Seer is a service that provides AI capabilities to Sentry by running inference on Sentry issues and providing user insights. It is currently in early development and not yet compatible with self-hosted Sentry instances. The tool requires access to internal Sentry resources and is intended for internal Sentry employees. Users can set up the environment, download model artifacts, integrate with local Sentry, run evaluations for Autofix AI agent, and deploy to a sandbox staging environment. Development commands include applying database migrations, creating new migrations, running tests, and more. The tool also supports VCRs for recording and replaying HTTP requests.

OpenCommit is a tool that auto-generates meaningful commits using AI, allowing users to quickly create commit messages for their staged changes. It provides a CLI interface for easy usage and supports customization of commit descriptions, emojis, and AI models. Users can configure local and global settings, switch between different AI providers, and set up Git hooks for integration with IDE Source Control. Additionally, OpenCommit can be used as a GitHub Action to automatically improve commit messages on push events, ensuring all commits are meaningful and not generic. Payments for OpenAI API requests are handled by the user, with the tool storing API keys locally.

AI Town is a virtual town where AI characters live, chat, and socialize. This project provides a deployable starter kit for building and customizing your own version of AI Town. It features a game engine, database, vector search, auth, text model, deployment, pixel art generation, background music generation, and local inference. You can customize your own simulation by creating characters and stories, updating spritesheets, changing the background, and modifying the background music.

Dravid (DRD) is an advanced, AI-powered CLI coding framework designed to follow user instructions until the job is completed, including fixing errors. It can generate code, fix errors, handle image queries, manage file operations, integrate with external APIs, and provide a development server with error handling. Dravid is extensible and requires Python 3.7+ and CLAUDE_API_KEY. Users can interact with Dravid through CLI commands for various tasks like creating projects, asking questions, generating content, handling metadata, and file-specific queries. It supports use cases like Next.js project development, working with existing projects, exploring new languages, Ruby on Rails project development, and Python project development. Dravid's project structure includes directories for source code, CLI modules, API interaction, utility functions, AI prompt templates, metadata management, and tests. Contributions are welcome, and development setup involves cloning the repository, installing dependencies with Poetry, setting up environment variables, and using Dravid for project enhancements.

ChatGPT Smart Speaker is a project that enables speech recognition and text-to-speech functionalities using OpenAI and Google Speech Recognition. It provides scripts for running on PC/Mac and Raspberry Pi, allowing users to interact with a smart speaker setup. The project includes detailed instructions for setting up the required hardware and software dependencies, along with customization options for the OpenAI model engine, language settings, and response randomness control. The Raspberry Pi setup involves utilizing the ReSpeaker hardware for voice feedback and light shows. The project aims to offer an advanced smart speaker experience with features like wake word detection and response generation using AI models.

VectorFlow is an open source, high throughput, fault tolerant vector embedding pipeline. It provides a simple API endpoint for ingesting large volumes of raw data, processing, and storing or returning the vectors quickly and reliably. The tool supports text-based files like TXT, PDF, HTML, and DOCX, and can be run locally with Kubernetes in production. VectorFlow offers functionalities like embedding documents, running chunking schemas, custom chunking, and integrating with vector databases like Pinecone, Qdrant, and Weaviate. It enforces a standardized schema for uploading data to a vector store and supports features like raw embeddings webhook, chunk validation webhook, S3 endpoint, and telemetry. The tool can be used with the Python client and provides detailed instructions for running and testing the functionalities.

Feeds Fun is a self-hosted news reader tool that automatically assigns tags to news entries. Users can create rules to score news based on tags, filter and sort news as needed, and track read news. The tool offers multi/single-user support, feeds management, and various features for personalized news consumption. Users can access the tool's backend as the ffun package on PyPI and the frontend as the feeds-fun package on NPM. Feeds Fun requires setting up OpenAI or Gemini API keys for full tag generation capabilities. The tool uses tag processors to detect tags for news entries, with options for simple and complex processors. Feeds Fun primarily relies on LLM tag processors from OpenAI and Google for tag generation.

The user interface (and the main server) of the simulator of aids and social benefits for young people. It is based on the free socio-fiscal simulator Openfisca.

Bito CLI provides a command line interface to the Bito AI chat functionality, allowing users to interact with the AI through commands. It supports complex automation and workflows, with features like long prompts and slash commands. Users can install Bito CLI on Mac, Linux, and Windows systems using various methods. The tool also offers configuration options for AI model type, access key management, and output language customization. Bito CLI is designed to enhance user experience in querying AI models and automating tasks through the command line interface.

Dir-assistant is a tool that allows users to interact with their current directory's files using local or API Language Models (LLMs). It supports various platforms and provides API support for major LLM APIs. Users can configure and customize their local LLMs and API LLMs using the tool. Dir-assistant also supports model downloads and configurations for efficient usage. It is designed to enhance file interaction and retrieval using advanced language models.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

This repository showcases how to build a simple LLM-based chatbot for answering questions based on documents using retrieval augmented generation (RAG) technique. It also provides guidance on deploying the chatbot using Podman or on the OpenShift Container Platform. The workshop associated with this repository introduces participants to LLMs & RAG concepts and demonstrates how to customize the chatbot for specific purposes. The software stack relies on open-source tools like streamlit, LlamaIndex, and local open LLMs via Ollama, making it accessible for GPU-constrained environments.

Vectara Answer is a sample app for Vectara-powered Summarized Semantic Search (or question-answering) with advanced configuration options. For examples of what you can build with Vectara Answer, check out Ask News, LegalAid, or any of the other demo applications.

Redbox is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarise civil service documents. It increases organisational memory by indexing documents and can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best. The project uses a microservice architecture with each microservice running in its own container defined by a Dockerfile. Dependencies are managed using Python Poetry. Contributions are welcome, and the project is licensed under the MIT License. Security measures are in place to ensure user data privacy and considerations are being made to make the core-api secure.

pyrfuniverse is a python package used to interact with RFUniverse simulation environment. It is developed with reference to ML-Agents and produce new features. The package allows users to work with RFUniverse for simulation purposes, providing tools and functionalities to interact with the environment and create new features.

Seer is a service that provides AI capabilities to Sentry by running inference on Sentry issues and providing user insights. It is currently in early development and not yet compatible with self-hosted Sentry instances. The tool requires access to internal Sentry resources and is intended for internal Sentry employees. Users can set up the environment, download model artifacts, integrate with local Sentry, run evaluations for Autofix AI agent, and deploy to a sandbox staging environment. Development commands include applying database migrations, creating new migrations, running tests, and more. The tool also supports VCRs for recording and replaying HTTP requests.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

LLM-RGB is a repository containing a collection of detailed test cases designed to evaluate the reasoning and generation capabilities of Language Learning Models (LLMs) in complex scenarios. The benchmark assesses LLMs' performance in understanding context, complying with instructions, and handling challenges like long context lengths, multi-step reasoning, and specific response formats. Each test case evaluates an LLM's output based on context length difficulty, reasoning depth difficulty, and instruction compliance difficulty, with a final score calculated for each test case. The repository provides a score table, evaluation details, and quick start guide for running evaluations using promptfoo testing tools.

Mastra is an opinionated Typescript framework designed to help users quickly build AI applications and features. It provides primitives such as workflows, agents, RAG, integrations, syncs, and evals. Users can run Mastra locally or deploy it to a serverless cloud. The framework supports various LLM providers, offers tools for building language models, workflows, and accessing knowledge bases. It includes features like durable graph-based state machines, retrieval-augmented generation, integrations, syncs, and automated tests for evaluating LLM outputs.

SWE-Lancer is a benchmark repository containing datasets and code for the paper 'SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?'. It provides instructions for package management, building Docker images, configuring environment variables, and running evaluations. Users can use this tool to assess the performance of language models in real-world freelance software engineering tasks.

Dash AI is an open-source coding assistant for Flutter developers. It is designed to not only write code but also run and debug it, allowing it to assist beyond code completion and automate routine tasks. Dash AI is powered by Gemini, integrated with the Dart Analyzer, and specifically tailored for Flutter engineers. The vision for Dash AI is to create a single-command assistant that can automate tedious development tasks, enabling developers to focus on creativity and innovation. It aims to assist with the entire process of engineering a feature for an app, from breaking down the task into steps to generating exploratory tests and iterating on the code until the feature is complete. To achieve this vision, Dash AI is working on providing LLMs with the same access and information that human developers have, including full contextual knowledge, the latest syntax and dependencies data, and the ability to write, run, and debug code. Dash AI welcomes contributions from the community, including feature requests, issue fixes, and participation in discussions. The project is committed to building a coding assistant that empowers all Flutter developers.

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.

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

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.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

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.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.