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python3-flask-compress-1.14-1.lbn36.noarch: Flask-Compress allows you to easily compress your Flask application's responses with gzip. The preferred solution is to have a server (like Nginx) automatically compress the static files for you. If you don't have that option Flask-Compress will solve the problem for you.

python3-flask-caching-2.3.1-2.lbn36.noarch: Flask-Caching Adds easy cache support to Flask

python3-flask-caching-2.3.1-2.lbn36.noarch: Flask-Caching Adds easy cache support to Flask

python3-flask-bootstrap-3.3.7.1-17.fc36.noarch: FlaskBootstrap packages Bootstrap into an extension that mostly consists of a blueprint named 'bootstrap'. It can also create links to serve Bootstrap from a CDN and works with no boilerplate code in your application

python3-flask-basicauth-0.2.0-2.fc36.noarch: HTTP basic authentication for Flask

python3-flask-babel-4.0.0-1.lbn36.noarch: Adds i18n/l10n support to Flask applications with the help of the Babel library.

python3-flask-babel-4.0.0-1.lbn36.noarch: Adds i18n/l10n support to Flask applications with the help of the Babel library.

python3-flask-babel-4.0.0-1.lbn36.noarch: Adds i18n/l10n support to Flask applications with the help of the Babel library.

python3-flask-appbuilder-4.8.0-1.lbn36.noarch: Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Extensive configuration of all functionality, easily integrate with normal Flask/Jinja2

python3-flask-appbuilder-4.8.0-1.lbn36.noarch: Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Extensive configuration of all functionality, easily integrate with normal Flask/Jinja2

python3-flask-appbuilder-4.8.0-1.lbn36.noarch: Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Extensive configuration of all functionality, easily integrate with normal Flask/Jinja2

python3-flask-appbuilder-4.8.0-1.lbn36.noarch: Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Extensive configuration of all functionality, easily integrate with normal Flask/Jinja2

python3-flask-3.0.3-5.lbn36.noarch: Flask is called a “micro-framework” because the idea to keep the core simple but extensible. There is no database abstraction layer, no form validation or anything else where different libraries already exist that can handle that. However Flask knows the concept of extensions that can add this functionality into your application as if it was implemented in Flask itself. There are currently extensions for object relational mappers, form validation, upload handling, various open authentication technologies and more. Python 3 version.

python3-elasticsearch-9.0.2-1.lbn36.noarch: The official Python client for Elasticsearch. Features Translating basic Python data types to and from JSON Configurable automatic discovery of cluster nodes Persistent connections Load balancing (with pluggable selection strategy) across available nodes Failed connection penalization (time based - failed connections won't be retried until a timeout is reached) Support for TLS and HTTP authentication Thread safety across requests Pluggable architecture Helper functions for idiomatically using APIs together

python3-apache-beam-2.47.0-1.lbn36.x86_64: Apache Beam is a unified programming model for both batch and streaming data processing, enabling efficient execution across diverse distributed execution engines and providing extensibility points for connecting to different technologies and user communities.

python3-sqlalchemy-spanner-1.6.2-2.lbn36.noarch: Spanner dialect for SQLAlchemy represents an interface API designed to make it possible to control Cloud Spanner databases with SQLAlchemy API. The dialect is built on top of the Spanner DB API, which is designed in accordance with PEP-249. Known limitations are listed here. All supported features have been tested and verified to work with the test configurations. There may be configurations and/or data model variations that have not yet been covered by the tests and that show unexpected behavior. Please report any problems that you might encounter by creating a new issue. Cloud Spanner product documentation SQLAlchemy product documentation Quick Start In order to use this package, you first need to go through the following steps: Select or create a Cloud Platform project. Enable billing for your project. Enable the Google Cloud Spanner API. Setup Authentication. Installation Stable released version of the package is available on PyPi: pip install sqlalchemy-spanner To install a

python3-snowflake-connector-3.15.0-1.lbn36.x86_64: This package includes the Snowflake Connector for Python, which conforms to the Python DB API 2.0 specification: Documentation is available at: code is also available at: Release Notes- v2.8.2(November 18,2022) - Improved performance of OCSP response caching - During the execution of GET commands we no longer resolve target location on the local machine - Improved performance of regexes used...

python3-pgvector-0.3.6-1.lbn36.noarch: pgvector-python pgvector support for Python Supports Django, SQLAlchemy, SQLModel, Psycopg 3, Psycopg 2, asyncpg, and Peewee Installation Run: pip install pgvector And follow the instructions for your database library: Django SQLAlchemy SQLModel Psycopg 3 Psycopg 2 asyncpg Peewee Or check out some examples: Embeddings with OpenAI Sentence embeddings with SentenceTransformers Hybrid search with SentenceTransformers (Reciprocal Rank Fusion) Hybrid search with SentenceTransformers (cross-encoder) Sparse search with Transformers Image search with PyTorch Image search with perceptual hashing Implicit feedback recommendations with Implicit Explicit feedback recommendations with Surprise Recommendations with LightFM Horizontal scaling with Citus Bulk loading with COPY Django Create a migration to enable the extension from pgvector.django import VectorExtension class Migration(migrations.Migration): operations = [ VectorExtension() ] Add a vector field to your model fro

python3-opensearch-py+kerberos-2.8.0-1.lbn36.noarch: This is a metapackage bringing in kerberos extras requires for python3-opensearch-py. It makes sure the dependencies are installed.

python3-opensearch-py+async-2.8.0-1.lbn36.noarch: This is a metapackage bringing in async extras requires for python3-opensearch-py. It makes sure the dependencies are installed.

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