Which vector databases are supported?

This skill covers popular options including Chroma for local development, Pinecone for managed services, and Weaviate for hybrid search, alongside libraries like FAISS.

Can I combine keyword search with semantic search?

Yes, the skill includes patterns for Hybrid Search, which combines BM25 keyword matching with dense vector retrieval to improve the relevance of retrieved results.

How does this skill handle long documents?

It provides strategies for recursive character splitting, token-based splitting, and semantic chunking to ensure data fits optimally within the LLM's context window.

How do I reduce hallucinations in my LLM app?

By using retrieval strategies and grounded prompt engineering techniques included in this skill, the LLM is instructed to answer based strictly on the provided context.

What is RAG and why should I use it?

Retrieval-Augmented Generation (RAG) allows LLMs to access external data not in their training set, ensuring responses are factual, up-to-date, and grounded in your specific documents.

RAG Implementation Toolkit

Name: RAG Implementation Toolkit
Author: HermeticOrmus

byHermeticOrmus

Ciencia de Datos y ML

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

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This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) within LLM applications, enabling developers to connect AI models with external knowledge bases. It covers the entire pipeline from document loading and recursive chunking to advanced retrieval strategies like hybrid search, multi-query generation, and reranking. By integrating this skill, users can build reliable Q&A systems, documentation assistants, and research tools that provide accurate, source-cited responses while significantly reducing model hallucinations through grounded context.

Características Principales

Vector database integration including Chroma, Pinecone, and Weaviate
Contextual compression and reranking for improved accuracy
Advanced document chunking and metadata filtering strategies
Grounded prompt engineering templates with source citations
Hybrid and semantic search retrieval techniques
0 GitHub stars

Casos de Uso

Building Q&A systems over proprietary internal business documents
Creating documentation assistants with source-backed answers
Developing research tools that perform semantic searches across large datasets

Acerca de

Características Principales

Vector database integration including Chroma, Pinecone, and Weaviate
Contextual compression and reranking for improved accuracy
Advanced document chunking and metadata filtering strategies
Grounded prompt engineering templates with source citations
Hybrid and semantic search retrieval techniques
0 GitHub stars

Casos de Uso

Building Q&A systems over proprietary internal business documents
Creating documentation assistants with source-backed answers
Developing research tools that perform semantic searches across large datasets