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Ready to build a “Chat with your code” application with ollama, Weaviate, and LlamaIndex 🦙 on Lightning AI ⚡️ Studio? We’ve just published a Lightning AI Studio template that you can simply copy. You’ll be up and running with your Retrieval-Augmented Generation (RAG) pipeline in minutes. Jump right in here:

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Weaviate AI Database

19,241 subscribers

12,435 görüntüleme • 2 yıl önce •via X (Twitter)

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Aniket2 yıl önce

@ollama @llama_index @LightningAI Great work @helloiamleonie 👏

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Neil Bhatt2 yıl önce

@ollama @llama_index @LightningAI Awesome!

Pablo Eder profil fotoğrafı
Pablo Eder2 yıl önce

@ollama @llama_index @LightningAI Is this running locally?

Lightning AI ⚡️ profil fotoğrafı
Lightning AI ⚡️2 yıl önce

@weaviate_io @ollama @llama_index on the cloud on a Lightning Studio. click “Open in Studio” to get a private copy of this

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SGM2 yıl önce

@ollama @llama_index @LightningAI @LightningAi based on this above video I got an idea what if we could have an inbuilt assistant in lightning Ai that can assiste users with the platform or even help them build things in lightning Ai

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New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

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New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

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