Getting Started: Your First Context Management System

Overview

This guide walks you through building a complete context management system from scratch. By the end, you'll have a working system that stores, retrieves, and serves context to AI applications.

Prerequisites

• Basic understanding of REST APIs
• Familiarity with a programming language (examples in Python)
• Access to a database (PostgreSQL recommended)
• An AI model API for testing integration

Step 1: Design Your Context Schema

Start by defining what context you'll store. A minimal schema includes: user identifier, context type, content, timestamps, and metadata. Consider future needs—add flexibility without over-engineering.

CREATE TABLE contexts (
  id UUID PRIMARY KEY,
  user_id UUID NOT NULL,
  context_type VARCHAR(50) NOT NULL,
  content JSONB NOT NULL,
  created_at TIMESTAMP DEFAULT NOW(),
  metadata JSONB
);

Step 2: Build the Storage Layer

Implement CRUD operations for context. Use connection pooling for production. Add indexes on common query patterns. Implement soft deletes for audit compliance.

Step 3: Create the API

Expose context operations through a REST API. Implement authentication, validate inputs, handle errors gracefully. Start simple—you can add GraphQL later if needed.

Step 4: Integrate with AI

Connect your context system to an AI model. Fetch relevant context, format for the model, include in prompts. Measure impact on response quality.

Next Steps

Once basics work, explore caching, advanced querying, and scaling strategies covered in other guides.