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python3-aioredis-2.0.1-2.lbn36.noarch: aioredis asyncio (3156) Redis client library. The library is intended to provide simple and clear interface to Redis based on asyncio. Features Feature Supported hiredis parser :white_check_mark: Pure-python parser :white_check_mark: Low-level & High-level APIs :white_check_mark: Pipelining support :white_check_mark: Multi/Exec support :white_check_mark: Connections Pool :white_check_mark: Pub/Sub support :white_check_mark: Sentinel support :white_check_mark: ACL support :white_check_mark: Streams support :white_check_mark: Redis Cluster support :no_entry_sign: Tested Python versions 3.6, 3.7, 3.8, 3.9 Tested for Redis servers 5.0, 6.0 Support for dev Redis server through low-level API Installation The easiest way to install aioredis is by using the package on PyPi: pip install aioredis Requirements Python 3.6+ hiredis Benchmarks Benchmarks can be found here: https:/github.com/popravich/python-redis-benchmark Contribute Issue Tracker: https:/gith

python3-aioodbc-0.3.3-10.fc36.noarch: Library for accessing a ODBC databases from the asyncio.

python3-aiomysql+sa-0.2.0-4.lbn36.noarch: This is a metapackage bringing in sa extras requires for python3-aiomysql. It makes sure the dependencies are installed.

python3-aiomysql+sa-0.2.0-4.lbn36.noarch: This is a metapackage bringing in sa extras requires for python3-aiomysql. It makes sure the dependencies are installed.

python3-aiomysql+rsa-0.2.0-4.lbn36.noarch: This is a metapackage bringing in rsa extras requires for python3-aiomysql. It makes sure the dependencies are installed.

python3-aiomysql-0.2.0-4.lbn36.noarch: aiomysql is a “driver” for accessing a MySQL database from the asyncio (PEP-3156/tulip) framework. It depends on and reuses most parts of PyMySQL . aiomysql tries to be like awesome aiopg library and preserve same api, look and feel.

python3-aiomysql-0.2.0-4.lbn36.noarch: aiomysql is a “driver” for accessing a MySQL database from the asyncio (PEP-3156/tulip) framework. It depends on and reuses most parts of PyMySQL . aiomysql tries to be like awesome aiopg library and preserve same api, look and feel.

python3-aiomisc-17.7.7-1.lbn36.noarch: Miscellaneous utils for asyncio. As a programmer, you are no stranger to the challenges that come with building and maintaining software applications. One area that can be particularly difficult is making architecture of the software that using asynchronous I/O. This is where aiomisc comes in. aiomisc is a Python library that provides a collection of utility functions and classes for working with asynchronous I/O in a more intuitive and efficient way. It is built on top of the asyncio library and is designed to make it easier for developers to write asynchronous code that is both reliable and scalable. With aiomisc, you can take advantage of powerful features like worker pools, connection pools, circuit breaker pattern, and retry mechanisms such as asyncbackoff and asyncretry to make your asyncio code more robust and easier to maintain. In this documentation, we’ll take a closer look at what aiomisc has to offer and how it can help you streamline your asyncio service development. Inst

python3-aiolimiter-1.1.0-1.lbn36.noarch: aiolimiter Introduction An efficient implementation of a rate limiter for asyncio. This project implements the Leaky bucket algorithm, giving you precise control over the rate a code section can be entered: from aiolimiter import AsyncLimiter rate_limit = AsyncLimiter(100, 30) async def some_coroutine(): async with rate_limit: await do_something() It was first developed as an answer on Stack Overflow.

python3-aiofiles-24.1.0-1.lbn36.noarch: aiofiles is an Apache2 licensed library, written in Python, for handling local disk files in asyncio applications.

jvector-3.0.6-1.lbn36.noarch: Exact nearest neighbor search (k-nearest-neighbor or KNN) is prohibitively expensive at higher dimensions, because approaches to segment the search space that work in 2D or 3D like quadtree or k-d tree devolve to linear scans at higher dimensions. This is one aspect of what is called “the curse of dimensionality.” With larger datasets, it is almost always more useful to get an approximate answer in logarithmic time, than the exact answer in linear time. This is abbreviated as ANN (approximate nearest neighbor) search. There are two broad categories of ANN index: Partition-based indexes, like LSH or IVF or SCANN Graph indexes, like HNSW or DiskANN Graph-based indexes tend to be simpler to implement and faster, but more importantly they can be constructed and updated incrementally. This makes them a much better fit for a general-purpose index than partitioning approaches that only work on static datasets that are completely specified up front. That is why all the major commercial vector indexes use graph approaches. JVector is a graph index in the DiskANN family tree.

kamailio-xmlrpc-6.0.0-1.lbn36.x86_64: XMLRPC transport and encoding for Kamailio RPCs and MI commands.

kamailio-xhttp-pi-6.0.0-1.lbn36.x86_64: Web-provisioning interface for Kamailio.

kamailio-utils-6.0.0-1.lbn36.x86_64: Non-SIP utitility functions for Kamailio.

kamailio-unixodbc-6.0.0-1.lbn36.x86_64: UnixODBC database connectivity for Kamailio.


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