![LeanAide](/statics/github-mark.png)
LeanAide
Tools based on AI for helping with Lean 4
Stars: 69
![screenshot](/screenshots_githubs/siddhartha-gadgil-LeanAide.jpg)
LeanAide is a work in progress AI tool designed to assist with development using the Lean Theorem Prover. It currently offers a tool that translates natural language statements to Lean types, including theorem statements. The tool is based on GPT 3.5-turbo/GPT 4 and requires an OpenAI key for usage. Users can include LeanAide as a dependency in their projects to access the translation functionality.
README:
LeanAide or LeanAIde (accidental pun) is work in progress to build AI based tools to help development with the Lean Theorem Prover. For now it has one tool under development, which translates statements written in natural language in a doc-string like format to Lean types (including theorem statements).
Build this repository and fetch pre-loaded embeddings with the following commands:
lake exe cache get # download prebuilt mathlib binaries
lake build mathlib
lake build
lake exe fetch_embeddings
Our translation is based on GPT 3.5-turbo/GPT 4, to use which you need an OpenAI key. To get started please configure environment variables using the following bash commands or equivalent in your system at the base of the repository (the angle brackets are not to be included in the command), and then launch VS code.
export OPENAI_API_KEY=<your-open-ai-key>
code .
Alternatively, you can create a file with path rawdata/OPENAI_API_KEY
(relative to the root of LeanAIDE) containing your key.
After this open the folder in VS code (or equivalent) with Lean 4 and go to the file LeanCodePrompts/TranslateDemo.lean
. Any statement written using syntax
similar to #theorem "There are infinitely many primes"
will be translated into Lean code. You will see examples of this in the demo files. Once the translation is done, a Try this
hyperlink and code-action will appear. Clicking on this will add the translated code to the file.
Alternatively, you can translate a statement using the following command in the Lean REPL:
lake exe translate "There are infinitely many primes"
Using in your own project still involves some issues, for example it is slow.
As we use precompiled embeddings, we must have toolchains matching. For using with v4.15.0
(so far the only version supported), include this project as a dependency in lakefile.lean
using the following (or use the equivalent in lakefile.toml
):
require LeanCodePrompts from git "https://github.com/siddhartha-gadgil/LeanAide"@"4.15.0"
The run from a terminal the following to download the prebuilt embeddings.
lake exe fetch_embeddings
If you import LeanAide
and Mathlib
to a file, the translation will be available.
The principal author of this repository is Siddhartha Gadgil.
The first phase of this work (roughly June 2022-October 2023) was done in collaboration with:
- Anand Rao Tadipatri
- Ayush Agrawal
- Ashvni Narayanan
- Navin Goyal
We had a lot of help from the Lean community and from collaborators at Microsoft Research. Our server is hosted with support from research credits from Google.
More recently (since about October 2024) the work has been done in collaboration with:
- Anirudh Gupta
- Vaishnavi Shirsath
- Ajay Kumar Nair
- Malhar Patel
- Sushrut Jog
This is supported by ARCNet, ART-PARK, IISc.
Our work is described in a note at the 2nd Math AI workshop and in more detail (along with related work) in a preprint.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for LeanAide
Similar Open Source Tools
![LeanAide Screenshot](/screenshots_githubs/siddhartha-gadgil-LeanAide.jpg)
LeanAide
LeanAide is a work in progress AI tool designed to assist with development using the Lean Theorem Prover. It currently offers a tool that translates natural language statements to Lean types, including theorem statements. The tool is based on GPT 3.5-turbo/GPT 4 and requires an OpenAI key for usage. Users can include LeanAide as a dependency in their projects to access the translation functionality.
![LLMs-World-Models-for-Planning Screenshot](/screenshots_githubs/GuanSuns-LLMs-World-Models-for-Planning.jpg)
LLMs-World-Models-for-Planning
This repository provides a Python implementation of a method that leverages pre-trained large language models to construct and utilize world models for model-based task planning. It includes scripts to generate domain models using natural language descriptions, correct domain models based on feedback, and support plan generation for tasks in different domains. The code has been refactored for better readability and includes tools for validating PDDL syntax and handling corrective feedback.
![2p-kt Screenshot](/screenshots_githubs/tuProlog-2p-kt.jpg)
2p-kt
2P-Kt is a Kotlin-based and multi-platform reboot of tuProlog (2P), a multi-paradigm logic programming framework written in Java. It consists of an open ecosystem for Symbolic Artificial Intelligence (AI) with modules supporting logic terms, unification, indexing, resolution of logic queries, probabilistic logic programming, binary decision diagrams, OR-concurrent resolution, DSL for logic programming, parsing modules, serialisation modules, command-line interface, and graphical user interface. The tool is designed to support knowledge representation and automatic reasoning through logic programming in an extensible and flexible way, encouraging extensions towards other symbolic AI systems than Prolog. It is a pure, multi-platform Kotlin project supporting JVM, JS, Android, and Native platforms, with a lightweight library leveraging the Kotlin common library.
![aici Screenshot](/screenshots_githubs/microsoft-aici.jpg)
aici
The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.
![llm.c Screenshot](/screenshots_githubs/karpathy-llm.c.jpg)
llm.c
LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.
![BentoDiffusion Screenshot](/screenshots_githubs/bentoml-BentoDiffusion.jpg)
BentoDiffusion
BentoDiffusion is a BentoML example project that demonstrates how to serve and deploy diffusion models in the Stable Diffusion (SD) family. These models are specialized in generating and manipulating images based on text prompts. The project provides a guide on using SDXL Turbo as an example, along with instructions on prerequisites, installing dependencies, running the BentoML service, and deploying to BentoCloud. Users can interact with the deployed service using Swagger UI or other methods. Additionally, the project offers the option to choose from various diffusion models available in the repository for deployment.
![ChatAFL Screenshot](/screenshots_githubs/ChatAFLndss-ChatAFL.jpg)
ChatAFL
ChatAFL is a protocol fuzzer guided by large language models (LLMs) that extracts machine-readable grammar for protocol mutation, increases message diversity, and breaks coverage plateaus. It integrates with ProfuzzBench for stateful fuzzing of network protocols, providing smooth integration. The artifact includes modified versions of AFLNet and ProfuzzBench, source code for ChatAFL with proposed strategies, and scripts for setup, execution, analysis, and cleanup. Users can analyze data, construct plots, examine LLM-generated grammars, enriched seeds, and state-stall responses, and reproduce results with downsized experiments. Customization options include modifying fuzzers, tuning parameters, adding new subjects, troubleshooting, and working on GPT-4. Limitations include interaction with OpenAI's Large Language Models and a hard limit of 150,000 tokens per minute.
![gemma Screenshot](/screenshots_githubs/google-deepmind-gemma.jpg)
gemma
Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology. This repository contains an inference implementation and examples, based on the Flax and JAX frameworks. Gemma can run on CPU, GPU, and TPU, with model checkpoints available for download. It provides tutorials, reference implementations, and Colab notebooks for tasks like sampling and fine-tuning. Users can contribute to Gemma through bug reports and pull requests. The code is licensed under the Apache License, Version 2.0.
![nx_open Screenshot](/screenshots_githubs/networkoptix-nx_open.jpg)
nx_open
The `nx_open` repository contains open-source components for the Network Optix Meta Platform, used to build products like Nx Witness Video Management System. It includes source code, specifications, and a Desktop Client. The repository is licensed under Mozilla Public License 2.0. Users can build the Desktop Client and customize it using a zip file. The build environment supports Windows, Linux, and macOS platforms with specific prerequisites. The repository provides scripts for building, signing executable files, and running the Desktop Client. Compatibility with VMS Server versions is crucial, and automatic VMS updates are disabled for the open-source Desktop Client.
![generative-ai-sagemaker-cdk-demo Screenshot](/screenshots_githubs/aws-samples-generative-ai-sagemaker-cdk-demo.jpg)
generative-ai-sagemaker-cdk-demo
This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.
![PromptAgent Screenshot](/screenshots_githubs/XinyuanWangCS-PromptAgent.jpg)
PromptAgent
PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.
![GhostOS Screenshot](/screenshots_githubs/ghost-in-moss-GhostOS.jpg)
GhostOS
GhostOS is an AI Agent framework designed to replace JSON Schema with a Turing-complete code interaction interface (Moss Protocol). It aims to create intelligent entities capable of continuous learning and growth through code generation and project management. The framework supports various capabilities such as turning Python files into web agents, real-time voice conversation, body movements control, and emotion expression. GhostOS is still in early experimental development and focuses on out-of-the-box capabilities for AI agents.
![CoLLM Screenshot](/screenshots_githubs/zyang1580-CoLLM.jpg)
CoLLM
CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.
![airflow Screenshot](/screenshots_githubs/apache-airflow.jpg)
airflow
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
![council Screenshot](/screenshots_githubs/chain-ml-council.jpg)
council
Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.
![aisuite Screenshot](/screenshots_githubs/andrewyng-aisuite.jpg)
aisuite
Aisuite is a simple, unified interface to multiple Generative AI providers. It allows developers to easily interact with various Language Model (LLM) providers like OpenAI, Anthropic, Azure, Google, AWS, and more through a standardized interface. The library focuses on chat completions and provides a thin wrapper around python client libraries, enabling creators to test responses from different LLM providers without changing their code. Aisuite maximizes stability by using HTTP endpoints or SDKs for making calls to the providers. Users can install the base package or specific provider packages, set up API keys, and utilize the library to generate chat completion responses from different models.
For similar tasks
![LeanAide Screenshot](/screenshots_githubs/siddhartha-gadgil-LeanAide.jpg)
LeanAide
LeanAide is a work in progress AI tool designed to assist with development using the Lean Theorem Prover. It currently offers a tool that translates natural language statements to Lean types, including theorem statements. The tool is based on GPT 3.5-turbo/GPT 4 and requires an OpenAI key for usage. Users can include LeanAide as a dependency in their projects to access the translation functionality.
For similar jobs
![sweep Screenshot](/screenshots_githubs/sweepai-sweep.jpg)
sweep
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.
![teams-ai Screenshot](/screenshots_githubs/microsoft-teams-ai.jpg)
teams-ai
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.
![ai-guide Screenshot](/screenshots_githubs/Crataco-ai-guide.jpg)
ai-guide
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.
![classifai Screenshot](/screenshots_githubs/10up-classifai.jpg)
classifai
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 Screenshot](/screenshots_githubs/mckaywrigley-chatbot-ui.jpg)
chatbot-ui
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 Screenshot](/screenshots_githubs/bricks-cloud-BricksLLM.jpg)
BricksLLM
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 Screenshot](/screenshots_githubs/fetchai-uAgents.jpg)
uAgents
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 Screenshot](/screenshots_githubs/griptape-ai-griptape.jpg)
griptape
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.