Search Results for "database" - Page 3
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Showing 1946 open source projects for "database"
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SAFRS
SqlAlchemy Flask-Restful Swagger Json:API OpenAPI
SAFRS exposes SQLAlchemy database models as a JSON:API web service and generates the corresponding swagger/OpenAPI. Database objects such as the User class from the demo.py example can be extended to include relationships with other objects. The demo_relationship.py contains the following extension of the User class where a relationship with the Book class is implemented. -
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peepDB
CLI tool and python library to inspect databases fast
peepDB is an open-source command-line tool and Python library designed for developers and database administrators who need a fast and efficient way to inspect their database tables without writing SQL queries. With support for MySQL, PostgreSQL, and MariaDB, peepDB is lightweight, secure, and incredibly easy to use. -
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Flask-Migrate
SQLAlchemy database migrations for Flask applications using Alembic
Flask-Migrate is a Flask extension that integrates SQLAlchemy database migrations into Flask applications by leveraging Alembic, making schema changes easier and safer across development and deployment environments. It provides a set of command-line routines tied into Flask’s CLI such that developers can generate migration scripts, apply upgrades, and roll back changes with simple commands, keeping the database structure in sync with evolving application models. -
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Xiyan MCP Server
A Model Context Protocol (MCP) server
The XiYan MCP Server is a Model Context Protocol (MCP) server that enables natural language queries to databases, powered by XiYan-SQL, a state-of-the-art text-to-SQL model. It allows users to interact with databases using conversational language, simplifying data retrieval processes. -
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Vanna 2.0
Chat with your SQL database
Vanna is an open-source Python framework that enables natural language interaction with databases by converting user questions into executable SQL queries using large language models. The framework uses a retrieval-augmented generation architecture that learns from database schemas, documentation, and past query examples to generate accurate queries tailored to a specific dataset. Vanna can be integrated into many environments, including notebooks, web applications, messaging platforms, and data dashboards, making it flexible for analytics and data exploration workflows. The system streams query results, visualizations, and summaries directly to user interfaces, allowing non-technical users to interact with complex data systems through conversational queries. ... -
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yt-fts
Search all of YouTube from the command line
yt-fts, short for YouTube Full Text Search, is an open-source command-line tool that enables users to search the spoken content of YouTube videos by indexing their subtitles. The program automatically downloads subtitles from a specified YouTube channel using the yt-dlp utility and stores them in a local SQLite database. Once indexed, users can perform full-text searches across all transcripts to quickly locate keywords or phrases mentioned within the videos. The tool returns search results with timestamps and direct links to the exact moment in the video where the phrase occurs. In addition to traditional keyword search, the system supports experimental semantic search capabilities using embeddings from AI services and vector databases. ... -
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pagodo
Automate Google Hacking Database scraping and searching
pagodo automates Google searching for potentially vulnerable web pages and applications on the Internet. It replaces manually performing Google dork searches with a web GUI browser. There are 2 parts. The first is ghdb_scraper.py that retrieves the latest Google dorks and the second portion is pagodo.py that leverages the information gathered by ghdb_scraper.py. This version of pagodo also supports native HTTP(S) and SOCKS5 application support, so no more wrapping it in a tool like... -
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pgai
A suite of tools to develop RAG, semantic search, and other AI apps
pgai is a suite of PostgreSQL extensions developed by Timescale to empower developers in building AI applications directly within their databases. It integrates tools for vector storage, advanced indexing, and AI model interactions, facilitating the development of applications like semantic search and Retrieval-Augmented Generation (RAG) without leaving the SQL environment. -
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Opsdroid
An open source chat-ops bot framework
Designed to take messages from chat services and execute Python functions (skills) based on their contents. Connector modules transfer messages between opsdroid and a particular chat service. Database modules allow opsdroid to persist information in a database of your choice. Parsers/Matchers modules gather the meaning of what was said and match a skill. Skills are what make opsdroid tick. They define how opsdroid should respond and what actions to take. -
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Full Stack FastAPI and PostgreSQL
Full stack, modern web application generator
...Celery worker that can import and use models and code from the rest of the backend selectively. REST backend tests based on Pytest, integrated with Docker, so you can test the full API interaction, independent on the database. As it runs in Docker, it can build a new data store from scratch each time. -
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Enferno
Modern Flask framework optimized for AI-assisted development
Enferno is a framework for building developer-first cloud backends using PostgreSQL and TypeScript. It offers primitives for defining data models, APIs, and access rules directly in code, enabling quick iteration and deployment. Enferno is designed to accelerate SaaS and internal tool development by combining the benefits of traditional backends with developer ergonomics. -
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Prisma Client Python
Prisma Client Python is an auto-generated and fully type-safe database
prisma-client-py is an auto-generated Python ORM for Prisma schema files, bringing Prisma's developer experience to Python projects. It provides a type-safe, intuitive interface for interacting with databases like PostgreSQL and MySQL. prisma-client-py is ideal for Python developers who want static typing, code completion, and seamless integration with modern backend stacks. -
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OSV.dev
Open source vulnerability DB and triage service
osv.dev (Open Source Vulnerabilities) is Google’s open source platform and API for aggregating, managing, and analyzing vulnerability data across multiple ecosystems. It powers the osv.dev website, providing a unified, queryable database of vulnerabilities that map directly to open source packages and versions. The system hosts vulnerability data for ecosystems such as PyPI, npm, Go, Maven, and Debian, among others. The platform includes a web UI, API, and a Go-based dependency scanner that checks software dependencies, container images, SBOMs (SPDX, CycloneDX), and Git repositories for known vulnerabilities. ... -
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Ibis
Expressive analytics in Python at any scale
Ibis is a Python library to help you write expressive analytics at any scale, small to large. Its goal is to simplify analytical workflows and make you more productive. Ibis gives you the benefit of a programming language. You don't need to sacrifice maintainability to get to those insights! Ibis builds on top of and works with existing Python tools. Ibis provides a full-featured replacement for SQL SELECT queries, but expressed with Python code. All tables in Ibis are immutable. To select a... -
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OSXPhotos
Python app to work with pictures and associated metadata
...Only iPhoto 9.6.1 (the final release) has been tested. This package will read Photos databases for any supported version on any supported macOS version. E.g. you can read a database created with Photos 5.0 on MacOS 10.15 on a machine running macOS 10.12 and vice versa. -
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Pony ORM
Pony Object Relational Mapper
Pony ORM is a Python ORM that enables developers to write database queries using generator expressions and Pythonic syntax, making code more readable and intuitive. It automatically translates Python expressions into SQL and supports multiple databases including SQLite, MySQL, PostgreSQL, and Oracle. With an emphasis on simplicity and maintainability, Pony ORM is suitable for both small projects and complex applications. -
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SQLModel
SQL databases in Python, designed for simplicity, compatibility
SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python-type annotations, and powered by Pydantic and SQLAlchemy. -
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web2py
Free and open source full-stack enterprise framework
Free open source full-stack framework for rapid development of fast, scalable, secure and portable database-driven web-based applications. Written and programmable in Python (version 3 and 2.7). Everything you need in one package including fast multi-threaded web server, SQL database and web-based interface. No third-party dependencies but works with third-party tools. Create, modify, deploy and manage applications from anywhere using your browser. -
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Django Rules
Awesome Django authorization, without the database
...A good Django citizen. Seamless integration with Django views, templates and the Admin for testing for object-level permissions. Efficient and smart. No need to mess around with a database to figure out whether John really wrote that book. Simple. Dive in the code. You'll need 10 minutes to figure out how it works. Powerful. rules comes complete with advanced features, such as invocation context and storage for arbitrary data, skipping evaluation of predicates. -
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Superduper
Superduper: Integrate AI models and machine learning workflows
...This allows developers to completely avoid implementing MLOps, ETL pipelines, model deployment, data migration, and synchronization. Using Superduper is simply "CAPE": Connect to your data, apply arbitrary AI to that data, package and reuse the application on arbitrary data, and execute AI-database queries and predictions on the resulting AI outputs and data. -
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Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. This project includes many examples of Jupyter notebooks. It is recommended to explore them. ... -
22
oterm
the terminal client for Ollama
...Its interface is designed to be simple and intuitive, enabling developers to launch conversations quickly using a single command. Oterm supports persistent chat sessions that store conversations, system prompts, and parameter configurations locally in a database. This allows users to maintain multiple conversations and reuse previous context across sessions. The tool also integrates with the Model Context Protocol so it can interact with external tools and prompts provided through MCP servers. -
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RAG API
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL
rag_api is an open-source REST API for building Retrieval-Augmented Generation (RAG) systems using LLMs like GPT. It lets users index documents, search semantically, and retrieve relevant content for use in generative AI workflows. Designed for rapid prototyping, it is ideal for chatbot development, document assistants, and knowledge-based LLM apps. -
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Open Gauss
Project-scoped Lean workflow orchestrator from Math, Inc.
Open Gauss is an enterprise-grade open-source relational database management system designed to handle large-scale data processing with high performance, reliability, and security. It is based on the PostgreSQL ecosystem but significantly extends its capabilities through architectural optimizations, AI-driven features, and enterprise-level enhancements. The database organizes data using the relational model, storing structured information in tables composed of rows and columns while supporting standard SQL for querying and management. ... -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
...You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy. SQLite, MySQL, and PostgreSQL databases can be monitored. Just replace the ENGINE property of your database, replacing django.db.backends with django_prometheus.db.backends. You may want to monitor the creation/deletion/update rate for your model. This can be done by adding a mixin to them. This is safe to do on existing models (it does not require a migration). Note that the exported metrics are counters of creations, modifications and deletions done in the current process. ...
