memory-system

Milvus Marketplace

A data retrieval development assistant based on Claude Code Skills.

Core Philosophy

Focus: Data Retrieval Domain

We specialize in the data retrieval vertical:

• Vector search, semantic search
• RAG, knowledge base Q&A
• Image search, recommendation systems
• Milvus / Zilliz Cloud

Two-Layer Solution Architecture

┌─────────────────────────────────────────────────────────┐
│              Scenario Plugins (6 plugins)               │
│   rag-toolkit, retrieval-system, multimodal-retrieval,  │
│   rec-system, memory-system, data-analytics             │
│   = Pre-built solutions = AI era caching mechanism      │
└─────────────────────────────────────────────────────────┘
                           ↑
                     Match / Combine
                           ↑
┌─────────────────────────────────────────────────────────┐
│                      core plugin                         │
│       Methodology (pilot) + Atomic operators            │
│              (embedding, chunking, ...)                 │
└─────────────────────────────────────────────────────────┘

Core Ideas:

1. Scenario plugins = AI cache: Pre-built scenario solutions act like cache - use directly if matched, no need to build from scratch
2. core = methodology + operators: Universal development methodology + composable atomic capabilities
3. Ship 60% first, iterate to 80%: Deliver working solutions quickly, then optimize based on feedback

Scenario Classification Principles

Scenarios are classified by architectural differences, not by industry or model:

• Different architecture = different scenario: Fundamentally different processes and code structures
• Only model/parameters differ = vertical applications within same scenario: Covered through configuration tables

plugins/retrieval-system/skills/
├── semantic-search/          # Category: architecture definition
│   ├── SKILL.md              # Generic workflow + model selection table
│   └── verticals/            # Subcategory: vertical application guides
│       ├── legal.md          # Legal search
│       ├── academic.md       # Academic papers
│       └── ecommerce.md      # E-commerce search

Workflow

User describes requirement
      │
      ▼
   pilot activates
      │
      ├─→ Clarify data and query
      │
      ├─→ Can match a scenario?
      │       ├─ Yes → Use pre-built solution
      │       └─ No → Combine core operators
      │
      ├─→ Generate code → User tests
      │
      └─→ Collect feedback → Iterate

Installation

1. Add Marketplace

/plugin marketplace add zilliztech/milvus-marketplace

2. Install Plugins

# Core tools (required)
/plugin install core@milvus-marketplace

# Install scenario plugins as needed
/plugin install rag-toolkit@milvus-marketplace        # RAG solutions
/plugin install retrieval-system@milvus-marketplace   # Text search
/plugin install multimodal-retrieval@milvus-marketplace # Image/video/multimodal
/plugin install rec-system@milvus-marketplace         # Recommendations
/plugin install memory-system@milvus-marketplace      # Chat memory
/plugin install data-analytics@milvus-marketplace     # Duplicate detection, clustering

3. Start Using

Simply describe what you want to build:

"Help me build a document Q&A system"
"I want to implement semantic search"
"Build an image search application"

The pilot will automatically activate, clarify requirements, and help you orchestrate the toolchain and generate code.

Plugin Overview

core - Core Capabilities

Type	Skill	Purpose
Controller	pilot	AI application navigator - understands requirements, orchestrates tools, delivers code
Operator	embedding	Text/image vectorization
Operator	chunking	Document chunking
Operator	indexing	Milvus index management
Operator	data-ingestion	Batch data import
Operator	rerank	Search result reranking
Operator	pdf-extract	PDF text extraction
Operator	vlm-caption	Image captioning (VLM)
Environment	local-setup	Local Milvus deployment

Scenario Plugins - Pre-built Solutions (6 Plugins, 17 Skills)

retrieval-system - Text Search

Skill	Architecture	Vertical Applications
semantic-search	embedding → vector search	Legal, academic, news, e-commerce, code, patents
hybrid-search	vector + BM25 keyword + score fusion	E-commerce, legal, academic
filtered-search	vector search + scalar filtering	E-commerce, recruitment, real estate
multi-vector-search	multi-vector field joint search	Products, papers, resumes

rag-toolkit - RAG / Q&A