Saravanan666/pypi-org huggingface dataset:
Package: rage
Easy Windows-Registry display & manipulation in Python.fromrageimportRegistryKey# Open some keykey=RegistryKey(some_registry_path)# Print names of all subkeysforkey_nameinkey.enum_keys():printkey# Get a subkeysubkey=key["subkey-name"]# Print out all the subkey's valuesforname,valueinsubkey.values:printrepr(name),value# Set one of the valueseditable_subkey=subkey.get_editable()editable_subkey.values["my value name"]=RegSZ("Value value!")# Delete a valuedeleditable_subkey.values["value to delete"]# Delete a subkeyeditable_subkey.delete_subkey("subkey name",recurse=False)
Package: raghavcalculator
CalculatorThis is a basic calculator which performs basic arithmetic operationspipinstallraghavcalculator[from raghavcalculator import Calculator as c]Addition[c.add_numbers(num1,num2)]LicenseThis program is free (as in speech) software under the GPLv3. Please see thelicensefile for more.Change Log0.0.13 (02/07/2022)Fixing a minor bugAdding a unique libraryAdding a few changesFirst Release
Package: ragmap
RAGmap 🗺️🔍OverviewRAGmap is a simple RAG visualization tool for exploring document chunks and queries in embedding space.Inspired by DeepLearning.ai's short course onAdvanced Retrieval for AI with Chromaand Gabriel Chua's award-winningRAGxplorer.Updates:👨‍💻RAGmapis now available as a standalone Python package!🌐 Try thelive demohosted on Streamlit Community Cloud.📢 RAGmap now supportsHugging Face 🤗models!What's inside?RAGmap supports the following features:Multiple document formats 📄PDFDOCXPPTXMultiple embedding modelsHugging Face 🤗Amazon Bedrock ⛰️Titan Text EmbeddingsTitan Multimodal EmbeddingsCohere Embed EnglishCohere Embed MultilingualDimensionality reduction (2D and 3D)UMAPt-SNEPCANatural language queriesAdvanced query augmentationGenerated Answers (HyDE)Multiple Queries... and more!☝️⚠️Important notice:As of January 2024, chromadb'sAmazonBedrockEmbeddingFunctiononly works with Titan models. Feel free to upvote thisPRto add support forCohere Embed models.PrerequisitesAmazon BedrockEnable access to the embedding (Titan Embeddings,Cohere Embed) and text (Anthropic Claude) models via Amazon Bedrock.For more information on how to request model access, please refer to theAmazon Bedrock User Guide(Set up > Model access)How to useOption 1 💻Install dependenciespip install -r requirements.txtRun the applicationstreamlit run app.pyPoint your browser tohttp://localhost:8501Option 2 🐳Run the following command to start the applicationdocker-compose upOnce the service is up and running, head over tohttp://localhost:8501Option 3 👨‍💻Install theragmappackagepip install ragmapStart building your own apps.Check out theexamplesfolder to get started!Example:Amazon shareholder lettersReferences(AWS)What is Retrieval-Augmented Generation?(DeepLearning.ai)Advanced Retrieval for AI with Chroma
Package: ragna-aws
ragna-awsAWS extensions forRagna. Currently supportsAWS S3asragna.core.DocumentAWS Bedrockasragna.core.AssistantsInstallation$pipinstallragna-awsUsagePut the following, or a subset thereof, into your Ragna configuration file, e.g.ragna.toml:# ...[core]# ...document="ragna_aws.S3Document"assistants=[# ..."ragna_aws.Claude","ragna_aws.ClaudeInstant","ragna_aws.Command","ragna_aws.CommandLight","ragna_aws.Jurassic2Mid","ragna_aws.Jurassic2Ultra","ragna_aws.Llama2Chat13B","ragna_aws.Llama2Chat70B","ragna_aws.TitanTextExpress","ragna_aws.TitanTextLite",]
Package: ragnar
RagnarRagnar is a lightweight Extract-Transform-Load (ETL) framework for Python 3.5+.Free software: MIT licenseDocumentation:https://ragnar.readthedocs.io.FeaturesKeeps a functional programming philosophy.Code reuse instead of “re-inventing the wheel” in each script.Customizable for your organization’s particular tasks.ExampleA pipeline that applies capital letters to the list and then filters through the one starting with “B”:>>>fromragnar.streamimportStream>>>st=Stream(["apple","banana","cherry"])>>>st.do(lambdax:x.upper())<ragnar.stream.Streamobjectat0x7fbe8e3509d0>>>>st.filter(lambdax:x.startswith("B"))<ragnar.stream.Streamobjectat0x7fbe8e3509d0>>>>forrowinst:...print(row)BANANAHistory0.1.0 (2019-04-11)First release on PyPI.
Package: ragrank
Welcome to Ragrank! This toolkit is designed to assist you in evaluating the performance of your LLM applications. Whether you're working on chatbots, text generators, or any other natural language processing (NLP) task, our toolkit can help you assess various aspects of your model's performance.What does it do?Our free LLM evaluation toolkit offers functionalities to assess:Accuracy of Facts:Evaluate how accurately your model generates factual information.Understanding of Context:Measure how well your model comprehends and maintains context within a conversation or text.Tone Analysis:Analyze the tone of generated text to ensure it aligns with your desired tone or sentiment.And More:Additional evaluation metrics and functionalities to help you gauge the effectiveness of your LLM applications.How to UseInstallation:Clone or download this repository to your local machine.Usage:Utilize the provided evaluation scripts and tools to evaluate your LLM applications. Detailed instructions for each evaluation metric are included within their respective directories.Customization:Feel free to customize and extend the toolkit to suit your specific evaluation needs. Contributions and feedback are welcomed!ContributingIf you'd like to contribute to the LLM Evaluation Toolkit, please follow these steps:Fork this repository.Create a new branch (git checkout -b feature/improvement).Make your changes.Commit your changes (git commit -am 'Add new feature').Push to the branch (git push origin feature/improvement).Create a new Pull Request.LicenseThis project is licensed under theApatche Licence.Feedback and SupportIf you encounter any issues, have questions, or would like to provide feedback, please open an issue on the GitHub repository.Happy evaluating!
Package: ragraph
RaGraphRaGraph is a package to create, manipulate, and analyze graphs consisting of nodes and edges. Nodes
usually represent (hierarchies of) objects and edges the dependencies or relationships between them.These graphs, or networks if you will, lend themselves well to applied analyses like clustering and
sequencing, as well as analyses involving the calculation of various insightful metrics.User documentationFor all user-facing documentation, please head over to our beautiful documentation over athttps://ragraph.ratio-case.nl!Development installationThis project is packaged usingpoetry. Packaging information as well
as dependencies are stored inpyproject.toml.Installing the project and its development dependencies can be done usingpoetry install -E all.TestsTests can be run usingpoetry run pytest.LintingLinting config is included inpyproject.tomlfor both Black and Ruff.Contributions and licenseTo get contributing, feel free to fork, pick up an issue or file your own and get going for your
first merge! We'll be more than happy to help.For contribution instructions, head over to theopen-source GitLab
repository!All code snippets in the tutorial and how-to guide sections of the package documentation are free to
use.If you find any documentation worthwhile citing, please do so with a proper reference to our
documentation!RaGraph is licensed following a dual licensing model. In short, we want to provide anyone that
wishes to use our published software under the GNU GPLv3 to do so freely and without any further
limitation. The GNU GPLv3 is a strong copyleft license that promotes the distribution of free,
open-source software. In that spirit, it requires dependent pieces of software to follow the same
route. This might be too restrictive for some. To accommodate users with specific requirements
regarding licenses, we offer a proprietary license. The terms can be discussed by reaching out to
Ratio.
Package: ragstack-ai
RAGStackRAGStackis an out-of-the-box solution simplifying Retrieval Augmented Generation (RAG) in GenAI apps.RAGStack includes the best open-source for implementing RAG, giving developers a comprehensive Gen AI Stack leveragingLangChain,CassIO, and more. RAGStack leverages the LangChain ecosystem and is fully compatible with LangSmith for monitoring your AI deployments.For each open-source project included in RAGStack, we select a version lineup and then test the combination for compatibility, performance, and security. Our extensive test suite ensures that RAGStack components work well together so you can confidently deploy them in production.RAGStack uses theAstra DB Serverless (Vector) database, which provides a highly performant and scalable vector store for RAG workloads like question answering, semantic search, and semantic caching.Quick InstallWith pip:pipinstallragstack-aiDocumentationDataStax RAGStack DocumentationQuickstartExamplesContributing and building locallyClone this repo:gitclonehttps://github.com/datastax/ragstack-aiThe project usespoetry.
To install poetry:pipinstallpoetryInstall dependenciespoetryinstallBuild the package distributionpoetrybuild
Package: ragstoriches
ragstorichesis a combined library/framework to ease writing web scrapers
using gevent and requests.A simple example to tell the story:#!/usr/bin/env python# -*- coding: utf-8 -*-fromurlparseimporturljoinimportrefrombs4importBeautifulSoupfromragstoriches.scraperimportScraperrr=Scraper(__name__)@rr.scraperdefindex(requests,context,url='http://eastidaho.craigslist.org/search/act?query=+'):soup=BeautifulSoup(requests.get(url).text)forrowinsoup.find_all(class_='row'):yield'posting',context,urljoin(url,row.find('a').attrs['href'])nextpage=soup.find(class_='nextpage')ifnextpage:yield'index',context,urljoin(url,nextpage.find('a').attrs['href'])@rr.scraperdefposting(requests,context,url):soup=BeautifulSoup(requests.get(url).text)infos=soup.find(class_='postinginfos').find_all(class_='postinginfo')title=soup.find(class_='postingtitle').text.strip()id=re.findall('\d+',infos[0].text)[0]date=infos[1].find('date').text.strip()body=soup.find(id='postingbody').text.strip()printtitleprint'='*len(title)print'post *%s*, posted on%s'%(id,date)printbodyprintInstall the library andBeautifulSoup 4usingpip install ragstoriches beautifulsoup4, then save the above ascraigs.py,
finally run withragstoriches craigs.py.You will get a bunch of jumbled input, so next step is redirectingstdoutto a file:ragstoriches craigs.py > output.mdTry giving different urls for this scraper on the command-line:ragstorichescraigs.pyhttp://newyork.craigslist.org/mnh/acc/>output.md# hustleragstorichescraigs.pyhttp://orangecounty.craigslist.org/wet/>output.md# writing OCragstorichescraigs.pyhttp://seattle.craigslist.org/w4m/>output.md# sleepless in seattleThere are a lot of commandline-options available, seeragstoriches--helpfor a list.Writing scrapersA scraper module consists of some initialization code and a number of
subscrapers. Scraping starts by calling the a scraper namedindexon the
scraperrrin the moduel (see the example above).Therequestsargument should be treated like therequestsmodule (it actually is an instance of requests
Pool). As long as you use it for fetching webpages, you never have to worry
about blocking or exceeding concurrency limits.Thecontextvariable is arbitrary, but by convention a dictionary. It’s a
way of passing state from one scraper to another or sharing it. It is only
passed on byragstorichesand never touched otherwise.Theurlis the url to scrape and parse.Return values of scrapers are ignored. However, if a scraper is a generater
(i.e. contains a yield statement), any value it yields must be a 3-tuple
consisting of the name of a scraper, a context object and another url. These
are added to the queue of jobs to scrape.Good friends ofragstorichesare theurlparse.urljoinfunction
andBeautifulSoup4.Usage as a libraryYou can use ragstoriches as a library as well by not using the commandline
tools but simply importing a scraper and running it with thescrape()method. Remember to monkey-patch using gevent first.See the source files for details, as there is not that much documentation
available at this point.
Package: ragwrangler
RAGWrangler - A simple RAG (Retrieval-Augumented Generation) Task ManagerRAGWrangler is a Python project designed to streamline the management of RAG tasks using generative language models and the Weaviate database.With this tool, you can automatically create, retrieve, and store the outputs of RAG tasks in a structured manner in Weaviate, allowing for easy management and tracking of outputs, ultimately saving time and resources.FeaturesAutomated Task Handling: Simplify the creation and management of tasks with a straightforward Python class interface.Weaviate Integration: Seamlessly store and retrieve task outputs using Weaviate database integration.Support for Multiple Language Models: You can easily swap out language models as desired.Logging: Utilize integrated logging for effortless debugging and tracking of task statuses.InstallationThe tool should be compatible with Python 3.8 and higher, although development primarily utilized Python 3.9.To get started, install the necessary Python packages using the command below:pipinstallragwranglerQuickstartSeeexample_usage.pyto see a brief example of how to use the tool.UsageYou can connect to any Weaviate instance to save your data.
Refer to theWeaviate documentationfor more information on how to set up & connect to a Weaviate instance.The primary class you will interact with isRAGTask.RAGTaskInstantiate aRAGTaskfor each generative task to be handled.Provide it with a Weaviate instance, and a builder function.
You will also need to set the OpenAI API key.importweaviate,osfromragwranglerimportRAGTask,set_openai_api_keyclient=weaviate.Client(url=os.environ['WCS_URL'],auth_client_secret=weaviate.AuthApiKey(os.environ['WCS_ADMIN_KEY']),additional_headers={"X-OpenAI-Api-Key":os.environ["OPENAI_APIKEY"]})set_openai_api_key(os.environ["OPENAI_APIKEY"])quiz_rag=RAGTask(client=client,task_prompt_builder=revision_quiz_json_builder)Whererevision_quiz_json_builderis a function that takes in a source text and returns a task prompt for the LLM.ExtendRAGTaskby defining custom task prompt builder functions that dictate how to generate prompts from the source text, which are then used to derive outputs.Working with TasksTo create a new task, instantiate an object ofRAGTask(or its extension) with a source text:quiz_rag=RAGTask(client=client,task_prompt_builder=revision_quiz_json_builder)To obtain the task output, utilize theget_outputmethod, specifying a model name if desired:output=task.get_output(source_text="Your source text here",model_name="gpt-3.5-turbo")LicenseThis project is licensed under the MIT License.Copyright© 2023 JP Hwang
Package: ragxplorer
RAGxplorer 🦙🦺RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations.Quick Start ⚡InstallationpipinstallragxplorerUsagefromragxplorerimportRAGxplorerclient=RAGxplorer(embedding_model="thenlper/gte-large")client.load_pdf("presentation.pdf",verbose=True)client.visualize_query("What are the top revenue drivers for Microsoft?")A quickstart Jupyter notebook tutorial on how to useragxplorercan be found athttps://github.com/gabrielchua/RAGxplorer/blob/main/tutorials/quickstart.ipynbOr as a Colab notebook:Streamlit Demo 🔎The demo can be found here:https://ragxplorer.streamlit.app/View the projecthereContributing 👋Contributions to RAGxplorer are welcome. Please read ourcontributing guidelines (WIP)for details.License 👀This project is licensed under the MIT license - see theLICENSEfor details.Acknowledgments 💙DeepLearning.AI and Chroma for the inspiration and code labs in theirAdvanced Retrivalcourse.The Streamlit community for the support and resources.
Package: raho
raho: Simple symmetric encryption built on cryptographyrahois a simple wrapper library for thecryptographymodule.InstallationpipinstallrahoAnd in your Python file:>>>importrahoUsageWith Fernets>>>fernet=raho.generate_fernet()>>>message=raho.encrypt('he is hiding behind the rock',fernet)>>>message'Z0FB...'>>>raho.decrypt(message,fernet)'he is hiding behind the rock'With passwords>>>message=raho.encrypt_with_password('they know water','dragon123')>>>raho.decrypt_with_password(message,'dragon123')'they know water'With key files>>>fernet=raho.generate_key_file('key-file')>>>message=raho.encrypt_with_key_file('falcon flies at dawn','key-file')>>>raho.decrypt_with_key_file(message,'key-file')'falcon flies at dawn'Command lineSeeraho --helpfor command-line usage examples.More informationFernets andcryptographyDevelopment Documentation
Package: rai
rai, RadiotherapyAI's non-clinical open source autocontouring libraryThis Open Source Unregulated Software is provided in the hope that it might be
useful but WITHOUT ANY WARRANTY. It isnotintended for clinical use, and
is instead intended for research use only.If you are looking for the Regulated Medical Device,RAIContours, that is
based upon the source code within this repository, when it has completed its
regulatory approval, it will be available for download directly fromhttps://radiotherapy.ai/. Prior to regulatory approval, you may be able to
utiliseRAIContoursunder a clinical trial notification or equivalent. If you
are interested in undergoing a clinical trial withRAIContoursplease reach
out toclinical-trials@radiotherapy.ai.GoalCreate an AI Software as a Medical Device that saves lives built entirely from
open source software. Being a part of making it easier for researchers to
translate from "bench-top to bedside", helping anyone to build regulated tools
that go on to save more lives than any one of us alone could ever hope to
achieve.InstallationThis open source research library is able to be installed by running:pip install raiThe regulatory documentationAll of the regulatory documentation is available under a creative commons
license and is built uponthe amazing templatesprovided byOpenRegulatory.The source code for the regulatory documentation is available within thedocs directory. The online rendering of the documentation is viewable
athttps://docs.radiotherapy.aiThe regulatory task is currently a work in progress. Right now I am just
learning the regulatory ropes while putting all of my warts up here for all to
see. There will be mistakes. If you find them please let me know. Also, if you
want to help out, please get in touch. My email address issimon.biggs@radiotherapy.ai.
Package: raiblocks
This library contains a python wrapper for the RaiBlocks RPC server
which tries to make it a little easier to work with by converting RPC responses
to native python ones and exposing a pythonic api for making RPC calls.Also included are utilities such as converting rai/xrb and interesting accountsInstallationpip install raiblocksDocumentationhttps://raiblocks-python.readthedocs.io/RPC clientYou can browse the availableRPC methods listor check theRPC Client API documentationfor examples of usage.WarningThe RPC clientDOES NOThandle timeouts or retries
automatically since this could lead to unwanted retries of requests
causingdouble spends. Keep this in mind when implementing retries.>>>fromraiblocksimportRPCClient>>>rpc=RPCClient('http://localhost:7076')>>>rpc.version(){'rpc_version':1,'store_version':10,'node_vendor':'RaiBlocks 9.0'}>>>rpc.peers(){'[::ffff:75.171.168.5]:7075':4,'[::ffff:108.44.38.183]:1032':4}Conversion>>>fromraiblocksimportconvert>>>convert(12,from_unit='XRB',to_unit='raw')Decimal('1.2E+31')>>>convert(0.4,from_unit='krai',to_unit='XRB')Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>ValueError:floatvaluescanleadtounexpectedprecisionloss,pleaseuseaDecimalorstringeg.convert('0.4','krai','XRB')>>>convert('0.4',from_unit='krai',to_unit='XRB')Decimal('0.0004')Known Accounts / Constants>>>fromraiblocksimportGENESIS_BLOCK_HASH>>>GENESIS_BLOCK_HASH'991CF190094C00F0B68E2E5F75F6BEE95A2E0BD93CEAA4A6734DB9F19B728948'>>>fromraiblocksimportKNOWN_ACCOUNT_IDS>>>KNOWN_ACCOUNT_IDS['xrb_1ipx847tk8o46pwxt5qjdbncjqcbwcc1rrmqnkztrfjy5k7z4imsrata9est']'Developer Fund'>>>fromraiblocksimportKNOWN_ACCOUNT_NAMES>>>KNOWN_ACCOUNT_NAMES['Burn']'xrb_1111111111111111111111111111111111111111111111111111hifc8npp'DevelopmentSetupvirtualenv venv
source venv/bin/activate
pip install -r requirements.pip -r requirements-dev.pip
python setup.py developRunning tests# regular
pytest

# coverage
./coverageBuilding docscd docs

# generate once
make html

# live building
make liveMaking a releaseUpdate CHANGELOG.rstbumpversion [major|minor|patch]python setup.py upload
Package: rai-core-flask
rai_core_flaskProvide environment detection for enabling Flask server without configuring at instantiation. Environments are implemented as classes that will either return None if the environment check fails, or return an object with all the appropriate environment settings if the check passes. The flask service can then use it to build out urls, attach required headers, and render inline visualizations with the correct methods.Installationpip install -e .Usagefrom rai_core_flask import FlaskHelperflask_service = FlaskHelper(ip=<ip-to-listen-on>, port=<port-to-listen-on>)Ifipis not specified then it listens onlocalhostby default.
Ifportis not specified then it finds an open port in the range 8704 to 8993
and listens on it.To register a function to listen on an route:@flask_service.app.route("/your-api-route/<int:param_name>", methods=["GET", "POST"])
def my_func(param_name):
    return somethingRelease ProcessIncrement the version number insetup.pyActivate your virtual environment.Run therelease.shscript.
Package: raida
# raidaA hobby project focused on encoding data in the Raiblock Block lattice# Requirements to Write Data to the Raiblocks Block LatticeI'm working on a spec and corresponding python library for this, but do not want to release any part of it until the Raiblock client matures a bit.  I don't want tons of users wrecklessly adding uncompressed data to the network.  It's bad enough we'll probably have many blocks of spam even without a library to make it easy. Check back to this repo later.# Requirements to Read Data from the Raiblocks Block LatticeBelow is a POC.  I'd like to work with others on a globally logical compression scheme and conventions/metadata to speed up reading and indexing.* python 3* a fully synchronized Raiblocks Node running on port 7076```pip install raiblocksgit clone https://github.com/jnmclarty/raida.gitpython example_read.py```
Package: raiden-api-client
raiden api clientThe raiden api client wraps the API ofraiden. It simplifies the usage ofraiden's apito a few lines.InstallationUse the package managerpipto install raiden API client.pipinstallraiden_api_clientUsageimportraiden_api_clientraiden=raiden_api_client.RaidenAPIWrapper(ip="localhost",port=8545)# Parity running at localhost:8545raiden.transfer(partner="0x0000000000000000000000000000000000000000",amount=1,)# Transfering 1 Token to 0x00Further examples can be found in the example folder.Available Methods-mint_tokens(receiver,token,amount)->AttrDict-register_token(token)->AttrDict-open_channel(partner,token,deposit,settle_timeout=500)->AttrDict-transfer(partner,token,amount,identifier=None)->AttrDict-fund_channel(partner,token,deposit)->AttrDict-close_channel(partner,token)->AttrDict-leave_token_network(token)->List[str]-get_channels(token=None,partner=None)->List[AttrDict]-get_payments(partner=None,token=None)->List[AttrDict]-get_token_network(token=None)->AttrDict-get_raiden_version()->AttrDict-get_address()->AttrDict-get_pending_transfer(token=None,partner=None)->List[AttrDict]-get_connections()->AttrDict-get_node_status()->AttrDictCheckout theraiden api docsfor further information about the returned objects.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT