![]() Return File(stream, "application/msword", "Result.docx") Ī complete working example of mail merge Word document in ASP.NET Core can be downloaded from mail merge Word document.zipīy executing the program, you will get the Word document as follows. MemoryStream stream = new MemoryStream() Saves the Word document to MemoryStream ![]() String fieldNames = ĭ(fieldNames, fieldValues) ![]() Using (WordDocument document = new WordDocument(fileStreamPath, FormatType.Docx)) Objects can be “files” or “directories”.FileStream fileStreamPath = new Formatting.docx", FileMode.Open, FileAccess.Read, FileShare.ReadWrite) Objects have a size and can have a modification time (if the underlying storage system permits it) Objects inside the managed folder are identified by a “path” corresponding to their position w.r.t. The managed folder follows the usual conventions pertaining to file-like objects: Managed folders can also be used in Python and R notebooks, and in webapps. To use a managed folder as output, click on the “Add” button of outputs, and select “Create folder” at the bottom. To use a managed folder as input, select it in the inputs selector Managed folders are primarily intended to be used as input or output for code recipes (Python, R, Scala), though some visual recipes dealing with unstructured data also use managed folders as output (Export, Download, Merge Folder).Ī managed folder can be used both as input or output of Python, R, PySpark and SparkR recipes. Thanks to managed folders, DSS can help you even when it does not know about your data. Write another recipe that reads from the same managed folder to make a prediction recipeĪnything else you might think of. Write a first Python recipe that has a managed folder as output, and write the saved VW model in it. DSS does not have full-fledged integration in VW. You want to use Vowpal Wabbit to create a model. ![]() You have some files that DSS cannot read, but you have a Python library which can read them: upload the files to a manged folder, and use your Python library in a Python recipe to create a regular dataset Furthermore, you can upload and download files from the managed folder using the Public REST API. In there, you can read and write any kind of data, and you can do so on any filesystem-like connection (local filesystem of course, but also HDFS, S3, FTP…).ĭSS does not try to read or write structured data from managed folders, but they appear in Flow and can be used for dependencies computation. If you need to store and manipulate data in a format not supported natively by DSS, or to use training algorithms without a scikit-learn interface, then DSS offers unstructured storage handles in the form of “Managed Folders”. API Node & API Deployer: Real-time APIsĭSS comes with a large number of supported formats, machine learning engines, … But sometimes you want to do more.Automation scenarios, metrics, and checks.
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