wehub-resource-sync
70 lines
1.9 KiB
Markdown
70 lines
1.9 KiB
Markdown
# Segmentation
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The Segmentation pipeline segments text into semantic units.
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## Example
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The following shows a simple example using this pipeline.
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```python
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from txtai.pipeline import Segmentation
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# Create and run pipeline
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segment = Segmentation(sentences=True)
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segment("This is a test. And another test.")
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# Load third-party chunkers
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segment = Segmentation(chunker="semantic")
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segment("This is a test. And another test.")
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```
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The Segmentation pipeline supports segmenting `sentences`, `lines`, `paragraphs` and `sections` using a rules-based approach. Each of these modes can be set when creating the pipeline. Third-party chunkers are also supported via the `chunker` parameter.
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## Configuration-driven example
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Pipelines are run with Python or configuration. Pipelines can be instantiated in [configuration](../../../api/configuration/#pipeline) using the lower case name of the pipeline. Configuration-driven pipelines are run with [workflows](../../../workflow/#configuration-driven-example) or the [API](../../../api#local-instance).
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### config.yml
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```yaml
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# Create pipeline using lower case class name
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segmentation:
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sentences: true
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# Run pipeline with workflow
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workflow:
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segment:
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tasks:
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- action: segmentation
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```
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### Run with Workflows
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```python
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from txtai import Application
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# Create and run pipeline with workflow
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app = Application("config.yml")
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list(app.workflow("segment", ["This is a test. And another test."]))
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```
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### Run with API
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```bash
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CONFIG=config.yml uvicorn "txtai.api:app" &
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curl \
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-X POST "http://localhost:8000/workflow" \
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-H "Content-Type: application/json" \
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-d '{"name":"segment", "elements":["This is a test. And another test."]}'
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```
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## Methods
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Python documentation for the pipeline.
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### ::: txtai.pipeline.Segmentation.__init__
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### ::: txtai.pipeline.Segmentation.__call__
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