your machine, eliminating the need for external API calls and ensuring data privacy and performance.

Question 3

What is Vector Go and its primary function?

Accepted Answer

Vector Go is a high-performance Model Context Protocol (MCP) server designed for local semantic search and project context management. It provides AI agents with a 'global brain' of your codebase using local embeddings and vector storage, keeping all source code on your machine.

Question 4

Does Vector Go support real-time indexing for active development?

Accepted Answer

Yes, Vector Go features a built-in debounced file watcher that synchronizes the vector index in real-time as you save files, ensuring your AI agents always have the most up-to-date codebase context.

Question 5

How does Vector Go optimize resource usage for multiple instances?

Accepted Answer

It utilizes an intelligent Master/Slave architecture. This setup ensures only one instance loads heavy embedding models and runs the file watcher, drastically reducing RAM usage for slave instances and optimizing overall system resources.

Vector Go

Vector Go

主要功能

使用案例

主要功能

使用案例