Open Source Python Enterprise Software for Windows Server

Python Enterprise Software for Windows Server

Browse free open source Python Enterprise Software for Windows Server and projects below. Use the toggles on the left to filter open source Python Enterprise Software for Windows Server by OS, license, language, programming language, and project status.

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  • Context for your AI agents Icon
    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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  • 1
    bitfarm-Archiv Document Management - DMS
    bitfarm-Archiv is a powerful Document Management (DMS), Enterprise Content Management (ECM) and Knowledge Management System (KMS) with Workflow Components. Help us! As we live in the internet age, the best thing, you can help, is to write a short statement about your scenario and your use of the DMS, along with your experiences and put it on your own website or in a blog or forum. It would help us best, if you can also add a hyperlink to our site http://www.bitfarm-archiv.com. By this you help the software to gain a better presence in the web which helps distribute it. This, however, will allow us to acquire more enterprise customers which gives us more resources, e.g. for further development of the GPL version.
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    Downloads: 8 This Week
    Last Update:
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  • 2
    ERP / BI / NFe
    Downloads: 0 This Week
    Last Update:
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  • 3
    QAL

    QAL

    Query Abstraction Layer

    Project has moved to: https://github.com/OptimalBPM/qal QAL is a collection of libraries for mining, transforming and writing data from and to a number of places. Sources and destinations include different SQL and NoSQL backends, file formats like .csv, XML and excel. Even untidy HTML web pages. It has a database abstraction layer that supports connectivity to Postgres, MySQL, DB2, Oracle, MS SQL server. JSON and MongoDB is coming. It uses XML/JSON formats(self-generated SQL schemas) for representing queries, transformation and merging, making it scriptable. This means that QAL can be backend agnostic about a subset of SQL features and data types. Of course custom SQL:s are also supported. It is currently distributed as a Python 3 Library (pip3 install python3-qal) and Debian .deb package. It is related the Optimal BPM project, see its Optimal Sync application for usage examples. The text of this page is released under the Creative Commons Zero Waiver 1.0 (CC0).
    Downloads: 0 This Week
    Last Update:
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  • 4
    TableauPublisher

    TableauPublisher

    Sync .twb files to Tableau Server (prevent report chaos!)

    Prevent report chaos on Tableau Server! Python script that can publish a collection .twb files to Tableau Server. Can publish just the files that have changed. Files are stored in subversion and checked out to a local file system. Comparison of what has changed checks last commit time in svn vs last update time on Tableau Server. Can also run in 'nopublish' mode which just confirms what it would do, but doesn't take any action. Requires tabcmd, python 2.5, pysvn, minidom, dateutil, optparse
    Downloads: 0 This Week
    Last Update:
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  • Cloud data warehouse to power your data-driven innovation Icon
    Cloud data warehouse to power your data-driven innovation

    BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.

    BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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