Saravanan666/pypi-org huggingface dataset:
Package: cognica-server
Cognica ServerCognica is a cloud-ready multi-purpose database and real-time search engine for the modern era. It provides a key-value store, document store, and full-text search engine with dense vector similarity support. It also provides a deep-learning model serving APIs using Torch.It offers Key-Value, Document, Time Series, and Vector data models, allowing you to choose the data model as needed.Key-Value: Available for caching, storing web site sessions, or managing all kinds of status managementDocument: Collections, documents, query languages, flexible indexing, and moreTime Series: Provides real-time time series data processingVectorVectorize various data such as documents, images, and videos and store them as vector embeddingsSupport similarity search between vector embeddingsUnlike other products that can only use one embedding model at a time, it is possible to use two or more embedding models simultaneouslyProvides a secondary index that can maximize database core performance such as search and query speed. You can fine-tune search performance by combining a single or multiple indexes in a single database.Unique / Non-unique IndexesClustered / Non-clustered IndexesPartial IndexesFull-Text Search IndexesVector Search IndexesThe full-text search engine is built on top of the document store as an index, so you can create more than one full-text search index with different configurations for different purposes without having multiple copies of the same documents.Cognica is mainly written in C++ to achieve high performance and utilize hardware resources efficiently. We genuinely care about the runtime performance of the database, so we write almost every single line of the code very carefully.RequirementsIt supports various operating systems and can be run in the following environments.Ubuntu >= 21.04 (x86_64, aarch64)RHEL >= 9 (x86_64, aarch64)MacOS >= 11.0 (x86_64, arm64)InstallationYou can install it via pip on Python 3.9 or higher.$pipinstallcognica-serverYou can initialize the data storage path throughcognica-init.$cognica-initYou can run the server with the following command.$cognicarunserver-cconf/default.yamlFor more details, please refer to theGetting Started.Current StatusThe current version of Cognica Server is fully functional and stable, suitable for production environments. Users should note, however, that the API is evolving and future releases may introduce changes that break backward compatibility.
Package: cognify
Cognify - Insai Cognition LabGetting StartedTo get started, you just need install the cognify library. The libary is constantly evolving so stay tuned for new updates.InstallBegin by installing the cognify library, by running in your terminalpip install cognifyNext, you need to add a separate config file containing the database credentials. This file isprovided upon request. It will need to be added to the folder where cognify was installed.To find the installation location simply runpip show cognifyThis should give you the location of the cognify libraryNavigate to that the cognify folder and place thesettings.inifile that will be provided inside the folder.
Package: cognite-ai
cognite-aiA set of AI tools for working with CDF in Python.MemoryVectorStoreStore and query vector embeddings created from CDF. This can enable a bunch of use cases where the number of vectors aren't that big.Install the package%pip install cognite-aiThen you can create vectors from text (both multiple lines or a list of strings) like thisfrom cognite.ai import MemoryVectorStore
from cognite.client import CogniteClient

client = CogniteClient()
vector_store = MemoryVectorStore(client)

vector_store.store_text("Hi, I am a software engineer working for Cognite.")
vector_store.store_text("The moon is orbiting the earth, which is orbiting the sun.")
vector_store.store_text("Coffee can be a great way to stay awake.")

vector_store.query_text("I am tired, what can I do?")Smart data framesChat with your data using LLMs. Built on top ofPandasAIversion 1.5.8. If you have loaded data into a Pandas dataframe, you can runInstall the package%pip install cognite-aiChat with your datafrom cognite.client import CogniteClient
from cognite.ai import load_pandasai

client = CogniteClient()
SmartDataframe, SmartDatalake = await load_pandasai()

workorders_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorders", limit=-1)
workitems_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workitems", limit=-1)
workorder2items_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2items", limit=-1)
workorder2assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2assets", limit=-1)
assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "assets", limit=-1)

smart_lake_df = SmartDatalake([workorders_df, workitems_df, assets_df, workorder2items_df, workorder2assets_df], cognite_client=client)
smart_lake_df.chat("Which workorders are the longest, and what work items do they have?")


s_workorders_df = SmartDataframe(workorders_df, cognite_client=client)
s_workorders_df.chat('Which 5 work orders are the longest?')Configure LLM parametersparams = {
    "model": "gpt-35-turbo",
    "temperature": 0.5
}

s_workorders_df = SmartDataframe(workorders_df, cognite_client=client, params=params)
Package: cognite-cdffs
cdffsA file system interface (cdffs) to allow users to work with CDF Files using thefsspecsupported/compatible python packages (pandas,xarrayetc).fsspecprovides an abstract file system interface to work with local/cloud storages and based on the protocol name (example,s3orabfs) provided in the path,fsspectranslates the incoming requests to storage specific implementations and send the responses back to the upstream package to work with the desired data.Referfsspec documentationto get the list of all supported/compatible python packages.Installationcdffsis available on PyPI. Install using,pipinstallcognite-cdffsUsageImportant steps to follow when working with CDF Files using thefsspecsupported python packages.Importcdffspackagefromcogniteimportcdffs# noqaFollow instructions fromAuthenticationto authenticate.Read/write the files from/to CDF usingfsspecsupported packages. Example,Readzarrfiles using usingxarray.ds=xarray.open_zarr("cdffs://sample_data/test.zarr")Writezarrfiles usingxarray.ds.to_zarr("cdffs://sample_data/test.zarr",storage_options={"file_metadata":metadata})Refercdffs.readthedocs.iofor more details.ContributingWant to contribute? Check outCONTRIBUTING.
Package: cognite-correlation
CorrelationCorrelation analysis tool for contextualization of time series data.DocumentationDocumentation can be foundherePrerequisitesPython3 (>= 3.5) and pipInstallationUse pip for installation:$pipinstallcognite-correlationExamplesSome examples can be found in thedocumentation,
and some notebooks with examples can be found in theexamples/directory.LicenseCognite Packages use the Apache 2.0 License. SeeLICENSE
Package: cognite-extractor-utils
Cognite Pythonextractor-utilsTheextractor-utilspackage is an extension of the Cognite Python SDK intended to simplify the development of data
extractors or other integrations for Cognite Data Fusion.Documentation is hostedhere, including aquickstart tutorial.The changelog is foundhere.OverviewThe best way to start a new extractor project is to use thecogexCLI. You can install that from PyPI:pipinstallcognite-extractor-managerTo initialize a new extractor project, runcogexinitin the directory you want your extractor project in. ThecogexCLI will prompt you for some information about your
project, and then set up a poetry environment, git repository, commit hooks with type and style checks and load a
template.ExtensionsSome source systems have a lot in common, such as RESTful APIs or systems exposing as MQTT. We therefore have extensions
toextractor-utilstailroed to these protocols. These can be found in separate packages:REST extensionMQTT extensionContributingThe package is open source under theApache 2.0 license, and contribtuions are welcome.This project adheres to theContributor Covenant v2.0as a code of conduct.Development environmentWe usepoetryto manage dependencies and to administrate virtual environments. To developextractor-utils, follow the following steps to set up your local environment:Install poetryif you haven't already.Clone repository:$ git clone git@github.com:cognitedata/python-extractor-utils.gitMove into the newly created local repository:$ cd python-extractor-utilsCreate virtual environment and install dependencies:$ poetry installCode requirementsAll code must passblackandisortstyle
checks to be merged. It is recommended to install pre-commit hooks to ensure this locally before commiting code:$ poetry run pre-commit installEach public method, class and module should have docstrings. Docstrings are written in theGoogle
style. Please include unit and/or
integration tests for submitted code, and remember to update thechangelog.
Package: cognite-extractor-utils-mqtt
Cogniteextractor-utilsMQTT extensionThe MQTT extension forCogniteextractor-utilsprovides a way
to easily write your own extractors for systems exposing an MQTT interface.The library is currently under development, and should not be used in production environments yet.OverviewThe MQTT extension for extractor utils subscribes to MQTT topics, automatically serializes the payload into user-defined
DTO classes, and handles uploading of data to CDF.The only part of the extractor necessary to for a user to implement areDescribing the payload schema using PythondataclassesImplementing a mapping from the source data model to the CDF data modelAs an example, consider this example payload:{"elements":[{"pumpId":"bridge-pump-1453","startTime":"2022-02-27T12:13:00","duration":16,},{"pumpId":"bridge-pump-254","startTime":"2022-02-26T16:12:23","duration":124,},]}We want to make an extractor that can turn these MQTT messages into CDF events. First, we need to create some data
classes describing the expected schema of the payloads:@dataclassclassPumpEvent:pumpId:strstartTime:strduration:int@dataclassclassPumpEventList:elements:List[PumpEvent]Then, we can create anMqttExtractorinstance, subscribe to the appropriate topic, and convert the payload into CDF
events:extractor=MqttExtractor(name="PumpMqttExtractor",description="Extracting pumping events from an MQTT source",version="1.0.0",)@extractor.topic(topic="mytopic",qos=1,response_type=PumpEventList)defsubscribe_pump_events(events:PumpEventList)->Iterable[Event]:external_id_prefix=MqttExtractor.get_current_config_file()forpump_eventinevents.elements:start_time=arrow.get(pump_event.startTime)end_time=start_time.shift(seconds=pump_event.duration)yieldEvent(external_id=f"{external_id_prefix}{pump_event.pumpId}-{uuid.uuid4()}",start_time=start_time.int_timestamp*1000,end_time=end_time.int_timestamp*1000,)withextractor:extractor.run()A demo example is provided in theexample.pyfile.ContributingSee thecontribution guide forextractor-utilsfor details on contributing.
Package: cognite-geospatial-sdk
Cognite Geospatial SDKDevelopment on this repository is discontinued. Please look atCognite Python SDKandCognite JS SDK.Cognite Geospatial SDK for developers and data scientists working with Cognite Data Fusion (CDF).PythonA Python Geospatial SDK for the geospatial API.PrerequisitesIn order to start using the Python SDK, you needPython3 (>= 3.5) and pipAn API key. Never include the API key directly in the code or upload the key to github. Instead, set the API key as an environment variable. See the usage example for how to authenticate with the API key.This is how you set the API key as an environment variable on Mac OS and Linux:$exportCOGNITE_API_KEY=<yourAPIkey>InstallationTo install this package:$pipinstallcognite-geospatial-sdk
Package: cognite-jupyterlab-copilot
cognite_jupyterlab_copilotA JupyterLab extension .RequirementsJupyterLab = 3.5.3 # match jupyterlab versions across toolingInstallTo install the extension, execute:pipinstallcognite_jupyterlab_copilotUninstallTo remove the extension, execute:pipuninstallcognite_jupyterlab_copilotContributingDevelopment installNote: You will need NodeJS to build the extension package.[Conda]A sandboxed virtual python environmenthere.
Is strongly recommended, just after runningcondacreate-nmy_happy_conva_env-cconda-forgejupyterlab=3.5.3jupyterlite-core
condaactivatemy_happy_conva_envyour life will be easier in every way.jlpmThejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab.jlpmwill work right away if you usedconda.# Clone the repo to your local environment# Change directory to the cognite_jupyterlab_copilot directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallcognite_jupyterlab_copilotIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedcognite_jupyterlab_copilotwithin that folder.Cognite SDKTokenIf you have trouble getting an auth token try adding something like this toauth.ts:// localdev in raw jupyter labif(window.location.hostname==='localhost'){resolve(newCogniteClient({appId:'LLM-hub-server',project:'lervik-industries',// <-- your project name herebaseUrl:'https://api.cognitedata.com',// <-- your API endpoint heregetToken:async()=>// "imma dangerous secret token"}));}Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.
Package: cognite-jupyterlab-metrics
cognite_jupyterlab_metricsA JupyterLab extension.RequirementsJupyterLab = 3.5.3InstallTo install the extension, execute:pipinstallcognite_jupyterlab_metricsUninstallTo remove the extension, execute:pipuninstallcognite_jupyterlab_metricsContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the cognite_jupyterlab_metrics directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallcognite_jupyterlab_metricsIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedcognite_jupyterlab_metricswithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: cognite-jupyterlab-theme
cognite_jupyterlab_themeA JupyterLab extension adding a "Cognite" theme.RequirementsJupyterLab = 3.5.3 (to match the version atDSHubLite)InstallTo install the extension, execute:pipinstallcognite_jupyterlab_themeUninstallTo remove the extension, execute:pipuninstallcognite_jupyterlab_themeContributingDevelopment installNote: You will need NodeJS to build the extension package.nvmis the recommended way to manage local NodeJS versions.[Conda]A sandboxed virtual python environmenthereis strongly recommended, just after runningcondacreate-nmy_happy_conva_env-cconda-forgejupyterlab=3.5.3jupyterlite-core
condaactivatemy_happy_conva_envyour life will be easier in every way.jlpmThejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab.jlpmwill work right away if you usedconda.# Clone the repo to your local environment# Change directory to the cognite_jupyterlab_theme directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallcognite_jupyterlab_themeIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedcognite_jupyterlab_themewithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.
Package: cognite-power-ops
Welcome to PowerOps SDKWhat is it?The PowerOps SDK is a domain-specific SDK for interacting with Cognite Data Fusion (CDF) for the power operations' domain.Main Featurescognite.powerops.client.PowerOpsClientused to interact with CDF in a domain-specific language.Resource Sync,resync, used to sync configuration files with CDF through the CLI toolpowerops.Installationpipinstallcognite-power-opsConfigurationConfiguration of thePowerOpsClientandresyncis done through settings files.Settings FilesThe settings file are in.tomlformat. By default, the SDK will look for two settings files:settings.tomlin the current directory..secrets.tomlin the current directory.The motivation for splitting them is to avoid checking in secrets into Git.Example of settings files:settings.toml:[cognite]login_flow="interactive"project="<cdf-project>"tenant_id="<tenant-id>"cdf_cluster="<cdf-cluster>"client_id="<client-id>"[powerops]read_dataset="uc:000:powerops"write_dataset="uc:000:powerops"monitor_dataset="uc:po:monitoring"cogshop_version="".secrets.toml[cognite]client_secret="<client-secret>"Note:You can configure which settings files to use by setting the environment variableSETTINGS__FILESto a semicolon-separated list of file names.importosos.environ["SETTINGS_FILES"]=".my_settings.toml;.secrets.my_secrets.toml"UsageRun ResyncSee available commands:$powerops--helpExample of showing planed changes:$poweropsplantests/data/demoDayaheadPowerOpsClientfromcognite.powerops.clientimportPowerOpsClientclient=PowerOpsClient.from_settings()client.shop.runs.trigger()For more examples, see the examples section of the documentation.