neo4j Reviews

Plotly Reviews

neo4j mentions (36)

• How to give Claude Code persistent memory with a self-hosted mem0 MCP server

  The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 6 months ago

• The Context Graph Manifesto

  Perhaps the biggest promoter of the term has been Philip Rathle from Neo4j, which offers the best-known graph database system for storing knowledge graphs. But here's where the confusion starts: Is a knowledge graph something you store, or is it how you store something? It's not just a knowledge graph—it's also a graph database. That distinction matters, but the boundaries are blurry. - Source: dev.to / 7 months ago

• 6 retrieval augmented generation (RAG) techniques you should know

  The key difference lies in the retrieval mechanism. Vector databases focus on semantic similarity by comparing numerical embeddings, while graph databases emphasize relations between entities. Two solutions for graph databases are Neptune from Amazon and Neo4j. In a case where you need a solution that can accommodate both vector and graph, Weaviate fits the bill. - Source: dev.to / over 1 year ago

• LLM to extract and auto generate knowledge graph - step by step, in ~100 lines of python

  Neo4j is a leading graph database that is easy to use and powerful for knowledge graphs. - Source: dev.to / over 1 year ago

• 10 Ways AI Can Speed Up your Mobile App Development

  Neo4j is one of the most popular graph databases. It offers powerful querying capabilities through its Cypher query language. - Source: dev.to / over 1 year ago

Plotly mentions (34)

• How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better

  Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 5 months ago

• Python for Data Visualization: Best Tools and Practices

  Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago

• Generative AI Powered QnA & Visualization Chatbot

  Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago

• Build a Stock Dashboard in less than 40 lines of Python code!🤓

  In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / over 1 year ago

• Essential Deep Learning Checklist: Best Practices Unveiled

  How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago