GenAI

This article discusses the Security planning for the sample Retail-mart application. It shows the architecture and data flow diagram of the example application.

Feature Serving provides structured data for RAG applications and makes data in the Databricks platform available to applications deployed outside of Databricks.

Discover a simple way to query Neo4j through your favorite HTTP client with Neo4j Query API.

Open source LLM engineering platform - LLM observability, metrics, evaluations, prompt management.

We created a collection of user-tested micro-interaction design patterns and research-backed guidance for building generative AI applications.

Learn how we’re using generative AI to transform our approach to UX design and to rethink how our teams create products together.

Discover a systematic approach to enhance Retrieval-Augmented Generation (RAG) systems for improved performance and user satisfaction.

CoAgents, the building blocks for the next generation of AI-native apps.

In CopilotKit's blog, see how to use: • Real-time state sharing to match user… — LangChain (@LangChainAI)

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.

Hello, AI and data professionals! Today, we’re exploring hierarchical indices — a method significantly improving information retrieval in…

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...

Will we still need to learn query languages in the future?

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.

Explore inverted thinking to build a terrible RAG system and learn pitfalls to avoid for better recommendations.

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)

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!!

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...

Learn how to add domain specific vocabulary to your RAG with Agents using LangChain and LangGraph and make smarter and more useful RAG.

Use the GraphRAG Python package to transform unstructured data into knowledge graphs and enhance GenAI retrieval accuracy and relevance.