Saravanan666/pypi-org huggingface dataset:
Package: open511
Some utilities for theOpen511 APIformat.
Includes a validator, a tool to convert between Open511 serializations,
a Web interface for validation and conversion, and some utility/parsing
code.RequirementsPython 2.7 or 3.5, libxml2. Linux or MacOS. (It might work on Windows,
but hasn’t been tested. We’d be happy to work with anyone interested in
running this on Windows.)InstallationThis package is a Python application. The current best practice is to
install into an isolated Python environment, created with thevirtualenvpackage for Python 2, orpyvenvfor Python 3. Things
should still work if you don’t create an environment, but you may need
to run the setup commands below as root.Clone this repository, then runpython setup.py install. Or, to
install the latest released version, runeasy_install open511.Usageopen511-validate filename.xml

open511-validate http://demo.open511.org/api/events/

open511-convert --help

open511-convert filename.xml > filename.json

open511-convert filename.json > filename.xmlConversionsAvailable output formats: Open511 JSON (json), Open511 XML
(xml),MASAS-compatible Atom
(atom),KML(kml)Input formats: Open511 XML or JSON, andTraffic Management Data
Dictionary(TMDD) XMLYou can convert from any input format to any output format, e.g.open511-convertinput.tmdd-fkml output.kmlTMDDDue to the size and complexity of the TMDD specification, some input
files may not be supported. Please contact us if you have problems with
a particular TMDD input file, and we’ll try to get it working!To produce production-ready Open511 XML from TMDD, you need to specify
provide some information on your Open511 deployment via environment
variables. SetOPEN511_EVENTS_URLto the URL to your Open511 events
endpoint,OPEN511_JURISDICTION_URLto the URL of the appropriate
Open511 jurisdiction resource, andOPEN511_JURISDICTION_IDto the
Open511 ID of your jurisdiction. If these are not set, example values
will be used.More details on the conversion algorithm is indocs.Web interfaceA Web interface, available athttp://validator.open511.org/, is in
open511/webtools/init.py. Install the dependencies (listed in
requirements.txt, or runeasy_install Flask requests), then runpython open511/webtools/__init__.pyto start up a local server.
Package: openabm
Open-ABMOpen ABM runs on MESA and wraps basic MESA implementations with easy-to-use helper functions for fast and replicable ABM/multi-agent system prototyping.If you're viewing this on PyPI, please see our Github repo for documentation and getting started:https://github.com/cma43/Open-ABMTo get a quick start, please see our oabm_example.py file. For a fuller introduction, see our Jupyter notebook in the 'Introductory Walkthrough' folder. Also, keep in mind that you may access the source code of any template models under the 'examples' folder.All current starting models were built by the MESA team. Users are encouraged to explore MESA and its tools after getting started with Open ABM here:https://mesa.readthedocs.io/en/master/tutorials/intro_tutorial.html
Package: openaccess_epub
OpenAccess_EPUB is a program for converting OpenAccess Academic Journal
articles into the ePub format.Documentation for the project is hosted on
[Read the Docs](http://openaccess-epub.readthedocs.org)Getting StartedYou can install OpenAccess_EPUB from the source at the
[project page](https://github.com/SavinaRoja/OpenAccess_EPUB).or you can install with pip.pip install openaccess_epubOpenAccess_EPUB is only compatible with Python3, so make sure that you install
it with the correct version if you have more than one Python version.Additional requirements are [docopt](https://github.com/docopt/docopt) and
[lxml](http://lxml.de/), which pip will attempt to install for you.Once you’ve successfully installed OpenAccess_EPUB and its dependencies,
access the interface usingoaepubThis can be followed by various subcommands and help
regarding the usage can be retrieved byoaepub -hoaepub convert -hetc.Use theoaepub configurecommand to set up some local OpenAccess_EPUB
configuration interactively before converting articles to EPUB.How to ContributeIf you would like to contribute to the project, there are many ways to do so.
If you know Python and would like to assist in development, contact me and we
can discuss how you may help. Feedback regarding bugs, presentation, formatting,
and accessibility are all quite valuable.AuthorPaul Barton (SavinaRoja)
Package: openacli
OpenAclIWanted a tool to use OpenAI on the command line.  Also supports showing images.Installation>pipinstallopenacliUsageYou'll need anOpenAI API keyto use this library.Chat>oacli
me>"<prompt with quotes>"ai><someresponse>Image>oacli-i"<some prompt with quotes>"Python Pillow will bring up and imagePrompt>oacli"<some prompt with quotes>"<responsefromOpenAI>SettingsSeveral configuration settings are exposed through environment variables.  If they aren't provided, the defaults below will be applied.# OpenAI Settings
OPENAI_DEFAULT_ENGINE="text-davinci-003"
OPENAI_DEFAULT_TEMPERATURE=.5
OPENAI_DEFAULT_FREQUENCY_PENALTY=0
OPENAI_DEFAULT_PRESENCE_PENALTY=0
OPENAI_DEFAULT_MAX_TOKENS=2048
OPENAI_DEFAULT_IMAGE_NUMBER=1
OPENAI_DEFAULT_IMAGE_SIZE="1024x1024"

# OAclI Settings
OACLI_INPUT_COLOR=Green
OACLI_OUTPUT_COLOR=RedExamplesChatImagePrompt
Package: openacquisition
openacquisitionPython library to create data acquisition algorithms for mass spectrometersRequirementsInstallation$pipinstallopenacquisitionUsageContributingInterested 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.LicenseOpenAcquisitionwas created by theGoldfarb Labat Washington University School of Medicine in St. Louis. It is licensed under the terms of the MIT license.
Package: open-aea
Open AEA FrameworkA framework for autonomous economic agent (AEA) development with no vendor lock-inThis is a fork ofthe original AEA projectwith the benefit that all vendor lock-in at the package level is removed. This means, there are no dependencies on centrally hosted registries for agent packages and the AEA itself does not prefer any package vendor over another. Where feasible, compatibility with the original AEA framework is maintained.Get started developing AEAsCreate and launch a clean virtual environment with Python 3.10 (any Python>=3.8 works):pipenv --python 3.10 && pipenv shellInstall the package fromPyPI:pip install open-aea[all]Or, if you usezshrather thanbash:pip install "open-aea[all]"Then, build your agent as described in thedocs.CiteIf you are using our software in a publication, please
consider to cite it with the following BibTex entry:@misc{agents-aea,
  Author = {Marco Favorito and David Minarsch and Ali Hosseini and Aristotelis Triantafyllidis and Diarmid Campbell and Oleg Panasevych and Kevin Chen and Yuri Turchenkov and Lokman Rahmani and Jiří Vestfál and James Riehl and 8baller and Adamantios Zaras and David Vilela and Michiel Karrenbelt and Viraj Patel},
  Title = {Open Autonomous Economic Agent (AEA) Framework},
  Year = {2021},
}
Package: open-aea-cosmpy
CosmPyA python library for interacting with cosmos based blockchain networksInstallingTo install the library use:pip3installcosmpyGetting StartedBelow is a simple example for querying an account's balances:fromcosmpy.aerial.clientimportLedgerClient,NetworkConfig# connect to Fetch.ai network using default parametersledger_client=LedgerClient(NetworkConfig.fetchai_mainnet())alice:str='fetch12q5gw9l9d0yyq2th77x6pjsesczpsly8h5089x'balances=ledger_client.query_bank_all_balances(alice)# show all coin balancesforcoininbalances:print(f'{coin.amount}{coin.denom}')DocumentationBefore running this command make sure your development environment is set up. CheckDevelopment setupfor more details.To see the documentation:makedocs-liveThen navigate to the following URL in your browser:http://127.0.0.1:8000/cosmpy/ExamplesUnder theexamplesdirectory, you can find examples of basic ledger interactions usingcosmpye.g. transferring tokens, staking, deploying and interacting with a smart contract, and performing atomic swaps.To contributePlease seeCONTRIBUTINGandDEVELOPINGguides.Extra ResourcesGitHub RepoBug ReportsDiscussionsGetting Started
Package: open-aea-flashbots
web3-flashbotsThis library works by injecting flashbots as a new module in the Web3.py instance, which allows submitting "bundles" of transactions directly to miners. This is done by also creating a middleware which captures calls toeth_sendBundleandeth_callBundle, and sends them to an RPC endpoint which you have specified, which corresponds tomev-geth.To apply correct headers we use theflashbotmethod which injects the correct header on POST.Quickstartfrometh_account.signers.localimportLocalAccountfromweb3importWeb3,HTTPProviderfromflashbotsimportflashbotfrometh_account.accountimportAccountimportosETH_ACCOUNT_SIGNATURE:LocalAccount=Account.from_key(os.environ.get("ETH_SIGNER_KEY"))w3=Web3(HTTPProvider("http://localhost:8545"))flashbot(w3,ETH_ACCOUNT_SIGNATURE)Now thew3.flashbots.sendBundlemethod should be available to you. Look inexamples/simple.pyfor usage examples.GoerliTo use goerli, add the goerli relay RPC to theflashbotfunction arguments.flashbot(w3,ETH_ACCOUNT_SIGNATURE,"https://relay-goerli.flashbots.net")Development and testingInstallpoetryPoetry will automatically fix your venv and all packages needed.poetryinstallTips: PyCharm has a poetry pluginSimple Goerli ExampleSeeexamples/simple.pyfor environment variable definitions.poetryshellETH_SENDER_KEY=<sender_private_key>\PROVIDER_URL=https://eth-goerli.alchemyapi.io/v2/<alchemy_key>\ETH_SIGNER_KEY=<signer_private_key>\pythonexamples/simple.pyLintingIt's advisable to run black with default rules for lintingsudopipinstallblack# Black should be installed with a global entrypointblack.
Package: open-aea-web3
web3.pyA Python library for interacting with Ethereum.Python 3.7.2+ supportQuickstartGet started in 5 minutesortake a tourof the library.DocumentationFor additional guides, examples, and APIs, see thedocumentation.Want to help?Want to file a bug, contribute some code, or improve documentation? Excellent! Read up on our
guidelines forcontributing,
then check out issues that are labeledGood First Issue.Questions on implementation or usage? Join the conversation ondiscord.
Package: openagents
Mask-PredictDownload modelDescriptionDatasetModelMASK-PREDICT[WMT14 English-German]download (.tar.bz2)MASK-PREDICT[WMT14 German-English]download (.tar.bz2)MASK-PREDICT[WMT16 English-Romanian]download (.tar.bz2)MASK-PREDICT[WMT16 Romanian-English]download (.tar.bz2)MASK-PREDICT[WMT17 English-Chinese]download (.tar.bz2)MASK-PREDICT[WMT17 Chinese-English]download (.tar.bz2)Preprocesstext=PATH_YOUR_DATAoutput_dir=PATH_YOUR_OUTPUTsrc=source_languagetgt=target_languagemodel_path=PATH_TO_MASKPREDICT_MODEL_DIRpython preprocess.py --source-lang ${src} --target-lang ${tgt} --trainpref $text/train --validpref $text/valid --testpref $text/test  --destdir ${output_dir}/data-bin  --workers 60  --srcdict ${model_path}/maskPredict_${src}${tgt}/dict.${src}.txt --tgtdict ${model_path}/maskPredict${src}_${tgt}/dict.${tgt}.txtTrainmodel_dir=PLACE_TO_SAVE_YOUR_MODELpython train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512  --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir ${model_dir}Evaluationpython generate_cmlm.py ${output_dir}/data-bin  --path ${model_dir}/checkpoint_best_average.pt  --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10  --decoding-strategy mask_predictLicenseMASK-PREDICT is CC-BY-NC 4.0.
The license applies to the pre-trained models as well.CitationPlease cite as:@inproceedings{ghazvininejad2019MaskPredict,title={Mask-Predict: Parallel Decoding of Conditional Masked Language Models},author={Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},year={2019},}
Package: openagua-client
openagua-clientA very thin wrapper around the OpenAgua web API.UsageTo use this, you need an API key from OpenAgua. Once you have that, you can supply it from an environment variable calledOA_SECRET_KEYor directly as an argument to the client.Here are two examples demonstrating each approach.Example 1In this example, the API key is in the environment variableOA_SECRET_KEY. Note that this may be loaded dynamically using, for example,dot_envfromopenagua_clientimportClientasOAoa=OA()network=oa.get_network(77)Example 2The key is supplied directly. Note that it is best practice to never hard code secrets. Therefore, that is not done in this example, though technically it could be done. Using environment variables for keys is best practice.importosfromopenagua_clientimportClientasOAoa_api_key=os.environ['MY_OPENAGUA_API_KEY']oa=OA(api_key=oa_api_key)network=oa.get_network(77)
Package: openai-agent
OpenAI AgentEasy-to-use OpenAI Agent with support for the latest Function call feature.UsageCreate your own functions using comprehensive documentation comments and type annotations.defadd(a:int,b:int):"""Add two numbers.:param a: First number.:param b: Second number."""returna+bLoad the functions as function objects.fromopenai_agent.completionsimportget_function_completionfromopenai_agent.functionsimportFunctionfromopenai_agent.messagesimportUserMessageresponse=get_function_completion(messages=[UserMessage(content="Calculate 2 + 2")],functions=[Function.load_from_func(add)],)print(response.content)
Package: openai-agent-llm
openai-agent-llm为本地私有部署大模型提供openai接口兼容开发者只需要定义推理接口内容即可安装pipinstallopenai_agent_llmimportjsonimportosimportuuidimportuvicornfromstarlette.middleware.corsimportCORSMiddlewarefromopenai_agent_llmimportLLMAPIfromopenai_agent_llmimportModelServerfromwebsockets.sync.clientimportconnectfromloguruimportloggerserver=os.environ.get("server","wss://mikeee-chatglm2-6b-4bit.hf.space/queue/join")classChatGLMInt4SpiderServer(ModelServer):http_request=Nonedefdeal_prompt(self,params):prompt=params.get("prompt")iftype(prompt)==str:prompt=prompt.replace("Human:","问:").replace("AI:","答:")iflen(prompt)>2andprompt[-2:0]=="答:":prompt=prompt[:-2]eliftype(prompt)==list:prompt=""foriinparams.get("prompt",[]):role=i.get("role","")ifrole=="system":passelse:prompt=prompt+i.get("content","")+"\n"else:prompt="hi"# if len(prompt) * CutWordMultiple > 1024:#     prompt = prompt[int(len(prompt) * 2) / 3:]returnpromptdefsend_init(self,websocket,session_hash):value=websocket.recv()logger.info(value)websocket.send(json.dumps({"fn_index":1,"session_hash":session_hash},ensure_ascii=False))foriinrange(2):value=websocket.recv()logger.info(value)definfer(self,params):prompt=self.deal_prompt(params)url=serverrequest_id=str(uuid.uuid4())withconnect(url)aswebsocket:self.send_init(websocket=websocket,session_hash=request_id)websocket.send(json.dumps({"data":[False,prompt,[],8192,0.85,0.95,None,None],"event_data":None,"fn_index":1,"session_hash":request_id},ensure_ascii=False))text=""whileTrue:value=websocket.recv()try:data=json.loads(value)except:continuemsg=data.get("msg")ifdata.get("msg")=="process_completed":finish=Trueelse:finish=Falseifmsg=="process_starts":continueelifmsg=="process_generating":data_=data.get("output",{}).get("data",[])[0][0][1][3:-4].replace("<br>","\n")token=data_[len(text):]text=data_stop=NoneiffinishisTrue:stop="stop"yield{"text":token,"usage":{"prompt_tokens":0,"completion_tokens":0,"total_tokens":0,},"finish_reason":stop,}iffinishisTrue:print()breakapp=LLMAPI()app.init(model_server_class=ChatGLMInt4SpiderServer)app.add_middleware(CORSMiddleware,allow_origins=["*"],allow_credentials=True,allow_methods=["*"],allow_headers=["*"],)if__name__=='__main__':uvicorn.run(app=app,port=9000)
Package: openaiapi
openaiapiA light wrapper around the OpenAI API usingapiron.Installation$python-mpipinstallopenaiapiUsageFirst, make anOPENAI_API_KEYenvironment variable available whose value is your OpenAI API key. Once you have an API in place, you can use theOpenAIclass to generate content:>>>fromopenaiapiimportOpenAI>>>OpenAI.completions(json={"model":"text-davinci-003","prompt":"What is your quest?"}){...,'choices':[{'text':'\n\nMy quest is to find my purpose and fulfill it.',...}],...}}See the package documentation for the full list of available endpoints, and seethe OpenAI documentationfor the accepted/required parameters to each endpoint.
Package: openai-api-call
中文文档移步这里。Openai API callA Python wrapper for OpenAI API, supporting multi-turn dialogue, proxy, and asynchronous data processing.Installationpipinstallopenai-api-call--upgradeUsageSet API Key and Base URLMethod 1, write in Python code:importopenai_api_callopenai_api_call.api_key="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"openai_api_call.base_url="https://api.example.com"Method 2, set environment variables in~/.bashrcor~/.zshrc:exportOPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"exportOPENAI_BASE_URL="https://api.example.com"ExamplesExample 1, simulate multi-turn dialogue:# first chatchat=Chat("Hello, GPT-3.5!")resp=chat.getresponse()# continue the chatchat.user("How are you?")next_resp=chat.getresponse()# add response manuallychat.user("What's your name?")chat.assistant("My name is GPT-3.5.")# save the chat historychat.save("chat.json",mode="w")# default to "a"# print the chat historychat.print_log()Example 2, process data in batch, and use a checkpoint filecheckpoint:# write a function to process the datadefmsg2chat(msg):chat=Chat(api_key=api_key)chat.system("You are a helpful translator for numbers.")chat.user(f"Please translate the digit to Roman numerals:{msg}")chat.getresponse()checkpoint="chat.jsonl"msgs=["%d"%iforiinrange(1,10)]# process the datachats=process_chats(msgs[:5],msg2chat,checkpoint,clearfile=True)# process the rest data, and read the cache from the last timecontinue_chats=process_chats(msgs,msg2chat,checkpoint)Example 3, process data in batch (asynchronous), print hello using different languages, and use two coroutines:fromopenai_api_callimportasync_chat_completion,load_chatslangs=["python","java","Julia","C++"]chatlogs=["print hello using%s"%langforlanginlangs]async_chat_completion(chatlogs,chkpoint="async_chat.jsonl",ncoroutines=2)chats=load_chats("async_chat.jsonl")LicenseThis package is licensed under the MIT license. See the LICENSE file for more details.update logCurrent version1.0.0is a stable version, with the redundant featurefunction callremoved, and the asynchronous processing tool added.Beta versionSince version0.2.0,Chattype is used to handle dataSince version0.3.0, you can use different API Key to send requests.Since version0.4.0, this package is mantained bycubenlp.Since version0.5.0, one can useprocess_chatsto process the data, with a customizedmsg2chatfunction and a checkpoint file.Since version0.6.0, the featurefunction callis added.
Package: openai-async
openai-asyncA light-weight, asynchronous client for OpenAI API - chat completion, text completion, image generation and embeddings.For the full documentation, go to theopenAI website.Installationpip install openai-asyncUseChat completionresponse = await openai_async.chat_complete(
    _OPEN_AI_API_KEY,
    timeout=2,
    payload={
        "model": "gpt-3.5-turbo",
        "messages": [{"role": "user", "content": "Hello!"}],
    },
)
print(response.json()["choices"][0]["message"])
>>> {"role": "assistant", "content": "\n\nHello there! How can I assist you today?"}Visit theofficial documentationfor more info.Text completionimport openai_async

response = await openai_async.complete(
    "<API KEY>",
    timeout=2,
    payload={
        "model": "text-davinci-003",
        "prompt": "Correct this sentence: Me like you.",
        "temperature": 0.7,
    },
)
print(response.json()["choices"][0]["text"].strip())
>>> "I like you."Visit theofficial documentationfor more info.Image generationimport openai_async
import urllib.request
from PIL import Image

response = await openai_async.generate_img(
    "<API KEY>",
    timeout=8,
    payload={
        "prompt": "a white siamese cat",
        "n": 1,
        "size": "512x512"
    },
)
urllib.request.urlretrieve(response.json()["data"][0]["url"], "img.png")
Image.open("img.png").show()Embeddingsimport openai_async

response = await openai_async.embeddings(
    <API_KEY>,
    timeout=2,
    payload={"model": "text-embedding-ada-002", "input": "a cat"},
)
print(response.json()["data"][0]["embedding"])
>>> [-0.019408401,
-0.009246278,
-0.014390069,
-0.012294915,
-0.0025609178,
0.021252638,
...]Visit theofficial documentationfor more info.Get an API keyTo generate an openAI API key, while in theopenAI website, click on your username in the top right corner, then go to "View API keys" and create a key.DisclaimerThis repository has no connection whatsoever to openAI.
Package: openai-billing
openai-billing-playwrightFetch openai billing info from command lineInstall itDownloadthe latest releaseand unzip.Use itRunopenai-billing.exefrom command line.openai-billing.exe--help

openai-billing.exeu@gmai.compw1u2@gmai.compw2# check two pairsopenai-billing.exeu@gmai.compw1u2@gmai.compw2# or when special characters (&, * etc) are in passwordsopenai-billing.exe"u@gmai.com pw*1 u2@gmai.com pw2&"# check pairs in clipboardopenai-billing.exeopenai_billing# check pairs in a fileopenai-billing.exe--filenameemail-pw-pairs.txt# use a proxyopenai-billing.exe--proxysoscks5://127.0.0.1:1080u@gmai.compw1u2@gmai.compw2# turn on browser: to see the whole thing in actionopenai-billing.exeu@gmai.compw1--headful# turn on debugsetLOGURU_LEVEL=TRACE
openai-billing.exeu@gmai.compw1--headfulUse it with pythonpipinstallopenai-billing# pip install git+https://github.com/ffreemt/openai-billing-playwright# poetry add git+https://github.com/ffreemt/openai-billing-playwright# git clone https://github.com/ffreemt/openai-billing-playwright && cd openai-billing-playwrightpython-mopenai_billing# oropenai-billing# helppython-mopenai_billing--help# check two pairspython-mopenai_billingu@gmai.compw1u2@gmai.compw2# or special characters (&, * etc) in passwordspython-mopenai_billing"u@gmai.com pw*1 u2@gmai.com pw2&"# check pairs in clipboardpython-mopenai_billing# check pairs in a filepython-mopenai_billing--filenameemail-pw-pairs.txt# use a proxypython-mopenai_billing--proxysoscks5://127.0.0.1:1080u@gmai.compw1u2@gmai.compw2# turn on browser: to see the whole thing in actionpython-mopenai_billingu@gmai.compw1--headful# turn on debugsetLOGURU_LEVEL=TRACE
python-mopenai_billingu@gmai.compw1--headful
Package: openai-chat-parser
OpenAI Chat Parser is a command-line tool that downloads, extracts, and parses OpenAI chat conversation archives. The tool organizes conversations into separate folders with appropriate file formats based on the conversation content.InstallationYou can install the OpenAI Chat Parser package from PyPI using pip:pipinstallopenai-chat-parserUsageAfter installing the package, you can use theopenai-chat-parsercommand-line tool to process chat archives.To run the OpenAI Chat Parser, execute the following command:openai-chat-parserThe script will prompt you to enter the ZIP URL of the chat archive:Enter the zip URL:Enter the URL and pressEnter. The tool will download, extract, and parse the chat conversations, organizing them into separate folders.ConfigurationBefore using the OpenAI Chat Parser, you can configure the package by modifying theconfig.jsonfile. This file contains settings like the destination path, archive folder, export folder, and download retries.Here’s an example of theconfig.jsonfile:{"chats_home":"~/Chats","downloads_folder":"downloads","archive_folder":"archive","conversations_folder":"conversations","zip_download_retries":3,"retry_wait_time_seconds":5}You can customize the settings in theconfig.jsonfile to suit your needs.LicenseThis project is licensed under the MIT License.