ai VS pygwalker

The Vercel AI SDK is built for OpenAI APIs and includes a range of tools for utilizing OpenAI APIs.

The AI is calling a function (https://platform.openai.com/docs/guides/function-calling) that is mapped to developer-controlled logic. The easiest thing to do is render specific UIs for every function call, but you can render however you'd like.

We kept the code snippets in the blog post brief, but you can see the full code for the demo here: https://github.com/vercel/ai/tree/main/examples/next-ai-rsc

In this blog post, we'll build a Next.js chatbot that runs on your computer. We'll use Ollama to serve the OpenHermes 2.5 Mistral LLM (large language model) locally, the Vercel AI SDK to handle stream forwarding and rendering, and ModelFusion to integrate Ollama with the Vercel AI SDK. The chatbot will be able to generate responses to user messages in real-time.

I recently set up the Next-Langchain-starter from Vercel, and it is phenomenal: Easy to setup, easy to modify. So far I've only done a few modifications:

Vercel AI SDK Playground Documentation

I used infrastructure as code (IaC) to provision and deploy Vercel's next-openai example. IaC is useful because it applies the same rigor of application code development to infrastructure provisioning. Instead of manual point and click in a cloud console which can be unrepeatable or error-prone, you just store and change all infrastructure configurations as code in source control .

You can use LLM providers other than hugging face such as Anthropic, Langchain adn OpenAI as well. Follow this doc to change the route handler(/api/chat/route.ts) and refer to the section of your chosen LLM provider.

**References: If you got stuck anywhere feel free to reach out. The Vercel documentation is excellent and the Github repo is also easily clonable if you don't have time to code from the start and just want to get stuck in customising.

The most popular data visualization python library in 2023. It turns your dataframe into an interactive data exploration app like tableau/powerBI with one line of code. It provides simple drag-and-drop/chat interface for you to build charts. It can run in juypter notebook, which means you do not need to switch between your code and the visualization app. Besides, you can also build interactive spitial visualization on maps with it. And it also has Javascript and R version.

GWalkR is the R binding of Graphic-Walker, if you want to use it in python, check the python version: PyGWalker: https://github.com/Kanaries/pygwalker

+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.

+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.

RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.