rag-builder
CommunityBuild scalable RAG with vector databases.
Software Engineering#rag#Qdrant#multi-project#embedding#semantic-search#vector-search#document-ingestion
Authormindmorass
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill enables teams to build Retrieval-Augmented Generation pipelines by indexing documents as vector embeddings and enabling fast semantic search across multiple projects.
Core Features & Use Cases
- Ingest documents and chunk content into a vector store with per-project isolation.
- Perform semantic search over stored passages and retrieve relevant results with metadata.
- Use Case: A developer team stores project documentation, notes, and code snippets in a single vector store and retrieves context during brainstorming or coding sessions.
Quick Start
Start a Qdrant vector store locally, install dependencies, and run the MCP-based rag server to ingest and search documents.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: rag-builder Download link: https://github.com/mindmorass/reflex/archive/main.zip#rag-builder Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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