Exploring the Powerful "Show Me" Plugin in ChatGPT

The "Show Me" plugin of ChatGPT is a fascinating feature, capable of generating diagrams for a better understanding of concepts and explanations. It utilizes a sort of API provided by the plugin, interpreting the request and providing responses in the desired format. You can view an example of a diagram created by this plugin here.

To add to its versatility, this plugin can also assist in tasks such as visualizing genealogical lines, as evidenced in the biblical lineage of Tudor shown for illustrative purposes.

Initially known as the "Diagram It" plugin, it's certainly gaining popularity among users. However, accessing these plugins might require specific steps, depending on your subscription. For users with GPTplus subscriptions, it's worth exploring how to enable these plugins.

Furthermore, it seems the "Show Me" plugin is particularly valuable to those involved in architecture or any field that requires the visual representation of concepts. However, it's not just confined to that; it could also be used in conjunction with the Wolfram maths plugin for more complex computations and visualizations.

Lastly, it's not the only way to visualize data. You can also utilize tools like Mermaid JS to write scripts and generate graphs.

Stay tuned for more exciting updates and possibilities in the realm of AI and chatbots.

Tags: ChatGPT, ShowMePlugin, AI, DataVisualization, MermaidJS

Exploring the Capabilities of ChatGPT: A Summary

ChatGPT is an AI language model that can process large amounts of text data, including code examples, and can provide insights and answer questions based on the text input provided to it within its token limit of 4k tokens. However, it cannot browse the internet or access external links or files outside of its platform, except for a select few with plugin access.

Users have reported that ChatGPT can start to hallucinate data after a certain point due to its token … click here to read

ChatGPT and the Future of NPC Interactions in Games

Fans of The Elder Scrolls series might remember Farengar Secret-Fire, the court wizard of Dragonsreach in Skyrim. His awkward voice acting notwithstanding, the interactions players had with him and other NPCs were often limited and repetitive. However, recent developments in artificial intelligence and natural language processing might change that. ChatGPT, a language model based on OpenAI's GPT-3.5 architecture, can simulate human-like conversations with players and even remember past interactions. With further development, NPCs in future games could have unique goals, decisions, … click here to read

AI Models for Chatting

If you're interested in using AI models for chatting, there are several options available that you can explore. Here are some popular choices:

SillyTavern - A frontend for AI chat models.
Simple Proxy for Tavern - A backend proxy server for AI chat models.
Koboldcpp - A backend for AI chat models.

Here are some recommended AI models that you can … click here to read

Exploring Chat Models: rwkv/raven 1.5B and fastchat-t5 3B

If you are looking for chat models to enhance your conversational AI applications, there are several options available. Two popular models worth exploring are rwkv/raven 1.5B and fastchat-t5 3B .

rwkv/raven 1.5B is a powerful model that can generate responses for conversations. You can find the model as ggml, which stands for "generalized generative model language." It offers an extensive corpus and has a context … click here to read

Using Langchain and GPT-4 to Create a PDF Chatbot

Users discussed how to create a PDF chatbot using the GPT-4 language model and Langchain. They shared a step-by-step guide on setting up the ChatGPT API and using Langchain's Documentreader `PyPDFLoader` to convert PDF files into a format that can be fed to ChatGPT. The users also provided a link to a GitHub repository that demonstrates this process: https://github.com/mayooear/gpt4-pdf-chatbot-langchain .

One user mentioned using GPT-4 for writing a novel and pointed out the model's limitations in referencing data from conversations that … click here to read

Chat2DB: A Database Client with AI Flair

In the realm of database management, Chat2DB stands out as a unique and powerful tool. It's not just your average database client; it infuses traditional functionalities with a dash of AI, making it a compelling option for developers and data enthusiasts alike.

What is Chat2DB?

Chat2DB is an open-source database client that lets you interact with your databases using natural language. Gone are the days of cryptic SQL queries; with Chat2DB, you can simply ask questions in plain English … click here to read

ChatPaper: A Glimpse into AI-Powered Research Assistance

Feeling overwhelmed by the ever-growing mountain of research papers? Fear not, fellow scholars, for ChatPaper has arrived! This innovative tool, created by a PhD student in reinforcement learning, harnesses the power of AI to streamline your research workflow.

What is ChatPaper?

Imagine a personal research assistant that can:

Summarize arXiv papers in under a minute: ChatPaper leverages ChatGPT3 to generate concise and informative summaries of research papers, helping you quickly grasp the key points … click here to read

The Evolution and Challenges of AI Assistants: A Generalized Perspective

AI-powered language models like OpenAI's ChatGPT have shown extraordinary capabilities in recent years, transforming the way we approach problem-solving and the acquisition of knowledge. Yet, as the technology evolves, user experiences can vary greatly, eliciting discussions about its efficiency and practical applications. This blog aims to provide a generalized, non-personalized perspective on this topic.

In the initial stages, users were thrilled with the capabilities of ChatGPT including coding … click here to read

Building an AI-Powered Chatbot using lmsys/fastchat-t5-3b-v1.0 on Intel CPUs

Discover how you can harness the power of lmsys/fastchat-t5-3b-v1.0 language model and leverage Intel CPUs to build an advanced AI-powered chatbot. Let's dive in!

Python Code:

 # Installing the Intel® Extension for PyTorch* CPU version python -m pip install intel_extension_for_pytorch # Importing the required libraries import torch from transformers import T5Tokenizer, AutoModelForSeq2SeqLM import intel_extension_for_pytorch as ipex # Loading the T5 model and tokenizer tokenizer = T5Tokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0") model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0", low_cpu_mem_usage=True) # Setting up the conversation prompt prompt …
                        click here to read

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