Adding Azure Translate to Your Semantic Kernel Copilot

In this recent blog post, I discussed how user intent analysis ensures that your chatbot follows designated pathways to address specific inquiries. I utilized the Semantic Kernel as my AI orchestration framework, which offers various methods to implement these pathways.

One approach is to create a plan for more complex paths comprising multiple tasks, such as triggering different prompts or activating plugins. However, some paths can be straightforward, involving merely the invocation of a plugin or function. For instance, one task could be to translate a text snippet or a previous response within your chatbot conversation.

While an LLM (Large Language Model) is capable of performing translations, there are alternative methods that might be more effective than simply inputting text into an LLM and hoping for an accurate translation. Exploring these alternatives can enhance the translation process, ensuring more reliable and contextually appropriate outcomes.

• When tasked with processing extensive texts, consume tokens can become costly, especially if you envision the chatbot as a Copilot capable of translating entire documents.
• LLMs are engineered to predict subsequent words rather than providing direct word-for-word or sentence-for-sentence translations. We’ve observed that if the prompt is slightly inaccurate, it can result in peculiar translations that do not align well with the original text. Therefore, it’s crucial to consider the context and the specific requirements of the translation task when utilizing LLMs for such purposes.

Given that my chatbot is built using the Semantic Kernel and hosted on Azure cloud, leveraging Azure Open AI, I have incorporated the Azure Translate service into my architecture. I’ve integrated this service as a plugin within the Semantic Kernel orchestrator. This integration allows me to analyze a user’s question and, if it pertains to translation, invoke the translation plugin to provide a swift and hopefully superior result for the end-user.

When setting up the Azure translation service, it offers sample code that utilizes an HTTP Client to call the Translation service API. However, with the recent release of the Azure Translation SDK, there is now a Translation client available. This client simplifies the process of building solutions, making it much more straightforward to integrate translation capabilities into applications.

The plugin is now primed for integration with our Copilot, where it can swiftly perform translations of text within the user’s question, as well as of the previous responses provided by the chatbot. This enhancement allows for seamless, real-time translation, enriching the user experience by ensuring clear and accurate communication.

With the translator plugin, our chatbot can swiftly execute text translations, enhancing accuracy and speed. While the chatbot currently excels in translating individual queries and responses, I’m exploring the possibility of extending its capabilities to include full document translations. For instance, documents located on SharePoint or in a storage account could be translated. This would involve leveraging the Document Translation feature of the Azure Translate service. However, due to the potential time required for such translations, it might be necessary to run the process asynchronously to avoid prolonged wait times for the user.

I’m considering various approaches to address these challenges and provide efficient solutions for comprehensive document translations. Let’s see what I come up with!