GenAI

GenAI

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Security planning for LLM-based applications
Security planning for LLM-based applications
This article discusses the Security planning for the sample Retail-mart application. It shows the architecture and data flow diagram of the example application.
·learn.microsoft.com·
Security planning for LLM-based applications
What is Databricks Feature Serving? - Azure Databricks
What is Databricks Feature Serving? - Azure Databricks
Feature Serving provides structured data for RAG applications and makes data in the Databricks platform available to applications deployed outside of Databricks.
With Databricks Feature Serving, you can serve structured data for retrieval augmented generation (RAG) applications, as well as features that are required for other applications, such as models served outside of Databricks or any other application that requires features based on data in Unity Catalog.
·learn.microsoft.com·
What is Databricks Feature Serving? - Azure Databricks
Langfuse
Langfuse
Open source LLM engineering platform - LLM observability, metrics, evaluations, prompt management.
·langfuse.com·
Langfuse
The RAG Playbook - jxnl.co
The RAG Playbook - jxnl.co
Discover a systematic approach to enhance Retrieval-Augmented Generation (RAG) systems for improved performance and user satisfaction.
·jxnl.co·
The RAG Playbook - jxnl.co
Lamini - Enterprise LLM Platform
Lamini - Enterprise LLM Platform
Lamini is the enterprise LLM platform for existing software teams to quickly develop and control their own LLMs. Lamini has built-in best practices for specializing LLMs on billions of proprietary documents to improve performance, reduce hallucinations, offer citations, and ensure safety. Lamini can be installed on-premise or on clouds securely. Thanks to the partnership with AMD, Lamini is the only platform for running LLMs on AMD GPUs and scaling to thousands with confidence. Lamini is now used by Fortune 500 enterprises and top AI startups.
·lamini.ai·
Lamini - Enterprise LLM Platform
Hierarchical Indices: Enhancing RAG Systems
Hierarchical Indices: Enhancing RAG Systems
Hello, AI and data professionals! Today, we’re exploring hierarchical indices — a method significantly improving information retrieval in…
·medium.com·
Hierarchical Indices: Enhancing RAG Systems
GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval - Blog | MLOps Community
GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval - Blog | MLOps Community
GraphRAG (by way of Neo4j in this case) enhances faithfulness (a RAGAS metric most similar to precision) when compared to vector-based RAG, but does not significantly lift other RAGAS metrics related to retrieval; may not offer enough ROI to justify the hype of the accuracy benefits given the performance overhead.
·home.mlops.community·
GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval - Blog | MLOps Community
UX for Agents, Part 2: Ambient
UX for Agents, Part 2: Ambient
This is our second post focused on UX for agents. We discuss ambient background agents, which can handle multiple tasks at the same time, and how they can be used in your workflow.
·blog.langchain.dev·
UX for Agents, Part 2: Ambient
UX for Agents, Part 1: Chat
UX for Agents, Part 1: Chat
At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. Check out that talk here. In this post I will dive deeper into UX for agents. Thanks to Nuno Campos, LangChain founding engineer for many of the original thoughts and analogies
·blog.langchain.dev·
UX for Agents, Part 1: Chat
Memory for agents
Memory for agents
At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. Check out that talk here. In this post I will dive more into memory. See the previous post on planning here, and the previous posts on UX here, here, and here.
·blog.langchain.dev·
Memory for agents
🧠I wrote some thoughts on memory for agents!
🧠I wrote some thoughts on memory for agents!
We released a bunch of new functionality for memory in LangGraph, and in doing so we thought hard about what memory actually means, and was is useful today Some highlights 👇 🛃Memory is application specific The best memory today… — Harrison Chase (@hwchase17)
·x.com·
🧠I wrote some thoughts on memory for agents!
Converting CSV Data to a Neo4j Graph Database To RAG system | GraphRAG from Scratch #demo
Converting CSV Data to a Neo4j Graph Database To RAG system | GraphRAG from Scratch #demo
I recently embarked on a data adventure with the Northwind Traders Sales Dataset that I discovered on GitHub in CSV format. My goal was to convert this dataset into a Neo4j graph database to explore the power of graph databases for data analysis. Here's a glimpse of my journey: Data Cleaning with Pandas: I used Python's pandas library to clean the data, merge tables, drop unwanted columns, and perform various data transformations. Pandas made it easy to handle and manipulate the data efficiently. Cypher Code for Data Insertion: After preparing the data, I wrote Cypher code to insert the cleaned data into a Neo4j graph database. This involved creating nodes and relationships to represent the data structure accurately. Neo4j Cloud Instance: I utilized a free Neo4j cloud instance to host my graph database. The cloud platform provided an easy-to-use interface and powerful features to manage and query my data. The combination of pandas for data preprocessing and Neo4j for graph representation has been incredibly powerful. It has opened up new possibilities for visualizing and analyzing the data relationships in ways that were not possible with traditional databases. If you're interested in data analysis, graph databases, or just love exploring new technologies, I highly recommend giving this a try. Feel free to reach out if you have any questions or want to share your own experiences! #DataScience #GraphDatabases #Neo4j #Python #Pandas #DataCleaning #Cypher #DataAnalysis #TechJourney #pandas #programming #knowledgegraph #rag #RetrievalAugementedGeneration #GraphRAG Buy me a coffee: https://www.buymeacoffee.com/princez3 Follow me on social media: Discord community server: https://discord.gg/xpyUaEppzU twitter: https://twitter.com/Prince_krampah Channel main page: https://www.youtube.com/c/CodeWithPrince Hope you enjoy today's video. Please show your love and support by just liking and subscribing to the channel so we can grow a strong and powerful community. Activate the 🔔 beside the subscribe button to get the notification!📩 If you have any questions or requests feel free to leave them in the comments below. Thank you for watching and see you in the next video!!
·youtube.com·
Converting CSV Data to a Neo4j Graph Database To RAG system | GraphRAG from Scratch #demo
GenAI GraphRAG and AI agents using Vertex AI Reasoning Engine with LangChain and Neo4j
GenAI GraphRAG and AI agents using Vertex AI Reasoning Engine with LangChain and Neo4j
Building and Deploying GenAI GraphRAG Applications and AI agents using Google Cloud’s Vertex AI Reasoning Engine with LangChain and Neo4j Authors: Michael Hunger,  Head of Product Innovation, Neo4j Maruti C, Partner Engineering Lead,Google Generative AI developers not familiar with orchestration too...
·googlecloudcommunity.com·
GenAI GraphRAG and AI agents using Vertex AI Reasoning Engine with LangChain and Neo4j
Designing UX for AI Applications (Part 12 of 18)
Designing UX for AI Applications (Part 12 of 18)
In this informative video, we dive into the world of designing user experiences (UX) for AI applications. Bethany Jepchumba explores the importance of building trust and transparency in AI systems to ensure user satisfaction and Responsible AI. In this video, we cover: Introduction to User Experience and Understanding User Needs. Designing AI Applications for Trust and Transparency. Designing AI Applications for Collaboration and Feedback. Recommended resources The full "Generative AI for Beginners" Course After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge! Best practices for building collaborative UX with Human-AI partnership Introduction to guidelines for human-AI interaction Related episodes Generative AI for Beginners
·learn.microsoft.com·
Designing UX for AI Applications (Part 12 of 18)