Hello prompt engineers, Earlier this year I tried to create a hardcoded cache of embedding vectors, only to be thwarted by the limitations of Kotlin (the combined size of the arrays of numbers exceeded Kotlin’s maximum function size). Now that we’ve added Sqlite to the solution to support memory and querying, we can use that infrastructu...
Hello prompt engineers, We first introduced OpenAI chat functions with a weather service and then a time-based conference sessions query. Both of those examples work well for ‘point in time’ queries or questions about a static set of data (e.g., the conference schedule). But each time the JetchatAI app is opened, it has no recolle...
Hello prompt engineers, Last week’s post introduced the OpenAI chat function calling to implement a live weather response. This week, we’ll look at how to use function calling to enhance responses when using embeddings to retrieve data isn’t appropriate. The starting point will be the droidcon SF sample we’ve covered previously: F...
Hello prompt engineers, OpenAI recently announced a new feature – function calling – that makes it easier to extend the chat API with external data and functionality. This post will walk through the code to implement a “chat function” in the JetchatAI sample app (discussed in earlier posts). Following the function calling document...
Hello AI enthusiasts, This week, we’ll be talking about how you can use Azure Cognitive Services to enhance the types of inputs your Android AI scenarios can support. What makes an LLM multimodal? Popular LLMs like ChatGPT are trained on vast amounts of text from the internet. They accept text as input and provide text as output....
Hello prompt engineers, A few weeks ago I added a custom datastore (the droidcon SF schedule) to the Jetchat OpenAI chat sample. One of the ‘hacks’ I used was generating the embeddings used for similarity comparisons on every startup and caching in memory: This results in ~70 web requests each time, plus the (albeit low) monetar...
Hello prompt engineers, A few weeks ago we finished a series of posts building an AI chatbot using the Jetchat sample with OpenAI. The sample uses the chat and image endpoints, but has the same limitation as many LLMs, which is that its knowledge is limited to the training data (for example, anything after September 2021 is not included)...
Hello prompt engineers and Jetpack Compose developers, Last week, we introduced the JetchatGPT sample to show you how to integrate the OpenAI chat API into an Android app. Instead of building requests manually like in previous blog posts, we showed you how to use the openai-kotlin client library to make it easier to interact with the chat en...
Hello prompt engineers, The last three blogs have been about exploring the OpenAI API completion, edit, and image endpoints from Android, using HttpClient and crafting JSON requests and responses. This post is about implementing the chat API in an Android app, using the open-source client library openai-kotlin on GitHub. The library make...
Hello prompt engineers, We’re back with another blog post on using OpenAI in Android applications! So far in this blog series, we’ve covered: Last week, we talked about the different API endpoint options and showed you some examples of how to use the API. Today, we’ll be focusing more on the API and how yo...
