• https://news.ycombinator.com/item?id=46616529

In-depth tutorials on LLMs, RAGs and real-world AI agent applications. - ai-engineering-hub/fastest-rag-milvus-groq at main · patchy631/ai-engineering-hub

This project builds the fastest stack to build a RAG application with retrieval latency < 15ms.

It leverages binary quantization for efficient retrieval coupled with Groq’s blazing fast inference speeds.

Lessons learned from building RAG systems for Usul AI and enterprise clients, processing over 13 million pages.

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems. The rag-cookbooks GitHub repository provides a collection of Jupyter notebooks offering tutorials, best practices, and practical use cases for implementing Retrieval Augmented Generation (RAG) systems.

• https://github.com/hugobarona/banking-multi-agent-workshop/blob/main/csharp/src/MultiAgentCopilot/Models/Banking/OfferTerm.cs#L5
• https://github.com/PennStateLefty/semantic-kernel/tree/8232075e5c73f1827514ee583b3f5104ddf087d7/dotnet/src/VectorDataIntegrationTests/CosmosNoSqlIntegrationTests/CRUD
• https://github.com/seetampradhan/CosmosVectorSearch/tree/main#

Prototype and productionize AI features with a modern JS/TS stack

This is Dot, a standalone open source app meant for easy use of local LLMs and RAG in particular to interact with documents and files similarly to Nvidia’s Chat with RTX. Dot itself is completely standalone and is packaged with all dependencies including a copy of Mistral 7B, this is to ensure the app is as accessible as possible and no prior knowledge of programming or local LLMs is required to use it.

🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄. - vanna-ai/vanna: 🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.