{"enrichment":{"faq":[{"a":"OpenSearch skill guides you through building complete search applications from scratch. Start by setting up indexes, ingesting your documents (PDFs, text, etc.), and choosing your search strategy\u2014BM25 for traditional full-text, semantic for meaning-based retrieval, or hybrid combining both. Deploy ML models for embeddings, configure your search UI, and iteratively tune relevance. The skill covers the entire end-to-end workflow.","q":"How do I build a search app with OpenSearch?"},{"a":"OpenSearch skill covers multiple search approaches: BM25 for keyword-based full-text search, semantic search using embeddings and ML models, hybrid search combining sparse and dense vectors, and agentic search strategies. Each approach suits different use cases\u2014choose based on your content type, query patterns, and relevance requirements. The skill helps you implement and evaluate each.","q":"What search strategies does OpenSearch skill support?"},{"a":"OpenSearch skill walks you through semantic search setup by deploying ML models to generate embeddings, configuring vector indexes (including KNN search), and implementing dense vector retrieval. You'll learn to ingest documents, transform them into embeddings, and query using semantic similarity. The skill also covers hybrid approaches that combine vector search with traditional BM25.","q":"How do I set up semantic or vector search with embeddings?"},{"a":"OpenSearch skill teaches document processing and ingestion for search applications. You'll learn to extract content from PDFs and other formats, structure documents for indexing, and set up BM25 or semantic indexes. The skill covers the full pipeline from raw documents to searchable indexes, including preprocessing and optimization for retrieval quality.","q":"How can I ingest and index documents like PDFs for search?"},{"a":"OpenSearch skill includes guidance on evaluating and tuning search relevance. You'll learn to measure search quality, adjust ranking parameters, optimize your index configuration, and compare different search strategies. The skill helps you iterate on relevance metrics and deploy improvements to your search application.","q":"How do I evaluate and improve search quality in OpenSearch?"},{"a":"OpenSearch skill covers hybrid search, which combines sparse vector (BM25) and dense vector (semantic) approaches for better relevance. Agentic search strategies use AI agents to orchestrate complex retrieval workflows. The skill teaches implementation patterns for both, helping you build sophisticated search systems that adapt to diverse query types and content.","q":"What is hybrid search and agentic search in OpenSearch?"}],"shadow_tags":["vector-retrieval","information-discovery","ml-powered-search","document-indexing","search-optimization","retrieval-augmented-generation","semantic-matching","search-infrastructure"],"summary_rewrite":"This skill guides you through building complete search applications on OpenSearch, covering index setup, document processing, and multiple search strategies including BM25, semantic, hybrid, and agentic approaches. Deploy ML models, ingest PDFs and other documents, and evaluate search quality end-to-end."},"files":[{"bytes":1131,"path":"skills/opensearch-skills/search/SKILL.md","sha256":"c02706065e05a8d1087ebd5837c8dbbd6b431e67ae8a3f8a175cfaab4fbf974d","url":"https://skillfed.io/files/opensearch-project/opensearch-agent-skills/search/50d52117/SKILL.md"}],"id":"opensearch-project/opensearch-agent-skills/search","links":{"html":"https://skillfed.io/opensearch-project/opensearch-agent-skills/search","md":"https://skillfed.io/opensearch-project/opensearch-agent-skills/search.md","repo":"https://github.com/opensearch-project/opensearch-agent-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":30,"language":"Python","last_updated":"2026-07-22","license":"Apache-2.0","name":"search","publisher":"opensearch-project","stars":37},"relations":{"similar":[{"id":"opensearch-project/opensearch-agent-skills/opensearch-skills"},{"id":"opensearch-project/opensearch-agent-skills/opensearch-launchpad"},{"id":"opensearch-project/opensearch-agent-skills/aws-setup"},{"id":"opensearch-project/opensearch-agent-skills/ingest"},{"id":"opensearch-project/opensearch-agent-skills/managed-ingestion-service"},{"id":"opensearch-project/opensearch-agent-skills/document-processing"},{"id":"opensearch-project/opensearch-agent-skills/cloud"},{"id":"opensearch-project/opensearch-agent-skills/observability"},{"id":"opensearch-project/opensearch-agent-skills/trace-analytics"},{"id":"pproenca/dot-skills/opensearch-function-scoring-algorithms"}]},"slug":{"owner":"opensearch-project","repo":"opensearch-agent-skills","skill":"search"},"version":"50d52117"}