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:

Tutorials

Advanced RAG
Embedding/Rerank Models
Vector Databases

RAG Projects

Embedchain

Embedchain streamlines the creation of personalized LLM applications, offering a seamless process for managing various types of unstructured data.

LangChain

LlamaIndex

GraphRAG

微軟開源一個基於圖譜的檢索與推理增強的解決方案。GraphRAG 透過從預檢索、後檢索到提示壓縮的過程中考慮知識圖譜的檢索與推理,為回答生成提供了一種更精準和相關的方法。

neo4j

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.

RAG 評估

評估生成(Generation)指標

評估檢索(Retrieval)指標

Ragas

Revision #76
Created 30 April 2024 20:21:55 by Admin
Updated 23 July 2024 16:27:50 by Admin