Saravanan666/pypi-org huggingface dataset:
Package: lki
Lirian Su(LKI)'s Configuration, 我的配置你好，我是苏子岳。
我的开发环境是 Windows 系统，
但我也时常会在 Unix 系统上游荡 (云服务器或者是 WSL)。
因为经常折腾环境，
所以我专门开了一个项目来同步我的各类配置。
很不巧，这个叫做LKI的项目后来被 GitHub 当作了同名自我介绍项目了，
假如你对我本人感兴趣，可以读一下我的自我介绍。配置风格我很懒，
主要使用各类缩写、热键执行命令。
这个项目包含了大量缩写，
但这些改动都是额外附加的，
它们并不改变原有的命令习惯。为了方便安装，
我把配置安装过程写成了 python 脚本。
（所以我拿到电脑第一时间是装一个 python 环境）快速安装首先，确保环境中有 Python 3.7+，然后使用pip快速安装：pip install lki && lki install本命令同时支持 Unix, MacOS, Windows 环境
发现有问题就给我报 Issue使用指南~/.gitconfig中包含了大量的 git alias：# 查看 git 配置$cat~/.gitconfig# git 的基础缩写$gitci# `git commit`$gitbr# `git branch`$gitpf# `git push -f`$gitsv# `git save` <=> `git stash`$gitld# `git load` <=> `git stash pop`# git 的常用缩写$gitcm# amend last commit$gitlogg# log ing graph$gitpd# push dev with gitlab merge request created$gityes# show what happened yesterday~/.profile中包含了大量的 bash alias：# 查看 bash 配置$cat~/.profile# 常用缩写$pleasevisudo# `sudo visudo`$gst# `git status`$pvsync--dev# `pipenv sync --dev`# 子命令复合缩写$dpa# `docker ps -a`$kgp# `kubectl get pods`$gcm# `git cm`$gsh# ssh through gate# 个人向的，你们用不上的缩写# 我写这就是给你们看看我平常跑那些命令$svl# go into my blog repo$dspmtest# run django test for zaihui/server project$stp# exec bash into k8s clusterVim 的配置，参见另外一个项目LKI/dotvim关于本项目的功能还有很多，
但是这里的空白太少我写不下了 XD开源协议宽松的 MIT License,
意味着你可以做任何更改，
甚至把作者名都改成你。我有问题要问没关系，不论是对项目的问题还是对我个人的问题，
或者是你觉得哪条命令不够好用，
都欢迎来项目里直接提 Issue。
Package: lk-logger
LK Logger中文版An alternative to Python built-inprintfunction.Features:Show source map, function name and varnames in console.Easy and simple.# add thisimportlk_loggerlk_logger.setup()# then remain using `print` as usual...print('Hello world')print(123,456,789)...It will replace Python's built-inprintfunction to take care all the leftovers.Simplify writing:# beforea,b=1,2print('a ={}, b ={}, a + b ={}'.format(a,b,a+b))# afterprint(a,b,a+b)(Another example)Non-intrusive. Once import lk-logger, there's no more modifications to your code. Just follow the way you are usingprintas usual.Code highlight.Installpipinstalllk-loggerThe latest version is 5.4.0 or higher.Quick Startimportlk_loggerlk_logger.setup(show_varnames=True)print('hello world')a,b=1,2print(a,b,a+b)print(a,b,(c:=a+b),c+3)Screenshot:Advanced UsageUsually the above example is enough to use.The advanced feature is"markup"shorthand.A markup is a string starts with ':', layed on the first or the last position of positional arguments, and consists of multiple marks.For example:print(':i','monday')print(':i','tuesday')print(':i','wednesday')It prints weekdays with a numeric prefix:Another one:importlk_loggerlk_logger.setup()print('this is a divider',':d')print(':v0','this is a TRACE message')print(':v1','this is a DEBUG message')print(':v2','this is a INFO  message')print(':v3','this is a WARN  message')print(':v4','this is a ERROR message')print(':v5','this is a FATAL message')Here is a list of all available marks:MarkDescription:ddivider line:iindex:fflush:llong/loose format (multiple lines):pparent layer:rrich style:sshort/simple/single line format:ttimestamp:vverbosity/log levelMarkup options::d0     default divider line (default)
:d1+    user defined (if not, fallback to :d0)

:i0     reset index
:i1     a simple increamental index (starts from 1) (default)

:f0     instantly print a message (default)
:f1     instantly print a message, and drain out message queue in other thread
:f2     wait until all messages in queue are printed, then print this message

:l0     let lk-logger decides how to format long message (default)
:l1     force expand all nodes

:p0     current frame
:p1     parent frame (default)
:p2     grand parent frame
:p3     great grand parent frame
:p4     great great grand parent frame
:p5+    great great great ... grand parent frame
        note: be careful using :p2+, it may crash if the frame count exceeds
        depth limit

:r0     rich style. see also rich's documentation (default)
:r1     rich renderable object, e.g. rich.table.Table, rich.panel.Panel, etc.

:s0     do not show varnames (default)
:s1     do not show source map, function name and varnames

:t0     reset timer
:t1     show time elapsed since last timer starts (default)

:v0     trace (default color)
:v1     debug (grey) (default)
:v2     info (blue)
:v3     warning (yellow)
:v4     error (red)
:v5     fatal (white text on red background)
:v6+    user defined (if not, fallback to :v0)Detailed examples:Seeexamples/all_markup_usages.py.Screenshot:
Package: lkmeans
LKMeansLKMeans is a Python library that extends the functionality of the KMeans algorithm by allowing clusterization using the Minkowski metric instead of the traditional Euclidean distance. The Minkowski metric provides better quality performance in certain scenarios, making LKMeans a valuable tool for clustering analysis.FeaturesClusterization using the Minkowski metricImproved quality performance compared to traditional KMeansEasy integration into existing machine learning pipelinesInstallationYou can install LKMeans:From sourceexportPYTHONPATH=${PYTHONPATH}:$(pwd)/lkmeansFrom PyPIpipinstalllkmeansUsageUsing LKMeans is straightforward. Here's a simple example that demonstrates how to use the library:importnumpyasnpfromlkmeansimportLKMeans# Generate some random dataX=np.random.rand(100,2)# Create an instance of LKMeans with the desired number of clusters and Minkowski parameterlkmeans=LKMeans(n_clusters=3,p=0.8)# Fit the model to the datalabels=lkmeans.fit_predict(X)centers=lkmeans.cluster_centers_inertia=lkmeans.inertia_print(labels,centers,inertia)In this example, we first import the LKMeans class from the LKMeans library. We create an instance of LKMeans with the desired number of clusters (n_clusters) and the desired Minkowski distance parameter (p), and fit the model to the data using the fit method.ContributingContributions to LKMeans are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.LicenseLKMeans is licensed under the MIT License. See theLICENSEfile for more information.Cite@misc{LKMeans2023,author={Aleksei Trutnev},title={Clustering high-dimensional data with Minkowski distance},year={2023},url={https://github.com/alexgiving/LKMeans}}ContactFor any questions or inquiries, please contactalexgiving@mail.ru.Enjoy clustering with LKMeans!
Package: lkml
lkmlA speedy LookML parser and serializer implemented in pure Python.Read the documentation onRead the Docs.lkml.loadparses LookML strings to Python objects or JSON strings.lkml.dumpserializes (generates) LookML strings from Python objects.Why should you uselkml?Tested onover 160K lines of LookMLfrom public repositories on GitHubParses a typical view or model file in <10 ms(excludes I/O time)Written in pure, modern Python 3.7 withno external dependenciesAfull unit test suitewith excellent coverageInterested in contributing tolkml? Check out thecontributor guidelines.How do I install it?lkmlis available to install onpipvia the following command:pip install lkml
Package: lkml2cube
lkml2cubelkml2cube is a tool to convert LookML models into Cube data models.UsageThere are two main commands,cubesandviews. Both commands read all the files in the provided input parameter, including those referenced by the LookML keywordincludes.
The difference is that thecubescommand only creates Cube'scubemodel definitions, while theviewscommand createscubeandviewmodel definitions. In LookML syntax, the join relationships are defined at the explore level (equivalent to Cube'sview). That's the reason explores need special treatment and also the reason for explores to be ignored in the lkml2cubecubescommand.Installpipinstalllkml2cubeConvert LookML views into Cube YAML definition.lkml2cubecubespath/to/file.view.lkml--outputdirexamples/Show Python dict representation of the LookerML objectlkml2cubecubes--parseonlypath/to/file.view.lkmlConvert LookML Explores into Cube's views YAML definition.lkml2cubeviewspath/to/file.explore.lkml--outputdirexamples/
Package: lkmlfmt
lkmlfmtlkmlfmt formats your LookML files including embedded SQL and HTML.InstallationpipinstalllkmlfmtCLIUselkmlfmtcommand to format your LookML file(s).
For further information, use--helpoption.lkmlfmt[OPTIONS][FILE]...APIfromlkmlfmtimportfmtlkml=fmt("""\view: view_name {derived_table: {sql:with cte as (select col1, col2 from tablenamewhere ts between current_date()-7 and current_date())select {% if true %} col1 {% else %} col2 {% endif %} from cte;;}}""")assertlkml=="""\view: view_name {derived_table: {sql:withcte as (select col1, col2from tablenamewhere ts between current_date() - 7 and current_date())select{% if true %} col1{% else %} col2{% endif %}from cte;;}}"""GitHub ActionsTo check if your LookML files are formatted.on:[pull_request]jobs:format-check:runs-on:ubuntu-lateststeps:-uses:actions/checkout@v3-uses:actions/setup-python@v4with:# '>=3.11' is requiredpython-version:'3.11'# you should specify the version of lkmlfmt!-run:pip install lkmlfmt-run:lkmlfmt --check path/to/lookml/file/or/directoryTo format arbitrary branch and create pull request.on:[workflow_dispatch]jobs:format-pr:runs-on:ubuntu-lateststeps:-uses:actions/checkout@v3-uses:actions/setup-python@v4with:# '>=3.11' is requiredpython-version:'3.11'# you should specify the version of lkmlfmt!-run:pip install lkmlfmt-run:lkmlfmt path/to/lookml/file/or/directory# check the documentation especially about workflow permissions# https://github.com/marketplace/actions/create-pull-request-uses:peter-evans/create-pull-request@v5with:branch:format/${{ github.ref_name }}FeedbackI'm not ready to accept pull requests, but your feedback is always welcome.
If you find any bugs, please feel free to create an issue.See alsoIn default, lkmlfmt formats embedded sql using sqlfmt.sqlfmtYou can install plugins to change the format of embeded looker expression, sql or html.
They are distributed under their own licenses, so please check if they are suitable for your purpose.lkmlfmt-djhtml
Package: lkmlfmt-djhtml
lkmlfmt-djhtmlA plugin forlkmlfmt.Installationpipinstalllkmlfmtlkmlfmt-djhtmlCLIlkmlfmt--pluginlkmlfmt_djhtmlAPIfromlkmlfmtimportfmtlkml=fmt("""\view: view_name {dimension: column_name {html:{% if value == "foo" %}<img src="https://example.com/foo"/>{% else %}<img src="https://example.com/bar"/>{% endif %} ;;}}""",plugins=["lkmlfmt_djhtml"])assertlkml=="""\view: view_name {dimension: column_name {html:{% if value == "foo" %}<img src="https://example.com/foo"/>{% else %}<img src="https://example.com/bar"/>{% endif %};;}}"""
Package: lkops
lk_opsstatementThe implementation of some open sourceoperators,modulesandlossesis based on the principle of out of the box.Each implementation should be attached withapplication examples,sourcesandgeneral principle descriptions.structureconn_redis.py: redis连接池示例_mysql.py:mysql单连接，查询，插入，更新，执行mysql连接池 执行_neo4j.py: neo4j连接，执行cypherencoderelative_position_representation.py: torch相对位置编码tools_threadpool.py: 线程池，异步机制_count_neo4j.py: 图谱数据统计_ecg_api_count.py: ecg调用统计_export_kg_data_to_yml.py:: 导出图谱食材食谱数据到ymlmodules.py: 计时装饰器, json number EncoderutilsLogger.py: devops logger配置Records2022.11.07 14:03 update Logger.py
2022.11.09 17:52 定时任务
2022年12月26日18:14:01 更新发布方式
2022年12月27日18:15:31 poetry 测试releasepypi authu: ticoAgp: 19990523ONLYrelease command修改版本号执行pythonsetup.pybdist_wheelsdist
twineuploaddist/*--verbose注：具体用到的tool依赖需要在实际项目中声明安装，处于精简考虑 发布的lkops不做任何依赖绑定
Package: lk-utils
LK Utilslk-utils is a set of utilities to make data processing more simple and fluent.Installpipinstalllk-utilslk-utils requires python 3.8 or higher version.Usagesubprocnew thread decoratorfromlk_utilsimportnew_threaddefmain(files:list[str])->None:forfinfiles:handle_file(f)@new_thread()defhandle_file(file:str)->None:# do something...fetch results from threadsfromlk_utilsimportnew_threaddefmain(files:list[str])->None:pool=[]forfinfiles:thread=handle_file(f)pool.append(thread)...forthreadinpool:result=thread.join()print(result)@new_thread()defhandle_file(file:str)->str:# do something...run cmd argsfromlk_utilsimportrun_cmd_argsfromlk_utilsimportrun_cmd_shellrun_cmd_args('python','-m','pip','list')run_cmd_shell('python -m pip list')advanced filter:fromlk_utilsimportrun_cmd_argsdefpip_install(dest:str,url_index:str=None)->None:run_cmd_args(('python','-m','pip'),('install','-r','requirements.txt'),('-t',dest),('-i',url_index),)mklink, mklinksfromlk_utilsimportmklink,mklinksmklink('/from_dir','/to_dir_1')mklinks('/from_dir','/to_dir_2')filesniffget current dir, get relative pathimportosfromlk_utilsimportfilesniffasfsprint(fs.currdir()==os.path.dirname(__file__).replace('\\','/'))# -> Trueprint(fs.relpath('..')==os.path.dirname(fs.currdir()))# -> Truelist files/dirsfromlk_utilsimportfilesniffasfsforpath,nameinfs.find_files('.'):# this is an generator.print(path,name)#   the first element is the **abspath**, the second is path's#   basename (<- os.path.basename(path))forpathinfs.find_file_paths('.'):# this is a list[str]print(path)fornameinfs.find_file_names('.'):# this is a list[str]print(name)# more:#   fs.findall_files#   fs.findall_file_paths#   fs.findall_file_names##   fs.find_dirs#   fs.find_dir_paths#   fs.find_dir_names##   fs.findall_dirs#   fs.findall_dir_paths#   fs.findall_dir_namesread_and_writeloads and dumpsfromlk_utilsimportread_and_writeasrwdata_r=rw.loads(file_i)#   it recognizes json, yaml, pkl as sturctured data. others are treated as#   plain text.data_w=...rw.dumps(data_w,file_o)#   it recognizes json, yaml, pkl as sturctured data. others are treated as#   plain text.below are marked as deprecated.excelexcel reader and writerfromlk_utilsimportexcelasexlreader=exl.ExcelReader(file_i)#   accepts '.xls' and '.xlsx' files....# TODO:CompleteExamplewriter=exl.ExcelWriter(file_o)#   accepts only '.xlsx' files....# TODO:CompleteExamplewriter.save()nlpTODO
Package: ll2location
ll2locationInstallationpip install ll2locationUsage example: Find from all countryfrom latlong.gadm import Gadm
from latlong.latlong import LatLong

#init all gadm package in all country, it may take a long time, maybe a few hours
gadm = Gadm()  
gadm.get_shp_fully()

#init lat long
lat = 10.773213
long = 106.671818

#from lat long to location
latlng = LatLong()
code, country_info, detail_info = latlng.to_location(10.773213, 106.671818)
print(detail_info)The output could be:{'GID_3': 'VNM.25.10.4_1',
  'GID_0': 'VNM',
  'COUNTRY': 'Vietnam',
  'GID_1': 'VNM.25_1',
  'NAME_1': 'Hồ Chí Minh',
  'NL_NAME_1': 'NA',
  'GID_2': 'VNM.25.10_1',
  'NAME_2': 'Quận 10',
  'NL_NAME_2': 'NA',
  'NAME_3': 'Phường 12',
  'VARNAME_3': 'Ward 12',
  'NL_NAME_3': 'NA',
  'TYPE_3': 'Phường',
  'ENGTYPE_3': 'Ward',
  'CC_3': 'NA',
  'HASC_3': 'NA'}Usage example: Find from specific country codefrom latlong.latlong import LatLong
from latlong.gadm import Gadm, GetGadm

#show country code infomation
get_gadm = GetGadm()
codes_detail = get_gadm.get_codes_detail()
print(codes_detail)

#init gadm package in specific country
code = "VNM"
gadm = Gadm()
gadm.get_shp_by_code(code)

#init lat long
lat = 10.773213
long = 106.671818

#from lat long to location in specific country
latlng = LatLong()
detail_info = latlng.to_location_by_country_code(code, lat, long)

print(detail_info)The output could be:{'GID_3': 'VNM.25.10.4_1',
 'GID_0': 'VNM',
 'COUNTRY': 'Vietnam',
 'GID_1': 'VNM.25_1',
 'NAME_1': 'Hồ Chí Minh',
 'NL_NAME_1': 'NA',
 'GID_2': 'VNM.25.10_1',
 'NAME_2': 'Quận 10',
 'NL_NAME_2': 'NA',
 'NAME_3': 'Phường 12',
 'VARNAME_3': 'Ward 12',
 'NL_NAME_3': 'NA',
 'TYPE_3': 'Phường',
 'ENGTYPE_3': 'Ward',
 'CC_3': 'NA',
 'HASC_3': 'NA'}LICENSEFromhttps://gadm.org/license.html.GADM license
The data are freely available for academic use and other non-commercial use. Redistribution or commercial use is not allowed without prior permission.Using the data to create maps for publishing of academic research articles is allowed. Thus you can use the maps you made with GADM data for figures in articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are allowed (but not required) to publish these articles (and the maps they contain) under an open license such as CC-BY as is the case with PLoS journals and may be the case with other open access articles. Data for the following countries is covered by a a different license Austria: Creative Commons Attribution-ShareAlike 2.0 (source: Government of Ausria)
Package: lla
llaInterpreter for a basic logic language, inspired by two-element boolean algebra.DemoHere's a quick demo, based on the example that prompted its creation:>>> from lla.interpreter import Interpreter
>>> variables = {
...     'ssh': True,
...     'website_up': True,
...     'search_up': False,
...     'elasticsearch.http': False,
...     'elasticsearch.process': True,
... }
>>> interpreter = Interpreter(variables)
>>> interpreter.run('ssh & website_up')
True
>>> interpreter.run('ssh & website_up & search_up')
False
>>> interpreter.run('!search_up & !elasticsearch.http & elasticsearch.process')
True
>>> interpreter.run('undefined_variable')
Traceback (most recent call last):
    ...
lla.interpreter.UndefinedVariableException: Undefined variable: undefined_variableBy replacing thevariablesdict with a class implementing__getitem__,
it becomes incredibly flexible.OverviewHere's the gist of it:All variables are booleans.You can't define variables in the language itself, but it be provided adictor any object implementing__getitem__.Only fully-resolvable statements are supported.
a. Anything else raises a Python exception.You provide the interpreter a single statement and it gets simplified to a single boolean value, which is then returned.DependenciesIn theory, all you need is Python 3.6+.ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/duckinator/lla.The code for lla is available under theMIT License.
Package: llab-omero-iviewer
OMERO.iviewerAn OMERO.web app for visualizing images in OMERO.RequirementsOMERO 5.6.0 or newer.Installing from PyPIThis section assumes that an OMERO.web is already installed.Install the app usingpip:$ pip install -U omero-iviewerAdd iviewer custom app to your installed web apps:$ omero config append omero.web.apps '"omero_iviewer"'To replace the default omero.web viewer:$ omero config set omero.web.viewer.view omero_iviewer.views.indexTo enable the “open with” feature:$ omero config append omero.web.open_with '["omero_iviewer", "omero_iviewer_index",
  {"supported_objects":["images", "dataset", "well"],
   "script_url": "omero_iviewer/openwith.js", "label": "OMERO.iviewer"}]'Now restart OMERO.web as normal.UsageA guide to using OMERO.iviewer can be found onhttps://omero-guides.readthedocs.io/en/latest/iviewer/docs/iviewer.htmlLicenseOMERO.iviewer is released under the AGPL.Copyright2017-2022, The Open Microscopy Environment
Package: llama2-py
llama2.pydemowhy this fork?This repository serves as a fork that provides a Python-based implementation ofllama2.c. Designed for an extensive audience, it aims to be a straightforward "reference implementation" suitable for educational purposes.The currentllama2.crepository comprises two Python files intended for model training and one C file for inference. Our goal is to bridge the existing gap by offering a clear-cut reference implementation encapsulating all transformer logic within a concise Python file, not exceeding 500 lines of code.Though the original Facebook/llama is written on Python, its complexity is rather high due to multiple dependencies and sophisticated optimizations implemented within. This often makes it hard to follow, particularly for those new to the field.Please note, the current performance of our implementation is relatively slow, clocking in at approximately~1 tok/sec. This leaves ample scope for significant performance optimizations. We welcome any contributions towards enhancing the efficiency of this project.feel the magicFirst, navigate to the folder when you keep your projects and clone this repository to this folder:gitclonehttps://github.com/tairov/llama2.py.gitThen, open the repository folder:cdllama2.pyNow, let's just run a baby Llama 2 model in Pythonwgethttps://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.binJust run the Pythonpython3llama2.pystories15M.bin0.8256"Dream comes true this day"

Dreamcomestruethisday.Totheirsurprise.Abiggamewaseasyandeveryonewasgoingontheday.Jackandtheywereplayingbeneath:life,free,butter!Therewasthetimetothinkoftheuniverse.Therewasveryhappy,funandthejoyandthefollowingdownbelowofthisdaytheyweretherewasalotofawide,newcamping.
Jackandtheyhadhappened.Thetownwasthesavingupabovethecampofthewavesshoroftheirlaughter,friendlyjourneyoffriendshiptoone.Thenightskyshowoftheend.Littleceremony,happyagain.

OnceuponhisfamilyofabigdaywhenJack.Theywerefilledforeshadowedhappyandtheywerethejoyfilledthis,different:theKingoftheirappreciationtheyweretoawavetothespringlimit.TheywerebecomingRuby,happyandthesunsetoflifeofanamazingfriendshipandhehadarobot.

Onceupona4,happytothewonderfulexperienceofthecelebrationoftheirfriendship.Eventheplayground.
JackandSammyfishingadventureforeshiumofawishingbeingfreetime,happy.Thegenerousadventureforeshlymadeit.Thechanceto
achievedtok/s:1.3463711338028914use as a packagePyPi llama2-pypipinstallllama2-py>>>importllama2>>>llama2.run({"checkpoint":"out/model.bin","temperature":0.0,"steps":256,"prompt":None})
Onceuponatime,therewas...performancePerformance is awful at the moment.
On my Mac M1 Max --~1.3 tok / secLicenseMIT
Package: llama-agi
🤖 Llama AGI 🦙This python package allows you to quickly create Auto-GPT-like agents, using LlamaIndex and Langchain.SetupInstall using pip:pipinstallllama-agiOr install from source:gitclonehttps://github.com/run-llama/llama-lab.gitcdllama-lab/llama_agi
pipinstall-e.Example UsageThe following shows an example of setting up theAutoAGIRunner, which will continue completing tasks (nearly) indefinitely, trying to achieve it's initial objective of "Solve world hunger."fromlangchain.agentsimportload_toolsfromlangchain.llmsimportOpenAIfromllama_agi.execution_agentimportToolExecutionAgentfromllama_agi.runnersimportAutoAGIRunnerfromllama_agi.task_managerimportLlamaTaskManagerfromllama_agi.toolsimportsearch_notes,record_note,search_webpagefromllama_indeximportServiceContext,LLMPredictor# LLM setupllm=OpenAI(temperature=0,model_name='text-davinci-003')service_context=ServiceContext.from_defaults(llm_predictor=LLMPredictor(llm=llm),chunk_size_limit=512)# llama_agi setuptask_manager=LlamaTaskManager([args.initial_task],task_service_context=service_context)tools=load_tools(["google-search-results-json"])tools=tools+[search_notes,record_note,search_webpage]execution_agent=ToolExecutionAgent(llm=llm,tools=tools)# launch the auto runnerrunner=AutoAGIRunner(task_manager,execution_agent)objective="Solve world hunger"initial_task="Create a list of tasks"sleep_time=2runner.run(objective,initial_task,sleep_time)More examples can be found in theexamplesfolder!Llama EcosystemLlamaIndex (connecting your LLMs to data):https://github.com/jerryjliu/llama_indexLlamaHub (community library of data loaders):https://llamahub.ai