Skill UI

A comprehensive guide to implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.

This guide details setting up robust Continuous Integration and Continuous Deployment (CI/CD) pipelines for Exa integrations. It covers unit tests, live integration testing using GitHub Actions secrets, and health checks. Use this when automating testing workflows or ensuring Exa API connectivity during builds and release gates.

A comprehensive guide to diagnosing and resolving common errors and status codes (400, 401, 429, etc.) encountered when integrating with the Exa API. Provides specific solutions for malformed requests, authentication failures, rate limit issues, and content fetching errors, ensuring smooth API debugging and integration.

A comprehensive guide for deploying Exa-powered search APIs to production environments. This skill covers best practices for integrating Exa into Vercel Edge Functions, Docker containers, and Google Cloud Run. It includes detailed steps for secret management, implementing Redis caching for performance, and setting up health check endpoints, ensuring a robust and scalable production search solution.

A comprehensive guide containing minimal working examples for all core Exa search operations. It demonstrates basic metadata search, deep content extraction (text, highlights, summary), finding semantically similar pages, and retrieving content for specified URLs. Ideal for developers starting a new Exa integration or learning advanced RAG patterns.

A comprehensive runbook providing structured procedures for responding to Exa search API outages and integration failures. It guides users through initial triage, analyzing specific HTTP error codes (e.g., 401, 429, 5xx), implementing mitigation strategies, and completing thorough post-incident reviews and root cause analyses.

This guide identifies and avoids anti-patterns and common integration mistakes when working with Exa's neural search API. It covers crucial topics such as distinguishing between keyword and neural searches, properly retrieving content, handling date filters, and managing client instances. Use this when reviewing Exa-powered code, onboarding new team members, or auditing existing integrations for robustness and correctness.

A comprehensive guide and toolkit for performing load testing, capacity planning, and implementing scaling strategies for Exa API integrations. It covers best practices for managing rate limits (QPS), using tools like k6, implementing caching (LRU), and optimizing search architectures for high-throughput, low-latency applications, such as RAG systems.

This guide provides a comprehensive setup for a fast and reproducible local development workflow tailored for Exa API integrations. It covers establishing optimal project structure, implementing detailed unit tests using mock responses, and setting up robust integration tests. Ideal for developers who need to rapidly build, test, and iterate on applications relying on Exa's search and content fetching capabilities.

A comprehensive guide for migrating search pipelines from traditional keyword-based APIs (Google, Bing, Tavily) to Exa's advanced neural search capabilities. This guide covers building an adapter layer, implementing feature-flag traffic shifting, translating legacy query syntax, and leveraging Exa's unique features like semantic and similarity search for superior results.

A comprehensive guide and code implementation for setting up observability, metrics, and alerting for Exa search integrations. This skill tracks crucial KPIs such as search latency (P50/P95), error rates, result usage, and cache hit ratios. It is essential for monitoring search quality, debugging performance bottlenecks, and managing API resource usage in production environments.

This guide provides advanced technical patterns for optimizing Exa API performance in production workloads. It covers critical strategies such as selecting the optimal search type based on latency budgets (e.g., instant vs. neural), implementing robust result caching using LRU, minimizing content retrieval size, and executing parallel queries to maximize throughput. Essential for building reliable, low-latency, and cost-effective search integrations.