This article discusses the Security planning for the sample Retail-mart application. It shows the architecture and data flow diagram of the example application.
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.
A new wave of AI apps with agent-native UX is emerging, from Replit Agent to v0. Using LangGraph + 's new CoAgents extension, developers can build agent-native React applications.
In CopilotKit's blog, see how to use:
• Real-time state sharing to match user…
— LangChain (@LangChainAI)
The landscape of LLM guardrails: intervention levels and techniques
Explore the techniques to build guardrails for Large Language Models (LLMs) to ensure safe, reliable, and accurate outputs. Learn about rule-based methods, LLM metrics, LLM judges, and prompt engineering.
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.
I've been building agents for almost 1.5 years and can confidently 99% of the "ai browsing" demos are useless.
the reality is consumers won't have millions of AI agents working 24/7 for them, bargaining and shopping to save $20. there will just be an ai shopping app.
compute…
— Sully (@SullyOmarr)
LangGraph is one of the most versatile Python libraries for building AI agents. We can combine LangChain's LangGraph with Ollama and Llama 3.1 to build highl...
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.
CalPitch: Enhancing BD with Llamaindex + GPT-4o - Calsoft AI
CalPitch is our AI-powered sales outreach tool designed to transform how our business development team connects with prospects through cutting-edge technology.
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.
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
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.
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)
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
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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...
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