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): ...

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

To recap, instead of producing independent chunk embeddings, contextualized chunk embedding models like voyage-context-3 process the entire doc in a single pass to embed each chunk.This leads to document-aware chunk embeddings that generate semantically aware retrieval in RAG.Check the visual below 👇Thanks to the #MongoDB team for working with me on this thread! ...

In terms of practical usage...voyage-context-3 is a drop-in replacement for standard embeddings without downstream workflow changes.So you can start using it by just changing the model name.Find the docs here: https://t.co/6OP6JTjm4w ...

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 ...

voyage-context-3 supports 2048, 1024, 512, and 256 dimensions with quantization.Compared to OpenAI-v3-large (float, 3072d), voyage-context-3 (int8, 2048):- delivers 83% lower vector DB costs- provides 8.60% better retrieval qualityCheck this 👇 https://t.co/OqBhucXCN5 ...

Across 93 retrieval datasets, spanning nine domains (web reviews, law, medical, long documents, etc.):voyage-context-3 outperforms:- all models across all domains- OpenAI-v3-large by 14.2%- Cohere-v4 by 7.89%- Jina-v3 by 23.66%Check this 👇 https://t.co/Jnmf1GDGbW ...

Technically, unlike traditional chunk embedding, the model processes the entire doc in a single pass to embed each chunk.This way, it sees all the chunks at the same time to generate global document-aware chunk embeddings.This gives semantically aware retrieval in RAG. https://t.co/LFIGCGwjtC ...