How does it handle document chunking?

Instead of fixed character limits, it uses semantic chunking which splits documents at logical boundaries (like ## headers) to maintain context and improve retrieval relevance.

Why is Qdrant used for the vector store?

Qdrant is selected for its production-grade performance and its ability to handle complex payload filtering, which is essential for tenant isolation and metadata-heavy retrieval.

What RAG architectures are supported by this skill?

It includes implementation patterns for 8 architectures: Simple RAG, Memory-aware, Branched, HyDE (Hypothetical Document Embeddings), Adaptive, CRAG (Corrective RAG), Self-RAG, and Agentic RAG.

Does this skill support incremental updates to the vector store?

Yes, it provides a change detection pattern using SHA-256 file hashes to compare local files against the Qdrant index, ensuring only new or modified files are processed.

Can I use this for basic keyword search?

No, this skill is specifically designed for semantic search and advanced RAG pipelines. For simple keyword lookups, standard database queries are more appropriate.

Building RAG Systems

Name: Building RAG Systems
Author: panaversity

bypanaversity

•

Ciencia de Datos y ML

Architect and implement production-grade Retrieval-Augmented Generation systems using LangChain and Qdrant vector stores.

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This skill provides specialized guidance for building robust, enterprise-ready RAG pipelines and autonomous knowledge agents. It covers everything from sophisticated document ingestion—featuring semantic chunking and incremental updates via file hash detection—to advanced retrieval patterns like HyDE, CRAG, and Self-RAG. By leveraging LangChain orchestration and Qdrant’s high-performance vector database, developers can implement high-accuracy AI systems that include tenant isolation, metadata filtering, and automated evaluation with RAGAS.

Características Principales

Semantic chunking strategies based on document structure rather than fixed-size splits
Context expansion techniques to walk chunk chains for improved generation accuracy
Implementation patterns for 8 advanced RAG architectures including HyDE, CRAG, and Self-RAG
Incremental ingestion pipelines with SHA-256 change detection to optimize indexing
Advanced Qdrant payload indexing for multi-tenant isolation and complex filtering
96 GitHub stars

Casos de Uso

Developing semantic search engines for large-scale enterprise documentation
Building autonomous Digital FTEs for specialized domains like legal, finance, or healthcare
Implementing self-correcting RAG systems for high-accuracy research applications

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Características Principales

Semantic chunking strategies based on document structure rather than fixed-size splits
Context expansion techniques to walk chunk chains for improved generation accuracy
Implementation patterns for 8 advanced RAG architectures including HyDE, CRAG, and Self-RAG
Incremental ingestion pipelines with SHA-256 change detection to optimize indexing
Advanced Qdrant payload indexing for multi-tenant isolation and complex filtering
96 GitHub stars

Casos de Uso

Developing semantic search engines for large-scale enterprise documentation
Building autonomous Digital FTEs for specialized domains like legal, finance, or healthcare
Implementing self-correcting RAG systems for high-accuracy research applications