**Why it's important:** Every action in Catalog—from filtering to labeling—is performed on one or more data rows. | | Datasets | **What it is:** A *dataset* is a top-level container that holds a collection of data rows, often grouped by a specific project, data source, or collection period. You organize your data into datasets when you upload it to Labelbox.
Why it's important: Datasets provide the initial organization for your data and serve as the starting point for exploration in Catalog. | | Slices | **What it is:** A *slice* is a dynamic, saved query that represents a subset of your data. Think of it as a "smart folder" or a saved search that always stays up-to-date.
**Why it's important:** Slices let you continuously monitor specific subsets of your data without re-applying filters. When new data is added to the dataset that matches the slice's filters, it automatically appears in the slice. This is perfect for tracking data quality issues or monitoring for specific edge cases. | | Batches | **What it is:** A *batch* is a static, fixed group of data rows that you can send to a labeling project.
**Why it's important:** Batches are the primary mechanism for queuing up work for human labelers. Once created, a batch does not change unless you manually add or remove items, providing a stable workload for your labeling projects. | | Embeddings | \*\*What it is: \*\**Embeddings* are powerful numerical representations (vectors) of your data that capture its semantic meaning. They are the engine behind Catalog's AI-powered search features. Labelbox can generate these for you, or you can provide your own.
**Why it's important:** Embeddings allow you to search for data based on meaning and similarity, not just metadata. They power features like **similarity search** ("find more images like this one") and **natural language search** ("find images of a dog playing in a park"), making it possible to find relevant data in a more intuitive and powerful way. | ## Gallery view The gallery view is the default, providing a visual grid of your data. It's designed for rapid visual scanning and exploration.
Click the **Download** button to save the results directly to your computer. **Important:** Don't close your browser until the download is complete, or the file will be incomplete. | | Python SDK script | For large datasets, it's better to download using a Python script. The download panel will provide you with a sample script you can copy. This allows the download to happen in the background, so you don't have to worry about closing your browser. You'll need a Labelbox API key to run the script.
Click the **Code sample** button to view a sample Python script to download your export results based on the selected options. | # Find text Source: https://docs.labelbox.com/docs/find-text Search for text and uncover assets in Catalog. When working with text data such as [text](/reference/import-text-annotations), [conversational text](/reference/export-conversational-text-annotations), [documents](/reference/import-document-annotations), or [HTML](/docs/html-editor), you can use the **find text** filter in Catalog to surface data rows that contain specific keywords.
**Action:** Ensure the file is not corrupt and meets format requirements. | | FetchFailed | The data row URL could not be reached.
**Action:** Check that the URL is correct and that the Labelbox backend has access. | | FetchTimeout | The data row could not be fetched due to a timeout error.
**Action**: Re-process the data rows. | | Forbidden | Access to the URL was denied.
**Action:** Check your cloud storage permissions or pre-signed URL validity. | | InternalError | An internal error prevented the data row from being processed.
**Action**: Re-process the data rows. | | InvalidEspg | The data row has invalid ESPG metadata. | | NotFound | The file does not exist at the specified URL.
**Action:** Verify the URL is correct. | | TooManyBands | The geospatial data row has too many bands. | | TooManyRequests | The data row could not be fetched due to a rate-limiting error. | ## Warnings A warning indicates a non-critical issue. The data row can be used, but your experience may be degraded. | Warning | Description & action | | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | InvalidCors | The file is served with invalid CORS headers, which may impact the labeling experience.
**Action:** Check that the URL is being served with the correct CORS headers. |
Give your ontology a descriptive name (e.g., "Vehicle Identification - US Roads"). | | Objects | *Objects* in this context are specific items you want to identify and annotate in your data (e.g., *car*, *person*, *building*) Labelbox will search for existing features by name to encourage reuse.
Click **+ Add** to create or reuse object features. | | Classifications | *Classifications* are questions or attributes used to categorize or describe objets or the overall data (e.g., *Is the traffic light red or green?*, *What is the weather condition?*).
Click **+ Add** to create or reuse classification tasks. | | Relationships | *Relationships* are connections or interactions that exist between different labeled objects (e.g., a *driver* is *inside* a *car*).
Click **+ Add** to define relationships between your objects. | 5. Click **Create ontology** to complete the setup.
![](\"https://storage.googleapis.com/labelbox-datasets/image_sample_data/a-stunning-high-resolution-image-of-a-vibrant-red--FKZ_pkdaRDKZxQC1hJ45yw-9OoHdU5OQzyhpL-cwSz1aw.jpeg\")
",
"modelConfigName": "ideogram_image_1"
},
{
"title": "Image 2",
"content": "![](\"https://storage.googleapis.com/labelbox-datasets/image_sample_data/a-stunning-close-up-image-of-a-vibrant-red-tulip-t-mqx4mXo_S5ubGn7kU-oxJA-9OoHdU5OQzyhpL-cwSz1aw.jpeg\")
",
"modelConfigName": "ideogram_image_2"
}
]
}
```
 # Import HTML annotations Source: https://docs.labelbox.com/reference/import-html-annotations Developer guide for importing annotations on HTML data and sample import formats.
Get to production AI faster
Save time by creating and managing your training data, people, and processes in a single place — so you can focus on building the next big thing.
```
-
## Create multimodal chat evaluation projects
The two types of multimodal chat evaluation projects have different project creation methods and data row setups:
* For **offline** multimodal chat evaluation projects, use `create_offline_model_evaluation_project` and import data rows of existing conversations.
* For **live** multimodal chat evaluation projects, use `client.create_model_evaluation_project` and either:
* (Recommended) Create data rows and send them to projects, like other types of projects.
* Generate empty data rows upon project creation, which can't create data rows with attachments and metadata.
## Set up offline multimodal chat evaluation projects
Use `client.create_offline_model_evaluation_project` to create offline multimodal chat evaluation projects. This method takes the same parameters as `client.create_project`: a `name` and an optional `description`.
-
## Create response creation projects
You can create response creation projects using `client.create_response_creation_project`, which uses the same parameters as `client.create_project` but provides better validation to ensure the project is set up correctly. Additionally, you need to import text data rows to be used as prompts.