Skip to content

MyAIAssistant

Project Goals

MyAIAssistant helps users organize tasks, reference subject-matter knowledge, and leverage AI for semantic search, note summarization, task extraction and recommandation. The tool links knowledge artifacts to tasks to provide better context when addressing work.

Based on Stephen Covey's "7 Habits of Highly Effective People" the system helps manage priorities efficiently using the Eisenhower Matrix (Urgent/Important classification).

With a drag-and-drop user interface it is easy to continuously re-prioritize tasks.

Access the webApp local once started.

It also helps to manage organizations for which a user works with, and the related projects for one organisation. The term organisation was selected instead of client or customer, as the tool can be used by students or non-profit organizations.

Why I built this?

There are ton of to do apps, why sort of new one? The main drivers for creating this app:

  • How I can augment myself and my day-to-day outcomes?
  • Address what are the things I can do more, related ot what I value? I think the answer is learning, but how to organize time for it, and how to really track, learning activities?
  • I used paper or digital notes based Eisenhower matrix to track my tasks, but I think with AI can enhance the plannification, the decomposition and the semantic search of my current knowledge.
  • Address how to maximize my time on desired activities and minimize time on overhead?
  • While tracking task with app, it is easy to build weekly report, an essential tool for management.
  • NotebookLM, Glean, RAG, GraphRAG... are excellent tools to start managing knowledge and query it, but they are not related to my work directly. I think it may be possible to fill this gap.
  • The tool should also helps to address the efficient use of AI, by helping the step of clear thinking (getting a clear statement of the problem), clear writing (by formalizing in simple sentences the problem, to explain what you want to do), to build prompts for more efficient AI.
  • Apply agentic architecture for a simple day-to-day application, as a learning experience.

Core Features

Feature Status Description
Kanban-style Todo Management Completed Todos categorized by Importance/Urgency (Eisenhower Matrix)
Organization Management Completed Track organizations with stakeholders, team, strategy, and related products
Project Management Completed Manage projects with status lifecycle (Draft, Active, On Hold, Completed, Cancelled) linked to organizations
Knowledge Base Completed Metadata storage referencing documents, notes, and website links
Semantic Search (RAG) Completed AI-powered search across the knowledge base using embeddings
LLM Chat Support Completed AI chat for task planning and knowledge base queries
Task/Note Integration Planned Automatic linking of Todos to relevant knowledge artifacts

Knowledge Management

The knowledge management helps managing personal content for supporting queries on knowledge corpus and helps on the task recommendations. The KM manages the following element:

  • Title
  • Location/uri of the source document
  • Status: one of active, pending, indexed, error, archived
  • Document type: Folder, website, markdown, pdf

See how to do document and knowledge management

Task Management

Organization Management

Organizations represent external entities you work with. Each organization record includes:

  • Name: Organization or company name
  • Stakeholders: Key decision makers and contacts
  • Team: Internal team members assigned to the organization
  • Strategy/Notes: Overall relationship strategy and important notes
  • Related Products: Products, services, or solutions relevant to the organization

Project Management

Projects track specific work items within organizations. Each project includes:

  • Name: Project identifier
  • Description: Project goals and scope
  • Organization: Optional link to parent organization
  • Status: Lifecycle state (Draft, Active, On Hold, Completed, Cancelled)
  • Tasks: Markdown-formatted bullet list of actionable items
  • Past Steps: Historical record of completed actions

Projects can be filtered by organization and status. The Projects view displays active and draft counts for quick status assessment.

Reporting

It helps to build weekly report on metrics like:

  • Project started, actives, or closed
  • Number of meetings
  • Organization roadblocks addressed
  • Assets completed or started
  • Task created, completed

Quick Start

See user guide

Project Principles

  1. Run locally - All core features work without external API dependencies
  2. External configuration - Environment-based configuration for flexibility
  3. Privacy-first - Data stays on local infrastructure
  4. Efficient prioritization - Eisenhower Matrix helps focus on high-impact work

Activities

Create Knowledge base

Tool to vectorize folder content into ChromaDB for RAG.

This script scans a folder for supported documents (markdown, text, HTML), processes them through the RAG pipeline, stores embeddings in ChromaDB, and saves metadata to the knowledge base database.

The tool is reentrant: re-running it will update existing documents if their content has changed, and skip unchanged files.

Features

Code is: backend/tools/vectorize_folder.py

This CLI tool leverages the existing backend services:

  • DocumentLoader for loading markdown and HTML files
  • RecursiveTextSplitter for chunking content

  • ChromaDB with all-MiniLM-L6-v2 embeddings

  • Recursive folder scanning for supported file types

  • Configurable chunk size and overlap
  • Category and tags metadata support
  • Progress logging with file-by-file status
  • Collection statistics view
  • Handles .md, .markdown, .txt, and .html files