Day 18 challenge
Goal: build a production-ready RAG system using PostgreSQL and SupabaseTheme: context engineering week - information retrieval systemsTime investment: ~30 minutes
What you’ll accomplish today
- Set up a Supabase PostgreSQL database
- Load and index an Amazon product catalog as example data
- Connect your agent to Supabase for intelligent retrieval
- Implement semantic search queries that enhance agent responses
- Build retrieval patterns that scale to real-world data volumes
This requires setting up external services (Supabase). You’ll need to create
accounts and configure database connections. The concepts apply to any
PostgreSQL-based retrieval system.
Step 1: Understanding retrieval systems
Before building, understand what makes retrieval powerful:Retrieval architecture components
- Knowledge Base: Structured or unstructured data that agents can search
- Embedding Model: Converts text to vector representations for similarity search
- Vector Database: Stores and searches embeddings efficiently
- Retrieval System: Finds relevant information based on user queries
- Generation: Language model uses retrieved context to provide informed responses
When to use retrieval vs. fine-tuning
Retrieval is ideal for:- Dynamic information that changes frequently
- Large knowledge bases that exceed context windows
- Information that needs to be traceable and verifiable
- Scenarios where you need to add new information without retraining
- Changing behavior patterns or writing style
- Domain-specific reasoning capabilities
- When information is stable and doesn’t change often
Retrieval thinking Think of retrieval as giving your agent access to a
dynamic, searchable library rather than memorizing everything up front.
Set up Supabase
Supabase provides PostgreSQL with built-in vector search capabilities:Create your Supabase project
- Visit Supabase.com and create a free account
- Create a new project and note your project URL and API keys
- Set up authentication and database permissions
Load Amazon product catalog data
Let’s create a realistic product dataset for testing:Sample product data
Create sample product data that represents a typical e-commerce catalog. Navigate to the SQL editor in Supabase and run the following SQL:Connect your agent to Supabase
Now integrate your retrieval system with your Hypermode agent:Add Supabase connection
- Navigate to your agent’s connections in the About section
- Add Supabase connection and configure with your project credentials
- Test the connection by querying the products table
Create a retrieval-enabled agent
If you don’t have a suitable agent, create one with Concierge:Configure retrieval workflows
Add these patterns to your agent’s instructions:Implement retrieval queries
Test your retrieval system with real queries:Basic product search
- Query the products table
- Return relevant products with explanations
- Format results with specific details and reasoning
Complex requirement matching
Comparison queries
Retrieval quality Good retrieval systems return not just similar products,
but contextually relevant ones that actually answer the user’s underlying
need.
Contextual retrieval
Help your agent understand search context:Advanced query examples
Test sophisticated retrieval patterns:Performance optimization and monitoring
Ensure your retrieval system performs well at scale:Indexing optimization
Query performance monitoring
Retrieval quality metrics
Track these metrics to ensure good RAG performance:- Retrieval precision: How many retrieved items are relevant?
- Retrieval recall: How many relevant items are retrieved?
- Response time: How fast are queries executing?
- User satisfaction: Are users finding what they need?
What you’ve accomplished
In 30 minutes, you’ve built a production-ready RAG system: Database foundation: set up PostgreSQL with vector search capabilities Data pipeline: loaded and indexed structured product catalog with embeddings Agent integration: connected your agent to Supabase for dynamic information retrieval Semantic search: implemented vector similarity search for intelligent product discovery Advanced patterns: explored hybrid search, filtering, and contextual retrieval Performance optimization: learned indexing and monitoring strategies for production useThe power of retrieval systems
Retrieval transforms static agents into dynamic information systems: Before retrieval Agents limited to training data and general knowledge After retrieval Agents with access to current, specific, searchable knowledge bases This foundation enables agents to provide accurate, up-to-date information from your own data sources.Tomorrow - Day 19
Implement document-based retrieval systems using MongoDB Atlas for
unstructured data like product reviews and feedback.
Pro tip for today
Test retrieval quality with diverse queries:Time to complete: ~30 minutes Skills learned RAG system architecture, PostgreSQL vector search, embedding generation, semantic retrieval, hybrid search patterns, performance optimization Next: day 19 - Document retrieval with MongoDB for unstructured data
Remember Retrieval quality depends on both the relevance of retrieved
information and how well your agent uses that information to generate
responses. Both sides matter equally.