TxtAI MCP Server

# Textractor   The Textractor pipeline extracts and splits text from documents. This pipeline extends the [Segmentation](../segmentation) pipeline. Each document goes through the following process. - Content is retrieved if it's not local - If the document `mime-type` isn't plain text or HTML, it's converted to HTML via the [FiletoHTML](../filetohtml) pipeline - HTML is converted to Markdown via the [HTMLToMarkdown](../htmltomd) pipeline - Content is split/chunked based on the [segmentation parameters](../segmentation/#txtai.pipeline.Segmentation.__init__) and returned The [backend](../filetohtml/#txtai.pipeline.FileToHTML.__init__) parameter sets the FileToHTML pipeline backend. If a backend isn't available, this pipeline assumes input is HTML content and only converts it to Markdown. See the [FiletoHTML](../filetohtml) and [HTMLToMarkdown](../htmltomd) pipelines to learn more on the dependencies necessary for each of those pipelines. ## Example The following shows a simple example using this pipeline. ```python from txtai.pipeline import Textractor # Create and run pipeline textract = Textractor() textract("https://github.com/neuml/txtai") ``` See the link below for a more detailed example. | Notebook | Description | | |:----------|:-------------|------:| | [Extract text from documents](https://github.com/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | Extract text from PDF, Office, HTML and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | | [Chunking your data for RAG](https://github.com/neuml/txtai/blob/master/examples/73_Chunking_your_data_for_RAG.ipynb) | Extract, chunk and index content for effective retrieval | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/73_Chunking_your_data_for_RAG.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 textractor: # Run pipeline with workflow workflow: textract: tasks: - action: textractor ``` ### Run with Workflows ```python from txtai import Application # Create and run pipeline with workflow app = Application("config.yml") list(app.workflow("textract", ["https://github.com/neuml/txtai"])) ``` ### 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":"textract", "elements":["https://github.com/neuml/txtai"]}' ``` ## Methods Python documentation for the pipeline. ### ::: txtai.pipeline.Textractor.__init__ ### ::: txtai.pipeline.Textractor.__call__