Vector Search Patterns

Skill Profile

(Select at least one profile to enable specific modules)

• [ ] DevOps
• [x] Backend
• [ ] Frontend
• [ ] AI-RAG
• [ ] Security Critical

Overview

Vector search (also known as semantic search or similarity search) enables finding similar items based on their meaning rather than exact keyword matches. It uses vector embeddings to represent data as points in high-dimensional space, where similarity is measured as distance between points. This skill covers embeddings fundamentals, vector databases (Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, pgvector, Redis), distance metrics, indexing algorithms, hybrid search, RAG patterns, chunking strategies, query optimization, scaling strategies, cost optimization, evaluation metrics, and common use cases.

Why This Matters

Vector search enables finding similar items based on meaning rather than exact matches, which is fundamental to modern AI applications. It powers recommendation systems, semantic search, RAG, and many other AI/ML applications. Proper implementation of vector search patterns can improve user experience by 10-100x while reducing infrastructure costs. This skill is essential for building production-grade AI systems.

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

Skill Composition

• Depends on: None
• Compatible with: None
• Conflicts with: None
• Related Skills: None

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

• Embedding model is chosen and consistent
• Vector database is accessible and configured
• Sufficient resources for embedding generation and storage
• Query volume justifies optimization

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | | Integration | DB / API | All external API calls or database connections must be mocked during unit tests | | E2E | User Journey | Critical user flows to test | | Performance | Latency / Load | Benchmark requirements | | Security | Vuln / Auth | SAST/DAST or dependency audit | | Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• [ ] Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• [ ] Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• [ ] Authorization: Validate user permissions before state changes

2. Performance & Resources

• [ ] Execution Efficiency: Consider time complexity for algorithms
• [ ] Memory Management: Use streams/pagination for large data
• [ ] Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• [ ] Design Pattern: Follow SOLID principles, use Dependency Injection
• [ ] Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• [ ] Logging Standards: Structured JSON, include trace IDs request_id
• [ ] Metrics: Track error_rate, latency, queue_depth
• [ ] Error Handling: Standardized error codes, no bare except
• [ ] Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• [ ] Tests passed + coverage met
• [ ] Lint/Typecheck passed
• [ ] Logging/Metrics/Trace implemented
• [ ] Security checks passed
• [ ] Documentation/Changelog updated
• [ ] Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure