Let’s create a zero-shot document question answering system using Flan-ULv2, a powerful encoder-decoder language model from Google based on T5. This system can surpass the performance of other models like GPT-3 and T5 in various downstream tasks. We’ll utilize the chainlang library to build an embedding database, search the database for the most similar documents to a given query, and then use the language model to retrieve the most likely answers.
Build a Chat Application with ChatEngine and OpenAI and ChatGPT integration tutorial. The frontend will consist of ChatEngine for our chat application, Redux Toolkit for our state management, Redux Toolkit Query for making api calls, Hero icons for our Icons and React Router for Navigation. For the backend we will be using Node Js as our runtime, Express Js as our backend framework, and OpenAI for Ai integration with our chat.
In this video we use the new OpenAI gpt-3.5-turbo model to create a ChatGPT application in React. This is one of the fastest GPT models to be released. This video is great for beginners to both React and ChatGPT, and is a great portfolio project. We create a chatbot that allows you to communicate directly with the ChatGPT API, which is a project that can be applied to a vast array of React projects!
In the past day OpenAI have released their new ChatGPT API. I made a quick video to talk about the announcement and to show how to access it in Python with the OpenAI Package and with LangChai
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Today, we’ve released a new set of OpenAI actions:
Chat — Interact with the Chat API
Solve specific problems — Use actions to Summarize Text, Classify Items into Categories, and more.
Create Transcription — Transcribe audio to text in any language
Create Embeddings — Create embeddings for search, recommendations, and other relatedness problems.
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In this tutorial, you’ll learn the basics of how to use LangChain to build scalable javascript/typescript large language model applications trained on your own custom data.
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As a researcher or scientific writer, you may often find yourself needing to quickly understand and summarize complex scientific articles. Luckily, there are tools that can help make this overwhelming process easier. In this blog post, we’ll look at how to use OpenAI’s GPT-3 language model and the Streamlit library to create a simple application for generating summaries of scientific articles.
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Identifying the right tools to enable for high performance machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed.
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