What to Ask Leina to Do with HoneyHive
Copy a prompt, add your details, and send it to Leina in your connected team chat.
Create a named dataset and insert supplied examples with a field mapping.
Create HoneyHive dataset [name] with description [description], then add these JSON examples [data], mapping [fields] to inputs, [fields] to history, and [fields] to expected answers.
Get Started with HoneyHive
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Connect Your Account
Connect a project-scoped HoneyHive API key from Settings > Project > API Keys.
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Try a Task
Tell Leina what you need in your team chat. Include the source, destination, and expected result.
Set Up Leina in Your Chat → -
Check the Result
Review the result and any changes in the connected app.
Supported HoneyHive Capabilities
This HoneyHive connection manages project datasets and datapoint associations. It can create datapoints from JSON examples but does not run evaluations or read execution traces.
- Dataset Management
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Find datasets by exact name or ID, create or edit them, and permanently delete selected datasets.
- List Datasets
- Create Dataset
- Datapoint Membership
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Insert mapped examples and remove a selected datapoint’s association with a dataset.
- Add Datapoints
- Remove Datapoint
View All 6 Actions
- List Datasets
- Create Dataset
- Update Dataset
- Delete Dataset
- Add Datapoints
- Remove Datapoint
Frequently Asked Questions
What mapping do my examples need?
Identify source field names for inputs, conversation history, and ground-truth answers. The mapping places those fields in the corresponding datapoint sections.
Does removing an example delete it?
Removing a datapoint association detaches it from the selected dataset without deleting the datapoint itself.
Can I run an evaluation here?
These actions manage datasets and examples. Evaluation runs and trace inspection are not exposed by this connection.
Your AI employee for HoneyHive
Tell Leina what you need in your team chat. Include the source, destination, and expected result.