Hello Android developers, 2023 was the year that machine learning and artificial intelligence really became mainstream, and we covered both topics with a focus on Android implementations. We published series on using the ONNX machine learning runtime, building Android apps with Microsoft Graph, and tutorials for Jetpack Compose developer...
Hello prompt engineers, This week, we are taking one last look at the new Assistants API. Previous blog posts have covered the Retrieval tool with uploaded files and the Code interpreter tool. In today’s post, we’ll add the function that we’d previously built to the fictitious Contoso employee handbook document chat. Configure fun...
Hello prompt engineers, Over the last few weeks, we’ve looked at different aspects of the new OpenAI Assistant API, both prototyping in the playground and using Kotlin in the JetchatAI sample. In this post we’re going to add the Code Interpreter feature which allows the Assistants API to write and run Python code in a sandboxed execution...
Hello prompt engineers, This week we’re continuing to discuss the new Assistant API announced at OpenAI Dev Day. There is documentation available that explains how the API works and shows python/javascript/curl examples, but in this post we’ll implement in Kotlin for Android and Jetpack Compose. You can review the code in this JetchatAI ...
Hello prompt engineers, Last week we looked at one of the new OpenAI features – Assistants – in the web playground, but good news: the OpenAI Kotlin library is already being updated with the new APIs and you can start to try them out right now in your Android codebase with snapshot package builds. With a few minor configuration changes y...
Hello prompt engineers, OpenAI held their first Dev Day on November 6th, which included a number of new product announcements, including GPT-4 Turbo with 128K context, function calling updates, JSON mode, improvements to GPT-3.5 Turbo, the Assistant API, DALL*E 3, text-to-speech, and more. This post will focus just on the Assistant API b...
Hello prompt engineers, Last week’s blog introduced a simple “chat over documents” Android implementation, using some example content from this Azure demo. However, if you take a look at the Azure sample, the output is not only summarized from the input PDFs, but it’s also able to cite which document the answer is drawn from (showing in ...
Hello prompt engineers, In last week’s discussion on improving embedding efficiency, we mentioned the concept of “chunking”. Chunking is the process of breaking up a longer document (ie. too big to fit under a model’s token limit) into smaller pieces of text, which will be used to generate embeddings for vector similarity comparisons wit...
Hello prompt engineers, I’ve been reading about how to improve the process of reasoning over long documents by optimizing the chunking process (how to break up the text into pieces) and then summarizing before creating embeddings to achieve better responses. In this blog post we’ll try to apply that philosophy to the Jetchat demo’s confe...
Hello prompt engineers, This week we’re taking a break from code samples to highlight the general availability of Azure AI Content Safety. In this blog series we’ve touched briefly on the using prompt engineering to restrict the types of responses an LLM will provide, such as setting the system prompt to set boundaries on what questions ...
