Is the generated code ready for production?

The scaffold provides a professional-grade structure with Pydantic settings, modular services, and evaluation scripts designed to be extended for production use.

What frameworks does rag-scaffold support?

It currently supports Python (LangChain, LlamaIndex, Vanilla), TypeScript (LangChain.js), and the Ailog API for managed RAG-as-a-Service.

Does it include advanced features like reranking?

Yes, you can opt-in to features like hybrid search (dense + BM25), reranking, streaming responses, and source citations during generation.

Can I choose my own vector database?

Yes, the skill provides configurations for Qdrant, Pinecone, ChromaDB, Weaviate, and Milvus.

RAG Scaffold

Name: RAG Scaffold
Author: floflo777

byfloflo777

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Data Science & ML

Generates production-ready Retrieval-Augmented Generation (RAG) pipeline boilerplate code with integrated best practices.

About

The rag-scaffold skill streamlines the initial development phase of RAG-based applications by providing a comprehensive, modular boilerplate. It supports multiple frameworks like LangChain and LlamaIndex, various vector databases, and top-tier LLM providers, ensuring that developers can jump from idea to prototype using industry-standard patterns for chunking, embedding, retrieval, and generation. Whether you are building a document search engine or a complex AI agent, this skill provides a solid architectural foundation including configuration management, citation handling, and evaluation scripts.

Key Features

Pre-integrated vector store setups for Qdrant, Pinecone, ChromaDB, and more
Multi-framework support including LangChain, LlamaIndex, and Vanilla Python/TypeScript
Automated project structure generation with Docker and environment templates
Modular architecture with separate services for embeddings, retrieval, and generation
17 GitHub stars
Advanced RAG features like hybrid search, reranking, and conversation memory

Use Cases

Developing a code assistant with specialized repository indexing and retrieval
Building a custom documentation search engine for internal company wikis
Prototyping a customer support bot that accesses private knowledge bases

About

Key Features

Pre-integrated vector store setups for Qdrant, Pinecone, ChromaDB, and more
Multi-framework support including LangChain, LlamaIndex, and Vanilla Python/TypeScript
Automated project structure generation with Docker and environment templates
Modular architecture with separate services for embeddings, retrieval, and generation
17 GitHub stars
Advanced RAG features like hybrid search, reranking, and conversation memory

Use Cases

Developing a code assistant with specialized repository indexing and retrieval
Building a custom documentation search engine for internal company wikis
Prototyping a customer support bot that accesses private knowledge bases