TxtAI MCP Server

# Tabular   The Tabular pipeline splits tabular data into rows and columns. The tabular pipeline is most useful in creating (id, text, tag) tuples to load into Embedding indexes. ## Example The following shows a simple example using this pipeline. ```python from txtai.pipeline import Tabular # Create and run pipeline tabular = Tabular("id", ["text"]) tabular("path to csv file") ``` See the link below for a more detailed example. | Notebook | Description | | |:----------|:-------------|------:| | [Transform tabular data with composable workflows](https://github.com/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | Transform, index and search tabular data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | ## 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 tabular: idcolumn: id textcolumns: - text # Run pipeline with workflow workflow: tabular: tasks: - action: tabular ``` ### Run with Workflows ```python from txtai import Application # Create and run pipeline with workflow app = Application("config.yml") list(app.workflow("tabular", ["path to csv file"])) ``` ### 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":"tabular", "elements":["path to csv file"]}' ``` ## Methods Python documentation for the pipeline. ### ::: txtai.pipeline.Tabular.__init__ ### ::: txtai.pipeline.Tabular.__call__