backend-rag-implementation
CommunityBuild intelligent RAG systems for grounded LLM responses.
Authorshredbx
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill empowers developers to build Retrieval-Augmented Generation (RAG) systems, enabling LLMs to provide accurate, factual, and cited responses by integrating with external knowledge bases, eliminating hallucinations.
Core Features & Use Cases
- Vector Databases & Embeddings: Store and retrieve document embeddings efficiently using tools like Pinecone or Chroma.
- Advanced Retrieval Strategies: Implement hybrid search, multi-query retrieval, and reranking for optimal context.
- Use Case: Build a Q&A chatbot that answers questions based on a company's internal documentation, ensuring all responses are grounded in the provided documents and include citations.
Quick Start
Use the backend-rag-implementation skill to set up a basic RAG system using Langchain, loading documents from a 'docs' directory, splitting them into chunks, and creating a Chroma vector store.
Dependency Matrix
Required Modules
langchainopenaichromadbpinecone-clientweaviate-clientsentence-transformersaiohttprequests
Components
referencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: backend-rag-implementation Download link: https://github.com/shredbx/demo-3d-model/archive/main.zip#backend-rag-implementation Please download this .zip file, extract it, and install it in the .claude/skills/ directory.