TxtAI MCP Server

# Segmentation   The Segmentation pipeline segments text into semantic units. ## Example The following shows a simple example using this pipeline. ```python from txtai.pipeline import Segmentation # Create and run pipeline segment = Segmentation(sentences=True) segment("This is a test. And another test.") # Load third-party chunkers segment = Segmentation(chunker="semantic") segment("This is a test. And another test.") ``` 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. ## Configuration-driven example 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). ### config.yml ```yaml # Create pipeline using lower case class name segmentation: sentences: true # Run pipeline with workflow workflow: segment: tasks: - action: segmentation ``` ### Run with Workflows ```python from txtai import Application # Create and run pipeline with workflow app = Application("config.yml") list(app.workflow("segment", ["This is a test. And another test."])) ``` ### Run with API ```bash CONFIG=config.yml uvicorn "txtai.api:app" & curl \ -X POST "http://localhost:8000/workflow" \ -H "Content-Type: application/json" \ -d '{"name":"segment", "elements":["This is a test. And another test."]}' ``` ## Methods Python documentation for the pipeline. ### ::: txtai.pipeline.Segmentation.__init__ ### ::: txtai.pipeline.Segmentation.__call__