rag-implementation

Set up a RAG system using LangChain and Chroma DB for local documents.

1|Updated Aug 13, 2022

Search Tags:#semantic search #embeddings #langchain #llm #rag #ai applications #vector database

One-click install

npx skills add https://github.com/sskim91/dotfiles --skill rag-implementation-sskim91

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: rag-implementation
Source: https://github.com/sskim91/dotfiles/tree/main/claude/skills/rag-implementation
Command: npx skills add https://github.com/sskim91/dotfiles --skill rag-implementation-sskim91

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, openai, chromadb, pinecone-client, weaviate-client, sentence-transformers, and includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables the creation of LLM applications that provide accurate, factual, and up-to-date responses by integrating external knowledge bases, effectively combating hallucinations and providing source citations.

Core Features & Use Cases

Vector Databases & Embeddings: Learn to use tools like Pinecone, Chroma, and OpenAI embeddings for semantic search.
Retrieval Strategies: Implement dense, sparse, hybrid, and multi-query retrieval to find the most relevant information.
Document Chunking: Master techniques like recursive character and semantic splitting for optimal context management.
Use Case: Build a Q&A chatbot for your company's internal documentation. Use this Skill to set up a RAG system that retrieves answers directly from your documents and cites the sources, ensuring accuracy and trust.

Quick Start

Help me set up a basic Retrieval-Augmented Generation (RAG) system using LangChain and Chroma DB for my local documents.