{"enrichment":{"faq":[{"a":"OpenSearch Launchpad guides you from requirements to a running search application. It covers architecture planning, data ingestion from PDFs and DOCX files, strategy selection (keyword, semantic, hybrid, or agentic), and deployment to local Docker or AWS. The skill launches a search UI ready for live queries.","q":"How do I build a search app with OpenSearch Launchpad?"},{"a":"OpenSearch Launchpad supports four search approaches: BM25 keyword search for exact term matching, semantic search using dense embeddings, hybrid search combining keyword and semantic methods, and agentic search for complex retrieval workflows. You select the strategy based on your requirements and data characteristics.","q":"What search strategies does OpenSearch Launchpad support?"},{"a":"OpenSearch Launchpad provides step-by-step configuration for vector search with embeddings. It handles embedding model selection, index creation with vector fields, and query execution. The skill supports both dense vectors and neural sparse embeddings, letting you compare approaches and choose the best fit for your use case.","q":"How do I set up semantic or vector search with embeddings?"},{"a":"Yes. OpenSearch Launchpad processes PDF and DOCX files for searchable indexes. It handles document parsing, chunking, and transformation into embeddings or keyword-indexed content. The ingestion pipeline prepares your documents for either semantic or keyword search strategies.","q":"Can OpenSearch Launchpad ingest PDF and DOCX documents?"},{"a":"OpenSearch Launchpad supports deployment to local Docker environments and Amazon OpenSearch Serverless on AWS. The skill configures infrastructure, manages index setup, and launches your search UI in either environment, making it easy to run locally for development or scale on AWS for production.","q":"Where can I deploy OpenSearch Launchpad applications?"},{"a":"OpenSearch Launchpad provides metrics and tuning guidance for search quality assessment. It covers relevance evaluation, ranking metrics like nDCG, and precision optimization. The skill helps you benchmark results and adjust configurations to improve search performance and user satisfaction.","q":"How does OpenSearch Launchpad evaluate search quality?"}],"shadow_tags":["search-infrastructure","vector-embeddings","retrieval-augmented-generation","full-text-indexing","semantic-retrieval","information-ranking","document-ingestion","query-optimization","multi-strategy-search","cloud-deployment"],"summary_rewrite":"OpenSearch Launchpad is your guided path from initial requirements to a fully operational search system. It handles data ingestion, strategy selection (keyword, semantic, hybrid, or agentic), and deployment to either local Docker or Amazon OpenSearch Serverless. The skill walks you through architecture planning and launches a search UI ready for queries."},"files":[{"bytes":9187,"path":"skills/opensearch-skills/search/opensearch-launchpad/SKILL.md","sha256":"ac63c0478f219eb86c7090e46255ba34cfe1fc79efd0c80e6ec2045958824941","url":"https://skillfed.io/files/opensearch-project/opensearch-agent-skills/opensearch-launchpad/98c0ba26/SKILL.md"}],"id":"opensearch-project/opensearch-agent-skills/opensearch-launchpad","links":{"html":"https://skillfed.io/opensearch-project/opensearch-agent-skills/opensearch-launchpad","md":"https://skillfed.io/opensearch-project/opensearch-agent-skills/opensearch-launchpad.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":"opensearch-launchpad","publisher":"opensearch-project","stars":37},"relations":{"similar":[{"id":"opensearch-project/opensearch-agent-skills/aws-setup"},{"id":"opensearch-project/opensearch-agent-skills/log-analytics"},{"id":"opensearch-project/opensearch-agent-skills/trace-analytics"},{"id":"opensearch-project/opensearch-agent-skills/opensearch-skills"},{"id":"opensearch-project/opensearch-agent-skills/managed-ingestion-service"},{"id":"opensearch-project/opensearch-agent-skills/cloud"},{"id":"opensearch-project/opensearch-agent-skills/document-processing"},{"id":"aws/agent-toolkit-for-aws/amazon-opensearch-service"},{"id":"aws/agent-toolkit-for-aws/storing-and-querying-vectors"},{"id":"opensearch-project/opensearch-agent-skills/aoss-nextgen-provisioning"}]},"slug":{"owner":"opensearch-project","repo":"opensearch-agent-skills","skill":"opensearch-launchpad"},"version":"98c0ba26"}