Github

CHatGPT PowerPoint 生成 How to build: an AI PowerPoint generator (Next.js, OpenAI, CopilotKit) https://github.com/CopilotKit/CopilotKit

ChatGPT SQL 生成 https://github.com/ademakdogan/ChatSQL

AWS: Kendra https://aws.amazon.com/jp/kendra/ https://github.com/aws-samples/aws-genai-llm-chatbot Amazon Kendra を触ってみた Amazon Kendra で簡単に検索システムを作ってみよう ! Video: Amazon Kendra による文書からの日本語セマンティック検索 データソース

生成 AI（ジェネレーティブ AI/GenAI） Softbank データマネジメントと生成 AI Snowflake の製品管理担当幹部が考える「生成 AI×DWH」の新たな可能性 AI 活用で目指すべきは“脱 DWH” データレイクと DWH のデータを統合管理する真の“データ活用”時代の「レイクハウス」 生成 AI の開発力強化に向けたプロジェクト「GENIAC」を開始します （Generative AI Accelerator Challenge） Microsoft がデータ分析基盤「Fabric」発表、DWH・AI・ストリーム分析を統合 プログラミングコード生成 AI GitHub Copilot Amazon CodeWhisperer BigQuery 総合職の新卒 1 年目が、自社のデータ構造に合わせた SQL 生成 AI を作ってみた BigQuery からデータを抽出して生成 AI に分析してもらうまでが SQL で完結する手順 Amazon Redshift/Amazon Q generative SQL 自然言語から SQL のクエリ生成ができる Amazon Q generative SQL を触ってみた（プレビュー） [速報] Amazon Redshift クエリエディタが Amazon Q generative SQL の新機能が発表されました（プレビュー） #AWSreInvent Amazon Bedrock LLM: Claude 2 アンソロピックのクロード 2 Vs GPT-4（徹底比較） OpenAI の GPT シリーズと Bedrock の Claude をコスト比較してみた PingCAP/TiDB ChatGPT を搭載した革新的な SQL ジェネレータ「Chat2Query」とは ChatGPT × MySQL Shell：SQL を AI に考えてもらうプラグイン Azure Azure Open AI Service で SQL を生成するプロンプト例 日本語プロンプト指示で SQL 文を生成できるデータベース開発支援ツール - システムインテグレータ Fabric Microsoft Fabric とは Microsoft Fabric のデータ ウェアハウスとは Microsoft Fabric の組み込みの AI モデルで自然言語に対する解析・分析をしてみる Snowflake Gen AI と LLM をデータに導入しよう AI 機能を使って、SQL なしで Snowflake のデータを分析する方法 Snowpark Container Services:Snowflake で洗練された生成 AI とフルスタック アプリを安全に展開して実行する Snowflake Cortex で AI を数秒で使用 Snowflake Cortex ML ベースの関数 Snowflake Cortex について調べてたら凄いワクワクしてきた！ Morph https://www.morphdb.io/ データのための、Notion や Figma のようなツールをつくりたい Figma や Miro のように直感的なデータ分析体験を！Canvas 機能のご紹介

EFS バックアップ AWS Backup https://github.com/hdknr/awsform/blob/main/terraform/modules/backups/main.tf AWS DataSync AWS DataSync を使って S3 から EFS へデータを転送する！

djanog-redis: cache.lock lock https://github.com/jazzband/django-redis/blob/d94a7f9644b96cc37743914fce899cca942c032a/django_redis/cache.py#L177 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 class RedisCache(BaseCache): ... @omit_exception def lock(self, *args, **kwargs): return self.client.lock(*args, **kwargs) def __init__(self, server: str, params: Dict[str, Any]) -> None: ... self._client_cls = options.get( "CLIENT_CLASS", "django_redis.client.DefaultClient" ) self._client_cls = import_string(self._client_cls) self._client = None ... DefaultClient https://github.com/jazzband/django-redis/blob/d94a7f9644b96cc37743914fce899cca942c032a/django_redis/client/default.py#L29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 from redis import Redis class DefaultClient: def __init__(self, server, params: Dict[str, Any], backend: BaseCache) -> None: ... self._clients: List[Optional[Redis]] = [None] * len(self._server) ... def lock( self, key: KeyT, version: Optional[int] = None, timeout: Optional[float] = None, sleep: float = 0.1, blocking_timeout: Optional[float] = None, client: Optional[Redis] = None, thread_local: bool = True, ): if client is None: client = self.get_client(write=True) key = self.make_key(key, version=version) return client.lock( key, timeout=timeout, sleep=sleep, blocking_timeout=blocking_timeout, thread_local=thread_local, ) def get_client( self, write: bool = True, tried: Optional[List[int]] = None, ) -> Redis: """ Method used for obtain a raw redis client. This function is used by almost all cache backend operations for obtain a native redis client/connection instance. """ index = self.get_next_client_index(write=write, tried=tried) if self._clients[index] is None: self._clients[index] = self.connect(index) return self._clients[index] # type:ignore Redis https://github.com/redis/redis-py/blob/6d77c6d715430c30f22147f8c572659d77380a9f/redis/client.py#L88 lock: https://github.com/redis/redis-py/blob/6d77c6d715430c30f22147f8c572659d77380a9f/redis/client.py#L439C1-L511C10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 class Redis(RedisModuleCommands, CoreCommands, SentinelCommands): def lock( self, name: str, timeout: Optional[float] = None, sleep: float = 0.1, blocking: bool = True, blocking_timeout: Optional[float] = None, lock_class: Union[None, Any] = None, thread_local: bool = True, ): """ Return a new Lock object using key ``name`` that mimics the behavior of threading.Lock. If specified, ``timeout`` indicates a maximum life for the lock. By default, it will remain locked until release() is called. ``sleep`` indicates the amount of time to sleep per loop iteration when the lock is in blocking mode and another client is currently holding the lock. ``blocking`` indicates whether calling ``acquire`` should block until the lock has been acquired or to fail immediately, causing ``acquire`` to return False and the lock not being acquired. Defaults to True. Note this value can be overridden by passing a ``blocking`` argument to ``acquire``. ``blocking_timeout`` indicates the maximum amount of time in seconds to spend trying to acquire the lock. A value of ``None`` indicates continue trying forever. ``blocking_timeout`` can be specified as a float or integer, both representing the number of seconds to wait. ``lock_class`` forces the specified lock implementation. Note that as of redis-py 3.0, the only lock class we implement is ``Lock`` (which is a Lua-based lock). So, it's unlikely you'll need this parameter, unless you have created your own custom lock class. ``thread_local`` indicates whether the lock token is placed in thread-local storage. By default, the token is placed in thread local storage so that a thread only sees its token, not a token set by another thread. Consider the following timeline: time: 0, thread-1 acquires `my-lock`, with a timeout of 5 seconds. thread-1 sets the token to "abc" time: 1, thread-2 blocks trying to acquire `my-lock` using the Lock instance. time: 5, thread-1 has not yet completed. redis expires the lock key. time: 5, thread-2 acquired `my-lock` now that it's available. thread-2 sets the token to "xyz" time: 6, thread-1 finishes its work and calls release(). if the token is *not* stored in thread local storage, then thread-1 would see the token value as "xyz" and would be able to successfully release the thread-2's lock. In some use cases it's necessary to disable thread local storage. For example, if you have code where one thread acquires a lock and passes that lock instance to a worker thread to release later. If thread local storage isn't disabled in this case, the worker thread won't see the token set by the thread that acquired the lock. Our assumption is that these cases aren't common and as such default to using thread local storage.""" if lock_class is None: lock_class = Lock return lock_class( self, name, timeout=timeout, sleep=sleep, blocking=blocking, blocking_timeout=blocking_timeout, thread_local=thread_local, )

git update-ref -d これだけだと devel/2023-12-14 を pull できない git branch -d devel error: cannot lock ref 'refs/remotes/origin/devel/2023-12-14': 'refs/remotes/origin/devel' exists; cannot create 'refs/remotes/origin/devel/2023-12-14' From github.com:spin-dd/taihei-epm-server ! [new branch] devel/2023-12-14 -> origin/devel/2023-12-14 (unable to update local ref) error: some local refs could not be updated; try running 'git remote prune origin' to remove any old, conflicting branches update-ref でクリアされる git update-ref -d refs/remotes/origin/devel

bash スクリプト bash シェルスクリプトで引数とオプションを解析する シェル変数のデフォルト値を設定する compdef 1 2 3 4 5 6 7 8 9 eval "$(uv generate-shell-completion zsh)" _uv_run_mod() { if [[ "$words[2]" == "run" && "$words[CURRENT]" != -* ]]; then _arguments '*:filename:_files' else _uv "$@" fi } 1 compdef _uv_run_mod uv uv というコマンドに対して、_uv_run_mod という補完関数を関連付けています。 uv の後に Tab キーを押すと、_uv_run_mod 関数で定義された補完候補が表示されます。

MPTT Tree ID Django + mysql： transaction.atomic() で select_for_update() を使ってレコードをロックしたときのメモ MySQL で採番機能（シーケンス）を実装する方法を整理する MySQL のロックについて公式ドキュメントを読みながら動作検証してみた〜テーブルレベルロック〜 分散ロック Distributed Locking in Django 1 2 3 4 from django.core.cache import cache with cache.lock("somekey"): do_some_thing() redis-py: https://github.com/redis/redis-py/blob/d3a3ada03e080f39144807c9fbe44876c40e0548/redis/client.py#L394 デッドロック Hook available for automatic retry after deadlock in django and mysql setup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 import django.db.backends.utils from django.db import OperationalError import time original = django.db.backends.utils.CursorWrapper.execute def execute_wrapper(*args, **kwargs): attempts = 0 while attempts < 3: try: return original(*args, **kwargs) except OperationalError as e: code = e.args[0] if attempts == 2 or code != 1213: raise e attempts += 1 time.sleep(0.2) django.db.backends.utils.CursorWrapper.execute = execute_wrapper

Elasic Cache for Redis バージョン 1 2 3 4 5 $ redis-cli --version redis-cli 5.0.7 $ redis-server -v Redis server v=5.0.7 sha=00000000:0 malloc=jemalloc-5.2.1 bits=64 build=66bd629f924ac924 接続: 1 REDIS_URL=redis://mycloud-stage-redis-nc.sigrvp.ng.0001.apne1.cache.amazonaws.com:6379/0 1 2 3 $ export $(cat .env|xargs) $ redis-cli -u $REDIS_URL PING PONG Python redis-py: https://github.com/redis/redis-py django-redis: https://github.com/jazzband/django-redis channels_redis: https://github.com/django/channels_redis クラスターモードへの接続 Cluster Client 1 2 3 4 5 6 7 8 9 import logging from redis.cluster import RedisCluster ENDPOINT = "mycloud-stage-redis-test.sigrvp.clustercfg.apne1.cache.amazonaws.com" logging.basicConfig(level=logging.INFO) redis = RedisCluster(host=ENDPOINT, port="6379") if redis.ping(): logging.info("Connected to Redis") django-redis: ...