Saravanan666/pypi-org huggingface dataset:
Package: aib-custom-logging-test
aib_custom_loggerThis serves as the official documentation for the PyPi package developed by the AIB MLOPS team called "aib-custom-logger".IntroductionThe "aib-custom-logger" pypi pkg offers the abilty to throw logs that contains meaningful metadata, which can be used in both debugging and monitoring.UsageThe pkg exists in the aib-mirror, once you install the pkg using "pip install aib-custom-logger", you can proceed to use it as follows:from aib_custom_logger_tst.aib_custom_logger import aib_logger
 
 my_logger=aib_logger(project_id,pipeline_name="{{$.pipeline_job_name}}")
 
 my_logger.log(
  f"Batch prediction job output info: {job.output_info}",
  category=aib_logger.Categories.DATA,
  action=aib_logger.Actions.CREATE,
  extra_labels=['batch_prediction','automl']
 )1.init()After you import the pkg you need to initialise an object of the aib_logger class. The constructor takes the following params:project (str):Required. The project id of to add to the google cloud client for logging.pipeline_name (str):Optional. The pipeline run name that this log was thrown from. This can be achieved by passing the placeholder "{{$.pipeline_job_name}}" which will be replaced by the run_name at run time. Default is empty string "".logger_name (str):Optional. The name of the custom logger which you can then query by. Default is "aib_custom_logger".2.Log()the log function takes the following params:msg (str):Required. The content you wish to log.category (aib_logger.Categories):Optional. An enum to choose from relating the category of the log. Default is "Other".action (aib_logger.Actions):Optional. An enum to choose from relating the action of the log. Default is "Other".severity (aib_logger.Severities):Optional. An enum to choose from relating the level of severity of the log. Default is "Info".extra_labels (list):Optional. Extra custom labels to add to the log.object (dict):Optional. An extra custom dictionary to be added.3.CategoriesThe following are the available categories (key=value) defined in the aib_logger.Categories enum:MODEL = "Model"DATA = "Data"METRIC = "Metric"OTHER = "Other"4.ActionsThe following are the available actions (key=value) defined in the aib_logger.Actions enum:CREATE="Create"UPDATE="Update"DELETE="Delete"UPLOAD="Upload"OTHER="Other"5.SeveritiesThe following are the available severities (key=value) defined in the aib_logger.Severities enum:INFO="INFO"WARNING="WARNING"ERROR="ERROR"QueryingIn order to see the logs being produced by your pipeline, you need to navigate to the logging page in GCP and start querying.logname:you can query for all the logs that were produced by a specific custom logger name.jsonpayload:the jsonpayload holds the content of your logs and the metadata. It is a key value object as seen below.LimitationsCurrently the custom logs yielded by this pkg can only appear in the logging page, and not the logs section in the vertex pipelines UI.
Package: aibench
AIBenchmarkBenchmark your model against other modelsAbout|Features|Technologies|Starting|License|AuthorInstallationRun this script in your terminal:$pipinstallaibenchAboutAIBenchmark is a package which lets you quickly get the benchmark of your model based on the popular datasets and compare with existing leaderboard. It also has a nice collection of metrics which you could easily import.We currently support 14 text-based and 2 image-based datasets for AutoBenchmarking aiming for regression/classification tasks. Available datasets could be found in aibenchmark/dataset.py file.Or run the following code:fromaibenchmark.datasetimportDatasetsListprint(list(DatasetsList.get_available_datasets()))Code example for benchmarking:fromaibenchmark.benchmarkimportBenchmarkfromaibenchmark.datasetimportDatasetInfo,DatasetsListbenchmark=Benchmark(DatasetsList.Texts.SST)dataset_info:DatasetInfo=benchmark.dataset_infoprint(dataset_info)test_features=dataset_info.data['Texts']model=torch.load(...)# Implement your code based on the type of model you use, your pre- and post-processing etc.outputs=model.predict(test_features)# Results of your model based on predictionsbenchmark_results=benchmark.run(predictions=outputs,metrics=['accuracy','precision','recall','f1_score'])# Metricsprint(benchmark_results)# Existing leaderboard for this datasetprint(benchmark.get_existing_benchmarks())FeaturesFast comparison of metrics of your model and other SOTA models for particular datasetSupporting 16+ most populat datasets, the list is always updating. Soon we willl support more than 1000 datasetsAll metrics in one place and we are adding new ones in a standardised wayTechnologiesThe following tools were used in this project:PytorchTransformersScikit-learn:memo: LicenseThis project is under license from MIT. For more details, see theLICENSEfile.Made byIgorandTimBack to top
Package: ai-benchmark
AI Benchmark Alphais an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying onTensorFlowmachine learning library, and is providing a lightweight and accurate solution for assessing inference and training speed for key Deep Learning models.In total, AI Benchmark consists of42 testsand19 sectionsprovided below:MobileNet-V2[classification]Inception-V3[classification]Inception-V4[classification]Inception-ResNet-V2[classification]ResNet-V2-50[classification]ResNet-V2-152[classification]VGG-16[classification]SRCNN 9-5-5[image-to-image mapping]VGG-19[image-to-image mapping]ResNet-SRGAN[image-to-image mapping]ResNet-DPED[image-to-image mapping]U-Net[image-to-image mapping]Nvidia-SPADE[image-to-image mapping]ICNet[image segmentation]PSPNet[image segmentation]DeepLab[image segmentation]Pixel-RNN[inpainting]LSTM[sentence sentiment analysis]GNMT[text translation]For more information and results, please visit the project website:http://ai-benchmark.com/alphaInstallation InstructionsThe benchmark requires TensorFlow machine learning library to be present in your system.On systems thatdo not have Nvidia GPUs, run the following commands to install AI Benchmark:pipinstalltensorflow
pipinstallai-benchmarkIf you want to check theperformance of Nvidia graphic cards, run the following commands:pipinstalltensorflow-gpu
pipinstallai-benchmarkNote 1:If Tensorflow is already installed in your system, you can skip the first command.Note 2:For running the benchmark on Nvidia GPUs,NVIDIA CUDAandcuDNNlibraries should be installed first. Please find detailed instructionshere.Getting StartedTo run AI Benchmark, use the following code:fromai_benchmarkimportAIBenchmarkbenchmark=AIBenchmark()results=benchmark.run()Alternatively, on Linux systems you can typeai-benchmarkin the command line to start the tests.To run inference or training only, usebenchmark.run_inference()orbenchmark.run_training().Advanced settingsAIBenchmark(use_CPU=None,verbose_level=1):use_CPU={True, False, None}:   whether to run the tests on CPUs  (if tensorflow-gpu is installed)verbose_level={0, 1, 2, 3}:   run tests silently | with short summary | with information about each run | with TF logsbenchmark.run(precision="normal"):precision={"normal", "high"}:   ifhighis selected, the benchmark will execute 10 times more runs for each test.Additional Notes and RequirementsGPU with at least 2GB of RAM is required for running inference tests / 4GB of RAM for training tests.The benchmark is compatible with bothTensorFlow 1.xand2.xversions.ContactsPlease contactandrey@vision.ee.ethz.chfor any feedback or information.
Package: aibo
🔈AiboOffline Smart Speaker Engine Powered by ChatGPThttps://user-images.githubusercontent.com/62988216/228871916-f8311a31-be59-4855-b7d9-873e3f9adc10.movFeaturesVoice Prompting: You and Aibo can communicate by voice.Security and Privacy: You don't need to worry about security and privacy.Models: You can choose your favorite AI model as your aibo.InstallationWith pipThis repository is tested on Python 3.8+, PyTorch 1.13.1+ and MacOS 11.5.2+.You should install aibo in avirtual environment. If you're unfamiliar with Python virtual environments, check out theuser guide.First, create a virtual environment with the version of Python you're going to use and activate it.Then, you will need to install PyTorch.
Please refer toPyTorch installation pageregarding the specific installation command for your platform.When one of those backends has been installed, aibo can be installed using pip as follows:pipinstallaiboUsageFirst, configure the model you want to use, API, and API key. You can also set the parameters required for online execution at this point.aiboinitStart aibo and start conversation in your language. Your conversation history is stored in the history directory.Offline execution is performed by appending "--offline" after this command.aibostartModelsWe support the following APIs for online/offline execution.modelonlineofflineChatGPT(gpt-3.5-turbo)⭕️❌Whisper⭕️⭕️
Package: aiboot
CubeAI模型服务化引擎 —— AI-Boot将AI模型推理程序封装成为Web互联网服务。目前提供RESTful API、WebSocket和可视化Web GUI等多种形式的互联网API接口服务，支持二进制数据传输和文件上传。依赖包地址https://pypi.org/project/aiboot依赖包引用$ pip install aiboot应用开发实例CubeAI智立方https://github.com/cube-ai/cubeaihttps://code.ihub.org.cn/projects/1484/repository/cubeai_model_examples
Package: aibo-server
aibo-serveris the backing Python server for theaibo.elEmacs package that leverages OpenAI's chat API to bring ChatGPT into EmacsInstallationThis package requires Python 3.11 or greaterpipinstallaibo-serverStart serverNormally the server will be run using theaibo.cli.startcommand in emacspython-maibo.cli.startbut it's also possible to run it usinguvicornuvicornaibo.server.main:app--port5000DatabaseThe server uses a simple sqlite database, by default stored in~/.aibo/database.dbCustomizationFor Python environment customizations, refer toconstants.py.
Package: aibro
AIbroAIbro is a serverless model training tool that helps data scientists train AI models on cloud platforms with one line of code.Documentation:https://doc.aipaca.ai/,Company Website:https://aipaca.ai/Potential Install Error:IfOSError: protocol not foundshows up, it is caused by missing/etc/protocols file.file. This command should be able to resolve the error:sudo apt-get -o Dpkg::Options::="--force-confmiss" install --reinstall netbase
Package: aibuildersart
แพ็คเกจนี้ใช้สำหรับโชว์รูปศิลปะ ascii ซึ่งได้ผลงานมาจากสมาชิกเพจ
AIBuilders ช่วยกันเขียนขึ้นมาวิธีติดตั้งเปิด CMD / Terminalpipinstallaibuildersartวิธีใช้งานfromaibuildersartimport*show=UncleEngineer()show.art()print("Author:{}".format(show.name))
Package: aica-api
Python AICA API ClientThe AICA API client module provides simple functions for interacting with the AICA API.pipinstallaica-apiThe client can be used to easily make API calls as shown below:fromaica_api.clientimportAICAaica=AICA()aica.set_application('my_application.yaml')aica.start_application()aica.load_component('my_component')aica.unload_component('my_component')aica.stop_application()To check the status of component predicates and conditions, the following blocking methods can be employed:fromaica_api.clientimportAICAaica=AICA()ifaica.wait_for_condition('timer_1_active',timeout=10.0):print('Condition is true!')else:print('Timed out before condition was true')ifaica.wait_for_predicate('timer_1','is_timed_out',timeout=10.0):print('Predicate is true!')else:print('Timed out before predicate was true')Compatability tableThe latest version of the AICA API client will generally support the latest API version in the AICA framework. For
older versions of the AICA framework, it may be necessary to install older versions of the client. Use the following
compatability table to determine which client version to use.API server versionMatching Python client versionv3.x>= v2.0.0v2.xv1.2.0<= v1.xUnsupportedRecent client versions also support the following functions to check the client version and API compatability.fromaica_api.clientimportAICAaica=AICA()# get the current client versionaica.client_version()# check compatability between the client version and API versionaica.check()
Package: aicapi2021-yw3760
AICAPI2021_YW3760This is a python package of an API CLIENT to retrieve the data of collections in the Atr Institute of Chicago. The outputs inlude: An table include related information about the searched artworks. An table include related information about the searched tours. An image of an artwork, searched by title and artist. An table include related information about the searched products.Installation$pipinstallAICAPI2021_YW3760UsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.LicenseAICAPI2021_YW3760was created by YW3760. It is licensed under the terms of the MIT license.CreditsAICAPI2021_YW3760was created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: aicapi-yw3760
aicapi_yw3760This is a python package of an API CLIENT to retrieve the data of collections in the Atr Institute of Chicago.
The outputs inlude:
An table include related information about the searched artworks.
An table include related information about the searched tours.
An image of an artwork, searched by title and artist.
An table include related information about the searched products.Installation$pipinstallaicapi_yw3760UsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseaicapi_yw3760was created by yw3760. It is licensed under the terms of the MIT license.Creditsaicapi_yw3760was created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: aicaptcha
AiCaptchaPython library for solving various types of captcha including Hcaptcha, ReCaptchaV3(enterprise/normal), ObjectRecognition and Audio.InstallationTo install the library, simply run the following command:pip install aicaptchaUsageTo use the library, import the AiCaptcha class from the aicaptcha module, create an instance of the class with your API key, and call the appropriate method to solve the captcha.Here's an example:fromaicaptchaimportAiCaptchaapi_key="your_api_key_here"solver=AiCaptcha(api_key)# Solve an Hcaptchahcaptcha_result=solver.Hcaptcha(site_key="hcaptcha_site_key_here",page_url="hcaptcha_page_url_here",user_agent="your_user_agent_here")print(hcaptcha_result.token)print(hcaptcha_result.key)# Solve a ReCaptchaV3 (enterprise/normal)recaptchav3_result=solver.ReCaptchaV3(type="recaptchav3_type(enterprise/normal)",site_key="recaptchav3_site_key_here",page_url="recaptchav3_page_url_here",action="recaptchav3_action_here")print(recaptchav3_result.token)# Solve a audio captchaaudio_captcha_result=solver.Audio(audio="audio_filename_or_content_or_url",numbers_sensitivity=False)print(audio_captcha_result.text)print(audio_captcha_result.confidence)# Solve the captcha specifying the name of the objectsobject_recognition_captcha_result=solver.ObjectRecognition(image="image_filename_or_content_or_url",max_results=5)print(object_recognition_captcha_result.results)ChannelTelegram :AiCaptchaSupportTelegram :supportfor free trial contact support.
Package: ai-care
AI-CareCurrent AI models are only capable of passively responding to user inquiries
and lack the ability to initiate conversations.
AI-Care endows AI with the capability to speak proactively.
With simple settings, AI-Care allows your AI to proactively care for you.Highlights✨💵Low Cost: In terms of both token usage and API call frequencies, AI-Care is designed to minimize these expenses.
It operates with an O(1) cost complexity, which means that the costs do not increase with the duration of its activation.🕊️Low Intrusiveness: AI-Care provides its services alongside existing systems,
with virtually zero intrusion into the original code.
This allows for easy integration of AI-Care services into existing systems.🌍Model Universality: Compatible with all LLM (Large Language Model) models,
AI-Care does not rely on function call features or specific ways in which the model is used.UsageDefine the "to_llm" and "to_user" interfaces. AICare uses the "to_llm" interface to send
messages to the LLM and uses the "to_user" interface to send messages to the user.defto_llm_method(chat_context,to_llm_messages:list[AICareContext])->str|Generator[str,None,None]:# Here you need to convert `to_llm_messages` into the message format of the LLM you are using# and send the message to the LLM.# The return value is the message content replied by the LLM.# If you are not using stream mode, directly return the reply message string.# You can also use stream mode, in which case a string generator should be returned.# If using stream mode, AICare will also automatically use stream mode when sending messages to the user.passdefto_user_method(to_user_message:str|Generator[str,None,None])->None:# Here you need to process messages from AICare as you would a normal LLM reply.# If using stream mode, this method should be able to receive and handle a string generator.# If the `to_llm` method uses stream mode, then this method should also use stream mode.passInstantiate AICare:fromai_careimportAICareai_care=AICare()Register "to_llm" and "to_user" methods:ai_care.register_to_llm_method(to_llm_method)ai_care.register_to_user_method(to_user_method)Using AICare# After each round of conversation or when AICare service is neededai_care.chat_update(chat_context)AI-Care settings# Set guidance informationai_care.set_guide(guide="your guide")# Set how long before AI-Care is activatedai_care.set_config(key="delay",value=60)# Set the number of times AI-Care selects “ASK_LATER”, setting it to 0 can disable this option.ai_care.set_config(key="ask_later_count_limit",value=1)# Set the system default recursive depth for ask.ai_care.set_config(key="ask_depth",value=1)# Set the maximum number of chat intervals the system automatically records.ai_care.set_config(key="n_chat_intervals",value=20)LicenseThis project is licensed under theMIT License.
Package: aicc-tools
aicc介绍aicc是一款面向MindSpore开发者和华为计算中心，致力于易化和规范化云上文件交互过程，简化云上日志显示，以此加速模型开发进度和降低云上集群调试难度。其包含三大功能：云上日志显示分类；云上文件交互传输； 云上分布式训练启动。aicc不仅能够帮助开发者便捷、快速的将本地（线下）代码迁移至计算中心，还可以一行代码解决云上分布式训练问题。利用云上文件交互传输功能，可解决云上大规模模型或大规模数据实时交互传输问题。简洁的日志显示和分类系统也极大地便利了开发者利用云上集群调试大模型。特性CFTS：文件交互传输系统AiLog：云上日志显示系统Distribute Training：云上分布式训练安装教程pipinstallaicc_tools使用说明云上环境初始化importaicc_toolsasac# moxing 交互环境注册（可选）避免文件交互过程中动态密钥失效而中断ac.mox_register(ak='#@#$',sk='*&^%',server='https://obs.cn-central-221.ovaijisuan.com')# 一行代码开启云上分布式训练"""Args:seed: 设置随机种子数use_parallel: 是否使用分布式训练，默认是True基本环境初始化配置。支持mindspore.context.set_context(**kwargs)内所有支持的参数配置context_config = {"mode": "GRAPH_MODE", "device_target": "Ascend", "device_id": 0}并行环境初始化配置。支持mindspore.context.set_auto_parallel_context(**kwargs)内所有支持的参数配置parallel_config = {"parallel_mode": "DATA_PARALLEL", "gradients_mean": True}Return:rank_id: 当前程序执行的device编号device_num: 使用的总的device数量"""local_rank,device_num=ac.cloud_context_init(seed=args.seed,use_parallel=use_parallel,context_config=context,parallel_config=parallel)云上文件交互系统# Step1 init cfts'''obs_path: 用于存储云上训练过程中产生的所有数据的obs桶路径， 通常是以obs或s3开头的链接rank_id: 当前程序执行的device编号, 默认按照一个节点只回传一张卡的数据upload_frequence：回传数据至obs桶的频次，默认是训练一个step回传一次数据keep_last：保持始终是最新的文件，删掉旧文件，默认是True'''cfts=ac.CFTS(obs_path="obs存储路径",rank_id=0,upload_frequence=1，keep_last=True)# 获取obs的数据集（可选）'''默认返回云上训练作业容器内实际的数据集存放路径，方便开发者一键移植到数据集读取和处理代码中'''ds_path=cfts.get_dataset(dataset_obs="obs数据集路径")# 获取obs的checkpoint模型参数文件（可选）'''默认返回云上训练作业容器内实际的模型文件存储路径，方便开发者进行后续加载和处理'''ckpt_path=cfts.get_checkpoint(checkpoint_obs="obs模型路径")# Step2 获取内置 callback， 注意所有需要保存文件的路径在云上已被默认，用户无需配置！## 保存checkpoint的callback函数（可选）'''function: cfts.checkpoint_monitor()集成了mindspore的ModelCheckpoint, CheckpointConfig，支持其所有超参'''ckpt_cb=cfts.checkpoint_monitor(prefix="mae")## 保存summary的callback函数（可选）'''function: cfts.summary_monitor()集成了mindspore的SummaryCollector所有支持功能'''summary_cb=cfts.summary_monitor(collect_freq=1)## 显示loss的callback函数（可选）'''function: cfts.loss_monitor()集成了mindspore的LossMonitor所有功能，改善日志打印体验'''loss_cb=cfts.loss_monitor(per_print_times=1)## 如果有自定义的callback函数，可直接加入到callback列表中，但需注意obs_monitor需放在列表最后一项custom_cb=CustomMonitor()# 不涉及文件保存custom_cb=CustomMonitor(save_dir=cfts.get_custom_path())# 涉及文件保存时，需传入cfts固定的用户路径，保证文件被回传！## 云上必须的文件回传callback函数！！！（必选）'''function: cfts.obs_monitor()规范云上文件与obs交互传输，云上必须使用该callback函数才能实现所有生成文件的回传！'''obs_cb=cfts.obs_monitor()## 用于model.train的callback定义callback=[loss_cb,summary_cb,ckpt_cb,custom_cb,obs_cb]云上日志显示系统# 日志系统，打印信息'''logger 可以自定义打印到 stdout 和文件的配置，可以实现不输出日志到 stdout、或者只有指定节点和设备才输出日志。可以控制输出到 stdout 和文件的日志的等级和格式，并可以控制单个日志文件的大小和最多保存多少个日志文件。'''logger=ac.get_logger(to_std=True,stdout_nodes=(0,1),stdout_devices=(0,1),stdout_level='ERROR',stdout_format=None,file_level='INFO',file_save_dir='./log/',file_prefix=None,max_file_size=50,max_num_of_files=5)logger.debug('debug message')logger.info('info message')logger.warning('warning message')logger.error('error message')logger.critical('critical message')使用样例参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request
Package: aich
AICH-EncryptionEncryption Algorithm for All Languages. Generates a Token In Hindi.Installationsudo pip install aichUsagefrom a2 import aichsearch_word = input('Enter your encryption phrase:')encrypted = aich.aichin(search_word)print (encrypted)search_word = input('Enter your decryption phrase:')encrypted = aich.aichout(search_word)print (encrypted)Encryption Example{Inputs: This is freedom ये है आज़ादी , Outputs: षनफदपफदशफखकफयमफदशफखकफयमफददफखयफदषफदषफदनफदगफदतफयमफशयगफशनखफयमफशकशफशनपफयमफशमदफशणटफशकटफशकएफशयदफशनम}
{Inputs: AICH , Outputs: नणफनशफनकफनप}Decryption Example{Inputs: षनफदपफदशफखकफयमफदशफखकफयमफददफखयफदषफदषफदनफदगफदतफयमफशयगफशनखफयमफशकशफशनपफयमफशमदफशणटफशकटफशकएफशयदफशनम, Outputs: This is freedom ये है आज़ादी}
{Inputs: नणफनशफनकफनप , Outputs: AICH}
Package: ai-changelog
ai_changelogai_changelogis a Python project that automatically generates changelog files summarizing code changes, using AI.It usesLangChainandOpenAImodels to analyze Git commit diffs and generate natural language descriptions of the changes. This allows maintaining an up-to-date changelog without manual effort.This README was originally written by Claude, an LLM from Anthropic.Usageusage:ai_changelog[-h][--provider{openai,anthropic,anyscale}][--modelMODEL][--temperatureTEMPERATURE][--max_tokensMAX_TOKENS][--hub_promptHUB_PROMPT][--context_linesCONTEXT_LINES][--max_concurrencyMAX_CONCURRENCY][-v]refs

Processcommandlinearguments.

positionalarguments:refsReferencecomparisonwithstandardgitsyntax

options:-h,--helpshowthishelpmessageandexit--provider{openai,anthropic,anyscale}ModelAPIprovider--modelMODELModelname--temperatureTEMPERATUREModeltemperature--max_tokensMAX_TOKENSMaxtokensinoutput--hub_promptHUB_PROMPTPrompttopullfromLangChainHub--context_linesCONTEXT_LINESNumberofcontextlinesforeachcommit--max_concurrencyMAX_CONCURRENCYNumberofconcurrentconnectionstollmprovider(0meansnolimit)-v,--verboseRunLangChaininverbosemode

http://github.com/joshuasundance-swca/ai_changelogLocal installTo generate a changelog locally:pipinstallai_changelog

ai_changelog--help
ai_changelogmain..HEAD# to summarize changes locallyDockerdockerpulljoshuasundance/ai_changelog:latest
dockerrun\--env-file.env\-v/local_repo_dir:/container_dir_in_repo\-w/container_dir_in_repo\joshuasundance/ai_changelog:latest\main..HEADGitHub WorkflowTheai_changelog_main_pr.ymlworkflow runs on pushes tomain.It generates summaries for the new commits and appends them toAI_CHANGELOG.md. The updated file is then committed back to the PR branch.ai_changelogorigin/main^..origin/main# in a GitHub action to summarize changes in response to a push to mainai_changelogorigin/main..HEAD# in a GitHub action to summarize changes in response to a PRAnother flow was made to commit an updated changelog to an incoming PR before it was merged, but that seemed less useful although it did work well.ConfigurationSet environment variables as needed for your provider of choice (default requiresOPENAI_API_KEY).Set LangSmith environment variables to enable LangSmith integration, if applicable.Use command line arguments.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.TODOTestingGet CodeLlama working reliably in CICD (currently hit or miss on structured output)