That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):A graph-powered all-in-one RAG system!RAG-Anything is a graph-driven, all-in-one multimodal document processing RAG system built on LightRAG.It supports all content modalities within a single integrated framework.100% open-source. https://t.co/XGpDK0Ctht ...

GitHub repo: https://t.co/Z1z6QE1v94 ...

A graph-powered all-in-one RAG system!RAG-Anything is a graph-driven, all-in-one multimodal document processing RAG system built on LightRAG.It supports all content modalities within a single integrated framework.100% open-source. https://t.co/XGpDK0Ctht ...

RT Avi Chawla (@_avichawla)8 RAG architectures all AI Engineers should know: https://t.co/I4wQetVJL0 ...

8 RAG architectures all AI Engineers should know: https://t.co/I4wQetVJL0 ...

RT Avi Chawla (@_avichawla)A new embedding model cuts vector DB costs by ~200x.It also outperforms OpenAI and Cohere models.Here's a complete breakdown (with visuals): ...

Embedding Models - A new embedding model reduces vector DB costs by approximately 200x [1] - The new model outperforms OpenAI and Cohere models [1] Industry Focus - The report provides a complete breakdown with visuals on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) [1]

Core Idea - Contextualized chunk embedding models, such as voyage-context-3, process entire documents to embed chunks, leading to document-aware embeddings [1] - This approach enables semantically aware retrieval in Retrieval-Augmented Generation (RAG) systems [1] Technology - Voyage-context-3 is highlighted as an example of a contextualized chunk embedding model [1] - The method contrasts with producing independent chunk embeddings [1] Collaboration - The MongoDB team is acknowledged for their collaboration on this topic [1]

Product Features - voyage-context-3 可以直接替换标准 embeddings，无需更改下游工作流程 [1] - 只需更改模型名称即可开始使用 [1]

Compared to OpenAI-v3-large (float, 3072d). voyage-context-3 (binary, 512):- 99.48% lower vector DB costs.- 0.73% better retrieval quality.Check this 👇 https://t.co/7pLYG2Vkot ...