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katanaml/sparrow

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5,162 星标·517 分支·Python·GPL-3.0·14 次浏览sparrow.katanaml.io↗

Sparrow

Sparrow 是一个 LLM 文档提取平台和基于视觉的推理引擎,旨在将图像和 PDF 转换为经过验证的结构化数据。它作为代理工作流编排器,将分类、提取和验证任务串联成多步流水线。

该系统的特色在于其后端无关的推理层,可管理本地 GPU、Apple Silicon 和云服务商上的模型。它利用基于坐标的视觉定位将提取的文本映射到精确的边界框坐标,并使用基于提示的模型引导来引导注意力并规范化数据格式。

该平台涵盖了文档智能工作流,包括用于保持结构完整性的专业图像表格处理,以及用于验证提取字段正确性的模式驱动验证。它还提供了一个用于监控 API 性能、使用分析和系统健康状况的文档分析仪表板。

架构包含一个基于插件的扩展系统,用于集成索引和编排中使用的第三方库。

Features

  • Intelligent Document Processing - Provides a platform for intelligent document processing, combining classification, extraction, and validation into multi-step pipelines.
  • Vision-Language Model Backends - Uses vision-capable language models to parse document layouts and convert visual content into structured data.
  • Agentic Workflow Pipelines - Implements automated pipelines that chain LLM instructions and external tools for complex document analysis and error recovery.
  • Hardware Acceleration Backends - Manages extraction pipelines across diverse hardware accelerators including local and cloud-based backends.
  • Image Text Extractions - Recognizes and extracts text and key-value pairs from images and PDFs as structured data.
  • Vision-Language Orchestrators - Manages model inference across local GPUs, Apple Silicon, and cloud providers to process visual document data.
  • Hardware-Agnostic Inference Layers - Provides a hardware-agnostic inference layer that routes processing to local GPUs, Apple Silicon, or cloud providers.
  • Structured Document Extraction - Converts visual document layouts into machine-readable structured formats using vision-capable models.
  • Vision-Language Inference - Implements a vision-language inference engine that executes multimodal models across various hardware backends.
  • Workflow Orchestration - Combines document classification and data extraction into a single AI workflow pipeline with visual monitoring.
  • Document Field Validations - Checks extracted document fields against schemas to verify the presence and correctness of required data.
  • Structured Data Extraction - Parses complex tables and text from documents into predefined schemas with bounding box coordinate mapping.
  • PDF Coordinate Extraction - Extracts precise bounding box coordinates for recognized text regions within PDF pages.
  • Agentic Workflow Orchestrators - Functions as an orchestrator that chains LLM classification, extraction, and validation tasks with integrated error recovery.
  • Pipeline Orchestrators - Chains classification, extraction, and validation tasks into sequenced pipelines with error recovery.
  • Schema-Driven Validations - Verifies extracted document fields against predefined structural definitions to ensure data correctness.
  • Extraction Coordinate Annotations - Generates bounding box coordinates for extracted elements to provide visual grounding for the data.
  • Visual Coordinate Mapping - Maps extracted text to precise bounding box coordinates for visual grounding within documents.
  • Tabular Data Extraction - Extracts complex tabular data from documents while maintaining structural integrity through specialized vision processing.
  • OCR Document Conversion - Converts images and PDFs into validated structured data using vision models and schema-based validation.
  • Local Model Backends - Supports running inference across a variety of backends including local GPUs, Apple Silicon, and cloud providers.
  • Attention Steering Hints - Uses hint-based configuration files to steer model attention and normalize extracted data formats.
  • Extraction Hinting - Uses hint-based model steering to guide attention and normalize data formats during the extraction process.
  • Document Processing Pipelines - Extracts and analyzes data across documents containing multiple pages using orchestrated pipelines.
  • Table Structure Detections - Identifies tabular grids and merges cells within document layouts to crop them for specialized inference.
  • Image-Based Table Extractors - Identifies tabular regions and crops them into images for specialized inference to preserve structural integrity.
  • Document Table Extractors - Maps large or multi-column tables from documents to structured schemas using an intermediate processing pipeline.
  • Document Analysis Dashboards - Provides a visual dashboard for monitoring API performance, usage analytics, and the operational health of extraction pipelines.
  • Data Processing - Solution for efficient data extraction from documents and images.
  • Data Processing Tools - Solution for efficient data extraction from documents and images.

Star 历史

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Sparrow 的开源替代方案

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查看 Sparrow 的所有 30 个替代方案→

常见问题解答

katanaml/sparrow 是做什么的?

Sparrow 是一个 LLM 文档提取平台和基于视觉的推理引擎,旨在将图像和 PDF 转换为经过验证的结构化数据。它作为代理工作流编排器,将分类、提取和验证任务串联成多步流水线。

katanaml/sparrow 的主要功能有哪些?

katanaml/sparrow 的主要功能包括:Intelligent Document Processing, Vision-Language Model Backends, Agentic Workflow Pipelines, Hardware Acceleration Backends, Image Text Extractions, Vision-Language Orchestrators, Hardware-Agnostic Inference Layers, Structured Document Extraction。

katanaml/sparrow 有哪些开源替代品?

katanaml/sparrow 的开源替代品包括: datalab-to/chandra — sChandra is a document processing platform that converts images, PDFs, Word documents, spreadsheets, and other formats… kreuzberg-dev/kreuzberg — Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into… bytedance/dolphin — Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital… chonkie-inc/chonkie — Chonkie is a text chunking library designed for retrieval-augmented generation pipelines. It functions as a semantic… run-llama/liteparse — A fast, helpful, and open-source document parser. getomni-ai/zerox — Zerox is a multimodal document parser and OCR tool that uses vision models to convert PDF files and images into…