lanarky

Lanarky is a Python web framework designed for building microservices using Large Language Models (LLMs). It is LLM-first, fast, modern, supports streaming over HTTP and WebSockets, and is open-source. The framework provides an abstraction layer for developers to easily create LLM microservices. Lanarky guarantees zero vendor lock-in and is free to use. It is built on top of FastAPI and offers features familiar to FastAPI users. The project is now in maintenance mode, with no active development planned, but community contributions are encouraged.

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron. We're releasing it with the community in the spirit of building in the open. Note that it is still very much early so don't expect 100% stability ^^' In case of problems or question, feel free to open an issue!

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

Generative AI models have shown tremendous usefulness in increasing accessibility and automation of a wide range of tasks. This repository contains the `biochatter` Python package, a generic backend library for the connection of biomedical applications to conversational AI. It aims to provide a common framework for deploying, testing, and evaluating diverse models and auxiliary technologies in the biomedical domain. BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs.

Complexity is a community-driven, open-source, and free third-party extension that enhances the features of Perplexity.ai. It provides various UI/UX/QoL tweaks, LLM/Image gen model selectors, a customizable theme, and a prompts library. The tool intercepts network traffic to alter the behavior of the host page, offering a solution to the limitations of Perplexity.ai. Users can install Complexity from Chrome Web Store, Mozilla Add-on, or build it from the source code.

AnythingLLM is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.

TiDB.AI is a conversational search RAG (Retrieval-Augmented Generation) app based on TiDB Serverless Vector Storage. It provides an out-of-the-box and embeddable QA robot experience based on knowledge from official and documentation sites. The platform features a Perplexity-style Conversational Search page with an advanced built-in website crawler for comprehensive coverage. Users can integrate an embeddable JavaScript snippet into their website for instant responses to product-related queries. The tech stack includes Next.js, TypeScript, Tailwind CSS, shadcn/ui for design, TiDB for database storage, Kysely for SQL query building, NextAuth.js for authentication, Vercel for deployments, and LlamaIndex for the RAG framework. TiDB.AI is open-source under the Apache License, Version 2.0.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.

A handy package for kickstarting AI contests. This Python framework simplifies the creation of an environment for adversarial agents, offering various functionalities for game setup, playing against remote agents, data generation, and contest organization. The package is user-friendly and provides easy-to-use features out of the box. Developed by an enthusiastic team of M.Sc candidates at Polytechnique Montréal, 'seahorse' is distributed under the 3-Clause BSD License.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

Auto-generated Diagrams from Airflow DAGs. This project aims to easily visualize Airflow DAGs on a service level from providers like AWS, GCP, Azure, etc. via diagrams. It connects to your Airflow installation to retrieve all DAGs and tasks, processes them using Fuzzy String Matching, and renders the results into a Python file for diagram generation. Contributions are welcome.

Ragna is a RAG orchestration framework designed for managing workflows and orchestrating tasks. It provides a comprehensive set of features for users to streamline their processes and automate repetitive tasks. With Ragna, users can easily create, schedule, and monitor workflows, making it an ideal tool for teams and individuals looking to improve their productivity and efficiency. The framework offers extensive documentation, community support, and a user-friendly interface, making it accessible to users of all skill levels. Whether you are a developer, data scientist, or project manager, Ragna can help you simplify your workflow management and boost your overall performance.

AiiDA (www.aiida.net) is a workflow manager for computational science with a strong focus on provenance, performance and extensibility. **Features** * **Workflows:** Write complex, auto-documenting workflows in python, linked to arbitrary executables on local and remote computers. The event-based workflow engine supports tens of thousands of processes per hour with full checkpointing. * **Data provenance:** Automatically track inputs, outputs & metadata of all calculations in a provenance graph for full reproducibility. Perform fast queries on graphs containing millions of nodes. * **HPC interface:** Move your calculations to a different computer by changing one line of code. AiiDA is compatible with schedulers like SLURM, PBS Pro, torque, SGE or LSF out of the box. * **Plugin interface:** Extend AiiDA with plugins for new simulation codes (input generation & parsing), data types, schedulers, transport modes and more. * **Open Science:** Export subsets of your provenance graph and share them with peers or make them available online for everyone on the Materials Cloud. * **Open source:** AiiDA is released under the MIT open source license

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

Lanarky is a Python web framework designed for building microservices using Large Language Models (LLMs). It is LLM-first, fast, modern, supports streaming over HTTP and WebSockets, and is open-source. The framework provides an abstraction layer for developers to easily create LLM microservices. Lanarky guarantees zero vendor lock-in and is free to use. It is built on top of FastAPI and offers features familiar to FastAPI users. The project is now in maintenance mode, with no active development planned, but community contributions are encouraged.

ChatFAQ is an open-source comprehensive platform for creating a wide variety of chatbots: generic ones, business-trained, or even capable of redirecting requests to human operators. It includes a specialized NLP/NLG engine based on a RAG architecture and customized chat widgets, ensuring a tailored experience for users and avoiding vendor lock-in.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

Superagent is an open source framework that enables any developer to integrate production ready AI Assistants into any application in a matter of minutes.

Chainlit is an open-source async Python framework which allows developers to build scalable Conversational AI or agentic applications. It enables users to create ChatGPT-like applications, embedded chatbots, custom frontends, and API endpoints. The framework provides features such as multi-modal chats, chain of thought visualization, data persistence, human feedback, and an in-context prompt playground. Chainlit is compatible with various Python programs and libraries, including LangChain, Llama Index, Autogen, OpenAI Assistant, and Haystack. It offers a range of examples and a cookbook to showcase its capabilities and inspire users. Chainlit welcomes contributions and is licensed under the Apache 2.0 license.

This repo contains sample applications that show how to use Neo4j with the generative AI capabilities in Google Cloud Vertex AI. We explore how to leverage Google generative AI to build and consume a knowledge graph in Neo4j.

API Improvement Proposals (AIPs) are design documents that provide high-level, concise documentation for API development at Google. The goal of AIPs is to serve as the source of truth for API-related documentation and to facilitate discussion and consensus among API teams. AIPs are similar to Python's enhancement proposals (PEPs) and are organized into different areas within Google to accommodate historical differences in customs, styles, and guidance.

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.

Speakeasy is a tool that helps developers create production-quality SDKs, Terraform providers, documentation, and more from OpenAPI specifications. It supports a wide range of languages, including Go, Python, TypeScript, Java, and C#, and provides features such as automatic maintenance, type safety, and fault tolerance. Speakeasy also integrates with popular package managers like npm, PyPI, Maven, and Terraform Registry for easy distribution.

ApiCat is an API documentation management tool that is fully compatible with the OpenAPI specification. With ApiCat, you can freely and efficiently manage your APIs. It integrates the capabilities of LLM, which not only helps you automatically generate API documentation and data models but also creates corresponding test cases based on the API content. Using ApiCat, you can quickly accomplish anything outside of coding, allowing you to focus your energy on the code itself.

Aiohttp pydantic is an aiohttp view to easily parse and validate requests. You define using function annotations what your methods for handling HTTP verbs expect, and Aiohttp pydantic parses the HTTP request for you, validates the data, and injects the parameters you want. It provides features like query string, request body, URL path, and HTTP headers validation, as well as Open API Specification generation.

Ain is a terminal HTTP API client designed for scripting input and processing output via pipes. It allows flexible organization of APIs using files and folders, supports shell-scripts and executables for common tasks, handles url-encoding, and enables sharing the resulting curl, wget, or httpie command-line. Users can put things that change in environment variables or .env-files, and pipe the API output for further processing. Ain targets users who work with many APIs using a simple file format and uses curl, wget, or httpie to make the actual calls.

OllamaKit is a Swift library designed to simplify interactions with the Ollama API. It handles network communication and data processing, offering an efficient interface for Swift applications to communicate with the Ollama API. The library is optimized for use within Ollamac, a macOS app for interacting with Ollama models.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.