Add Context-Aware Customer Support Agent with Langbase

[gwho]

Implemented a comprehensive educational project that teaches RAG (Retrieval Augmented Generation) from first principles using Langbase primitives.

Project Structure

• Bottom-up learning approach: Memory → Retrieval → Pipe → Orchestration
• 4 progressive scripts teaching each primitive component
• 4 mini-projects for hands-on tinkering and experimentation
• Comprehensive documentation with learning guides

Core Components

1. Memory Creation (1-memory-creation.ts)

   • Demonstrates parsing, chunking, and embedding pipeline
   • Uploads FAQ.txt knowledge base
   • Explains vector database indexing

2. Retrieval Testing (2-retrieval-test.ts)

   • Tests semantic search before LLM integration
   • Shows chunk retrieval and similarity scoring
   • Explains top_k parameter tuning

3. Pipe Creation (3-pipe-creation.ts)

   • Creates AI agent with system prompt
   • Configures model, temperature, and memory attachment
   • Demonstrates the cognition layer

4. Main Orchestration (main.ts)

   • Full RAG pipeline integration
   • Interactive and single-query modes
   • Production-ready architecture with debug mode

Mini-Projects

1. Personality Swap: Test different system prompts (pirate, sarcastic, etc.)
2. Knowledge Injection: Add new documents without code changes
3. Accuracy Tuner: Optimize top_k retrieval parameter
4. Multi-Format Challenge: Test CSV, PDF, and other file formats

Features

• Well-commented code explaining every concept
• No "magic" - uses explicit SDK calls
• Modular design for easy customization
• Comprehensive error handling and troubleshooting
• QUICKSTART.md for 5-minute setup
• Detailed README with learning path

Tech Stack

• TypeScript + Node.js
• Langbase SDK for RAG primitives
• Environment-based configuration
• Development tooling (tsx, TypeScript)

This project teaches AI agent development through hands-on building rather than using pre-made templates.