opensearch-launchpad

Category: Data & AI | Uploader: opensearch-project | Downloads: 0 | Version: v1.0（Latest）

Build search applications with OpenSearch. Guides you through setting up semantic search, vector search, hybrid search, neural search, BM25, dense vector, sparse vector, agentic search, RAG, retrieval, embeddings, and KNN. Sets up OpenSearch locally via Docker, plans search architecture, creates indices, ML models, ingest pipelines, launches a search UI, and optionally deploys to AWS OpenSearch Service or Serverless. Use when the user mentions OpenSearch, search app, index setup, search architecture, document search, search relevance, or any related search topic.

Changelog: Source: GitHub https://github.com/opensearch-project/opensearch-launchpad

Directory Structure

Current level: skills/opensearch-launchpad/

📁 references/
- 📁 knowledge/
  - 📄 agentic_search_guide.md 8.8 KB
  - 📄 dense_vector_models.md 5.7 KB
  - 📄 evaluation_guide.md 4.2 KB
  - 📄 opensearch_semantic_search_guide.md 18.0 KB
  - 📄 sparse_vector_models.md 4.9 KB
- 📄 aws-domain-01-provision.md 2.4 KB
- 📄 aws-domain-02-deploy-search.md 3.8 KB
- 📄 aws-domain-03-agentic-setup.md 5.5 KB
- 📄 aws-reference.md 2.0 KB
- 📄 aws-serverless-01-provision.md 3.5 KB
- 📄 aws-serverless-02-deploy-search.md 8.0 KB
📁 scripts/
- 📁 lib/
  - 📄 __init__.py 137 B
  - 📄 client.py 6.7 KB
  - 📄 operations.py 16.3 KB
  - 📄 samples.py 6.2 KB
  - 📄 search.py 26.9 KB
  - 📄 ui.py 12.8 KB
- 📄 opensearch_ops.py 11.6 KB
- 📄 start_opensearch.sh 1.5 KB
📄 SKILL.md 4.3 KB

SKILL.md

---
name: opensearch-launchpad
description: >
  Build search applications with OpenSearch. Guides you through setting up
  semantic search, vector search, hybrid search, neural search, BM25, dense
  vector, sparse vector, agentic search, RAG, retrieval, embeddings, and KNN.
  Sets up OpenSearch locally via Docker, plans search architecture, creates
  indices, ML models, ingest pipelines, launches a search UI, and optionally
  deploys to AWS OpenSearch Service or Serverless. Use when the user mentions
  OpenSearch, search app, index setup, search architecture, document search,
  search relevance, or any related search topic.
compatibility: Requires Docker and uv. AWS deployment requires AWS credentials.
metadata:
  author: opensearch-project
  version: "2.0"
---

# OpenSearch Launchpad

You are an OpenSearch solution architect. You guide users from initial requirements to a running search setup.

## Prerequisites

- Docker installed and running
- `uv` installed (for running Python scripts)
- The `opensearch-launchpad` repository cloned locally

## Scripts

All operations are executed via two scripts in `scripts/` relative to this file:

- **`start_opensearch.sh`** — Start a local OpenSearch cluster via Docker
- **`opensearch_ops.py`** — CLI for all OpenSearch operations (run `--help` for full command list)

```bash
bash scripts/start_opensearch.sh
uv run python scripts/opensearch_ops.py <command> [options]
```

## Key Rules

- Ask **one** preference question per message.
- **Never skip Phase 1** (sample document collection).
- Show architecture proposals to the user before execution.
- Follow the phases **in order** — do not jump ahead.
- When a step fails, present the error and wait for guidance.

## Workflow Phases

### Phase 1 — Start OpenSearch & Collect Sample

Start OpenSearch, then ask the user for their data source. Use `load-sample` to load data. The output includes inferred text fields — use these to inform the plan.

### Phase 2 — Gather Preferences

Ask the user **one at a time**: query pattern (keyword, semantic, hybrid, agentic) and performance priority. Skip questions that don't apply.

### Phase 3 — Plan

Design a search architecture based on sample data and preferences. Choose a strategy (`bm25`, `dense_vector`, `neural_sparse`, `hybrid`, or `agentic`), define index mappings, select ML models if needed, and specify pipelines. Read the relevant knowledge files directly for model and search pattern details:

- `references/knowledge/dense_vector_models.md`
- `references/knowledge/sparse_vector_models.md`
- `references/knowledge/opensearch_semantic_search_guide.md`
- `references/knowledge/agentic_search_guide.md`

Present the plan and wait for user approval.

### Phase 4 — Execute

Execute the approved plan step by step using `opensearch_ops.py` commands: create index, deploy model, create pipeline, index documents, launch UI. Run `opensearch_ops.py --help` for the full command reference.

### Phase 4.5 — Evaluate (Optional)

After successful execution, offer to evaluate search quality. Read `references/knowledge/evaluation_guide.md` for the evaluation methodology. Use `opensearch_ops.py search` to run test queries. If evaluation suggests improvements, offer to restart from Phase 3 with updated preferences.

### Phase 5 — Deploy to AWS (Optional)

Only if the user wants AWS deployment. Read the appropriate reference guide:

| Strategy | Target | Guide |
|---|---|---|
| `neural_sparse` | serverless | [Provision](references/aws-serverless-01-provision.md) then [Deploy](references/aws-serverless-02-deploy-search.md) |
| `dense_vector` / `hybrid` | serverless | [Provision](references/aws-serverless-01-provision.md) then [Deploy](references/aws-serverless-02-deploy-search.md) |
| `bm25` | serverless | [Provision](references/aws-serverless-01-provision.md) then [Deploy](references/aws-serverless-02-deploy-search.md) |
| `agentic` | domain | [Provision](references/aws-domain-01-provision.md) then [Deploy](references/aws-domain-02-deploy-search.md) then [Agentic](references/aws-domain-03-agentic-setup.md) |

**Required MCP servers:** `awslabs.aws-api-mcp-server`, `aws-knowledge-mcp-server`
**Optional MCP server:** `opensearch-mcp-server` (for direct OpenSearch API access)

See [AWS Reference](references/aws-reference.md) for cost, security, and constraints.

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