nixos-4screen/ai_dev_plan.md

Personal AI Agent: "About Me" Profile Generator

Project Goal
Build a showcase AI system that scans and summarizes your professional/personal work from self-hosted services (primarily Gitea for code/repos, plus Flatnotes/Trillium/HedgeDoc for notes/ideas/projects). The agent answers employer-style questions dynamically (e.g., "Summarize Giordano's coding projects and skills") with RAG-grounded responses, links, and image embeds where relevant.

Emphasize broad AI toolchain integration for skill development and portfolio impact: agentic workflows, RAG pipelines, orchestration, multi-LLM support. No frontend focus — terminal/API-triggered queries only.

Key Features

• Periodic/full scanning of services to extract text, summaries, code snippets, links, images.
• Populate & query a local vector DB (RAG) for semantic search.
• Agent reasons, retrieves, generates responses with evidence (links/images).
• Multi-LLM fallback (DeepSeek primary, Gemini/OpenCode trigger).
• Scheduled/automated updates via pipelines.
• Local/Docker deployment for privacy & control.

Tools & Stack Overview

Category	Tool(s)	Purpose & Why Chosen	Integration Role
Core Framework	LangChain / LangGraph	Build agent, tools, chains, RAG logic. Modular, industry-standard for LLM apps.	Heart of agent & retrieval
Crawling/Extraction	Selenium / Playwright + Firecrawl (via LangChain loaders)	Handle auth/dynamic pages (Gitea login/nav), structured extraction (Markdown/JSON).	Scan web views & APIs
Vector Database	Chroma	Local, lightweight RAG store. Easy Docker setup, native LangChain integration.	Store embeddings for fast semantic search
LLM(s)	DeepSeek (via API) + Gemini / OpenCode	DeepSeek: cheap, strong reasoning (primary). Gemini/OpenCode: terminal trigger/fallback.	Reasoning & generation
Data Pipeline / Scheduling	Apache Airflow (Docker)	Industry-best for ETL/ETL-like scans (DAGs). Local install via official Compose.	Schedule periodic scans/updates to Chroma
Visual Prototyping	Flowise	No-code visual builder on LangChain. Quick agent/RAG prototyping & debugging.	Experiment with chains before code
Script/Workflow Orchestration	Windmill	Turn Python/LangChain scripts into reusable, scheduled flows. Dev-first, high growth.	Reactive workflows (e.g., on-commit triggers)
Event-Driven Automation	Activepieces	Connect services event-based (e.g., Gitea webhook → re-scan). AI-focused pieces.	Glue for reactive triggers

High-Level Architecture & Flow

1. Ingestion Pipeline (Airflow + Crawlers)

   • Airflow DAG runs on schedule (daily/weekly) or manually.
   • Task 1: LangChain agent uses Selenium/Playwright tool to browse/authenticate to services (e.g., Gitea repos, Flatnotes/Trillium pages).
   • Task 2: Firecrawl loader extracts structured content (text, code blocks, links, image URLs).
   • Task 3: LangChain chunks, embeds (DeepSeek embeddings), upserts to Chroma vector DB.
   • Optional: Activepieces listens for events (e.g., Gitea push webhook) → triggers partial re-scan.

2. Agent Runtime (LangChain/LangGraph + DeepSeek)

   • Core agent (ReAct-style): Receives query (e.g., via terminal/OpenCode: "opencode query 'Giordano's top projects'").
   • Tools: Retrieve from Chroma (RAG), fetch specific pages/images if needed.
   • LLM: DeepSeek for cost-effective reasoning/summarization. Fallback to Gemini if complex.
   • Output: Natural response with summaries, links (e.g., Gitea repo URLs), embedded image previews (from scanned pages).

3. Prototyping & Orchestration Layer

   • Use Flowise to visually build/test agent chains/RAG flows before committing to code.
   • Windmill wraps scripts (e.g., scan script) as jobs/APIs.
   • Activepieces adds event-driven glue (e.g., new note in Trillium → notify/update DB).

Deployment & Running Locally

• Everything in Docker Compose: Airflow (official image), Chroma, Python services (LangChain agent), optional Flowise/Windmill containers.
• Secrets: Env vars for API keys (DeepSeek, service auth).
• Trigger: Terminal via OpenCode/Gemini CLI → calls agent endpoint/script.
• Scale: Start simple (manual scans), add Airflow scheduling later.

Skill Showcase & Portfolio Value

• Demonstrates: Agentic AI, RAG pipelines, web crawling with auth, multi-tool orchestration, cost-optimized LLMs, local/self-hosted infra.
• Broad coverage: LangChain ecosystem + industry ETL (Airflow) + modern AI workflow tools (Flowise/Windmill/Activepieces).
• Low cost: DeepSeek keeps API bills minimal (often <$5/month even with frequent scans/queries).

Next Steps (Implementation Phases)

1. Setup local Docker env + Chroma + DeepSeek API key.
2. Build basic crawler tools (Selenium + Firecrawl) for Gitea/Flatnotes.
3. Prototype agent in Flowise, then code in LangChain.
4. Add Airflow DAG for scheduled ingestion.
5. Integrate Windmill/Activepieces for extras.
6. Test queries, refine summaries/links/images.

This setup positions you strongly for AI engineering roles while building real, integrated skills.

** Extra tools to add.

• AutoMaker
• AutoCoder - These assist in set and forget long review AI
• OpenRouter - Single access point for any CLI with useage fee.
• Aider - CLI code and file editing with OpenROuter for any model
• Goose - integrates with system and MCP servers like ClawBot