Production-grade knowledge repository for AI/ML systems, data engineering, and automation at scale.
| Project | Stack | Description |
|---|---|---|
| Multi-Agent Research | LangGraph, Tavily, MCP | Supervisor-Worker architecture for deep research with citation tracking |
| Autonomous Browser Agent | LangGraph, Playwright, GPT-4o | LLM-powered agent that navigates web autonomously via DOM analysis |
| Legal RAG & Knowledge Graph | Neo4j, Qdrant, PyMuPDF | Hybrid search system for legal documents with citation support |
| Geospatial Data Pipeline | PostGIS, GDAL, Dask | Large-scale geospatial ETL with real-time visualization |
┌─────────────────────────────────────────────────────────────────────────┐
│ 🤖 LLM AGENTS │ 📊 DATA ENGINEERING │ ⚡ AUTOMATION │
│ ───────────────── │ ────────────────── │ ──────────── │
│ • LangChain/Graph │ • ETL at Scale │ • Browser Control │
│ • RAG Systems │ • Geospatial Analysis │ • n8n Workflows │
│ • Multi-Agent │ • Vector & Graph DBs │ • Event-Driven │
└─────────────────────────────────────────────────────────────────────────┘
knowledge_base/
│
├── 1-architecture-patterns/ # Clean Code, SOLID, System Design
│ ├── clean-architecture/
│ ├── clean-code/
│ ├── design-patterns/
│ ├── system-design/
│ └── algorithms/
│
├── 2-data-engineering/ # ETL, Scraping, Geospatial
│ ├── etl-pipelines/
│ ├── document-processing/ 📄 PDF, OCR, Tables
│ ├── geospatial-analysis/ ⭐ Differentiator
│ ├── db-optimization/
│ └── architecture/
│
├── 3-ai-ml/ # LLMs, RAG, Computer Vision
│ ├── llm-agents/ ⭐ Flagship (Multi-Agent, MCP)
│ ├── rag-systems/ ⭐ Flagship
│ ├── observability/ 🔍 LangSmith, RAGAS
│ ├── computer-vision/
│ └── ml-ops/
│
├── 4-automation/ # Browser, n8n, Event-Driven
│ ├── browser-automation/
│ ├── n8n-workflows/
│ └── event-driven/
│
├── 5-python-advanced/ # Concurrency, Testing, Patterns
│ ├── web-frameworks/
│ ├── data-stack/
│ └── testing/
│
├── 6-databases/ # Vector, Graph, Postgres
│ ├── vector-dbs/
│ ├── graph-dbs/
│ └── postgres-advanced/
│
├── 7-infrastructure/ # Docker, AWS Serverless
│ ├── docker/
│ └── aws-serverless/ 🚀 API Gateway, Mangum, SAM
│
└── 99-blueprints/ 🏆 Production Architectures
├── autonomous-browser-agent/
├── multi-agent-research/ ⭐ New (Supervisor Pattern)
├── legal-rag-graphdb/
├── geospatial-pipeline/
├── scalable-scraping/
└── data-pipelines/
- Nesne Yönelimli Programlama (OOP)
- Tamamlandı: Bölüm 1: Sınıflar ve Nesneler - Python nesne modelinin
__new__, descriptor'lar, metaclass'ler ve dataclass'ler gibi temel yapı taşları incelendi.
- Tamamlandı: Bölüm 1: Sınıflar ve Nesneler - Python nesne modelinin
| I want to... | Go to |
|---|---|
| Build an LLM Agent | llm-agents → browser-agent blueprint |
| Implement RAG | rag-systems → legal-rag blueprint |
| Scrape at Scale | etl-pipelines → scalable-scraping blueprint |
| Work with Geo Data | geospatial-analysis |
| Choose a Vector DB | vector-dbs |
| Dockerize Production | docker |
graph LR
subgraph "AI/ML Layer"
A[LangGraph] --> B[GPT-4o / Claude]
B --> C[RAG Pipeline]
B -.-> M[LangSmith]
end
subgraph "Data Layer"
D[(PostgreSQL)] --> E[(Neo4j)]
E --> F[(Qdrant)]
end
subgraph "Automation Layer"
G[Playwright] --> H[n8n]
H --> I[FastAPI]
end
C --> D
C --> F
A --> G
This repository is for personal reference and portfolio purposes.
Last updated: 2025-12-12
- Build pipelines that feed AI systems reliably
- Deploy ML models with production-grade observability
- Automate human-in-the-loop workflows intelligently
Problem: Law firm needs to process 10K+ contracts monthly, extract key clauses, and flag risks.
How this repo helps:
| Step | Reference |
|---|---|
| Document ingestion | 2-data-engineering/etl-pipelines/ |
| PDF parsing | 5-python-ecosystem/pdf-processing/ |
| Text chunking & embedding | 3-ai-and-agents/rag-systems/ |
| Vector storage | 6-databases/vector/ |
| Agent for Q&A | 3-ai-and-agents/llm-agents/ |
| Production deployment | 7-devops-cloud/containerization/ |
Time saved: Instead of 2 weeks of research, I have battle-tested patterns in 2 hours.
Problem: E-commerce client needs competitor pricing from 500+ websites, daily.
How this repo helps:
| Step | Reference |
|---|---|
| Static scraping patterns | 2-data-engineering/etl-pipelines/scraping-tools/beautifulsoup/ |
| Dynamic JS rendering | 4-automation-workflows/browser-automation/playwright/ |
| Anti-detection strategies | 99-blueprints-and-recipes/scalable-scraping-architecture/ |
| Pipeline orchestration | 2-data-engineering/etl-pipelines/pipeline-patterns/ |
| Data storage design | 6-databases/relational/ |
Outcome: Deployed in 1 week. Runs reliably at 100K pages/day.
Problem: Engineering team drowning in Confluence, Notion, and Slack. "Where is X documented?" kills productivity.
How this repo helps:
| Step | Reference |
|---|---|
| Multi-source ingestion | 2-data-engineering/etl-pipelines/ |
| Embedding pipeline | 3-ai-and-agents/rag-systems/ |
| Conversational agent | 3-ai-and-agents/llm-agents/ |
| Slack integration | 4-automation-workflows/event-driven-systems/ |
| Deployment | 7-devops-cloud/aws-serverless/ |
Impact: 40% reduction in "context-switching" interruptions. Engineers find answers in seconds.
# Clone and explore
cd knowledge_base
# Each folder is self-contained
# Start with your current problem, not from the beginning- Have a specific problem? → Go directly to the relevant folder
- Building something new? → Check
99-blueprints-and-recipes/first - Learning a concept? → Start from
0-foundations/and follow dependencies
┌────────────────────────────────────────┐
│ "Don't collect knowledge. │
│ Build systems that leverage it." │
│ │
│ — Engineering Ethos │
└────────────────────────────────────────┘
This document is a living artifact. It evolves with every production lesson learned.