Can I use this for code-specific search?

Yes, the skill includes specific recommendations for code-centric embedding models like voyage-code-3 to optimize for technical repositories.

Is this compatible with LangChain and LangGraph?

Absolutely. It includes specific templates and code patterns designed for LangGraph state machines and LangChain retrievers.

How does this skill help reduce LLM hallucinations?

By implementing Retrieval-Augmented Generation, the skill ensures the LLM grounds its answers in retrieved context from your specific documents rather than relying solely on its training data.

What vector databases does this skill support?

The skill provides implementation guidance and configuration examples for Pinecone, Weaviate, Chroma, Qdrant, Milvus, and pgvector.

What advanced retrieval techniques are covered?

It covers Hybrid Search (combining keyword and semantic results), Multi-Query retrieval, Contextual Compression, Parent Document Retrieval, and Hypothetical Document Embeddings (HyDE).

RAG Implementation & Semantic Search

Name: RAG Implementation & Semantic Search
Author: gwickman

bygwickman

0•

Data Science & ML

Builds advanced Retrieval-Augmented Generation (RAG) systems using vector databases, semantic search, and sophisticated retrieval patterns for grounded AI applications.

This skill provides a comprehensive framework for building production-grade Retrieval-Augmented Generation (RAG) systems within LLM applications. It guides developers through the entire lifecycle of a knowledge-grounded AI, from selecting embedding models and implementing efficient text chunking strategies to configuring vector databases like Pinecone, Weaviate, and pgvector. By utilizing advanced patterns such as HyDE (Hypothetical Document Embeddings), hybrid search, and cross-encoder reranking, this skill enables the creation of highly accurate Q&A systems and research tools that minimize hallucinations by grounding responses in proprietary or domain-specific data sources.

Key Features

01Support for leading vector databases like Pinecone, Weaviate, Chroma, and pgvector

02Ready-to-use LangGraph workflow templates for building stateful, agentic RAG chains

03Diverse document chunking strategies including recursive, token-based, and semantic splitting

04Implementation of advanced RAG patterns including HyDE, Multi-Query, and Parent Document Retrieval

05Retrieval optimization techniques such as Hybrid Search (BM25 + Dense) and RRF weighting

060 GitHub stars

Use Cases

01Building enterprise Q&A systems over proprietary documentation and internal knowledge bases

02Creating research assistants that provide factual responses with verified source citations

03Developing chatbots with real-time access to frequently updated external data sources

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gwickman/auto-dev-test-target-1 rag-implementation

For use in Claude.ai and ChatGPT

Download Skill