Engram: Personal RAG system for Obsidian indexing and vector search

Engram: Personal RAG System for Obsidian Indexing and Vector Search

Engram is a powerful personal Retrieval Augmented Generation (RAG) system designed to integrate seamlessly with your Obsidian vault. By indexing your notes and documents, Engram enables sophisticated vector search capabilities, transforming your personal knowledge base into a dynamic, queryable resource for AI applications. Functioning as an MCP server, it provides a robust backend for developers looking to leverage their own data within AI workflows.

What Engram Does

Engram's primary function is to create a vector representation of the content within your Obsidian vault. This process involves indexing your notes, markdown files, and any other supported document types. Once indexed, Engram allows you to perform semantic searches, retrieving information based on the meaning and context of your queries rather than just keywords. This makes it an ideal tool for building AI agents that need to access and understand large volumes of personal or project-specific data.

Key Features

• Obsidian Vault Integration: Directly indexes content from your Obsidian vault.
• Vector Search: Enables semantic search for nuanced information retrieval.
• MCP Server Functionality: Acts as an MCP server, providing an API for AI agents.
• Personal RAG System: Facilitates the creation of AI applications grounded in your own data.
• Developer-Focused: Designed for technical users and AI builders.

Who Engram is For

Engram is specifically built for developers and AI builders who are working with personal knowledge management systems like Obsidian. If you are developing AI agents, chatbots, or any application that requires access to a structured and semantically searchable personal knowledge base, Engram provides the foundational RAG capabilities. It's for those who want to move beyond simple keyword searches and unlock the deeper contextual understanding of their notes for AI-driven tasks.