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rag-toolkit

Name: rag-toolkit
Author: zilliztech

By zilliztech

RAG (Retrieval Augmented Generation) solutions: basic RAG, RAG with reranking, agentic RAG, and multi-hop RAG for complex reasoning

npx claudepluginhub zilliztech/milvus-marketplace --plugin rag-toolkit

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Stars

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Med: 0·Avg: 285

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Med: 0·Avg: 1

What's Inside

Skills4

agentic-rag

/skills/agentic-rag

Use when user needs an autonomous RAG agent that decides when and what to retrieve dynamically. Triggers on: agentic RAG, agent, autonomous retrieval, tool use, function calling, research agent, conversational RAG, dynamic retrieval, self-directed search, RAG with tools, intelligent assistant, adaptive retrieval.

multi-hop-rag

/skills/multi-hop-rag

Use when user needs multi-step reasoning with iterative retrieval for complex questions. Triggers on: multi-hop, multi-step RAG, complex questions, chain of retrieval, iterative retrieval, complex reasoning, cross-document reasoning, question decomposition, research questions, fact synthesis, connecting information across documents.

rag-with-rerank

/skills/rag-with-rerank

Use when user needs high-precision RAG with reranking for domains where accuracy is critical. Triggers on: rag rerank, precise RAG, cross-encoder, reranking RAG, legal QA, medical QA, high-precision QA, two-stage retrieval, semantic reranking, improve RAG accuracy, relevance scoring, document ranking.

rag

/skills/rag

Use when user wants to build RAG, Q&A system, or knowledge base with documents. Triggers on: RAG, retrieval augmented generation, Q&A system, knowledge base, document Q&A, chat with docs, ChatGPT for docs, LLM + retrieval, semantic search over documents, ground LLM with facts, reduce hallucination, enterprise search.

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Version0.1.0

Stars2

Forks2

MaintenanceGood

LicenseApache-2.0

Last CommitFeb 4, 2026

AddedFeb 3, 2026

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Available In

milvus-marketplace1

README

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:

Scenario plugins = AI cache: Pre-built scenario solutions act like cache - use directly if matched, no need to build from scratch
core = methodology + operators: Universal development methodology + composable atomic capabilities
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

View full README on GitHub

rag-toolkit

Popularity

What's Inside

Confidence

README

Milvus Marketplace

Core Philosophy

Focus: Data Retrieval Domain

Two-Layer Solution Architecture

Scenario Classification Principles

Workflow

Installation

1. Add Marketplace

2. Install Plugins

3. Start Using

Plugin Overview

core - Core Capabilities

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

retrieval-system - Text Search

rag-toolkit - RAG / Q&A

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Core Philosophy

Focus: Data Retrieval Domain

Two-Layer Solution Architecture

Scenario Classification Principles

Workflow

Installation

1. Add Marketplace

2. Install Plugins

3. Start Using

Plugin Overview

core - Core Capabilities

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

retrieval-system - Text Search

rag-toolkit - RAG / Q&A

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Health & Quality

More by zilliztech

memsearch

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