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3 dépôts

Awesome GitHub RepositoriesPipeline Orchestration

Systems for defining, chaining, and managing automated workflows and document processing tasks.

Distinguishing note: No existing candidates provided; this captures the core workflow automation capability.

Explore 3 awesome GitHub repositories matching devops & infrastructure · Pipeline Orchestration. Refine with filters or upvote what's useful.

Awesome Pipeline Orchestration GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • datalab-to/markerAvatar de datalab-to

    datalab-to/marker

    36,137Voir sur GitHub↗

    Marker is a comprehensive document processing platform designed to automate the conversion, extraction, and structuring of data from complex files. It functions as an orchestration engine that chains modular processing steps into versioned, reusable pipelines, allowing organizations to standardize document handling and automate repetitive business tasks at scale. The platform distinguishes itself through its support for secure, private infrastructure deployment, enabling users to run containerized services within their own environments to maintain strict data privacy. It features specialized

    The platform enables the creation of versioned and reusable configurations by chaining document processors to manage production deployments and iterative workflow updates.

    Python
    Voir sur GitHub↗36,137
  • datalab-to/suryaAvatar de datalab-to

    datalab-to/surya

    20,889Voir sur GitHub↗

    Surya is a document processing platform designed to transform unstructured files into structured, machine-readable data. It provides a comprehensive suite of tools for text recognition, layout analysis, and reading order detection, enabling the conversion of PDFs and images into formats such as JSON, HTML, or markdown. The platform is built to handle complex document workflows, offering capabilities for data extraction, document segmentation, and automated form completion. The platform distinguishes itself through a robust pipeline-based architecture that allows users to chain analysis tasks

    Connects multiple document processing tasks into versioned and reusable pipelines for complex extraction.

    Python
    Voir sur GitHub↗20,889
  • vibrantlabsai/ragasAvatar de vibrantlabsai

    vibrantlabsai/ragas

    12,659Voir sur GitHub↗

    Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

    Executes automated test runs by coordinating the interaction between target applications and defined performance metrics.

    Pythonevaluationllmllmops
    Voir sur GitHub↗12,659
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