aiocache

Asyncio cache manager for redis, memcached and memory

Stars: 1197

Visit

Aiocache is an asyncio cache library that supports multiple backends such as memory, redis, and memcached. It provides a simple interface for functions like add, get, set, multi_get, multi_set, exists, increment, delete, clear, and raw. Users can easily install and use the library for caching data in Python applications. Aiocache allows for easy instantiation of caches and setup of cache aliases for reusing configurations. It also provides support for backends, serializers, and plugins to customize cache operations. The library offers detailed documentation and examples for different use cases and configurations.

README:

aiocache ########

Asyncio cache supporting multiple backends (memory, redis and memcached).

.. image:: https://travis-ci.org/argaen/aiocache.svg?branch=master :target: https://travis-ci.org/argaen/aiocache

.. image:: https://codecov.io/gh/argaen/aiocache/branch/master/graph/badge.svg :target: https://codecov.io/gh/argaen/aiocache

.. image:: https://badge.fury.io/py/aiocache.svg :target: https://pypi.python.org/pypi/aiocache

.. image:: https://img.shields.io/pypi/pyversions/aiocache.svg :target: https://pypi.python.org/pypi/aiocache

.. image:: https://api.codacy.com/project/badge/Grade/96f772e38e63489ca884dbaf6e9fb7fd :target: https://www.codacy.com/app/argaen/aiocache

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/ambv/black

This library aims for simplicity over specialization. All caches contain the same minimum interface which consists on the following functions:

add: Only adds key/value if key does not exist.
get: Retrieve value identified by key.
set: Sets key/value.
multi_get: Retrieves multiple key/values.
multi_set: Sets multiple key/values.
exists: Returns True if key exists False otherwise.
increment: Increment the value stored in the given key.
delete: Deletes key and returns number of deleted items.
clear: Clears the items stored.
raw: Executes the specified command using the underlying client.

.. role:: python(code) :language: python

.. contents::

.. section-numbering:

Installing

pip install aiocache
pip install aiocache[redis]
pip install aiocache[memcached]
pip install aiocache[redis,memcached]
pip install aiocache[msgpack]

Usage

Using a cache is as simple as

.. code-block:: python

>>> import asyncio
>>> from aiocache import Cache
>>> cache = Cache(Cache.MEMORY) # Here you can also use Cache.REDIS and Cache.MEMCACHED, default is Cache.MEMORY
>>> with asyncio.Runner() as runner:
>>>     runner.run(cache.set('key', 'value'))
True
>>>     runner.run(cache.get('key'))
'value'

Or as a decorator

.. code-block:: python

import asyncio

from collections import namedtuple

from aiocache import cached, Cache
from aiocache.serializers import PickleSerializer
# With this we can store python objects in backends like Redis!

Result = namedtuple('Result', "content, status")


@cached(
    cache=RedisCache(), key="key", serializer=PickleSerializer(), port=6379, namespace="main")
async def cached_call():
    print("Sleeping for three seconds zzzz.....")
    await asyncio.sleep(3)
    return Result("content", 200)


async def run():
    await cached_call()
    await cached_call()
    await cached_call()
    cache = Cache(Cache.REDIS, endpoint="127.0.0.1", port=6379, namespace="main")
    await cache.delete("key")

if __name__ == "__main__":
    asyncio.run(run())

The recommended approach to instantiate a new cache is using the Cache constructor. However you can also instantiate directly using aiocache.RedisCache, aiocache.SimpleMemoryCache or aiocache.MemcachedCache.

You can also setup cache aliases so its easy to reuse configurations

.. code-block:: python

import asyncio

from aiocache import caches

You can use either classes or strings for referencing classes

caches.set_config({ 'default': { 'cache': "aiocache.SimpleMemoryCache", 'serializer': { 'class': "aiocache.serializers.StringSerializer" } }, 'redis_alt': { 'cache': "aiocache.RedisCache", 'endpoint': "127.0.0.1", 'port': 6379, 'timeout': 1, 'serializer': { 'class': "aiocache.serializers.PickleSerializer" }, 'plugins': [ {'class': "aiocache.plugins.HitMissRatioPlugin"}, {'class': "aiocache.plugins.TimingPlugin"} ] } })

async def default_cache(): cache = caches.get('default') # This always returns the SAME instance await cache.set("key", "value") assert await cache.get("key") == "value"

async def alt_cache(): cache = caches.create('redis_alt') # This creates a NEW instance on every call await cache.set("key", "value") assert await cache.get("key") == "value"

async def test_alias(): await default_cache() await alt_cache()

  await caches.get("redis_alt").delete("key")

if name == "main": asyncio.run(test_alias())

How does it work

Aiocache provides 3 main entities:

backends: Allow you specify which backend you want to use for your cache. Currently supporting: SimpleMemoryCache, RedisCache using redis_ and MemCache using aiomcache_.
serializers: Serialize and deserialize the data between your code and the backends. This allows you to save any Python object into your cache. Currently supporting: StringSerializer, PickleSerializer, JsonSerializer, and MsgPackSerializer. But you can also build custom ones.
plugins: Implement a hooks system that allows to execute extra behavior before and after of each command.

If you are missing an implementation of backend, serializer or plugin you think it could be interesting for the package, do not hesitate to open a new issue.

.. image:: docs/images/architecture.png :align: center

Those 3 entities combine during some of the cache operations to apply the desired command (backend), data transformation (serializer) and pre/post hooks (plugins). To have a better vision of what happens, here you can check how set function works in aiocache:

.. image:: docs/images/set_operation_flow.png :align: center

Amazing examples

In examples folder <https://github.com/argaen/aiocache/tree/master/examples>_ you can check different use cases:

Sanic, Aiohttp and Tornado <https://github.com/argaen/aiocache/tree/master/examples/frameworks>_
Python object in Redis <https://github.com/argaen/aiocache/blob/master/examples/python_object.py>_
Custom serializer for compressing data <https://github.com/argaen/aiocache/blob/master/examples/serializer_class.py>_
TimingPlugin and HitMissRatioPlugin demos <https://github.com/argaen/aiocache/blob/master/examples/plugins.py>_
Using marshmallow as a serializer <https://github.com/argaen/aiocache/blob/master/examples/marshmallow_serializer_class.py>_
Using cached decorator <https://github.com/argaen/aiocache/blob/master/examples/cached_decorator.py>_.
Using multi_cached decorator <https://github.com/argaen/aiocache/blob/master/examples/multicached_decorator.py>_.

Documentation

Usage <http://aiocache.readthedocs.io/en/latest>_
Caches <http://aiocache.readthedocs.io/en/latest/caches.html>_
Serializers <http://aiocache.readthedocs.io/en/latest/serializers.html>_
Plugins <http://aiocache.readthedocs.io/en/latest/plugins.html>_
Configuration <http://aiocache.readthedocs.io/en/latest/configuration.html>_
Decorators <http://aiocache.readthedocs.io/en/latest/decorators.html>_
Testing <http://aiocache.readthedocs.io/en/latest/testing.html>_
Examples <https://github.com/argaen/aiocache/tree/master/examples>_

.. _redis: https://github.com/redis/redis-py .. _aiomcache: https://github.com/aio-libs/aiomcache

For Tasks:

Click tags to check more tools for each tasks

cache data manage cache store data retrieve data clear cache

For Jobs:

python developer backend developer data engineer devops engineer software engineer

Alternative AI tools for aiocache

Similar Open Source Tools

aiocache

github

: 1.2k

aiohttp-debugtoolbar

aiohttp_debugtoolbar provides a debug toolbar for aiohttp web applications. It is a port of pyramid_debugtoolbar and offers basic functionality such as basic panels, intercepting redirects, pretty printing exceptions, an interactive python console, and showing source code. The library is still in early development stages and offers various debug panels for monitoring different aspects of the web application. It is a useful tool for developers working with aiohttp to debug and optimize their applications.

github

: 197

llm.nvim

llm.nvim is a universal plugin for a large language model (LLM) designed to enable users to interact with LLM within neovim. Users can customize various LLMs such as gpt, glm, kimi, and local LLM. The plugin provides tools for optimizing code, comparing code, translating text, and more. It also supports integration with free models from Cloudflare, Github models, siliconflow, and others. Users can customize tools, chat with LLM, quickly translate text, and explain code snippets. The plugin offers a flexible window interface for easy interaction and customization.

github

: 113

swarmzero

SwarmZero SDK is a library that simplifies the creation and execution of AI Agents and Swarms of Agents. It supports various LLM Providers such as OpenAI, Azure OpenAI, Anthropic, MistralAI, Gemini, Nebius, and Ollama. Users can easily install the library using pip or poetry, set up the environment and configuration, create and run Agents, collaborate with Swarms, add tools for complex tasks, and utilize retriever tools for semantic information retrieval. Sample prompts are provided to help users explore the capabilities of the agents and swarms. The SDK also includes detailed examples and documentation for reference.

github

: 202

langchainrb

Langchain.rb is a Ruby library that makes it easy to build LLM-powered applications. It provides a unified interface to a variety of LLMs, vector search databases, and other tools, making it easy to build and deploy RAG (Retrieval Augmented Generation) systems and assistants. Langchain.rb is open source and available under the MIT License.

github

: 1.3k

instructor

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

github

: 8.7k

instructor

Instructor is a Python library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses. Get ready to supercharge your LLM workflows!

github

: 7.7k

lmstudio.js

lmstudio.js is a pre-release alpha client SDK for LM Studio, allowing users to use local LLMs in JS/TS/Node. It is currently undergoing rapid development with breaking changes expected. Users can follow LM Studio's announcements on Twitter and Discord. The SDK provides API usage for loading models, predicting text, setting up the local LLM server, and more. It supports features like custom loading progress tracking, model unloading, structured output prediction, and cancellation of predictions. Users can interact with LM Studio through the CLI tool 'lms' and perform tasks like text completion, conversation, and getting prediction statistics.

github

: 663

clarifai-python

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

github

: 392

json-repair

JSON Repair is a toolkit designed to address JSON anomalies that can arise from Large Language Models (LLMs). It offers a comprehensive solution for repairing JSON strings, ensuring accuracy and reliability in your data processing. With its user-friendly interface and extensive capabilities, JSON Repair empowers developers to seamlessly integrate JSON repair into their workflows.

github

: 135

aiavatarkit

AIAvatarKit is a tool for building AI-based conversational avatars quickly. It supports various platforms like VRChat and cluster, along with real-world devices. The tool is extensible, allowing unlimited capabilities based on user needs. It requires VOICEVOX API, Google or Azure Speech Services API keys, and Python 3.10. Users can start conversations out of the box and enjoy seamless interactions with the avatars.

github

: 154

pinecone-ts-client

The official Node.js client for Pinecone, written in TypeScript. This client library provides a high-level interface for interacting with the Pinecone vector database service. With this client, you can create and manage indexes, upsert and query vector data, and perform other operations related to vector search and retrieval. The client is designed to be easy to use and provides a consistent and idiomatic experience for Node.js developers. It supports all the features and functionality of the Pinecone API, making it a comprehensive solution for building vector-powered applications in Node.js.

github

: 198

vim-ai

vim-ai is a plugin that adds Artificial Intelligence (AI) capabilities to Vim and Neovim. It allows users to generate code, edit text, and have interactive conversations with GPT models powered by OpenAI's API. The plugin uses OpenAI's API to generate responses, requiring users to set up an account and obtain an API key. It supports various commands for text generation, editing, and chat interactions, providing a seamless integration of AI features into the Vim text editor environment.

github

: 776

OpenAI

OpenAI is a Swift community-maintained implementation over OpenAI public API. It is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth, and other public benefits. The repository provides functionalities for text completions, chats, image generation, audio processing, edits, embeddings, models, moderations, utilities, and Combine extensions.

github

: 2.2k

memobase

Memobase is a user profile-based memory system designed to enhance Generative AI applications by enabling them to remember, understand, and evolve with users. It provides structured user profiles, scalable profiling, easy integration with existing LLM stacks, batch processing for speed, and is production-ready. Users can manage users, insert data, get memory profiles, and track user preferences and behaviors. Memobase is ideal for applications that require user analysis, tracking, and personalized interactions.

github

: 230

scaleapi-python-client

The Scale AI Python SDK is a tool that provides a Python interface for interacting with the Scale API. It allows users to easily create tasks, manage projects, upload files, and work with evaluation tasks, training tasks, and Studio assignments. The SDK handles error handling and provides detailed documentation for each method. Users can also manage teammates, project groups, and batches within the Scale Studio environment. The SDK supports various functionalities such as creating tasks, retrieving tasks, canceling tasks, auditing tasks, updating task attributes, managing files, managing team members, and working with evaluation and training tasks.

github

: 58

For similar tasks

aiocache

github

: 1.2k

palimpzest

Palimpzest (PZ) is a tool for managing and optimizing workloads, particularly for data processing tasks. It provides a CLI tool and Python demos for users to register datasets, run workloads, and access results. Users can easily initialize their system, register datasets, and manage configurations using the CLI commands provided. Palimpzest also supports caching intermediate results and configuring for parallel execution with remote services like OpenAI and together.ai. The tool aims to streamline the workflow of working with datasets and optimizing performance for data extraction tasks.

github

: 65

modelbench

ModelBench is a tool for running safety benchmarks against AI models and generating detailed reports. It is part of the MLCommons project and is designed as a proof of concept to aggregate measures, relate them to specific harms, create benchmarks, and produce reports. The tool requires LlamaGuard for evaluating responses and a TogetherAI account for running benchmarks. Users can install ModelBench from GitHub or PyPI, run tests using Poetry, and create benchmarks by providing necessary API keys. The tool generates static HTML pages displaying benchmark scores and allows users to dump raw scores and manage cache for faster runs. ModelBench is aimed at enabling users to test their own models and create tests and benchmarks.

github

: 56

db-ally

db-ally is a library for creating natural language interfaces to data sources. It allows developers to outline specific use cases for a large language model (LLM) to handle, detailing the desired data format and the possible operations to fetch this data. db-ally effectively shields the complexity of the underlying data source from the model, presenting only the essential information needed for solving the specific use cases. Instead of generating arbitrary SQL, the model is asked to generate responses in a simplified query language.

github

: 105

AirBnB_clone_v2

The AirBnB Clone - The Console project is the first segment of the AirBnB project at Holberton School, aiming to cover fundamental concepts of higher level programming. The goal is to deploy a server as a simple copy of the AirBnB Website (HBnB). The project includes a command interpreter to manage objects for the AirBnB website, allowing users to create new objects, retrieve objects, perform operations on objects, update object attributes, and destroy objects. The project is interpreted/tested on Ubuntu 14.04 LTS using Python 3.4.3.

github

: 98

ask-astro

Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.

github

: 181

ai-tutor-rag-system

The AI Tutor RAG System repository contains Jupyter notebooks supporting the RAG course, focusing on enhancing AI models with retrieval-based methods. It covers foundational and advanced concepts in retrieval-augmented generation, including data retrieval techniques, model integration with retrieval systems, and practical applications of RAG in real-world scenarios.

github

: 164

minio

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

github

: 46.0k

For similar jobs

resonance

Resonance is a framework designed to facilitate interoperability and messaging between services in your infrastructure and beyond. It provides AI capabilities and takes full advantage of asynchronous PHP, built on top of Swoole. With Resonance, you can: * Chat with Open-Source LLMs: Create prompt controllers to directly answer user's prompts. LLM takes care of determining user's intention, so you can focus on taking appropriate action. * Asynchronous Where it Matters: Respond asynchronously to incoming RPC or WebSocket messages (or both combined) with little overhead. You can set up all the asynchronous features using attributes. No elaborate configuration is needed. * Simple Things Remain Simple: Writing HTTP controllers is similar to how it's done in the synchronous code. Controllers have new exciting features that take advantage of the asynchronous environment. * Consistency is Key: You can keep the same approach to writing software no matter the size of your project. There are no growing central configuration files or service dependencies registries. Every relation between code modules is local to those modules. * Promises in PHP: Resonance provides a partial implementation of Promise/A+ spec to handle various asynchronous tasks. * GraphQL Out of the Box: You can build elaborate GraphQL schemas by using just the PHP attributes. Resonance takes care of reusing SQL queries and optimizing the resources' usage. All fields can be resolved asynchronously.

github

: 164

aiogram_bot_template

Aiogram bot template is a boilerplate for creating Telegram bots using Aiogram framework. It provides a solid foundation for building robust and scalable bots with a focus on code organization, database integration, and localization.

github

: 117

pluto

Pluto is a development tool dedicated to helping developers **build cloud and AI applications more conveniently** , resolving issues such as the challenging deployment of AI applications and open-source models. Developers are able to write applications in familiar programming languages like **Python and TypeScript** , **directly defining and utilizing the cloud resources necessary for the application within their code base** , such as AWS SageMaker, DynamoDB, and more. Pluto automatically deduces the infrastructure resource needs of the app through **static program analysis** and proceeds to create these resources on the specified cloud platform, **simplifying the resources creation and application deployment process**.

github

: 90

pinecone-ts-client

github

: 198

aiohttp-pydantic

Aiohttp pydantic is an aiohttp view to easily parse and validate requests. You define using function annotations what your methods for handling HTTP verbs expect, and Aiohttp pydantic parses the HTTP request for you, validates the data, and injects the parameters you want. It provides features like query string, request body, URL path, and HTTP headers validation, as well as Open API Specification generation.

github

: 63

gcloud-aio

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

github

: 288

aioconsole

aioconsole is a Python package that provides asynchronous console and interfaces for asyncio. It offers asynchronous equivalents to input, print, exec, and code.interact, an interactive loop running the asynchronous Python console, customization and running of command line interfaces using argparse, stream support to serve interfaces instead of using standard streams, and the apython script to access asyncio code at runtime without modifying the sources. The package requires Python version 3.8 or higher and can be installed from PyPI or GitHub. It allows users to run Python files or modules with a modified asyncio policy, replacing the default event loop with an interactive loop. aioconsole is useful for scenarios where users need to interact with asyncio code in a console environment.

github

: 452

aiosqlite

aiosqlite is a Python library that provides a friendly, async interface to SQLite databases. It replicates the standard sqlite3 module but with async versions of all the standard connection and cursor methods, along with context managers for automatically closing connections and cursors. It allows interaction with SQLite databases on the main AsyncIO event loop without blocking execution of other coroutines while waiting for queries or data fetches. The library also replicates most of the advanced features of sqlite3, such as row factories and total changes tracking.

github

: 1.1k