RT Avi Chawla (@_avichawla)Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and your embedding model costs are soaring while your pipeline struggles to scale. You want to switch to a model that prioritizes cost and latency in order to meet this new demand.But your existing embeddings live in o ...

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. https://t.co/lNSHKvmczqAvi Chawla (@_avichawla):Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and https://t.co/EtZ05xrK81 ...

Download Voyage-4-nano from HF: https://t.co/mXjgxriy6d ...

Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and your embedding model costs are soaring while your pipeline struggles to scale. You want to switch to a model that prioritizes cost and latency in order to meet this new demand.But your existing embeddings live in one vector space, while the ...

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. https://t.co/AZVktAeFEhAvi Chawla (@_avichawla):Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data: https://t.co/0nKUuBeJ70 ...

Here are 8 RAG architectures, explained visually: https://t.co/0j9eUVQIfZ ...

Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data:1) Chunk Indexing- The most common approach.- Split the doc into chunks, embed, and store them in a vector DB.- At query time, the closest chunks are retrieved directly.This is simple and effective, but large or noisy chunks can reduce precisi ...

RT Avi Chawla (@_avichawla)9 MCP, Agents, and RAG projects for AI engineers: https://t.co/fKTuaVMTc9 ...

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):9 MCP, Agents, and RAG projects for AI engineers: https://t.co/fKTuaVMTc9 ...

Find all these projects in our AI Engineering Hub, along with 90 more hands-on projects: https://t.co/z9IxdiEm8w(don't forget to star it ⭐ ) ...