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zilliz-launchpad

Ingest documents and index them in Milvus or Zilliz Cloud.

1|Updated Apr 15, 2026
Search Tags:#workflow#ui#docker#end-to-end#vector-search#milvus#zilliz-cloud
One-click install
npx skills add https://github.com/zilliztech/zilliz-launchpad --skill zilliz-launchpad
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: zilliz-launchpad
Source: https://github.com/zilliztech/zilliz-launchpad/tree/main/skills/zilliz-launchpad
Command: npx skills add https://github.com/zilliztech/zilliz-launchpad --skill zilliz-launchpad

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymilvus, openai, voyageai, cohere, httpx, Pillow, fastapi, uvicorn, pydantic, ragas, pypdf, pdfplumber, pdf2image, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Turn a sample document into a production-grade Milvus / Zilliz Cloud search app in minutes, guiding users through a six-phase flow from Collect to Deploy to ship a working search experience.

Core Features & Use Cases

  • End-to-end workflow: Collect, Configure, Plan, Execute, Evaluate, and Deploy to build, test, and deploy a searchable corpus.
  • Local-first to Cloud: prototype with Milvus Standalone and seamlessly promote to Zilliz Cloud.
  • Interactive UI: Next.js demo UI to explore results and verify quality.

Quick Start

Install the skill and run the six-phase flow to ingest your documents and launch the demo UI.

Frequently Asked Questions about zilliz-launchpad

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I ingest documents and build a vector search app with Milvus?▼

You can ingest PDF documents and index them in Milvus / Zilliz Cloud through a six-phase workflow: Collect, Configure, Plan, Execute, Evaluate, and Deploy. This process transforms raw files into a searchable corpus and launches a Next.js UI for immediate exploration.

Can I prototype locally with Milvus Standalone before deploying to Zilliz Cloud?▼

Yes, you can prototype with Milvus Standalone locally and seamlessly promote your searchable corpus to Zilliz Cloud. This local-first to Cloud workflow lets you build and test your vector search app before production deployment.

Does the vector search workflow support PDF document ingestion?▼

Yes, the workflow supports PDF document ingestion using pypdf, pdfplumber, and pdf2image. It extracts content from your PDFs and indexes it in Milvus or Zilliz Cloud for fast vector search retrieval.

What embedding models can I use for indexing documents in Zilliz Cloud?▼

You can use OpenAI, VoyageAI, and Cohere models to generate embeddings for indexing in Zilliz Cloud. The workflow integrates these dependencies during the Configure and Execute phases to vectorize your documents for retrieval.

How do I evaluate vector search quality after indexing documents in Milvus?▼

You evaluate vector search quality during the Evaluate phase using the ragas framework. This step measures retrieval performance on your indexed Milvus corpus before you proceed to the Deploy phase.

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