skills.rest
ils15
ils15COMMUNITY
Explore all skills by this author →
GitHub

rag-pipelines

Design and implement RAG pipelines with LangChain and vector stores.

10|5|Updated Dec 12, 2025
Search Tags:#document processing#RAG#pipeline design#vector stores#embedding models
One-click install
npx skills add https://github.com/ils15/pantheon --skill rag-pipelines-ils15
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-pipelines
Source: https://github.com/ils15/pantheon/tree/main/platform/windsurf/.windsurf/skills/rag-pipelines
Command: npx skills add https://github.com/ils15/pantheon --skill rag-pipelines-ils15

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, chroma, pinecone, weaviate, pgvector, gradio, streamlit, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit provides a comprehensive guide to designing Retrieval-Augmented Generation (RAG) pipelines, addressing the challenges of chunking, embeddings, retrieval strategies, evaluation, and demo patterns.

Core Features & Use Cases

  • Pipeline Design: Offers a structured approach to designing RAG pipelines with detailed explanations of each step.
  • Chunking Strategies: Discusses various chunking strategies suitable for different types of content.
  • Embedding Models: Explains the use of different embedding models and their dimensions.
  • Vector Stores: Details the selection of vector stores based on use cases and scaling requirements.
  • Retrieval Strategies: Covers similarity search, MMR, hybrid, and self-querying retrieval strategies.
  • Evaluation: Provides a framework for evaluating RAG pipelines using RAGAS metrics.
  • Demo Patterns: Offers examples of how to implement RAG pipelines using Gradio and Streamlit.

Quick Start

Implement a RAG pipeline using the provided chunking, embedding, and retrieval strategies to process documents and generate responses.

Frequently Asked Questions about rag-pipelines

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

FAQPage Schema
How do I design a RAG pipeline for efficient document processing?▼

Designing a RAG pipeline involves structuring chunking, embedding models, vector store selection, and retrieval strategies. This approach enables efficient document processing by generating accurate context for retrieval-augmented generation.

What chunking and embedding strategies work best for RAG pipelines?▼

Chunking strategies should match your content type, while embedding models must be selected based on their dimensions. Together, they optimize how text is broken down and vectorized for accurate retrieval in RAG pipelines.

How do I choose between Chroma, Pinecone, Weaviate, and pgvector for vector stores?▼

Choosing between Chroma, Pinecone, Weaviate, and pgvector depends on your specific use cases and scaling requirements. Each vector store offers distinct advantages for managing embeddings within RAG pipelines.

Can I evaluate RAG pipelines using RAGAS metrics?▼

Yes, you can evaluate RAG pipelines using RAGAS metrics. This framework provides a structured way to measure the performance and accuracy of your retrieval-augmented generation systems.

Do I need LangChain to implement retrieval strategies like MMR and hybrid search?▼

Yes, LangChain is required to implement retrieval strategies like MMR, hybrid, and self-querying search. It provides the necessary framework to integrate these strategies with your chosen vector stores.

How do I build a RAG demo using Gradio or Streamlit?▼

You can build a RAG demo using Gradio or Streamlit by following provided demo patterns. These frameworks allow you to quickly create interactive interfaces for your retrieval-augmented generation pipelines.

Related Skills

Explore More Software Engineering Skills→
agentsopeagentsope
287

agentsop-llamaindex

Build, debug, and evaluate LlamaIndex RAG systems for grounded document answers.

Community
Advanced
Dokkabei97Dokkabei97
2

ai-integration

Generate LLM API integration code for OpenAI, Anthropic, and Google.

Community
Intermediate
pingqLINpingqLIN

huggingface-gradio

Create interactive ML demos and web UIs in Python with Gradio's Interface, Blocks, and ChatInterface components.

Community
Intermediate
christian-bromannchristian-bromann
3

langchain-vector-stores

Index and retrieve embedded documents across multiple LangChain vector stores.

Community
Advanced