Does this skill include advanced retrieval strategies?

Yes, it covers hybrid search (combining keyword and semantic retrieval), multi-query expansion, reranking with cross-encoders, and contextual compression.

Can I use this skill for different types of document chunking?

Yes, it includes strategies for recursive character splitting, token-based splitting, semantic chunking, and markdown-specific header splitting.

Which vector databases are supported by this skill?

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

How does this skill help reduce AI hallucinations?

It provides implementation patterns for groundedness checks and source citations, ensuring the LLM generates responses based strictly on the retrieved context.

What libraries are used in the RAG implementation examples?

The examples primarily utilize LangChain, OpenAI embeddings, Sentence Transformers, and various vector database client libraries.

RAG System Implementation

Name: RAG System Implementation
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Builds robust Retrieval-Augmented Generation (RAG) systems to ground LLM applications in external knowledge bases and vector databases.

About

This skill provides comprehensive patterns and best practices for implementing Retrieval-Augmented Generation (RAG), enabling developers to build AI applications that leverage proprietary or domain-specific data. It covers the entire pipeline—from document chunking and embedding generation to vector database integration and advanced retrieval strategies like hybrid search and reranking. By following these implementation patterns, users can create highly accurate Q&A systems, research assistants, and documentation tools that provide grounded responses with verifiable source citations.

Key Features

Vector database integration patterns for Pinecone, Weaviate, Chroma, and FAISS
Contextual compression and reranking methods to improve answer precision
Multiple document chunking strategies for semantic and recursive splitting
RAG evaluation metrics for accuracy, retrieval quality, and groundedness
Advanced retrieval techniques including hybrid search, multi-query, and reranking
1 GitHub stars

Use Cases

Creating research assistants that provide accurate answers with source citations
Building proprietary Q&A systems over internal company documentation
Reducing LLM hallucinations by grounding AI responses in external factual data

About

Key Features

Vector database integration patterns for Pinecone, Weaviate, Chroma, and FAISS
Contextual compression and reranking methods to improve answer precision
Multiple document chunking strategies for semantic and recursive splitting
RAG evaluation metrics for accuracy, retrieval quality, and groundedness
Advanced retrieval techniques including hybrid search, multi-query, and reranking
1 GitHub stars

Use Cases

Creating research assistants that provide accurate answers with source citations
Building proprietary Q&A systems over internal company documentation
Reducing LLM hallucinations by grounding AI responses in external factual data