search
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.
Search helps you build and deploy a complete search application with OpenSearch from sample data to a running UI.
AI-generated summary based on this skill's SKILL.md
Install
opensearch-project/opensearch-agent-skills/search · repository language: Python
git clone https://github.com/opensearch-project/opensearch-agent-skills
cp -r opensearch-agent-skills/skills/opensearch-skills/search ~/.claude/skills/searchnpx skillfed install opensearch-project/opensearch-agent-skills/searchFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I build a search app with OpenSearch?
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—BM25 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.
What search strategies does OpenSearch skill support?
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—choose based on your content type, query patterns, and relevance requirements. The skill helps you implement and evaluate each.
How do I set up semantic or vector search with embeddings?
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.
How can I ingest and index documents like PDFs for search?
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.
How do I evaluate and improve search quality in OpenSearch?
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.
What is hybrid search and agentic search in OpenSearch?
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.
SKILL.md
rendered from the published skill — quoted content, verbatim
Search
Category skill for building search applications with OpenSearch.
Skills
| Skill | Description |
|---|---|
| opensearch-launchpad | End-to-end search application builder — from sample data to a running search UI with BM25, semantic, hybrid, or agentic search |
When to Use
Read opensearch-launchpad/SKILL.md when the user wants to: - Build a new search application - Set up an OpenSearch index with a specific search strategy - Deploy ML models for semantic or hybrid search - Evaluate and tune search quality - Process documents (PDF, DOCX) for search ingestion
Read as markdown · JSON record · Browse the source repository
File tree — 9 files
skills/opensearch-skills/search/SKILL.md
skills/opensearch-skills/search/opensearch-launchpad/SKILL.md
skills/opensearch-skills/search/opensearch-launchpad/agentic_search_guide.md
skills/opensearch-skills/search/opensearch-launchpad/dense_vector_models.md
skills/opensearch-skills/search/opensearch-launchpad/evaluation_guide.md
skills/opensearch-skills/search/opensearch-launchpad/local_ase.md
skills/opensearch-skills/search/opensearch-launchpad/opensearch_semantic_search_guide.md
skills/opensearch-skills/search/opensearch-launchpad/sparse_vector_models.md
skills/opensearch-skills/search/opensearch-launchpad/unstructured_preset.md