opensearch-launchpad
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.
OpenSearch Launchpad guides you through building a complete search application, from data collection to deployment on local or AWS infrastructure.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-22
OpenSearch Launchpad guides you through building a complete search application, from data collection to deployment on local or AWS infrastructure. 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.
Use it when
- OpenSearch Launchpad supports four search approaches: BM25 keyword search for exact term matching, semantic search using dense embeddings.
- OpenSearch Launchpad provides step-by-step configuration for vector search with embeddings.
Verify before relying
Read SKILL.md below before installing (8 files). Open directory: indexed for reading, not audited.
Similar skills
Install
opensearch-project/opensearch-agent-skills/opensearch-launchpad · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I build a search app with OpenSearch Launchpad?
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.
What search strategies does OpenSearch Launchpad support?
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.
How do I set up semantic or vector search with embeddings?
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.
Can OpenSearch Launchpad ingest PDF and DOCX documents?
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.
Where can I deploy OpenSearch Launchpad applications?
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.
How does OpenSearch Launchpad evaluate search quality?
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.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
OpenSearch Launchpad
You are an OpenSearch solution architect. You guide users from initial requirements to a running search setup.
Prerequisites
uvinstalled (for running Python scripts)- The skill directory available locally
- Target
local: Docker installed and running - Target
aws: AWS credentials configured (no Docker needed)
Optional MCP Servers
```json { "mcpServers": { "ddg-search": { "command": "uvx", "args": ["duckduckgo-mcp-server"] },
(truncated - see the full file via the links below)
File tree — 8 files
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
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Related skills
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ingest is a category skill for transforming unstructured files into JSONL chunks on your machine. It handles PDF, DOCX, and PPTX formats through the document-processing skill, which uses Docling to produce search-ready output without requiring cloud infrastructure or AWS.
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