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python3-openai-wandb-1.6.1-1.lbn36.noarch: Install support for Weights and Biases

python3-openai-embeddings-1.6.1-1.lbn36.noarch: Install dependencies for embedding utilities.

python3-openai-datalib-1.6.1-1.lbn36.noarch: Data libraries like numpy and pandas are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API.

python3-openai+voice_helpers-1.77.0-1.lbn36.noarch: This is a metapackage bringing in voice_helpers extras requires for python3-openai. It makes sure the dependencies are installed.

python3-openai+realtime-1.77.0-1.lbn36.noarch: This is a metapackage bringing in realtime extras requires for python3-openai. It makes sure the dependencies are installed.

python3-websockets-14.1-2.lbn36.x86_64: websockets is a library for developing WebSocket servers and clients in Python. It implements RFC 6455 with a focus on correctness and simplicity. It passes the Autobahn Testsuite. Built on top of Python’s asynchronous I/O support introduced in PEP 3156, it provides an API based on coroutines, making it easy to write highly concurrent applications.

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-datareader-0.10.0-2.fc36.noarch: Data readers extracted from the pandas codebase, should be compatible with recent pandas versions.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

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.

python3-opensearch-py-2.8.0-1.lbn36.noarch: opensearch-py is a community-driven, open source OpenSearch client licensed under the Apache v2.0 License. For more information, see opensearch.org.

python3-openlineage-sql-1.24.2-1.lbn36.x86_64: Python interface for the Rust OpenLineage lineage extraction library

python3-openlineage-python-1.24.2-1.lbn36.noarch: OpenLineage-python To install from source, run: $ python -m pip install . Configuration Config file The most common way to configure the OpenLineage Client is by .yaml file, which contains all the details of how to connect to your OpenLineage backend. The config file is located by: looking at the OPENLINEAGE_CONFIG environment variable looking for the openlineage.yml file in the current working directory looking for the openlineage.yml file in the $HOME/.openlineage directory. Different ways of connecting to OpenLineage backend are supported by the standardized Transport interface. This is an example config for specifying http transport: transport: type: "http" url: "https:/backend:5000" auth: type: "api_key" api_key: "f048521b-dfe8-47cd-9c65-0cb07d57591e" The type property is required. It can be one of the built-in transports or a custom one. There are four built-in transports, http, kafka, console and file. Custom transports type is a fully qualified class name that


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