Vector Memory FAQs

Question 1

What types of content can Vector Memory store?

Accepted Answer

Vector Memory supports storing information from multi-format files including PDF, TXT, and Markdown. Additionally, you can save ad-hoc text snippets as free-form notes without needing to create separate files.

Question 2

What is Vector Memory?

Accepted Answer

Vector Memory is an MCP server designed to provide AI assistants with a semantic long-term memory system. It allows AI models to store and recall information from various sources using natural language.

Question 3

How does Vector Memory find stored information?

Accepted Answer

It utilizes semantic search, enabling AI assistants to retrieve relevant information based on the meaning and context of a query, even without exact word matches. This ensures highly accurate and contextual recall.

Question 4

How can I manage the memory stored in Vector Memory?

Accepted Answer

The tool comes with built-in memory management utilities. You can use `vector-memory-manage` for interactive searching and selective deletion, and `vector-memory-cleanup` for quick cleanup commands and statistics.

Question 5

What are the core prerequisites to run Vector Memory?

Accepted Answer

To use Vector Memory, you need Python 3.12 or higher and a Redis server running locally on port 6379. Installation is straightforward via pip or uvx.

Vector Memory

Vector Memory

Key Features

Use Cases

Key Features

Use Cases