Azure OpenAI

using engini’s azure openai activities, you can create, get and update records to manage and define databases getting started with azure openai a azure openai account add a connection to azure openai in engini enter your engini account at https //app engini io navigate to connections page by clicking on the connections on the left sidebar or by clicking https //app engini io/connections click on the add connection option located at the topbar choose azure openai option from the available applications enter the following details in the “add connection” form and press save connection name enter a unique and descriptive name for this connection this name will help you identify and manage the connection in your engini account “azure openai” by default url enter your azure openai url here api key enter the access token you obtained from azure openai save settings – saves the connection configuration and applies the selected settings actions create transcription this action provides a setup for defining which model deployment to use and the specific file data to be processed for transcription deployment name this field requires the unique name of your azure openai deployment for transcription tasks, you must use the whisper model, as it is the only supported model for converting audio to text in this action file name a text field where you specify the name of the file being transcribed this helps in identifying and organizing the output in relation to the source file content (base64) this field requires the actual file content encoded in base64 format this allows the audio or document data to be transmitted as a text string for processing add field a functional button that allows users to include additional parameters or custom fields to the configuration if needed create translation this action allows users to submit an audio or document file to be translated into english through a specific model deployment deployment name you must enter the specific name of your azure openai deployment here for translation tasks, the whisper model is the exclusive requirement, as it is the only model supported for these specific audio translation operations file name a text input where the user enters the name of the source file this serves as a label to track and identify the specific content being processed content (base64) this field is for the actual data of the file, converted into a base64 string this encoding ensures the file content is transmitted correctly as text for the ai to process add field a functional option that allows the user to append additional configuration parameters to the action create chat completion this action is used to generate ai responses based on a provided conversational history or a set of instructions deployment name this field requires the name of your specific azure openai model deployment for chat completion, you should use large language models (llms) such as gpt 4o , gpt 4 turbo , or gpt 3 5 turbo messages\[] this field accepts an array of objects (initialized from an object array) representing the conversation history each object typically includes a "role" (system, user, or assistant) and the "content" of the message to provide context for the ai's response add field a button that allows users to add optional configuration parameters, such as "temperature" or "max tokens," to fine tune the model's output generate image this action enables the creation of original images from textual descriptions by utilizing the dall e 3 model deployment name this field requires the specific name of your azure openai deployment to generate images, you must use the dall e 3 model, as these are the specialized models for text to image tasks prompt a text area where you provide a detailed description of the image you want the ai to create the more specific the instructions, the more accurate the generated visual output will be add field a button that allows users to include optional parameters, such as image resolution or quality settings, to further customize the output initialize object array the initialize object array allows users to define and initialize a structured array of objects that can be used as input for subsequent steps, such as conversational history in chat completions variable name this field is for entering a unique name for the array being created this name acts as a reference variable that allows you to access this specific set of data later in your workflow object element type this dropdown menu allows you to select the specific schema or category for the objects within the array it ensures that the data structured in the array follows the correct format required by the intended ai model add field a functional button used to add more specific properties or initial values to the objects within the array during the setup process append to object array append to object array allows users to add a new data entry to an existing array that was previously created in the workflow, such as an array of chat messages variable this dropdown menu allows you to select a pre existing array created by the https //app archbee com/docs/ixqqblwfxopjg0nave78y/kszmw3brk97z6mt fvwti#dboz7 , once selected, the interface automatically generates input fields based on the specific object type (such as "messages") to let you add new data to that array send api request this interface allows users to perform a send api request action within the azure openai studio it is used to manually configure and execute custom http requests to specific azure openai endpoints for advanced integration or specialized tasks base url this field displays the primary endpoint address for your azure openai resource; the resource name is automatically fetched and populated from your connector settings, typically following the format https //{your resource name} openai azure com relative url this field is for entering the specific api path corresponding to the service you want to call, such as completions or embeddings method this dropdown specifies the http verb for the request, with post being the default for sending data payloads to the model body type this indicates the format of the data being sent, pre set to application/json to ensure compatibility with azure openai's api requirements body a text area where you enter the raw json payload containing the parameters and instructions for the api request add headers a functional button to include necessary metadata with the request, such as your api key for authentication add queries an option to append specific query parameters to the url to further refine the api call