TxtAI MCP Server

# Audio Stream   The Audio Stream pipeline is a threaded pipeline that plays audio segments. This pipeline is designed to run on local machines given that it requires access to write to an output device. ## Example The following shows a simple example using this pipeline. ```python from txtai.pipeline import AudioStream # Create and run pipeline audio = AudioStream() audio(data) ``` This pipeline may require additional system dependencies. See [this section](../../../install#environment-specific-prerequisites) for more. See the link below for a more detailed example. | Notebook | Description | | |:----------|:-------------|------:| | [Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) [▶️](https://www.youtube.com/watch?v=tH8QWwkVMKA) | Full cycle speech to speech workflow with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/65_Speech_to_Speech_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 audiostream: # Run pipeline with workflow workflow: audiostream: tasks: - action: audiostream ``` ### Run with Workflows ```python from txtai import Application # Create and run pipeline with workflow app = Application("config.yml") list(app.workflow("audiostream", [["numpy data", "sample rate"]])) ``` ### 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":"audiostream", "elements":[["numpy data", "sample rate"]]}' ``` ## Methods Python documentation for the pipeline. ### ::: txtai.pipeline.AudioStream.__init__ ### ::: txtai.pipeline.AudioStream.__call__