Saravanan666/pypi-org huggingface dataset:
Package: langchain-xfyun
🦜️🔗✨ LangChain-xfyun⚡ 在LangChain中流畅地使用讯飞星火大模型 ⚡⏩ 快速安装pip install langchain-xfyun🤔 这是什么？大型语言模型（LLM）正在成为一种变革性技术，它使开发人员能够构建以前无法构建的应用程序。然而，孤立地使用这些 LLM 通常不足以创建真正强大的应用程序，只有将它们与其他计算或知识来源相结合，才能发挥真正的威力。Fork fromlangchain添加了讯飞星火大模型的支持，让你可以在langchain中使用SparkLLM[TODO] 修改langchain内置prompt以适应SparkLLM其它关于langchain的信息可以参考LangChain's original README.md❓ 如何使用fromlangchain_xfyun.chat_modelsimportChatSparkfromlangchain_xfyun.promptsimportChatPromptTemplatefromlangchain_xfyun.chainsimportLLMChainllm=ChatSpark(app_id="your_app_id",api_key="your_api_key",api_secret="your_api_secret")prompt=ChatPromptTemplate.from_template("我有一个生产[{product}]商品的公司，请帮我取一个最合适的公司名称。只输出答案本身")chain=LLMChain(llm=llm,prompt=prompt,verbose=True)product="魔方"ans=chain.run(product)print(ans)像以前一样使用chat model，现在你可以使用ChatSpark而不是ChatOpenAI更详细的内容请参考langchain 官方文档💁 ContributingAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.For detailed information on how to contribute, seehere.
Package: langchain_zhipu
为什么要开发这个包？为了方便在 langchain 中使用，在官方SDK基础上做了如下额外工作：问题1: 智谱AI的官方SDK使用了 pydantic v2，这与 langchain（尤其是langserve）不兼容问题2: langchain.community 的国内包更新不及时，无法在 langchain 的 LCEL 语法中使用已支持全部 langchain 接口invokeainvokebatchabatchstreamastreamastream_eventsasteram_log已支持模型能力模型名称："glm-3-turbo", "glm-4", "glm-4v"逻辑推理和对话生成支持工具回调简单的例子fromzhipuai_pydantic_v1importChatZhipuAIllm=ChatZhipuAI()# invokellm.invoke("hi")# streamforsinllm.stream("hi"):print(s)# astreamasyncforsinllm.astream("hi"):print(s)使用glm-4vfromlangchain_zhipuimportChatZhipuAIfromlangchain_core.promptsimportChatPromptTemplatellm4v=ChatZhipuAI(model="glm-4v")prompt=ChatPromptTemplate.from_messages([("human",[{"type":"text","text":"图里有什么"},{"type":"image_url","image_url":{"url":"https://img1.baidu.com/it/u=1369931113,3388870256&fm=253&app=138&size=w931&n=0&f=JPEG&fmt=auto?sec=1703696400&t=f3028c7a1dca43a080aeb8239f09cc2f"}}]),])(prompt|llm4v).invoke({})接口指南智谱开放平台大模型接口 Python SDK（Big Model API SDK in Python），让开发者更便捷的调用智谱开放API官方SDK官方SDK能力简介对所有接口进行了类型封装。初始化client并调用成员函数，无需关注http调用过程的各种细节，所见即所得。默认缓存token。
Package: langchangetrack
Package for statistically significant language change.Free software: BSD licenseDocumentation:https://langchangetrack.readthedocs.org.FeaturesThis package provides tools to detect linguistic change in a temporal corpora.We do this by capturing linguistic change through different methods.Usagecd examples/distributional/
./ngrams_pipeline.sh ../data/temporal_corpus/ ./working/ ./output/ 1900 2000 5 locallinear 100 1000 “ngrams” ../data/temporal_corpus/common_vocab.txt 10cd examples/pos
./pos_pipeline.sh ../data/temporal_corpus/ ./working/ ./output/ 1900 1930 5 ../data/temporal_corpus/common_vocab.txt 10cd examples/freq
./freq_pipeline.sh ../data/temporal_corpus/ ./working/ ./output/ 1900 2000 5 ../data/temporal_corpus/common_vocab.txt 10Requirementswheel==0.23.0argparse>=1.2.1numpy>=0.9.1scipy>=0.15.1more_itertools>=2.2joblib>=0.8.3-r1gensim==0.10.3statsmodels>=0.5.0changepoint>=0.1.0nltk>=3.0.0textblob>=0.9.0textblob-aptagger>=0.2.0Installationcd langchangetrackpip install -r requirements.txtpython setup.py installHistory0.1.0 (2015-02-20)First release on PyPI.
Package: langcodes-py2
langcodes is a toolkit for working with and comparing the standardized codes
for languages, such as ‘en’ for English, ‘es’ for Spanish, and ‘zh-Hant’ for
Traditional Chinese. These are BCP 47 language codes, formerly known as ISO
language codes.langcodes is designed for Python 3, but this version of langcodes,
‘langcodes-py2’, is a backport to Python 2.7.The documentation for langcodes lives in its README file, which you can read
on GitHub:https://github.com/LuminosoInsight/langcodes
Package: langcrawler
langcrawlerlangcrawleris a language crawler written in Python.SeeLang Fetcheras a language fetcher fromlangcrawler.RequirementPostgreSQL >= 12.4Python >= 3.8Redis >= 6.0InstallationUbuntuaptupdate
aptinstall-ypython3-devpython3-pippython3-setuptools
pipinstalllangcrawlerWindows# Install Python, Microsoft Visual C++ and Windows SDK
pip install langcrawlerUpdatingpipinstalllangcrawler--upgradeRunninglangcrawler\--pg-address127.0.0.1:5432\--pg-loginpostgres/postgres\--redis-address127.0.0.1:6379\--redis-passredis\--repo-count1\--repo-hostgerrit,github,gitlab\--repo-langgo,javascript,php,python,rust,typescriptUsageusage:crawler.py[-h][--pg-addressPG_ADDRESS][--pg-loginPG_LOGIN][--redis-addressREDIS_ADDRESS][--redis-passREDIS_PASS][--repo-countREPO_COUNT][--repo-hostREPO_HOST][--repo-langREPO_LANG][-v]LanguageCrawler

optionalarguments:-h,--helpshowthishelpmessageandexit--pg-addressPG_ADDRESSpostgresaddress(host:port)--pg-loginPG_LOGINpostgreslogin(user/pass)--redis-addressREDIS_ADDRESSredisaddress(host:port)--redis-passREDIS_PASSredispass--repo-countREPO_COUNTrepositorycount--repo-hostREPO_HOSTrepositoryhost--repo-langREPO_LANGrepositorylanguage-v,--versionshowprogram'sversionnumberandexitDesignLicenseProject License can be foundhere.ReferenceSearch on GerritSearch on GitHubSearch on GitLab
Package: langdash
langdashA simple library for interfacing with language models.Currently in beta!Features:Support for guided text generation, text classification (through prompting) and vector-based document searching.Lightweight, build-it-yourself-style prompt wrappers in pure Python, with no domain-specific language involved.Token healing and transformers/RNN state reuse for fast inference, like inMicrosoft's guidance.First-class support for ggml backends.Documentation:Read on readthedocs.ioRepository:main/Gitlab mirrorInstallationUsepipto install. By default, langdash does not come preinstalled with any additional modules. You will have to specify what you need like in the following command:pip install --user langdash[embeddings,sentence_transformers]List of modules:core:embeddings:required for running searching through embeddingsbackends:Generation backends:rwkv_cpp,llama_cpp,ctransformers,transformersEmbedding backends:sentence_transformersNote:If running from source, initialize the git submodules in thelangdash/externfolder to compile foreign backends.UsageExamples:Text generationGenerating TV showsEmbedding searchSeeexamples folderfor full examples.Running the ExamplesAll examples can be ran with the following command:python examples/instruct.py [model type] [model name or path]You can specify additional model parameters using the-aeCLI argument, and passing a valid Python literal. For example, to run the chat example using the WizardLM model with context length of 4096, do:python examples/chat.py llama_cpp /path/to/ggml-wizardlm.bin -ae n_ctx 4096Some examples require you to specify the prompt format. Formats include:wizardlm(shortened Alpaca format without the first prompt line and# Instruction:), andalpaca(the full format). You will need to specify it for most of the examples:python examples/instruct.py llama_cpp /path/to/ggml-wizardlm.bin -ae n_ctx 4096 --prompt-format wizardlmFor a full list, see theexamples/_instruct_format.pyfile.LicenseApache 2.0
Package: langdb
Language Database (langdb)Hackable database powering Large Language Models (LLMs)BuildPut pypi credentials in~/.pypirc. The relevant section should look like this[distutils]
  index-servers =
    testpypi

[pypi]
  username = __token__
  password = pypi-...

[testpypi]
  username = __token__
  password = pypi-...python -m buildUpload to test index:twine upload --repository testpypi dist/*Upload to prod index:python -m build
twine upload dist/*
Package: langdetect
langdetectPort of Nakatani Shuyo'slanguage-detectionlibrary (version from 03/03/2014) to Python.Installation$ pip install langdetectSupported Python versions 2.7, 3.4+.Languageslangdetectsupports 55 languages out of the box (ISO 639-1 codes):af, ar, bg, bn, ca, cs, cy, da, de, el, en, es, et, fa, fi, fr, gu, he,
hi, hr, hu, id, it, ja, kn, ko, lt, lv, mk, ml, mr, ne, nl, no, pa, pl,
pt, ro, ru, sk, sl, so, sq, sv, sw, ta, te, th, tl, tr, uk, ur, vi, zh-cn, zh-twBasic usageTo detect the language of the text:>>>fromlangdetectimportdetect>>>detect("War doesn't show who's right, just who's left.")'en'>>>detect("Ein, zwei, drei, vier")'de'To find out the probabilities for the top languages:>>>fromlangdetectimportdetect_langs>>>detect_langs("Otec matka syn.")[sk:0.572770823327,pl:0.292872522702,cs:0.134356653968]NOTELanguage detection algorithm is non-deterministic, which means that if you try to run it on a text which is either too short or too ambiguous, you might get different results everytime you run it.To enforce consistent results, call following code before the first language detection:fromlangdetectimportDetectorFactoryDetectorFactory.seed=0How to add new language?You need to create a new language profile. The easiest way to do it is to use thelangdetect.jartool, which can generate language profiles from Wikipedia abstract database files or plain text.Wikipedia abstract database files can be retrieved from "Wikipedia Downloads" (http://download.wikimedia.org/). They form '(language code)wiki-(version)-abstract.xml' (e.g. 'enwiki-20101004-abstract.xml' ).usage:java -jar langdetect.jar --genprofile -d [directory path] [language codes]Specify the directory which has abstract databases by -d option.This tool can handle gzip compressed file.Remark: The database filename in Chinese is like 'zhwiki-(version)-abstract-zh-cn.xml' or zhwiki-(version)-abstract-zh-tw.xml', so that it must be modified 'zh-cnwiki-(version)-abstract.xml' or 'zh-twwiki-(version)-abstract.xml'.To generate language profile from a plain text, use the genprofile-text command.usage:java -jar langdetect.jar --genprofile-text -l [language code] [text file path]For more details seelanguage-detection Wiki.Original projectThis library is a direct port of Google'slanguage-detectionlibrary from Java to Python. All the classes and methods are unchanged, so for more information see the project's website or wiki.Presentation of the language detection algorithm:http://www.slideshare.net/shuyo/language-detection-library-for-java.
Package: lang-detect
# lang-detect: a tool to detect languageDetecting the language for a small piece of unicode text without any dependency
to other libraries.Currently we support detecting de, en, es, fr, it, ja, nl, pl, ru, zh-hans,
zh-hant, and zh-yue.After some simple testing, we found that the result for long sentence is better.## MethodWe focus on the Basic Multilingual Plane in unicode encoding, and current
language support set could be extended.For each language, we use a uniformed ngram vector to represent the language
itself. This vector can be seen at the data folder.When we detect a text, we generate the uniformed ngram vector for this text, and
just comparing the cosine value of the angle between the text vector and the
language vector.To get the language vector, we use feature articles on Wikipedia as corpus.## Usagecd to the project rootbin/langdetect YOUR_SENTENCE_HERE
Package: langdiversity
LangDiversityElevate your language models with insightful diversity metrics.LinksPaper:https://arxiv.org/abs/2308.11189Video:https://www.youtube.com/watch?v=BekDOLm6qBI&t=10s&ab_channel=NeuroSymbolicCheck outLangDiversity Hello Worldif you're new.Table of ContentsIntroductionInstallationUsageBibtexLicenseContactsIntroductionLangDiversity is a package that provides tools to calculate diversity measures for a given set of data. Specifically, it can compute measures like Shannon's entropy and Gini impurity. It also offers utilities to select prompts based on their diversity scores when interacting with models like OpenAI's GPT-3.5 Turbo.The primary goal of this project is to assist researchers and developers in analyzing the diversity of responses generated by language models, thereby aiding in the evaluation and fine-tuning of such models.InstallationpipinstalllangdiversityUsageDetailed documentation is availablehere.BibtexIf you used this software in your work please cite our paper@misc{ngu2023diversity,title={Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries},author={Noel Ngu and Nathaniel Lee and Paulo Shakarian},year={2023},eprint={2308.11189},archivePrefix={arXiv},primaryClass={cs.CL}}LicenseThis repository is licensed underBSD-3-ClauseContactsFor any inquiries or feedback, please contact:Noel Ngu:nngu2@asu.eduNathaniel Lee:nlee51@asu.eduPaulo Shakarian:pshak02@asu.edu
Package: lange
langeLazy lists (i.e. Haskell-like ranges) for Python.Latest release|Current code|API documentationInstallationfrom package# Set up a virtualenv.python3-mvenvvenvsourcevenv/bin/activate# Install from PyPIpipinstalllangefrom sourcecdmy-project
gitclonehttps://github.com/davips/lange../lange
pipinstall-e../langeFeaturesStable floating-point range generation, e.g.:0.8 - 0.6 == 0.2up to 28 digits (customizable).Infinite[1 2 ...]or bounded.O(1) access/evaluationlst[3443]ExamplesArithmetic Progression# Boundedfromlangeimportapprint(ap[0.4,0.8,...,2])"""[0.4 0.8 .+. 2.0]"""# Infinite + slicingprog=ap[0.4,0.8,...]print(prog[:5])"""[0.4 0.8 .+. 2.0]"""# As listprint(list(prog[:5]))"""[0.4, 0.8, 1.2, 1.6, 2.0]"""print(prog[:5].l)"""[0.4, 0.8, 1.2, 1.6, 2.0]"""# Towards negative.print(ap[1,-2,...,-8].l)"""[1, -2, -5, -8]"""Geometric Progression# Boundedfromlangeimportgpprint(gp[0.4,0.8,...,2])"""[0.4 0.8 1.6]"""# Infinite + slicingprog=gp[0.4,0.8,...]print(prog[:5])"""[0.4 0.8 .*. 6.4]"""# As listprint(list(prog[:5]))"""[0.4, 0.8, 1.6, 3.2, 6.4]"""print(prog[:5].l)"""[0.4, 0.8, 1.6, 3.2, 6.4]"""# Using negative step.print(gp[1,-2,...,130].l)"""[1, -2, 4, -8, 16, -32, 64, -128]"""Other projectsPlease access thiswebsitefor more information.
Package: langeval-cli
LangEvalEvaluation for AI apps and agent▄▄▌   ▄▄▄·  ▐ ▄  ▄▄ • ▄▄▄ . ▌ ▐· ▄▄▄· ▄▄▌
██•  ▐█ ▀█ •█▌▐█▐█ ▀ ▪▀▄.▀·▪█·█▌▐█ ▀█ ██•
██▪  ▄█▀▀█ ▐█▐▐▌▄█ ▀█▄▐▀▀▪▄▐█▐█•▄█▀▀█ ██▪
▐█▌▐▌▐█ ▪▐▌██▐█▌▐█▄▪▐█▐█▄▄▌ ███ ▐█ ▪▐▌▐█▌▐▌
.▀▀▀  ▀  ▀ ▀▀ █▪·▀▀▀▀  ▀▀▀ . ▀   ▀  ▀ .▀▀▀Table of ContentsInstallationDocumentsHow to useDevelopmentLicenseInstallationpip install langeval-cliDocumentsTODOs:Refactor RAG EvalSupport custom output parserSupport more provider.How to usesee./examplesfor more details.Development# Create virtual environmenthatchenvcreate# Activate virtual environmenthatchshell# Run testhatchruntest# Run linthatchrunlint:style# Version dumphatchversionpatch/minor/major# Buildhatchbuild# Upload to pypihatchpublishLicenseLangEvalis distributed under the terms of theMITlicense.
Package: langevitour
langevitour for PythonPython interface forlangevitoura HTML widget that randomly tours projections of a high-dimensional dataset with an animated scatter-plot.For more information see the langevitourGithub repoorwebsite.InstallationpipinstalllangevitourPython usageimportnumpyasnpfromlangevitourimportLangevitour# Generate a sample datasetX=[]group=[]n=20000defr():returnnp.random.normal(0,0.02)foriinrange(n):a=i/n*np.pi*2X.append([10+np.sin(a)/3+r(),20+np.sin(a*2)/3+r(),30+np.sin(a*3)/3,40+np.sin(a*4)/3,50+np.sin(a*5)/3])group.append(int(i*4/n))# Extra axes (specified as columns of a matrix)extra_axes=[[1],[2],[0],[0],[0]]extra_axes_names=["V1+2*V2"]tour=Langevitour(X,group=group,extra_axes=extra_axes,extra_axes_names=extra_axes_names,point_size=1,)tour.write_html("langevitour_plot.html")langevitour also works injupyter notebooks.
Package: langful
langful[
en_us
|zh_cn]installpip3 install langfulorpip install langfulget startlang fileps:langfulload json file firstor you can set tolangful.lang( json_first = False ).json{"key":"value","...":"..."}.langif you need highlight, you can installvscode-langful:GitHubVSCodekey = value # hi, this is a example
# hi, I am a example, too
... = ...initthere havetwoways to initby filesimportlangfullang=langful.lang()then you can put file at there :langen_us.langen_us.jsonzh_cn.langzh_cn.json...or use other directory name and setlangful.lang( "directory name" )by dictionaryimportlangfullang=langful.lang({"en_us":{"hi":"Hi","welcome":"Welcome"},"zh_cn":{"hi":"你好","welcome":"欢迎"}})functionreplacelang=langful.lang({"en_us":{"test":"{}.{}%"}})print(lang.replace("test",[33,3]))mergeimportlangfullang=langful.lang({"en_us":{"hi":"Hi","welcome":"Welcome"},"zh_cn":{"hi":"你好"}})print(lang.merge("en_us",["zh_cn"]))about[github|pypi]
Package: langfun
LangfunLangfun is a Python library that aims to make language models (LM) fun
to work with. Its design enables a programming model that flows naturally,
resembling the human thought process. It emphasizes the reuse and combination of
language pieces to form prompts, thereby accelerating innovation. In contrast to
other LM frameworks, which feed program-generated data into the LM, langfun
takes a distinct approach: It starts with natural language, allowing for
seamless interactions between language and program logic, and concludes with
natural language and optional structured output. Consequently, langfun can
aptly be described as Language as functions, capturing the core of its
methodology.Installpip install langfunOr install nightly build with:pip install langfun --preHello WorldimportlangfunaslfclassNumericAnswerExtractor(lf.LangFunc):"""Numeric answer extractor.Here is my question:{{question}}Here is the response:{{question()}}Can you help me extract a number from the response as the answer to myquestion? Your response should only contain a number in numeric form.If the answer is not a number or you cannot extract it, respond with UNKNOWN."""output_transform=lf.transforms.Match('\d+').to_int()l=NumericAnswerExtractor()withlf.context(lm=lf.llms.Gpt35(debug=True)):r=l(question=lf.LangFunc('What is result of {{x}} plus {{y}}?'),x='one',y='two')print('Result:',r.result)Disclaimer: this is not an officially supported Google product.
Package: langfuse
Langfuse Python SDKInstallation[!IMPORTANT]
The SDK was rewritten in v2 and released on December 17, 2023. Refer to thev2 migration guidefor instructions on updating your code.pip install langfuseFull documentation:https://docs.langfuse.com/sdk/pythonLangchain documentation:https://docs.langfuse.com/langchainDevelopmentAdd Poetry pluginspoetry self add poetry-dotenv-plugin
poetry self add poetry-bumpversionInstall dependenciespoetry install --all-extrasAdd Pre-commitpoetry run pre-commit installTestsSetupAdd .env based on .env.exampleRunRun allpoetry run pytest -s -v --log-cli-level=INFORun a specific testpoetry run pytest -s -v --log-cli-level=INFO tests/test_core_sdk.py::test_flushE2E tests involving OpenAI and Serp API are usually skipped, remove skip decorators intests/test_langchain.pyto run them.Update openapi specGenerate Fern Python SDK inlangfuseand copy the files generated ingenerated/pythoninto thelangfuse/apifolder in this repo.Execute the linter by runningpoetry run ruff format .Rebuild and deploy the package to PyPi.Publish releasepoetry version patchpoetry version prepatchfor pre-release versionspoetry installpoetry buildgit commit -am "chore: release v{version}"git pushgit tag v{version}git push --tagspoetry publishCreate PyPi API token:https://pypi.org/manage/account/token/Setup:poetry config pypi-token.pypi your-api-tokenCreate a release on GitHub with the changelog
Package: langgenius-client
langgenius-clientA LangGenius App Service-API Client, using for build a webapp by request Service-APIUsageapi_key = "your_api_key"

# Initialize CompletionClient
completion_client = CompletionClient(api_key)

# Create Completion Message using CompletionClient
completion_response = completion_client.create_completion_message(inputs={}, query="Hello", response_mode="blocking", user="user_id")
print(completion_response)

# Initialize ChatClient
chat_client = ChatClient(api_key)

# Create Chat Message using ChatClient
chat_response = chat_client.create_chat_message(inputs={}, message="Hello", user="user_id", response_mode="streaming")
print(chat_response)

# Get Chat History using ChatClient
chat_history = chat_client.get_chat_history(user="user_id")
print(chat_history)

# Get Conversation List using ChatClient
conversations = chat_client.list_conversations(user="user_id")
print(conversations)

# Rename Conversation using ChatClient
rename_conversation_response = chat_client.rename_conversation(conversation_id="conversation_id", name="new_name", user="user_id")
print(rename_conversation_response)
Package: langiso639
iso639A simple (really simple) library for working with ISO639-3 language codes.
Tested for Python 2.7 & 3.4.Includes data from Congress library:http://www.loc.gov/standards/iso639-2/php/code_list.php(+ updates)InstallationThe easiest way is usingpip:pip install langiso639If you are using Fedora 24+, you can install iso639 using dnf:dnf install python2-langiso639
# or
dnf install python3-langiso639Thanks, unknown Fedora packagers :-)Alternativespycountry:https://bitbucket.org/flyingcircus/pycountry- a more-featured packageiso639:https://github.com/noumar/iso639- another package with the same nameExample usageimportlangiso639>>>langiso639.to_name('sv')u'Swedish'>>>langiso639.to_native('sv')u'svenska'``
Package: langkit
LangKit is an open-source text metrics toolkit for monitoring language models. It offers an array of methods for extracting relevant signals from the input and/or output text, which are compatible with the open-source data logging librarywhylogs.💡 Want to experience LangKit? Go to thisnotebook!Table of Contents 📖MotivationFeaturesInstallationUsageModulesMotivation 🎯Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability space - a challenge worth solving, since the lack of visibility on the model's behavior can have serious consequences.Features 🛠️The currently supported metrics include:Text Qualityreadability scorecomplexity and grade scoresText RelevanceSimilarity scores between prompt/responsesSimilarity scores against user-defined themesSecurity and Privacypatterns - count of strings matching a user-defined regex pattern groupjailbreaks - similarity scores with respect to known jailbreak attemptsprompt injection - similarity scores with respect to known prompt injection attacksrefusals - similarity scores with respect to known LLM refusal of service responsesSentiment and Toxicitysentiment analysistoxicity analysisInstallation 💻To install LangKit, use the Python Package Index (PyPI) as follows:pip install langkit[all]Usage 🚀LangKit modules contain UDFs that automatically wire into the collection of UDFs on String features provided by whylogs by default. All we have to do is import the LangKit modules and then instantiate a custom schema as shown in the example below.importwhylogsaswhyfromlangkitimportllm_metricsresults=why.log({"prompt":"Hello!","response":"World!"},schema=llm_metrics.init())The code above will produce a set of metrics comprised of the default whylogs metrics for text features and all the metrics defined in the imported modules. This profile can be visualized and monitored in theWhyLabs platformor they can be further analyzed by the user on their own accord.More examples are availablehere.Modules 📦You can have more information about the different modules and their metricshere.