What is RAG in LLM application development?

Retrieval-Augmented Generation (RAG) is a technique that provides an LLM with specific, retrieved documents from an external source to ensure its answers are grounded in factual, up-to-date data.

How does this skill help reduce AI hallucinations?

By implementing grounding prompts and retrieval verification, the skill ensures the AI only answers based on provided context, significantly reducing the likelihood of false information.

Which vector databases does this skill support?

This skill provides implementation patterns for popular vector databases including Pinecone, Weaviate, Chroma, Milvus, Qdrant, and FAISS.

Can I use this skill with LangChain or LlamaIndex?

Yes, the skill includes standardized implementation patterns and code snippets compatible with LangChain and other popular AI orchestration frameworks.

RAG Implementation & Vector Search

Name: RAG Implementation & Vector Search
Author: HermeticOrmus

byHermeticOrmus

数据科学与机器学习

Builds production-ready Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in external data.

关于

This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) within LLM applications, allowing developers to connect AI models to proprietary knowledge bases. It covers the entire pipeline from document loading and optimized chunking to embedding generation and vector database integration. By utilizing advanced patterns like hybrid search, reranking, and contextual compression, this skill helps developers reduce hallucinations and build high-accuracy Q&A systems, research tools, and documentation assistants with verifiable source citations.

主要功能

Advanced retrieval patterns including hybrid search and multi-query retrieval
Vector Database integration for Pinecone, Chroma, Weaviate, and FAISS
Result reranking techniques using Cross-Encoders and MMR
Built-in evaluation metrics for groundedness and retrieval quality
0 GitHub stars
Multiple document chunking strategies like semantic and recursive splitting

使用场景

Building Q&A systems over proprietary corporate documentation
Creating research tools that provide accurate source citations
Implementing semantic search for large-scale natural language datasets

关于

主要功能

Advanced retrieval patterns including hybrid search and multi-query retrieval
Vector Database integration for Pinecone, Chroma, Weaviate, and FAISS
Result reranking techniques using Cross-Encoders and MMR
Built-in evaluation metrics for groundedness and retrieval quality
0 GitHub stars
Multiple document chunking strategies like semantic and recursive splitting

使用场景

Building Q&A systems over proprietary corporate documentation
Creating research tools that provide accurate source citations
Implementing semantic search for large-scale natural language datasets