Which vector databases are supported by this skill?

The skill provides implementation patterns for major providers including Pinecone, Weaviate, Chroma, Milvus, Qdrant, and pgvector.

How does it improve retrieval quality?

It implements advanced techniques like Hybrid Search (combining keyword and semantic search), Cross-Encoder reranking, and Hypothetical Document Embeddings (HyDE).

Retrieval-Augmented Generation (RAG) is a technique that provides LLMs with access to external, private, or real-time data to ensure responses are grounded in fact and specific context.

Does this help with document chunking?

Yes, it includes specialized logic for recursive character, token-based, and semantic chunking to optimize how your data is indexed for retrieval.

Is it compatible with LangGraph?

Absolutely. It features specific quick-start guides and advanced patterns for building stateful RAG workflows using LangGraph and Anthropic's Claude models.

RAG Implementation Suite

Name: RAG Implementation Suite
Author: duanbiao2000

byduanbiao2000

0•

Data Science & ML

Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and advanced retrieval patterns for LLM applications.

This skill empowers developers to bridge the gap between LLMs and private data by implementing end-to-end RAG pipelines. It provides comprehensive patterns for document chunking, embedding generation, vector store integration, and advanced retrieval strategies like hybrid search and reranking. Whether building a documentation assistant or a domain-specific Q&A tool, this skill offers the implementation logic and best practices to reduce hallucinations and ensure grounded, accurate AI responses using modern frameworks like LangGraph.

Key Features

010 GitHub stars

02Automated document chunking strategies including Recursive, Semantic, and Token-based splitting

03Contextual reranking and metadata filtering for superior retrieval accuracy

04Support for high-performance embedding models like Voyage-3 and Text-Embedding-3

05Seamless integration with leading vector databases like Pinecone, Chroma, and Weaviate

06Advanced retrieval patterns including Hybrid Search, Multi-Query, and HyDE

Use Cases

01Building a Q&A assistant for proprietary company documentation and internal wikis

02Reducing AI hallucinations in customer support chatbots via grounded data retrieval

03Creating a research tool that cites specific sources from a large-scale knowledge base

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add duanbiao2000/obsidiandoc26 rag-implementation

For use in Claude.ai and ChatGPT

Download Skill