(Beta) Visualize Datasets with Streaming
Visualize your datasets with real-time progress updates via Server-Sent Events
Notes
- To authenticate, you’ll need a Tako API key. It’s best practice to store it as an environment variable to avoid hardcoding sensitive credentials in your code.
- This endpoint returns a Server-Sent Events (SSE) stream, providing real-time progress updates during the visualization pipeline execution.
- The stream will emit
progressevents during processing and a finalresultevent with the complete visualization data.
Example Usage
When to Use Streaming vs Regular Endpoint
Use the streaming endpoint when:- You want to show progress indicators to users
- Processing large datasets that may take time
- Building interactive UIs that need real-time feedback
/v1/beta/visualize endpoint when:
- You only need the final result
- Building batch processing workflows
- Integrating with systems that don’t support streaming
Authorizations
Body
List of serialized CSV strings to visualize
The file id of the dataset to visualize. This is deprecated and will be removed in the future. Use file_ids instead.
Query with instructions to visualize the dataset
Whether to return debug information
The file ids of the datasets to visualize
The type of visualization component to use
bar, grouped_bar, stacked_bar, timeseries, area_timeseries, stacked_area_timeseries, pie, choropleth, map, scatter, boxplot, heatmap, timeline, waterfall, histogram, table, treemap Settings for controlling outputs of the knowledge search request
The model to use for the visualization
o3, o4-mini, gpt-5-mini, gpt-5-nano, llama-3.3-70b, gpt-oss-120b, zai-glm-4.6, zai-glm-4.7 The segment id of the dataset to visualize
The connected private index id of the dataset to visualize
Extra parameters to pass to the visualization
Response
Server-Sent Events stream with progress updates and final results
SSE stream with event types: progress (pipeline updates), result (final data)