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Hi everyone,I’d like to share an idea that could significantly streamline our workflows in FME Form, particularly when working with a lot of fields with the AttributeManager.Imagine this: an AI-powered enhancement that automatically detects and intelligently renames attribute fields based on your past workbenches or predefined naming preferences.🔍 Here’s how it could work:The AI would analyze field names like horizon_canopy_trees_percentage and suggest a cleaner, more intuitive name like CANOPY_PERCENTAGE. It could learn from your previous projects to suggest consistent naming conventions. You could set parameters such as: Use of capital letters or snake_case Abbreviation rules Context-aware naming (e.g., recognizing that “canopy” and “percentage” are key descriptors) Share an Excel file with fields name that the AI could associate with the fields in the AttributeManager.💡 Why this matters:Reduces manual renaming effort Promotes consistency across projects Makes workspaces easier to read and maintain Saves time, especially in large or complex datasetsI believe this could be a powerful addition to FME’s smart automation capabilities. I’d love to hear your thoughts, feedback, or any ideas to expand on this concept!
Working with readers that are either single merged feature types or datasets that do not expose attributes, e.g. CAD, XML, JSON or OSM PBF, as an author, I’d like to have as an option to be able to see attributes that are imported in the AttributeExposer to show the number of features that have values..Adding an option to show null, missing and empty values would be a nice feature too. When I import from cache, it would be nice to see the attribute value stats when Importing attributes. There’s room for columns to show the count for all.With this feature, the author can pick attributes that have values, keeping the destination cleaner. AI Assist recommends a Tester transformer plus counter to do this.The community hub transformer AttributeValueCounter was a good option to use for this but it does not work in FME ENGINE 2025.0.x. Just some random ideas, when Feature Cache is enabled for a workspace, during a translation run a background process can calculate the stats on the source feature types initially. Perhaps add that to the reading of the schema when a reader is configured to be added to the canvas.Get the unexposed attribute stats immediately optionally. Maybe just have a checkbox on the Import from Feature Cache or Import from Dataset dialog to allow the option.
Today we did an upgrade to FME Flow 2025.1. Unfortunately we noticed it is not possible to have a clean start (completely back to default) of FME Flow, without doing a new installation. Of course there is an option of overwrite items via a 'clean' back up file (.fsconfig). However, folders already present in FME Flow will not be overwritten if they are not present in the clean back up file. See also this link.It would be nice to have something like a 'clean up button' to restore the FME Flow default without a complete new installation. Is that possible?
Hi,in the record information you can nicely see data types (uint8, buffer, date, time, varchar) for all available attributes when selecting an individual feature: However, when accessing these information in Python we only get the basic types: Would it be possible to also get the full list of data types in Python?In my current use case, I’m supposed to convert all date attributes to another format. Since it’s a generic workflow, I don’t know in advance which attributes are containing dates. When looking at the schema feature I can see that the data types have been recognized correctly by FME. There is just no way easy way to get that information for each feature.Kind regards,Dennis
Reading a OGC API - Features (WFS 3.0) currently always returns data in WGS84 no matter what storage coordinate systems are available.It would be nice if you could choose to return data in the default storage coordinate system. Or failing that, the crs parameter should be available in the collection query parameter drop down
Users in my organization use SSO through SAML in Microsoft Entra to log into our FME Flow server. Disabling the fmeserver.saml.authentication.force setting (as documented under the FME Flow Settings section of SAML Configuration) allows login to be a single click experience on our FME Flow server. This setting allows the web browser to provide the login credential for the user’s Windows session on their computer to authenticate through the SAML connection. There should be a similar configuration option (or default configuration change) in FME Workbench in FME Form to allow us to let users have a single click login experience when they connect FME Workbench to our FME Flow server. I suspect that FME Workbench uses the WebView2 control to present a browser window to follow the authentication process when the user connects to FME Flow from FME Workbench through a SAML connection. Microsoft has documented a setting that can be enabled on the WebView2 control to allow passthrough of the Windows account credentials when authenticating through SAML called AllowSingleSignOnUsingOSPrimaryAccount (as documented at CoreWebView2EnvironmentOptions.AllowSingleSignOnUsingOSPrimaryAccount Property (Microsoft.Web.WebView2.Core) | Microsoft Learn). With our recent upgrade to FME Form/Flow 2025.1, we want to be able to share our web connections hosted on our FME Flow instance to all users of FME Form. This would be simplified if the SAML login process in FME Flow were a single click (because FME Form is forcing reauthenticating the SAML connection to FME Flow every time we open FME Form). Please allow us to configure (or change the default configuration for) FME Workbench to allow authentication through a SAML connection to our FME Flow server to be a single click experience like how we have FME Flow configured.
Most of my workflows have a lot of scripted parameters with various amounts of boilerplate code just to deal with handling defaults and fallbacks.The parameters with uppercase names are all scriptedThis is all just to handle both cases in the simple scenario where “Recent bound (current time if empty)” is, as the name implies, optionnal and defaulting to the moment where the script was launched if left empty. If I was dealing with attributes, it would literally just be this: I believe we should have a type of scripted parameter that allows using the same facilities and functions for parameters as we do with attributes, especially conditionnal values. This would make using default values and dropdown-presets, as well as format uniformization a lot easier to encode in the parameters without having to include (which often means duplicate) that logic in the transformers, or having to use Python scripts (which do not generate log files if they raise any errors) for simple operations like this.
At the moment the VoronoiDiagrammer only calculates distance "as the crow flies" this may not produce the best results. By taking land boundaries into account the transformer would produce much better results for land-based travel.
With the advent of Data Virtualization it seems like a good time to improve JSON support in FME!It would be great if there was a simple way to convert a feature to JSON format so that all attributes on the feature become elements in the JSON object and all lists items are converted to arrays in the JSON object.The result should be output to an attribute.I know that it’s possible to do this currently using the JSONTemplater but it requires manually building the template and it would be nice to avoid that. Just do whatever cleanup of attributes is required using the AttributeManager, then have FME convert to JSON without requiring extra input. The JSONTemplater can still be used for more complex cases.(I know that it’s also possible to use the JSON writer to create a simple file from a feature but this does not support lists/arrays and requires writing out to the filesystem.)
Often when I am analysing data with the statistic calculator I only want the summary statistics, but have to wait quite some time for the complete output to run before I can see the results.It would be useful to be able to turn off the Complete port, similar to the Cumulative port
When testing/debugging unexpected behaviour on FME Flow one of the first steps is to use FME Form to run it locally and take advantage of the sweet sweet feature caching.In some cases there can be quite a few parameters which need to be copied which is a real pain. A feature which would save a lot of time is to be able to auto populate the local settings of a workspace based on a FME Flow job.
It seems a lot of users are taking to using FeatureReader and FearureWriters in place of traditional readers and writers.I wonder if it’s time to give these two transformers a new color to help them stand out?
Please bring back FME wall of fame as it is very useful to demo skillset
I believe this was mentioned in a webinar, but being able to import an endpoint’s schema - from the dataset it is going to be accessing of course - without having to manually create every property would be a great thing to have. This could be from a local file, cloud source, anything. Beyond saving time, it should also ensure accuracy.
Database and Web Connections should be shared via FME Flow in the same way as Deployment Parameters.Doing so would not only be more intuitive, but it would provide more granular security since each user or role can be granted access to only the connections appropriate for that user or role.The process described below is unintuitive because is not consistent with Deployment Parameters, and it would be tedious to set up multiple “Shared Database” of connections each with different permissions.https://support.safe.com/hc/en-us/articles/25407698305037-Sharing-Database-and-Web-Connections-in-FME-Form
There are many transformers that have a tolerance parameter (ex Clipper, Dissolver). While they default to automatic calculated tolerance, for features in lat/long, that generally works out to something like 8.800000000000009e-14. It would be nice if we could set the tolerance on the workspace, and any transformer that does not have an explicit tolerance set would then use the workspace tolerance and not something sub-atomic.
***Note from Migration:*** Original Title was: A transformer, which combines AttributeValueMapper, AttributeRangeMapper and TestFilter I'm using AttributeValueMapper, AttributeRangeMapper and TestFilter quite a lot, but I'm missing a transformer, which would have similar interface as AttributeValueMapper, but you could set a Test Condition to the source value like in TestFilter. If the value of the source attribute passes the test condition, the destination attribute would get the value from the corresponding Destination Value.At the moment I usually have a TestFilter with AttributeCreators for each output port of the TestFilter. AttributeValueMapper is a good tool, but lacks the capabilities of TestFilter.Of course I could use Conditional Values in e.g. AttributeManager, but they're always hidden inside the transformer.
For example, if I have three or four different contractions of a month name and I want them all to map to ‘September’ - supporting logic from Tester like regex, ‘begins with’ or suchlike would be useful.
When working with a datasets that have a large number of attributes, it would be great to have some enhanced selecting/filtering in the Reader/Writer Attributes tab. For example, starts with, ends with, contains, other tests - like contains x but does not start with y. Regex support would be good, but something like a Tester merged into the R/W could also work. Thanks.
Migration Note: Idea originally posted on 2016-04-20
Proposal:Introduce FME AI Assist as a built-in assistant on the FME Community forum. This AI would provide instant, context-aware replies to forum questions, helping users get faster support and surfacing relevant documentation, tutorials, or workspace examples.Key Features:Auto-suggest answers based on existing Knowledge Center articles and forum history. Summarize long threads or highlight accepted solutions. Offer workspace-building tips or transformer suggestions. Learn from user feedback to improve over time.Avatar Suggestion:Let’s give it a friendly face! I propose using Zipster—the beloved FME mascot—as the avatar for FME AI Assist. Zipster already represents creativity and problem-solving, making it the perfect symbol for this helpful assistant.
For the HTTPCaller, the certificate authentication was introduced. (see )Is it possible to add it to the RabbitMQ Hub Transformer as well?
There are times where I would like to store more complex data as a parameter, I have resorted to json or comma-separated text (multi-line text parameter). This is mostly private parameters that specify data source, configuration or possible some initial data. In nature this data is tabular, most of the time.Maintaining json/text in a parameter dialog today is cludgy Getting the data into features requires multiple transformersNow, wouldn’t it be nice with a table parameter type? Using a parameterfetcher could possible read the table into a list, that one can just explode/expose. Maintaining the data in a dialog set for a table would also be easier… !
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