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RAG

檢索增強生成 - Retrieval Augmented Generation

RAG 主要用來解決大型語言模型（LLM）實際應用時的兩大侷限：幻覺/錯覺（hallucination）與資料時限。RAG 結合「資訊檢索（retrieval）」和「生成（generation）」：在文字生成之前，先從資料庫中檢索相關的資料放入上下文，以確保 LLM 可依照正確的最新資訊生成結果。

RAG 優點：

  • 降低 AI 幻覺
  • 提升資料數據安全
  • 減少模型微調
  • 改善資料時限
Tutorials
Autonomous RAG A flexible Q&A-chat-app for your selection of documents with langchain, Streamlit and chatGPT | by syrom | Medium 【圖解】4步驟教人資打造AI法律顧問！讓你的ChatGPT不再胡說八道|數位時代 BusinessNext (bnext.com.tw) 創建本地PDF Chatbot with Llama3 & RAG技術 #chatbot #chatgpt #llama3 #rag #chatpdf - YouTube 一些程式範例：https://github.com/Shubhamsaboo/awesome-llm-apps  Easy AI/Chat For Your Docs with Langchain and OpenAI in Python

GitHub Projects

Embedding Models

Vector Databases

Verba

Verba is a fully-customizable personal assistant for querying and interacting with your data, either locally or deployed via cloud. Resolve questions around your documents, cross-reference multiple data points or gain insights from existing knowledge bases. Verba combines state-of-the-art RAG techniques with Weaviate's context-aware database. Choose between different RAG frameworks, data types, chunking & retrieving techniques, and LLM providers based on your individual use-case.

PrivateGPT
LLMWare

The Ultimate Toolkit for Enterprise RAG Pipelines with Small, Specialized Models.

talkd/dialog

Talkd.ai—Optimizing LLMs with easy RAG deployment and management.

LangChain
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