Saravanan666/pypi-org huggingface dataset:
Package: vectorcord
A modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python.The Future of discord.pyPlease read thegistfor the future of this project. It’s been a good one.Key FeaturesModern Pythonic API usingasyncandawait.Proper rate limit handling.Optimised in both speed and memory.InstallingPython 3.8 or higher is requiredTo install the library without full voice support, you can just run the following command:# Linux/macOSpython3-mpipinstall-Uvectorcord# Windowspy-3-mpipinstall-UvectorcordOtherwise to get voice support you should run the following command:# Linux/macOSpython3-mpipinstall-U"vectorcord[voice]"# Windowspy-3-mpipinstall-Uvectorcord[voice]To install the development version, do the following:$gitclonehttps://github.com/DTS-11/VectorCord$cdVectorCord$python3-mpipinstall-U.[voice]Optional PackagesPyNaCl(for voice support)Please note that on Linux installing voice you must install the following packages via your favourite package manager (e.g.apt,dnf, etc) before running the above commands:libffi-dev (orlibffi-develon some systems)python-dev (e.g.python3.6-devfor Python 3.6)Quick ExampleimportvectorcordclassMyClient(vectorcord.Client):asyncdefon_ready(self):print('Logged on as',self.user)asyncdefon_message(self,message):# don't respond to ourselvesifmessage.author==self.user:returnifmessage.content=='ping':awaitmessage.channel.send('pong')client=MyClient()client.run('token')Bot Exampleimportvectorcordfromvectorcord.extimportcommandsbot=commands.Bot(command_prefix='>')@bot.command()asyncdefping(ctx):awaitctx.send('pong')bot.run('token')You can find more examples in the examples directory.LinksDocumentationOur Discord ServerOfficial Discord.py ServerDiscord API
Package: vectordash
# vectordash-cli
A command line tool for interacting with [Vectordash](http://vectordash.com) GPU instances.For a more detailed overview on how to get started, how the commands work, or general questions, please go to our [docs](https://docs.vectordash.com)!#### Usage Examplesvectordash secret <secret_token>- update’s the user’s secret token which is used for authenticationvectordash list- lists the machines the user can connect to; these are machines user is currently rentingvectordash ssh <machine_id>- connect the user to a machine via SSHvectordash push <machine_id> <from_path> <to_path>This uses scp to push files to the machine. If<to_path>is not included,
thenscppushes it to the machine’s home directory.vectordash pull <machine_id> <from_path> <to_path>Same command as above, except we’re copying files from the machine to the local machine.
If<to_path>is not provided, then copies the files to the current directory.
Package: vectordbs
vectordbsVectordbs is a python library that supports all vector databases specifically for LLM apps and frameworks.Currently all LLM frameworks and apps (Llama-Index, Open AI, Langchain, Baby AGI, etc) are all having to reimplement support for each vector database with a lot of redundant work, and this library is hoping to existing and new frameworks don't have to reinvent the wheel.List of Supported Vector databasesTaskSupportedChroma:x:Pinecone:x:Weaviate:x:Qdrant:x:Milvus:x:Redis:x:Opensearch:x:Postgres:x:Installpip install vectordbs
Package: vector-demo
vector-demoA basic implementation of a Vector as a Python object.Free software: MIT licenseDocumentation:https://vector-demo.readthedocs.io.FeaturesVector magnitude calculation.Calculation of dot product between two vectors.Calculation of Cosine of the angle between two vectors.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.1 (2018-03-27)Added angle method to get angle between vectors.0.2.1 (2017-09-14)Added angle method to get angle between vectors.0.1.1 (2017-09-13)Updating documentation and configuration.0.1.0 (2017-09-13)First public commit.
Package: vector-graphics
vector-graphicsA Wagtail app for vector graphics based on media-catalogue. Please bear with us while we prepare more detailed documentation.Compatibilityvector-graphics' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.xInstallationInstall usingpip:pipinstallvector-graphicsAddvector_graphicsto yourINSTALLED_APPSsetting:INSTALLED_APPS=[# ...'vector_graphics'# ...]
Package: vectorim
VectorimA simple, no BS vector database for experimentation and prototyping.
This Vector "DB" is not designed for performance or scale, and will probably choke your machine under more than a few thousand entries.
It is to be used in scripting and experimenting with new ideas, without the hassle of actually using a database. It just keeps the vectors next to your data in a class, and pickles it on each update (if enabled). This way the db can be saved across runs avoiding re-embeddding.
Usage:fromvectorimimportVectorim# create a databasevector_db=Vectorim([[1.0,0.4,...],...],["Vectorim","Is","The","Best","Database",...],file_path="my_db.pkl")# search!scores,data=vector_db.search([0.69,0.42,...],top_k=3)# that's it reallyFor the sake of laziness, vectors/matrices can be either lists/nested-lists of floats or numpy arrays of corresponding dimensions. OpenAI API returns a list of floats as of now, but working with numpy is nicer so internally everything is converted to that format. Shouldn't really bother you as it's hidden, just use any one of them.
Package: vectorium
VectoriumThis Python package provides a simple and flexible interface for creating, manipulating and querying vector databases. Vectorium makes it easy to add or remove vectors, compare them using various similarity metrics, and perform various aggregate operations.FeaturesFlexible Vector Database: Store and manage your high dimensional vectors in an efficient manner.Multiple Compare Functions: Includes functions like cosine similarity, euclidean distance, dot product.Various Aggregate Functions: Supports aggregation of result vectors using mean, sum, max, min or no operation.Vector Operations: Add or remove vectors from your database, save or load your vector collection, and update your vector database as needed.InstallationThis package is not yet available on PyPi. Please clone this repository to your local machine and import theVectorDatabaseclass.UsageThis is a brief example of how to use theVectorDatabaseclass:fromvectoriumimportVectorDatabaseimportnumpyasnp# Create a new database named 'my_collection'db=VectorDatabase('my_collection',dim=128)# Add a new vector associated with the key 'my_key'db.add('my_key',np.random.randn(128))# Compare an input vector with the databaseresults=db.compare(np.random.randn(128),func='cosine',aggregate='mean')# Remove a key from the databasedb.remove('my_key')# Save the databasedb.save()# Load the databasedb.load('my_collection_path')Class: VectorDatabaseParameterscollection- The name of the database. Will be used as a filename when saving/loading.dim(optional) - The dimensions of the vectors. If None, will be inferred from the first added vector.collection_path(optional) - The path where the database file will be stored.Methodsadd(key, vec)Add a new vector associated with the given key to the database. If the key already exists, the vector will be appended to the existing ones.remove(key)Removes the vectors associated with the given key from the database.compare(input_vector, func='cosine', aggregate='mean')Compares an input vector with the vectors in the database using the given compare function (default is 'cosine') and returns the results aggregated using the given aggregate function (default is 'mean').topk(input_vector, k=10, func='cosine', aggregate='mean', reverse=False)Similar tocompare, but only returns the topkresults.reset()Empties the database.save()Saves the database to a.npzfile with the name of the collection.load(collection_path)Loads the database from a.npzfile located at the given path.update()Updates the internal list representation of the database. This is called automatically after eachadd,remove,reset,loadandsaveoperation.RequirementsPython 3.6+NumPyPyTorchContributionPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: vectorized2d
Vectorized2DThis is a user-friendly wrapper to numpy arrays, for dealing with numerical problems in a vectorized fashion - in the 2D world.Provided objects include:Array2D- a user-friendly interface to numpy arrays of shape=Nx2.Vector2D- a user-friendly wrapper for arrays of 2D vectors that represent physical quantities.Point2D- a user-friendly wrapper to arrays of 2D points that represent spatial locations in a cartesian coordinate system.Coordinate- a user-friendly wrapper for arrays of 2D points that represent 2D spatial (geographical) coordinates
(longitude and latitude) in radians.InstallationThe easiest way to installvectorized2dand get updates is via the Python Package Index (PyPI):$pipinstallvectorized2dPerformanceVectorized2d usesNumbato gain enhanced performance compared to vanilla numpy.For example, (per-row) norm calculation:>>>importnumpyasnp>>>fromvectorized2dimportArray2D>>>a_np=np.random.random(size=(1000,2))>>>a_2d=a_np.view(Array2D)>>>%timeitnp.linalg.norm(a_np,axis=1)23.1µs±1.25µsperloop(mean±std.dev.of7runs,10000loopseach)>>>%timeitnp.sqrt(np.einsum('ij,ij->i',a_np,a_np))10.5µs±40.2nsperloop(mean±std.dev.of7runs,100000loopseach)>>>%timeita_2d.norm2.63µs±67.9nsperloop(mean±std.dev.of7runs,100000loopseach)
Package: vectorizedb
OverviewVectorizeDB is a Python package designed for the efficient storage and retrieval of high-dimensional vectors. It’s particularly useful in applications like machine learning and information retrieval. The package utilizes hnswlib for fast approximate nearest neighbor searches and LMDB for scalable and reliable storage.InstallationTo install VectorizeDB, ensure you have Python 3.10 or higher. It can be installed via pip:pipinstallvectorizedbUsageInitializationfromvectorizedbimportDatabase# Initialize a new databasedb=Database(path="path/to/db",dim=128,readonly=False,similarity="cosine")Adding Dataimportnumpyasnp# Add a vector with an associated keydb.add(key="sample_key",vector=np.random.rand(128))# Add a vector with metadatadb.add(key="another_key",vector=np.random.rand(128),metadata={"info":"sample metadata"})# Another way to add datadb["yet_another_key"]=(np.random.rand(128),{"info":"sample metadata"})Retrieving Data# Retrieve vector and metadata by keyvector,metadata=db["sample_key"]# Check if a key exists in the databaseexists="sample_key"indbSearching# Search for nearest neighbors of a vectorresults=db.search(vector=np.random.rand(128),k=5)forkey,vector,distance,metadatainresults:print(key,distance,metadata)Iterating Through Data# Iterate through all keys, vectors and metadata in the databaseforkey,vector,metadataindb:print(key,metadata)Updating Data# Update a vector in the databasedb.update_vector("sample_key",np.random.rand(128))# Update metadatadb.update_metadata("sample_key",{"info":"updated metadata"})Deleting Data# Delete a vector from the database by keydeldb["sample_key"]Database Length# Get the number of entries in the databaselength=len(db)LicenseVectorizeDB is released under the Apache License. For more details, see the LICENSE file included in the package.
Package: vectorizeit
Python ProjectvectorizeIntroductionTo import the project simply type>>>fromvectorizeitimportvectorizeafter installation and usevectorizeas a decorator
to iterate over arguments of list type.>>>@vectorize(keys=['a','b'])...deffoo(a,b):...returna,b>>>foo((1,2),['a','b'])(((1,'a'),(1,'b')),((2,'a'),(2,'b')))InstallThe latest stable version can always be installed or updated via pip:$pipinstallvectorizeitLicenseCode and documentation are available according to the license
(see LICENSE file in repository).
Package: vectorizer-ai
vectorizer-aiPython SDK forvectorizer.ai.What is it?Convert JPEG and PNG bitmaps to SVG vectors.Install$pipinstallvectorizer-aiUsageBasicfromvectorizer_aiimportVectorizerAIclient=VectorizerAI(api_id="VECTORIZER-AI-API-ID",api_secret="VECTORIZER-AI-API-SECRET",mode="production")svg=client.vectorize("/path/to/input.png")svg.save("/path/to/output.svg")You can also use:client.vectorize(image_base64="base64encodedimage==")# orclient.vectorize(image_url="https://imageurl.com/test.png")Advancedclient.vectorize(image_path="/path/to/image",input_max_pixels=100,processing_max_colors=256...)Reference:https://vectorizer.ai/apiAll parameters described in the API spec above replace period (.) with underscore (_). For example, if the parameter isinput.max_pixels, the SDK will useinput_max_pixels.ContributingFeel free to open a PR for any changes!Testing$python-munittestdiscover-stests-p'test_*.py'Made with ❤️ by@mitchbregs
Package: vectorlab
VectorLab is a library to help solve scientific and engineering problems of mathematics and machine learning. It is built upon various fabulous softwares and tries to fill the gap when playing around with those softwares.VectorLab consists of many modules including data generation, graph processing, series processing, statistics, optimization, ensemble mechanism and more.VectorLab is distributed under the 3-Clause BSD license.InstallationDependenciesVectorLab supportsPython 3.8+only.PyPI InstallationThe lastest version of VectorLab could be installed from PyPI.pip install -U vectorlabVectorLab also needs PyTorch and PyG support. It is highlyrecommendedto handle these two package installationsmanually.
If you only care for the CPU version of PyTorch and PyG, you can
also install vectorlab from PyPI with extensions.pip install -U -e vectorlab[.full]DevelopmentWe welcome contributions from all sources.Source codeYou can check the latest commit withgit:git clone https://github.com/Kr4t0n/vectorlab.gitTestingAfter the regular installation, you will needpytestto test VectorLab. You can install corresponding dependencies with:pip install -U -e vectorlab[.test]You can launch the test suite with:pytest vectorlab
Package: vectorlib-py
VectorLib: A library to create and manage vectors in Python.Installationpython-mpipinstallvectorlib-py# orpython-mpipinstallgit+https://github.com/AaravMalani/vectorlibUsageimportvectorlibu=vectorlib.Vector(0,10)v=vectorlib.Vector(-10,0,3)t=4a_avg=(v-u)/tprint(a_avg)# Vector(-2.5, -2.5, 0.75)print(a_avg.magnitude)# 3.61420807370024print(a_avg.dim)# (3,)print(a_avg.direction)# Vector(-0.6917144638660746, -0.6917144638660746, 0.2075143391598224)m=4f=m*aprint(f)# Vector(-10.0, -10.0, 3.0)print(f.dot(a_avg))# 52.25print(f.cross(a_avg))# Vector(0.0, 0.0, 0.0)
Package: vectorlite
VectorLiteDBVectorLiteis a simple vector database powered bysentence-transformersandhnswlib, designed for efficient storage and search of textual data.🚀 Quick Installpip install vectorliteUsageRun as api serverserve vectorliteNavigate tohttp://localhost:4440/docsRun integrated in codeInitializationfromvectorliteimportVectorLitevl=VectorLite()Adding Datadata=["Sample text 1","Sample text 2","Another example"]vl.create(data)Read All Dataall_data=vl.read_all()To limit the number of records returned:limited_data=vl.read_all(max_items=2)Read Specific Data by Indexitem=vl.read(1)Update Data by Indexvl.update(1,"Updated sample text 2")Delete Data by Indexvl.delete(1)Similarity Searchresults=vl.similarity_search("A related sample text",k=3)Semantic Searchresults=vl.semantic_search("A related sample text",k=3)
Package: vectormath
vectormathVector math utilities for Python built onNumPyWhyThevectormathpackage provides a fast, simple library of vector math
utilities by leveraging NumPy. This allows explicit
geometric constructs to be created (for example,Vector3andPlane)
without redefining the underlying array math.ScopeThevectormathpackage includesVector3/Vector2andVector3Array/Vector2Array.GoalsSpeed: All low-level operations rely on NumPy arrays. These are densely packed,
typed, and partially implemented in C. TheVectorArrayclasses in particular
take advantage of this speed by performing vector operations on all Vectors at
once, rather than in a loop.Simplicty: High-level operations are explicit and straight-forward.
This library should be usable by Programmers, Mathematicians, and Geologists.AlternativesNumPycan be used for any array operationsMany small libraries on PyPI (e.g.vectors)
implement vector math operations but are are only built with single vectors
in mind.Connectionspropertiesusesvectormathas the underlying framework for Vector properties.InstallationTo install the repository, ensure that you havepip installedand run:pip install vectormathFor the development version:git clone https://github.com/seequent/vectormath.git
cd vectormath
pip install -e .ExamplesThis example gives a brief demonstration of some of the notable features ofVector3andVector3Arrayimportnumpyasnpimportvectormathasvmath# Single Vectorsv=vmath.Vector3(5,0,0)v.normalize()print(v)# >> [1, 0, 0]print(v.x)# >> 1.0# VectorArrays are much faster than a for loop over Vectorsv_array=vmath.Vector3Array([[4,0,0],[0,2,0],[0,0,3]])print(v_array.x)# >> [4, 0, 0]print(v_array.length)# >> [4, 2, 3]print(v_array.normalize())# >> [[1, 0, 0], [0, 1, 0], [0, 0, 1]]# Vectors can be accessed individually or in slicesprint(type(v_array[1:]))# >> vectormath.Vector3Arrayprint(type(v_array[2]))# >> vectormath.Vector3# All these classes are just numpy arraysprint(isinstance(v,np.ndarray))# >> Trueprint(type(v_array[1:,1:]))# >> numpy.ndarrayCurrent version: v0.2.2
Package: vectormpp
Actian VectorMPP InstallerActian VectorMPP represents a sophisticated deployment of Actian’s Multi-node Vector Cluster within a Kubernetes environment.This deployment encompasses an extensive array of Kubernetes resources, accompanied by intricate configuration settings.To facilitate a streamlined installation process, we have developed the Actian VectorMPP Installer.The installer is distributed as a Python package, officially registered within the Python Package Index (PyPi).The installation process of the Actian VectorMPP Installer is straightforward:pip install vectormppFor assistance and command options, use:vectormpp -hThis command provides usage instructions and a list of available options, including but not limited to specifying work directories, setting log levels, and executing install or uninstall commands.The design behind the installerThe Actian VectorMPP Installer offers a range of functionalities designed to accommodate diverse customer needs, including those with varying Kubernetes cluster configurations and pre-existing Kubernetes resources.Central to this customization capability isActian VectorMPP Manifest— a collection of text files outlining the architecture of Actian VectorMPP.Thevectormppinstaller, leveragingManifest, acts as a versatile tool capable of applying these Kubernetes resources and configurations to customers' Kubernetes clusters.This approach enables potential use cases beyond Actian VectorMPP, applicable to any Kubernetes project withManifestdescribing its architecture.ExamplesDeploy Actian VectorMPP in a clean GKE clusterGet the corresponding Manifestclean-gkefrom our .
CloneActian VectorMPP Manifest repoto obtain the appropriate manifest file for a clean GKE cluster:git clone https://alm.actian.com/bitbucket/scm/~rxiao/vectormpp-manifest.gitCustomize your Actian VectorMPP installation by modifying the config.yaml file within the cloned repository:vim vectormpp-manifest/clean-gke/config.yamlExecute the installer using one of the following methods, specifying the path to your Manifest folder:vectormpp --manifest vectormpp-manifest/clean-gke --install
vectormpp -m vectormpp-manifest/clean-gke -iOr navigate to the Manifest directory and run:cd vectormpp-manifest/clean-gke && vectormpp -i
Package: vectorObjects
vectorObjectsObject approach to common dimensional vectorsThis project was create by converting and extending a small section of code within the now archivedpymeshio,
relating to Vectors and 3D object rotation matrices which have been extracted and generalised where possible. This was
due to some of these objects also having the potential to be used outside of this specific use case.vectorObjects also has the ability to allow you to create your own vectors, not pre defined within vectorObjects,
quickly via inheritance from a master class of methods. All source code is available on the projectgithub page
Package: vector-pkg-enderrayquaza
Vector pkgActual version : 2.0.2How to installpipinstallvector-pkg-enderrayquazaVector2dimportVector.Vector2dasvec2v=vec2.Vector2d(5,7.5)#Creates a instance of Vector2dprint(v.x)#Shows his composant x >>> 5print(v.y)#Idem                  >>> 7.5print(v.st)#Shows his standard   >>> 9.013878188659973u=vec2.Vector2d(-8,0.25)#Creates an other instance of Vector2dw=v+u#Makes a instance of Vector with composants (v.x + u.x; v.y + u.y)print(w.x,w.y)# >>> -3 7.75w=v-u#Idem but with a subtractionprint(w.x,w.y)# >>> 13 7.25k=5w=v*kprint(w.x,w.y)# >>> 25, 37.5#You can do this toov+=uv-=uv*=kp=v**u#Calculates the scalar product of v and uprint(p)# >>> -38.125a=v%u#Calculates the angle (in degrees) between v and uprint(a)# >>> 121.90015691773374z=complex(v)#Makes a complex number equal at x+yiprint(z)# >>> (5+7.5j)You can use the constant V0_ which is worth Vector2d(0, 0)Vector3dimportVector.Vector3dasvec3v=vec3.Vector3d(5,7.5,0)#Creates a instance of Vector3dprint(v.x)#Shows his composant x >>> 5print(v.y)#Idem                  >>> 7.5print(v.z)#Idem                  >>> 0print(v.st)#Shows his standard   >>> 9.013878188659973u=vec3.Vector3d(-8,0.25,2)#Creates an other instance of Vector3dw=v+u#Makes a instance of Vector with composants (v.x + u.x; v.y + u.y; v.z + u.z)print(w.x,w.y,w.z)# >>> -3 7.75 2w=v-u#Idem but with a subtractionprint(w.x,w.y,w.z)# >>> 13 7.25 -2k=5w=v*kprint(w.x,w.y,w.z)# >>> 25, 37.5 0#You can do this toov+=uv-=uv*=kp=v**u#Calculates the scalar product of v and uprint(p)# >>> -38.125a=v%u#Calculates the angle (in degrees) between v and uprint(a)# >>> 121.90015691773374You can use the constant V0_ which is worth Vector3(0, 0, 0)Vector4dIdem that Vector3d and Vector2d but with a 4th composant t (for time).VectorNdimportVector.VectorNdasvecNv=vecN.VectorNd([8,6,9,2.7,-4])The rest is like other Vector classes.
Package: vector-plot-extractor
vector-plot-extractortools for extracting data from vector plotsThis Python package has been tested on various PDF files, includingexample.pdf, under normal usage conditions. However, it has not undergone comprehensive testing and optimization yet. I will improve it in the future. Your suggestions and contributions are welcome.Documentation is currently in progress. Stay tuned for updates!DependenciesThis package mainly depends on the following packages:matplotlibnumpypymupdfThese will be automatically installed when runningpip install.InstallationTo install this package, you can simply usepip:pip install vector-plot-extractorIf you would like to try the latest development version, you may directly install from the project repository by:pip install git+https://github.com/ycwang-astro/vector-plot-extractor.gitUsageTo execute the main UI, run this in your terminalvpextract path/to/figure/fileTo import this package in a Python script:importvpextractorNoticeA bug in version 0.1.1 causes minor errors in the extracted scatter data. If you have used 0.1.1 version, follow the steps in thechangelog("Notes" under "0.1.2") to fix it.