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rag-builder

Store and retrieve document chunks with a vector database for RAG workflows.

Updated Aug 23, 2026
Search Tags:#embeddings#rag#search#mcp#retrieval#qdrant#vector-database
One-click install
npx skills add https://github.com/neverprepared/ink-bunny --skill rag-builder-neverprepared
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-builder
Source: https://github.com/neverprepared/ink-bunny/tree/main/reflex/plugins/reflex/skills/rag-builder
Command: npx skills add https://github.com/neverprepared/ink-bunny --skill rag-builder-neverprepared

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

RAG Builder simplifies creating retrieval-augmented generation pipelines by providing a ready-to-run server backed by a vector store, enabling fast ingestion, indexing, and search of documents across multiple projects.

Core Features & Use Cases

  • Ingest documents into a vector database with chunking for efficient retrieval.
  • Perform semantic search across project-specific collections with isolation.
  • Use pre-configured MCP server setup and embedding model integration for rapid deployment.
  • Build knowledge bases for teams and agent systems that rely on up-to-date documentation.

Quick Start

Launch the RAG server and ingest a sample document to begin querying immediately.

Frequently Asked Questions about rag-builder

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

FAQPage Schema
How do I build a RAG system with a vector database for document retrieval?▼

To build a RAG system, you can use this skill to ingest documents into a vector database with automatic chunking, enabling semantic search across isolated project collections.

How do I ingest documents into Qdrant for semantic search?▼

You ingest documents by using the pre-configured server to chunk and store them in Qdrant, which then allows you to perform semantic search across project-specific collections.

Does this RAG setup support multi-project collection isolation?▼

Yes, the RAG setup supports multi-project collection isolation, allowing you to maintain separate vector database collections so different projects can query their own document chunks independently.

Do I need an MCP server to run a retrieval-augmented generation pipeline?▼

You do not need to build one from scratch because this skill provides a pre-configured MCP server, handling the embedding model setup and collection management required for the pipeline.

What's the best way to keep an agent's knowledge base up-to-date with new documentation?▼

The best way to maintain an up-to-date knowledge base is to continuously ingest new documentation chunks into the vector database, enabling the agent system to retrieve the latest information during search.

What are the limitations of using pre-configured MCP servers for vector search?▼

The metadata does not specify limitations of the pre-configured MCP server, but it is designed for rapid deployment of vector search and embedding model integration rather than custom server configurations.

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