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Awesome GitHub RepositoriesDocument and Unstructured Extraction

Automated processes for parsing unstructured text, documents, or web content into structured, machine-readable formats.

Explore 53 awesome GitHub repositories matching data & databases · Document and Unstructured Extraction. Refine with filters or upvote what's useful.

Awesome Document and Unstructured Extraction GitHub Repositories

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  • unclecode/crawl4aiunclecode 的头像

    unclecode/crawl4ai

    68,644在 GitHub 上查看↗

    Crawl4AI is an AI-powered web crawling and data extraction engine designed to transform complex web content into structured formats. It functions as a headless browser orchestrator, enabling the navigation of dynamic websites, the execution of custom scripts, and the capture of visual assets like screenshots and PDFs. By integrating language models directly into the extraction workflow, the system converts raw HTML into clean, structured data or Markdown files optimized for downstream ingestion. The platform distinguishes itself through a distributed, self-hosted infrastructure that manages l

    Maps unstructured web content into predefined data structures using automated path selection or intelligent language model analysis.

    Python
    在 GitHub 上查看↗68,644
  • docling-project/doclingdocling-project 的头像

    docling-project/docling

    61,674在 GitHub 上查看↗

    Docling is a modular framework designed for document parsing, layout analysis, and structured data extraction. It transforms unstructured files and web content into a unified, hierarchical data model that preserves the spatial and semantic relationships between text, tables, images, and layout elements. By normalizing diverse input formats into a consistent internal representation, the library enables uniform processing across various document types. The project distinguishes itself through a schema-driven approach that maps document regions to strongly-typed objects, ensuring data accuracy t

    Defines specific input types and file formats to ensure that documents are processed according to custom requirements.

    Pythonaiconvertdocument-parser
    在 GitHub 上查看↗61,674
  • embedchain/embedchainembedchain 的头像

    embedchain/embedchain

    58,769在 GitHub 上查看↗

    Embedchain is an LLM memory management framework and RAG orchestration engine designed to provide AI agents with a persistent storage layer. It functions as a long-term memory pipeline that extracts facts from unstructured interactions and stores them as permanent knowledge base entries to retain user preferences and interaction history across sessions. The system employs a hybrid vector database interface that combines semantic embeddings with traditional keyword search. It utilizes an entity-linking knowledge graph to connect related information points and applies temporal ranking to distin

    Provides a pipeline to process unstructured interactions and isolate confirmed facts as permanent long-term memory entries.

    Python
    在 GitHub 上查看↗58,769
  • imartinez/privategptimartinez 的头像

    imartinez/privateGPT

    57,281在 GitHub 上查看↗

    PrivateGPT is a private AI document assistant and local knowledge base manager designed for querying private files and documents using retrieval-augmented generation. It functions as a local language model application and API gateway, allowing users to obtain cited answers from unstructured data without sending information to external servers. The system differentiates itself by acting as a tool integrator that connects language models to external functions, including web search, tabular data analysis, and custom action extensions. It provides a standardized API layer that allows local infere

    Parses various file formats and transforms unstructured text into machine-readable formats for local indexing.

    Python
    在 GitHub 上查看↗57,281
  • soxoj/maigretsoxoj 的头像

    soxoj/maigret

    33,154在 GitHub 上查看↗

    Maigret is an open-source intelligence framework designed for automated digital footprint discovery and identity investigation. It functions as a search engine that aggregates profile metadata by querying thousands of websites for specific usernames, mapping an individual's online presence across diverse platforms. The tool distinguishes itself through recursive discovery capabilities, which identify links within discovered profiles to expand the scope of an investigation automatically. It supports cross-platform identity correlation by mapping disparate accounts and pseudonymous personas, in

    Custom parsing logic maps unstructured HTML and API responses into a unified data format for consistent cross-platform analysis.

    Pythonblueteamclicybersecurity
    在 GitHub 上查看↗33,154
  • supermemoryai/supermemorysupermemoryai 的头像

    supermemoryai/supermemory

    27,334在 GitHub 上查看↗

    Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity s

    Parses unstructured text into individual, standalone data points to ensure information is stored in a granular, searchable format.

    TypeScriptcloudflare-kvcloudflare-pagescloudflare-workers
    在 GitHub 上查看↗27,334
  • cinnamon/kotaemonCinnamon 的头像

    Cinnamon/kotaemon

    25,139在 GitHub 上查看↗

    Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

    Extracts text content from various unstructured file formats including office documents, images, and emails.

    Pythonchatbotllmsopen-source
    在 GitHub 上查看↗25,139
  • openai/chatgpt-retrieval-pluginopenai 的头像

    openai/chatgpt-retrieval-plugin

    21,192在 GitHub 上查看↗

    This project is a retrieval-augmented generation pipeline designed for building custom ChatGPT plugins that allow language models to query private or professional documents. It implements a full retrieval workflow, from processing and indexing document chunks to retrieving relevant context for natural language queries. The system distinguishes itself through a hybrid retrieval approach that combines dense vector embeddings with sparse keyword matching, further refined by a two-stage semantic re-ranking process. It includes specialized data privacy tools for screening personally identifiable i

    Parses key information like authors and dates from unstructured text using a model to return structured JSON.

    Pythonchatgptchatgpt-plugins
    在 GitHub 上查看↗21,192
  • datalab-to/suryadatalab-to 的头像

    datalab-to/surya

    20,889在 GitHub 上查看↗

    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

    Transforms unstructured documents like PDFs and images into structured machine-readable formats for business pipelines.

    Python
    在 GitHub 上查看↗20,889
  • camel-ai/camelcamel-ai 的头像

    camel-ai/camel

    17,253在 GitHub 上查看↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Parses complex documents and images using OCR to convert unstructured files into machine-readable formats.

    Pythonagentai-societiesartificial-intelligence
    在 GitHub 上查看↗17,253
  • h4ckf0r0day/obscurah4ckf0r0day 的头像

    h4ckf0r0day/obscura

    16,110在 GitHub 上查看↗

    Obscura is a web scraping infrastructure and headless browser server designed for AI agents. It provides a system for AI models to control browser sessions, interact with websites, and extract web data using a WebSocket implementation of the Chrome DevTools Protocol. The project focuses on bot detection evasion by randomizing browser fingerprints, masking native functions, and blocking tracking scripts to mimic human behavior. It further secures identities through a traffic layer that routes network requests via HTTP or SOCKS5 proxies. The system supports large-scale data extraction through

    Transforms structured HTML trees into flattened markdown to optimize token usage for large language models.

    Rustantidetectantidetect-browserbrowser
    在 GitHub 上查看↗16,110
  • langbot-app/langbotlangbot-app 的头像

    langbot-app/LangBot

    15,311在 GitHub 上查看↗

    LangBot is an orchestration platform designed for building, managing, and deploying AI agents. It functions as a comprehensive framework for integrating large language models with custom workflows, enabling developers to connect intelligent agents to various messaging platforms and external tools. The platform distinguishes itself through a modular, plugin-based architecture that allows for the extension of agent capabilities via custom tools and file parsers. It features a secure, sandbox-isolated runtime environment that executes untrusted code and plugin logic within resource-constrained c

    Converts binary files into structured text content to prepare data for indexing and retrieval.

    Pythonagentcozedeepseek
    在 GitHub 上查看↗15,311
  • getmaxun/maxungetmaxun 的头像

    getmaxun/maxun

    15,049在 GitHub 上查看↗

    Maxun is an open-source web scraping and automation platform designed to transform dynamic website content into structured data. By leveraging artificial intelligence to interpret natural language prompts, the system identifies page elements and extracts information without requiring manual selector configuration. It serves as a bridge between raw web content and intelligent workflows, providing structured outputs in formats optimized for large language model ingestion and agent-based applications. The platform distinguishes itself through its ability to handle complex, authenticated, and dyn

    Provides automated parsing of unstructured web content and documents into structured, machine-readable formats.

    TypeScriptagentsapiautomation
    在 GitHub 上查看↗15,049
  • codelucas/newspapercodelucas 的头像

    codelucas/newspaper

    14,982在 GitHub 上查看↗

    Newspaper is a Python library designed for scraping, parsing, and analyzing web-based information. It functions as a framework for automated news aggregation and large-scale web content extraction, providing tools to download, clean, and structure text, metadata, and media from diverse online sources. The project distinguishes itself through a pipeline-oriented architecture that combines heuristic-based content extraction with natural language processing. It automatically identifies and isolates article bodies from web page boilerplate while simultaneously performing language detection, keywo

    Provides global and instance-specific settings for customizing extraction parameters like timeouts and content filtering.

    HTMLcrawlercrawlingnews
    在 GitHub 上查看↗14,982
  • othmanadi/planning-with-filesOthmanAdi 的头像

    OthmanAdi/planning-with-files

    14,139在 GitHub 上查看↗

    Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations. The platform distinguishes itself through a focus on data sovereignty and secure, private infrastructure deployment. It enables organizations to maintain full control over sensitive information by processing data locally o

    Extracts entities and relationships from documents, emails, and tickets to build a structured network of organizational knowledge.

    Pythonadalagentagent-skills
    在 GitHub 上查看↗14,139
  • unstructured-io/unstructuredUnstructured-IO 的头像

    Unstructured-IO/unstructured

    14,019在 GitHub 上查看↗

    Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

    Provides automated processes for parsing unstructured documents into structured, machine-readable formats for AI workflows.

    HTMLdata-pipelinesdeep-learningdocument-image-analysis
    在 GitHub 上查看↗14,019
  • 567-labs/instructor567-labs 的头像

    567-labs/instructor

    13,176在 GitHub 上查看↗

    Instructor is a framework designed for structured data extraction, validation, and language model integration. It functions as a library that transforms unstructured text into validated, type-safe objects by leveraging schema definitions and model-specific tool-calling capabilities. By acting as a validation middleware, the project ensures that language model outputs strictly conform to defined data structures. The library distinguishes itself through a robust validation-based retry loop that automatically re-submits failed responses with error feedback to iteratively correct schema complianc

    Configures underlying protocols like tool calling or constrained grammar sampling to optimize data extraction.

    Pythonopenaiopenai-function-calliopenai-functions
    在 GitHub 上查看↗13,176
  • nlp-compromise/compromisenlp-compromise 的头像

    nlp-compromise/compromise

    12,122在 GitHub 上查看↗

    Compromise is a natural language processing library and rule-based engine designed for English text manipulation, analysis, and parsing. It provides a toolkit for tokenizing text, identifying parts of speech, and performing linguistic analysis to achieve semantic understanding of unstructured strings. The project distinguishes itself through its ability to programmatically transform grammar, such as modifying verb tenses, noun plurality, and adjective forms. It also functions as a named entity recognizer capable of extracting people, places, organizations, dates, and contact information from

    Converts unstructured strings into organized data by identifying named entities, dates, and grammatical components.

    JavaScript
    在 GitHub 上查看↗12,122
  • h2oai/h2ogpth2oai 的头像

    h2oai/h2ogpt

    12,016在 GitHub 上查看↗

    h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services. The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of

    Extracts structured data from unstructured documents using optical character recognition and machine learning.

    Pythonaichatgptembeddings
    在 GitHub 上查看↗12,016
  • tmc/langchaingotmc 的头像

    tmc/langchaingo

    9,416在 GitHub 上查看↗

    langchaingo is an LLM application framework for Go designed for building language model-powered applications and autonomous agents. It serves as an orchestration library and tool integration framework that allows developers to link prompt sequences and model calls into complex, multi-step workflows. The project provides a toolkit for implementing retrieval-augmented generation pipelines by processing unstructured documents and retrieving relevant context via vector search. It includes a dedicated integration layer for indexing high-dimensional embeddings and performing similarity searches acr

    Provides automated processes for parsing unstructured text and documents into formats suitable for indexing.

    Go
    在 GitHub 上查看↗9,416
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  5. Document and Unstructured Extraction

探索子标签

  • DOM-to-Markdown TransformationsUtilities that parse raw HTML structures into clean, structured text formats for downstream consumption.
  • Extraction Configurations2 个子标签Configuration tools that define input types and file formats to guide document extraction processes.
  • Fact Extraction Pipelines1 个子标签Automated processes that isolate and save confirmed facts from unstructured interactions into structured memory entries. **Distinct from Document and Unstructured Extraction:** Specifically targets the extraction of discrete facts for long-term memory, not general document parsing.
  • Scalable ExtractorsExtracts text, tables, and charts from structured and unstructured documents at scale for downstream retrieval. **Distinct from Document and Unstructured Extraction:** Distinct from Document and Unstructured Extraction: emphasizes scalability and multi-element extraction (text, tables, charts) for retrieval pipelines.
  • Schema-Driven Extraction1 个子标签Tools that map unstructured web content into predefined data structures using automated path selection.