Meta's release of its latest Llama language model family this week, including the massive Llama-3 405B model, has generated a great deal of excitement among AI developers. These open-weights frontier models, which have been updated with a new license that allows unrestricted use of outputs, will enable significant improvements to AI-powered applications, and enable widespread commercial use of synthetic data. Less discussed, but no less important, are Meta's latest open moderation tools, including a new model called PromptGuard. PromptGuard is a small, lightweight classification model trained to detect malicious prompts, including jailbreaks and prompt injections. These attacks can be used to manipulate language models to produce harmful outputs or extract sensitive information. Companies building enterprise-ready applications must be able to detect and mitigate these attacks to ensure their models are safe to use, especially in sensitive and highly-regulated domains like healthcare, finance, and law. PromptGuard is a text classification model based on mDeBERTa-v3-base, a small transformer model with multilingual capabilities. Meta trained this model to output probabilities for 3 classes: BENIGN, INJECTION, and JAILBREAK. The JAILBREAK class is designed to identify malicious user prompts (such as the "Do Anything Now(opens in a new tab)" or DAN prompt, which instructs a language model to ignore previous instructions and enter an unrestricted mode). On the other hand, the INJECTION class is designed to identify retrieved contexts, such as a webpage or document, which have been poisoned with malicious content to influence the model's output. In our tests, we find that the model is able to identify common jailbreaks like DAN, but also labels benign prompts as injections. This likely happens because the model is trained to handle both prompts and retrieved contexts (such as web searches and news articles), and a benign prompt may appear similar to a malicious context. As stated in the model card: Application developers typically want to allow users flexibility in how they interact with an application, and to only filter explicitly violating prompts (what the ‘jailbreak’ label detects). Third-party content has a different expected distribution of inputs (we don’t expect any “prompt-like” content in this part of the input) This indicates that when applying the model to user prompts, you may want to ignore the INJECTION label, and only filter JAILBREAK inputs. On the other hand, when filtering third-party context to show to the model, such as a news article, you'd want to remove both JAILBREAK and INJECTION labels. We wrote a quick blog post about how you can use PromptGuard to protect your language models from malicious inputs. You can read more here: https://www.trytaylor.ai/blog/promptguard submitted by /u/Different-General700 [link] [comments]

https://alessandromolina.substack.com/p/python-data-engineering-july-2024 I have been working in the data engineering world for a few years, and have been contributing to many OSS projects that are foundations to it. This month I decided to start a dedicated newsletter to help everyone stay informed with what goes on in the data engineering world. The Python Data Engineering newsletter was born as a way to scratch my own itch of having to keep up with updates to all the components that are foundations for data engineering projects in Python, and aims to be different from the usual ones that focus on data science and analytics, as it concentrates more on the fundamental building blocks needed to create platforms on which data science can run. Hope it will be helpful for other people too and lightweight enough that it won’t introduce additional information overload for its readers. submitted by /u/_amol_ [link] [comments]

https://github.com/igrek51/wat What My Project Does This inspection tool is extremely useful for debugging in dynamically typed Python. It allows you to examine unknown objects at runtime. Specifically, you can investigate type, formatted value, variables, methods, parent types, signature, documentation, and even the source code. Let me know what you think. I would really appreciate your feedback. submitted by /u/igrek51 [link] [comments]

pgmq-sqlalchemy What My Project Does A more flexible PGMQ Postgres extension Python client using SQLAlchemy ORM, supporting both async and sync engines, sessionmakers, or built from dsn. Comparison The official PGMQ package only supports psycopg3 DBAPIs. For most use cases, using SQLAlchemy ORM as the PGMQ client is more flexible, as most Python backend developers won't directly use Python Postgres DBAPIs. Features Supports async and sync engines and sessionmakers, or built from dsn. Automatically creates the pgmq (or pg_partman) extension on the database if it does not exist. Supports all Postgres DBAPIs supported by SQLAlchemy, e.g., psycopg, psycopg2, asyncpg. See SQLAlchemy Postgresql Dialects for all dialects. Target Audience pgmq-sqlalchemy is a production package that can be used in scenarios that need a message queue for general fan-out systems or third-party dependencies retry mechanisms. Links PyPI GitHub Documentation submitted by /u/jason810496 [link] [comments]

Hey everyone! So I’ve created a very simple Python script to de-clutter your Downloads folder. demo What My Project Does This Python script sorts the files into different folders such as Audio, Video, Documents etc. according to the file extension. For example, a .pdf file will be moved to Documents. Usage Install it through pipx: pipx install dlorg Run $ dlorg to run the script. Target Audience Just a useful tool for most people. Comparison Supports a wide range of extensions, easily accessible through a single command, colored logging. Links Source Code (Github) Python package: PyPi EDIT: It is now installable through pipx. submitted by /u/at-pyrix [link] [comments]

Hello Python and Open Source Enthusiasts! I'm excited to introduce [Lambda Forge](https://docs.lambda-forge.com/home/getting-started/), a Python framework designed to make AWS Lambda function creation and deployment effortless. Whether you're a seasoned developer or just starting with serverless architectures, Lambda Forge has something to offer. What My Project Does Lambda Forge simplifies the process of creating, deploying, and managing AWS Lambda functions. It provides a robust CLI tool to generate Lambda function templates, authorizers, and service integrations with ease. The framework supports live development, allowing developers to test and debug Lambda functions locally while connected to real AWS resources. Additionally, Lambda Forge facilitates the creation and deployment of Lambda Layers, and it automatically generates documentation and architecture diagrams for your projects. Target Audience Lambda Forge is designed for developers who are building serverless applications using AWS Lambda. Whether you're working on production-grade applications or experimenting with serverless technologies, Lambda Forge provides the tools and structure to streamline your development workflow. It's suitable for both experienced developers looking for efficiency and newcomers seeking an easy entry point into serverless development. Key Features: **1. CLI Tool:** Lambda Forge includes a powerful CLI tool, `FORGE`, to help you create and manage serverless projects efficiently. **2. Modular Functions:** Easily create Lambda functions with a modular architecture that adheres to best coding practices. Example command: ```shell forge function hello_world --method "GET" --description "A simple hello world" --public ``` **3. Authorizers:** Secure your Lambda functions with structured authorizers. Example command: ```shell forge authorizer secret --description "An authorizer to validate requests based on a secret present on the headers" ``` **4. AWS Services Integration:** Generate dedicated service classes for seamless interaction with AWS resources. Example command: ```shell forge service sns ``` **5. Lambda Layers:** Simplify working with Lambda Layers, including custom and external libraries. Example command: ```shell forge layer --custom my_custom_layer ``` **6. Live Development:** Connect Lambda functions to your local environment for hot reload and easy debugging. Example command: ```shell forge live server ``` Comparison Lambda Forge stands out from other serverless frameworks due to its focus on ease of use and developer productivity. The CLI tool, `FORGE`, provides a highly intuitive interface for generating and managing serverless resources. The modular structure promotes clean code practices, and the integration with live development ensures a seamless debugging experience. Furthermore, Lambda Forge automates documentation and architecture diagram generation, saving developers time and effort. Its emphasis on single responsibility principles and dependency injection sets it apart from existing alternatives, offering a more structured and maintainable approach to serverless development. Explore more about Lambda Forge and join our community through the links below: [Github](https://github.com/GuiPimenta-Dev/lambda-forge) [Documentation](https://docs.lambda-forge.com/home/getting-started/) [Examples](https://docs.lambda-forge.com/examples/introduction/) [Telegram Bot (Powered with ChatGPT)](https://web.telegram.org/a/#6950159714) Happy coding! submitted by /u/No_Coffee_9879 [link] [comments]

Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

I just started working and learning all the KMeans, Clustering, etc. It's been fun so far. I received this value error and had a chuckle, so I thought I would share it. ValueError: could not convert string to float: 'American Cheese (slice)' submitted by /u/NoticeAwkward1594 [link] [comments]

if someone here is interested in drawing with Python Turtle here is a short video with a code example attached: https://www.youtube.com/watch?v=geUwYyFCg6A submitted by /u/Link0000054 [link] [comments]

Hello everyone, this is a project I've been working on from a few years for my own-use with my pets, and this year I decided to share it with the community. Project website: PuppySignal.com What my project does The functionality is pretty simple: when you log in, you create a profile for your pet, and a QR code will be generated and linked to its profile. When someone scans this code and decides to share its location, you will receive a notification on your phone with your pet's location, and the person who scanned it will see your contact information. It consists of a few projects built with React (web and documentation), React Native (mobile), and Python for the backend (FastAPI and SQLAlchemy). Target audience Pet owners, but anyone could create codes and attach them to items, or whatever they want. Comparison Many QR pet tag alternatives already exist. The difference between them and PuppySignal is that you don't have to buy a QR tag and wait for it to be delivered. You can just clone it, self-host it, print the QR code, and attach it to your pet's collar. Documentation: https://docs.puppysignal.com Repository: https://github.com/FLiotta/PuppySignal submitted by /u/godblessyerbamate [link] [comments]

Hey everyone! Last month we recieved a lot of encouraging feedback from you and used it to improve our framework. First up, we've completely rewritten how components are laid out internally.This was a large undertaking and has been in the works for several weeks now - and the results are looking great! We're seeing much faster layout times, especially for larger (1000+ component) apps. This was an entirely internal change, that not only makes Rio faster, but also paves the way for custom components, something we've been wanting to add for a while. From all the feedback the most common question we've encountered is, "How does Rio actually work?" The following topics have already been detailed in our wiki for the technical description: What are components? How does observing attributes work? How does Diffing, and Reconciliation work? We are working technical descriptions for: How does the Client-Server Communication work? How does our Layouting work? Thanks and we are looking forward to your feedback! 🙂 GitHub submitted by /u/Sn3llius [link] [comments]

What My Project Does: We use Boto3 (AWS SDK for Python) and the open source OpenZiti Python SDK to enable Zero Trust Network Access to a Private S3 Bucket with no inbound firewall ports, no need for sidecars, agents, or client proxies, nor any use of AWS Private Endpoints. Target Audience: Engineers and developers who need to connect distributed systems/apps to AWS S3 (though the technology can be used for many other use cases) Comparison: The company for whom we developed it previously used an AWS VPN client to connect from their robot to AWS Private S3 Bucket across the internet. Now they do not need to use a VPN, the zero trust overlay network is embedded in their Python apps running on/next to the robot. Further, they can close all inbound FW ports on AWS and only need outbound ports at source (opposed to inbound ports on both sides), no need for public DNS, L4 loadbalancers, and more. https://blog.openziti.io/extend-access-to-a-private-s3-bucket-using-python Source code can be found here - https://github.com/openziti/ziti-sdk-py/tree/main/sample/s3z#readme submitted by /u/PhilipLGriffiths88 [link] [comments]

Weekly Thread: Beginner Questions 🐍 Welcome to our Beginner Questions thread! Whether you're new to Python or just looking to clarify some basics, this is the thread for you. How it Works: Ask Anything: Feel free to ask any Python-related question. There are no bad questions here! Community Support: Get answers and advice from the community. Resource Sharing: Discover tutorials, articles, and beginner-friendly resources. Guidelines: This thread is specifically for beginner questions. For more advanced queries, check out our Advanced Questions Thread. Recommended Resources: If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance. Example Questions: What is the difference between a list and a tuple? How do I read a CSV file in Python? What are Python decorators and how do I use them? How do I install a Python package using pip? What is a virtual environment and why should I use one? Let's help each other learn Python! 🌟 submitted by /u/AutoModerator [link] [comments]

Hi everyone, You can play it here: https://jimmyrustles.com/chessopeningtheory Github: https://github.com/sgriffin53/openingexplorer_app What My Project Does This is a hobby project I made based on an old python GUI desktop script I made a few years ago. The idea is you can play openings on the board, and it'll update with the wiki article and some other stats (winning percentages, engine analysis, historical games) as you play through the openings. It's intended to be a tool to improve your opening knowledge at chess. Target Audience (e.g., Is it meant for production, just a toy project, etc. This is aimed at any chess enthusiasts who are interested in studying openings. I'm hoping it can be a useful tool for improvement. Comparison (A brief comparison explaining how it differs from existing alternatives.) The main comparison to this is the Lichess opening explorer, which is similar in many ways. The Lichess opening explorer gives you a page from the wiki (though a shortened version compared to mine) and gives the responses based on winning percentages. Mine is different in that it shows you the full wiki page complete with diagrams and annotations, compared to the shortened versions offered by Lichess. Mine also gives you short wiki descriptions for each popular response instead of listing them by winning chances. Let me know what you think of this project. It's just a small project I made for my website, but I'm hoping it can be a useful chess tool. submitted by /u/haddock420 [link] [comments]

Im 15 years old and I made a project that could be used in stores to put barcodes on products and scan them. This is one of my project that im the most proud of (I dont even know why lol) but I wanted to share it here to see if you guys could recommend me few improvements on this project and if you have other projects ideas. Thanks in advance and here is the link to the github project: https://github.com/TuturGabao/Store-Product-Management-SPM [link] [comments]

I just figured I could use ice cream to explain the 4 combinatorial iterators from the module itertools: combinations combinations_with_replacement permutations product I think this is a good idea because it is quite clear. Let me know your thoughts: combinations(iterable, r) This iterator will produce tuples of length r with all the unique combinations of values from iterable. (“Unique” will make use of the original position in iterable, and not the value itself.) E.g., what ice cream flavour combinations can I get? ```py Possible flavours for 2-scoop ice creams (no repetition) from itertools import combinations flavours = ["chocolate", "vanilla", "strawberry"] for scoops in combinations(flavours, 2): print(scoops) """Output: ('chocolate', 'vanilla') ('chocolate', 'strawberry') ('vanilla', 'strawberry') """ ``` combinations_with_replacement(iterable, r) Same as combinations, but values can be repeated. E.g., what ice cream flavour combinations can I get if I allow myself to repeat flavours? ```py Possible flavours for 2-scoop ice creams (repetition allowed) from itertools import combinations_with_replacement flavours = ["chocolate", "vanilla", "strawberry"] for scoops in combinations_with_replacement(flavours, 2): print(scoops) """Output: ('chocolate', 'chocolate') ('chocolate', 'vanilla') ('chocolate', 'strawberry') ('vanilla', 'vanilla') ('vanilla', 'strawberry') ('strawberry', 'strawberry') """ ``` permutations(iterable, r) All possible combinations of size r in all their possible orderings. E.g., if I get 2 scoops, how can they be served? This is a very important question because the flavour at the bottom is eaten last! ```py Order in which the 2 scoops can be served (no repetition) from itertools import permutations flavours = ["chocolate", "vanilla", "strawberry"] for scoops in permutations(flavours, 2): print(scoops) """Output: ('chocolate', 'vanilla') ('chocolate', 'strawberry') ('vanilla', 'chocolate') ('vanilla', 'strawberry') ('strawberry', 'chocolate') ('strawberry', 'vanilla') """ ``` product(*iterables, repeat=1) Matches up all values of all iterables together. (Computes the cartesian product of the given iterables.) E.g., if I can get either 2 or 3 scoops, and if the ice cream can be served on a cup or on a cone, how many different orders are there? ```py All the different ice-cream orders I could make from itertools import product possible_scoops = [2, 3] possibly_served_on = ["cup", "cone"] for scoop_n, served_on in product(possible_scoops, possibly_served_on): print(f"{scoop_n} scoops served on a {served_on}.") """Output: 2 scoops served on a cup. 2 scoops served on a cone. 3 scoops served on a cup. 3 scoops served on a cone. """ ``` Conclusion I think these 4 examples help understanding what these 4 iterators do if you don't have a maths background where “combinations” and “product” and “permutations” already have the meaning you need to understand this. In case you want to learn about the 16 other iterators in the module itertools, you can read this article. submitted by /u/RojerGS [link] [comments]

Hi everyone! 👋 I'm excited to share my new project, textscope, a Python library for relevance and subtheme detection in text. Key Features: 🔍 Relevance Analysis: Determines if a text is relevant to predefined profiles. 📚 Subtheme Detection: Identifies specific subthemes within texts. 💡 Flexible Configuration: Configure via config.py. 🛠️ Multilingual and Versatile: Supports multiple languages and adaptable to various scenarios. Installation: pip install textscope Usage Example: from textscope.relevance_analyzer import RelevanceAnalyzer model_name = 'intfloat/multilingual-e5-large-instruct' text = "This article discusses the latest advancements in AI and machine learning." analyzer = RelevanceAnalyzer(model_name) rel_score = analyzer.analyze(text) print(rel_score) # Returns a high relevance score for the topics. Check out the GitHub repository here: textscope Looking forward to your feedback and suggestions. Thanks for your time! submitted by /u/usc-ur [link] [comments]

A post detailing how to create and enforce isolation for a Python module through static analysis - useful due to Python's inability to define and enforce the dep graph! https://www.gauge.sh/blog/how-to-isolate-a-python-module-with-tach submitted by /u/the1024 [link] [comments]

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. What it does txtai was created back in 2020 starting with semantic search of medical literature. It has since grown into a framework for vector search, retrieval augmented generation (RAG) and large language model (LLM) orchestration/workflows. The goal of txtai is to be simple, performant, innovative and easy-to-use. It had vector search before many current projects existed. Semantic Graphs were added in 2022 before the Generative AI wave of 2023/2024. GraphRAG is a hot topic but txtai had examples of using graphs to build search contexts back in 2022/2023. There is a commitment to quality and performance, especially with local models. For example, it's vector embeddings component streams vectors to disk during indexing and uses mmaped arrays to enable indexing large datasets locally on a single node. txtai's BM25 component is built from the scratch to work efficiently in Python leading to 6x better memory utilization and faster search performance than the BM25 Python library most commonly used. I often see others complain about AI/LLM/RAG frameworks, so I wanted to share this project as many don't know it exists. Link to source (Apache 2.0): https://github.com/neuml/txtai Target Audience Developers who want to build AI/LLM/RAG applications with a simple approach. Comparison txtai is similar to LangChain and LlamaIndex. From a vector database standpoint, it's similar to ChromaDB. See the following post for a detailed comparison. https://www.reddit.com/r/txtai/comments/1e5nw3t/vector_search_rag_landscape_a_review_with_txtai/ submitted by /u/davidmezzetti [link] [comments]

It's been quite some time I've been working on this as a freetime project, but feel free to check it out : https://github.com/M3rcuryLake/Cassette What it does: Cassette enables the creation of succinct 30-second explanatory videos directly from your terminal. It leverages python's g4f module to generation transcript. Unreal speech for voiceovers and assemblyAI for subtitle generation. Cassette offers multiple customisation options and ensures efficient video production tailored for platforms like Instagram reels or YouTube shorts. Direct comparison: Brainrotjs has been great inspiration for this project, but it's pretty similar to other automated video generators and similar tools, except it's terminal based. Target audience: Cassette caters to creators looking to automate video production processes efficiently from their terminal. It appeals to developers, marketers, and content creators aiming to produce engaging, short-form videos for platforms like Instagram and YouTube, without the need for extensive manual editing or external software interface. submitted by /u/neptunym [link] [comments]