What is RAG in AI development?

Retrieval Augmented Generation (RAG) is a technique that enhances LLM responses by retrieving relevant information from an external knowledge base before generating a final answer.

Which vector databases does this skill support?

It provides implementation patterns and schemas for Pinecone, Chroma, pgvector, Weaviate, and Qdrant.

Does it handle document chunking?

Yes, it includes multiple strategies such as fixed-size, semantic, and recursive chunking to ensure your data is prepared optimally for vector search.

Is this skill optimized for production use?

Absolutely. It includes production best practices like error handling, caching, rate limiting for embeddings, and monitoring metrics.

Can I implement hybrid search with this skill?

Yes, the skill includes templates for combining semantic vector search with keyword-based search for improved retrieval accuracy.

RAG Implementation Patterns

Name: RAG Implementation Patterns
Author: vanman2024

byvanman2024

Ciencia de Datos y ML

Implements high-performance Retrieval Augmented Generation (RAG) pipelines including document chunking, vector database integration, and semantic search.

Acerca de

This skill provides comprehensive templates and implementation patterns for building production-ready RAG systems using the Vercel AI SDK. It covers the entire lifecycle of an AI knowledge system—from sophisticated document ingestion and optimized chunking strategies to embedding generation and advanced retrieval techniques like hybrid search and LLM-based re-ranking. Whether you are building context-aware chatbots or internal search engines, this skill provides the schemas and workflows needed to integrate with popular vector databases like Pinecone, pgvector, and Chroma effectively.

Características Principales

Utility scripts for batch embedding generation and configuration validation
Pre-configured schemas for Pinecone, Chroma, pgvector, and Weaviate
Advanced retrieval patterns including hybrid search and result re-ranking
0 GitHub stars
Complete RAG lifecycle templates from ingestion to generation
Multiple chunking strategies including fixed-size, semantic, and recursive

Casos de Uso

Creating agentic systems that dynamically retrieve context for complex tasks
Building a chatbot that answers questions using private documentation
Implementing semantic search for large-scale research or legal databases

Acerca de

Características Principales

Utility scripts for batch embedding generation and configuration validation
Pre-configured schemas for Pinecone, Chroma, pgvector, and Weaviate
Advanced retrieval patterns including hybrid search and result re-ranking
0 GitHub stars
Complete RAG lifecycle templates from ingestion to generation
Multiple chunking strategies including fixed-size, semantic, and recursive

Casos de Uso

Creating agentic systems that dynamically retrieve context for complex tasks
Building a chatbot that answers questions using private documentation
Implementing semantic search for large-scale research or legal databases