Does this skill include source attribution?

Yes, the pipeline is designed to capture metadata from source files to ensure every AI response can cite its specific chapter, section, or file.

Can I use this for non-technical documentation?

Absolutely; while optimized for technical textbooks, the architecture is adaptable for any markdown-based knowledge base, CMS, or document repository.

Which vector database is utilized by this skill?

This skill follows the implementation pattern for Qdrant, a high-performance vector search engine, for storing and querying embeddings.

How does it handle document chunking?

It implements a strategy of 800-character chunks with a 200-character overlap, specifically designed to preserve context and code blocks in technical markdown.

What embedding models does this skill support?

The skill is pre-configured for OpenAI's text-embedding-ada-002 but provides patterns that can be adapted for other vector-based models.

RAG Pipeline Architect

Name: RAG Pipeline Architect
Author: muhammad-anas35

bymuhammad-anas35

Data Science & ML

Implements end-to-end Retrieval-Augmented Generation workflows to enable accurate AI querying of specialized documentation and technical textbooks.

About

The RAG Pipeline skill empowers Claude to architect and maintain complex retrieval systems, specifically tailored for processing and querying dense technical content like the Physical AI textbook. It standardizes the workflow for document ingestion—including automated chunking, embedding generation with OpenAI, and vector storage in Qdrant—while optimizing the runtime query flow to ensure accurate, context-aware responses with proper source attribution. This skill is essential for developers building domain-specific Q&A systems that require high precision and semantic understanding of local knowledge bases.

Key Features

Vector search optimization using Qdrant with custom score thresholds
0 GitHub stars
Automated document ingestion and chunking from Docusaurus markdown files
Context building strategies to maximize LLM accuracy and minimize hallucination
Seamless integration with OpenAI text-embedding-ada-002 and GPT-4
Built-in metadata management for source attribution and chapter filtering

Use Cases

Building an AI assistant for technical textbooks or internal documentation
Refining semantic search accuracy for domain-specific technical queries
Automating the sync between a markdown knowledge base and a vector database

About

Key Features

Vector search optimization using Qdrant with custom score thresholds
0 GitHub stars
Automated document ingestion and chunking from Docusaurus markdown files
Context building strategies to maximize LLM accuracy and minimize hallucination
Seamless integration with OpenAI text-embedding-ada-002 and GPT-4
Built-in metadata management for source attribution and chapter filtering

Use Cases

Building an AI assistant for technical textbooks or internal documentation
Refining semantic search accuracy for domain-specific technical queries
Automating the sync between a markdown knowledge base and a vector database