Krrish D.’s Post

There are two ways of providing custom data to LLMs: 1) Fine Tuning: With gradient descent, RLHF, or other methods. 2) Retrieval Augmented Generation With large context windows and better prompting techniques, RAG is very popular. To try out that, I made a small project with OpenAI Agent of LlamaIndex, Qdrant as vector store, and React. The endpoint is deployed on AWS Lambda Function URL. It can answer questions about me and even tell you my availability from my Google calendar (via tool use). Check out the project live at https://lnkd.in/g_s8eWwu . Complete typescript code is available at https://lnkd.in/gXewraek. With high-quality small language models on the horizon, we might soon have them on edge devices with new voice interfaces. Imagine a world where you can do all your tasks just by asking! It's like having a genie, minus the lamp and limited wishes. #llamaindex #qdrant #AWS #RAG #AIAgent

My serverless Retrieval Augmented Generation (RAG) stack these days: - Vercel for hosting and serverless functions (+ AI SDK). - Upstash Redis for cache, Vector for vector store, and Qstash for messaging. (I really love Upstash.) - Cloudflare R2 for storage. - Neon for serverless Postgres. - unstructured.io for super simple RAG chunking. + OpenAI & Anthropic APIs (Haiku is amazing for fast/cheap/accurate checks along the way). It's amazing that because these are all serverless, I can run them for almost nothing on low-traffic projects.

Have you ever wanted to analyze some data directly out of DynamoDB? Maybe it's just a hassle to implement yet another analytics event? Well, then you're like HTML/CSS to Image. Check out this new use-case for Aggregations.io: Real-time analytics using DynamoDB Streams + Kinesis https://lnkd.in/eybyKX3w

Interested in learning some uses of Aggregations.io? Check out this post on how HCTI uses Aggregations.io to generate real time internal monitoring based on DynamoDB changes.

How easy is it to build an app with #ElasticSearch for meaningful conversations with your domain data? 🚀 In just 20 minutes, we’ll create a RAG (Retrieval Augmented Generation) app using Cohere and ELSER (Elastic Sparse Encoder) with your data. Let's go!💡 #DataScience #AI #AppDevelopment

We love our developers and want to make sure they have an amazing technical documentation for all of our products. 📖 This led our team to develop an AI chatbot that allows direct communication with MongoDB documentation. Take a look at this tutorial to learn how they did this. 👇 https://lnkd.in/gW_dVc2k

Want to learn how to unlock the power of Retrieval Augmented Generation (RAG)? 🚀 Watch our MongoDB.TV episode featuring Freeplay's Ian Cairns and Jeremy Silva to discover how to use Freeplay with MongoDB to streamline the complex process of experimenting, testing, and tuning RAG features for large-scale applications. Don't miss out: https://lnkd.in/e_-e7ApD #MongoDB #VectorDB #RAG #AI

We love our developers and want to make sure they have an amazing technical documentation for all of our products. 📖 This led our team to develop an AI chatbot that allows direct communication with MongoDB documentation. Take a look at this tutorial to learn how they did this. 👇 https://lnkd.in/gHQcmn5X

We love our developers and want to make sure they have an amazing technical documentation for all of our products. 📖 This led our team to develop an AI chatbot that allows direct communication with MongoDB documentation. Take a look at this tutorial to learn how they did this. 👇 https://lnkd.in/gqRhcKnY

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