Does it support advanced retrieval techniques?

Absolutely, it includes patterns for Hybrid Search, Multi-Query Retrieval, Contextual Compression, and Parent Document Retrieval.

What vector databases does this skill support?

The skill provides implementation patterns for major providers including Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

How do I evaluate my RAG system with this skill?

It provides a structured evaluation framework to measure accuracy, retrieval quality, and groundedness metrics for your LLM responses.

Can this skill help reduce LLM hallucinations?

Yes, by implementing grounded retrieval strategies and source citations, it ensures the LLM relies on specific context rather than its internal training data alone.

Is it compatible with LangChain?

Yes, the skill features multiple implementation examples using LangChain for document loading, splitting, and chain creation.

RAG Implementation Toolkit

Name: RAG Implementation Toolkit
Author: lifangda

bylifangda

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Data Science & ML

Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and optimized retrieval pipelines.

About

This skill provides a comprehensive framework for building production-grade LLM applications grounded in external knowledge. It offers expert guidance on vector database integration, document chunking strategies, and advanced retrieval patterns such as hybrid search and reranking. By implementing these patterns, developers can significantly reduce LLM hallucinations, enable domain-specific knowledge access, and provide verifiable source citations in AI-generated responses.

Key Features

Advanced document chunking and semantic splitting strategies
Reranking implementations using cross-encoders and MMR
Multi-strategy retrieval including hybrid search and multi-query patterns
Systematic RAG evaluation metrics for accuracy and groundedness
Vector database integration for Pinecone, Chroma, Weaviate, and more
16 GitHub stars

Use Cases

Building Q&A systems over proprietary internal documentation
Creating research tools with automated source citation and verification
Developing chatbots that access real-time or domain-specific data

About

Key Features

Advanced document chunking and semantic splitting strategies
Reranking implementations using cross-encoders and MMR
Multi-strategy retrieval including hybrid search and multi-query patterns
Systematic RAG evaluation metrics for accuracy and groundedness
Vector database integration for Pinecone, Chroma, Weaviate, and more
16 GitHub stars

Use Cases

Building Q&A systems over proprietary internal documentation
Creating research tools with automated source citation and verification
Developing chatbots that access real-time or domain-specific data