Solutions

What’s the real reason AI hasn’t yet delivered on its hype?

The easiest & fastest way to run customized and fine-tuned LLMs locally or on the edge

Leverage Azure Cosmos DB for generative AI workloads for automatic scalability, low latency, and global distribution to handle massive data volumes and real-...

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:

Deep dive into self-improving evaluators in LangSmith, motivated by the rise of LLM-as-a-Judge evaluators plus research on few-shot learning and aligning human preferences.

1. About this blogThis time, I’ll be developing an application designed for use within our FlyersSoft company, to improve workforce efficiency. Idea is to introduce CosmicTalent, an application designed to empower HR and managers in effectively navigating employee information. By leveraging CosmicTalent, users can efficiently filter and identify eligible employees based on specific task requirements. 🚀 Few key takeaways Advanatages of Azure CosmosDB Mongo vCore’s native vector search capabilities over Azure Vector Search.

Machine learning, Scala, and interactive computing

Samples to demonstrate pathways for Retrieval Augmented Generation (RAG) for Azure Data - microsoft/AzureDataRetrievalAugmentedGenerationSamples

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.

Explore the new LangChain RAG Template with Redis integration. Streamline AI development with efficient, adaptive APIs.

Build your own AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI.

Creating your own AI agent equipped with a sophisticated memory system. This guide provides a detailed walkthrough on leveraging the capabilities of Fireworks AI, MongoDB, and LangChain to construct an AI agent that not only responds intelligently but also remembers past interactions.