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Top 23 Python AI Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Open-Assistant
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
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MockingBird
🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time
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pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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chatgpt-on-wechat
基于大模型搭建的聊天机器人，同时支持 企业微信、微信 公众号、飞书、钉钉 等接入，可选择GPT3.5/GPT4.0/Claude/文心一言/讯飞星火/通义千问/Gemini/GLM-4/Claude/Kimi/LinkAI，能处理文本、语音和图片，访问操作系统和互联网，支持基于自有知识库进行定制企业智能客服。
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SuperAGI
<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
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haystack
:mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
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h2ogpt
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
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promptflow
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
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deeplake
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
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Project mention: Show HN: I made an app to use local AI as daily driver | news.ycombinator.com | 2024-02-27* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
For open assistant, the code: https://github.com/LAION-AI/Open-Assistant/tree/main/inference
Project mention: Step by step guide to create customized chatbot by using spaCy (Python NLP library) | dev.to | 2024-03-23Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
Project mention: SB-1047 will stifle open-source AI and decrease safety | news.ycombinator.com | 2024-04-29It's very easy to get started, right in your Terminal, no fees! No credit card at all.
And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.
You don't need OpenAI - nobody does.
Project mention: What’s the Difference Between Fine-tuning, Retraining, and RAG? | dev.to | 2024-04-08Check us out on GitHub.
Project mention: Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations | dev.to | 2024-04-26How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.
Project mention: License Plate Recognition with Home Assistant, Codeproject.ai, and Frigate NVR | news.ycombinator.com | 2024-04-26
Project mention: refacer VS facefusion - a user suggested alternative | libhunt.com/r/refacer | 2024-01-30
Project mention: Haystack DB – 10x faster than FAISS with binary embeddings by default | news.ycombinator.com | 2024-04-28I was confused for a bit but there is no relation to https://haystack.deepset.ai/
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
Project mention: Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023? | news.ycombinator.com | 2023-12-24As others have said you want RAG.
The most feature complete implementation I've seen is h2ogpt[0] (not affiliated).
The code is kind of a mess (most of the logic is in an ~8000 line python file) but it supports ingestion of everything from YouTube videos to docx, pdf, etc - either offline or from the web interface. It uses langchain and a ton of additional open source libraries under the hood. It can run directly on Linux, via docker, or with one-click installers for Mac and Windows.
It has various model hosting implementations built in - transformers, exllama, llama.cpp as well as support for model serving frameworks like vLLM, HF TGI, etc or just OpenAI.
You can also define your preferred embedding model along with various other parameters but I've found the out of box defaults to be pretty sane and usable.
[0] - https://github.com/h2oai/h2ogpt
Project mention: Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023? | news.ycombinator.com | 2023-12-24You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.
The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.
[1]: https://github.com/embedchain/embedchain
Project mention: Ask HN: Most efficient way to fine-tune an LLM in 2024? | news.ycombinator.com | 2024-04-04Gemma 7b is 2.4x faster than HF + FA2.
Check out https://github.com/unslothai/unsloth for full benchmarks!
Project mention: A suite of tools designed to streamline the development cycle of LLM-based apps | news.ycombinator.com | 2024-04-12
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python AI related posts
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A smooth and sharp image interpolation you probably haven't heard of
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Open-source SDK for adding custom code interpreters to AI apps
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Show HN: FileKitty – Combine and label text files for LLM prompt contexts
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Cold-(Brew) Outreach: Landing my first big client at a coffee shop
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SB-1047 will stifle open-source AI and decrease safety
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FLaNK AI Weekly for 29 April 2024
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Show HN: Cognita – open-source RAG framework for modular applications
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A note from our sponsor - SaaSHub
www.saashub.com | 3 May 2024
Index
What are some of the best open-source AI projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | stable-diffusion-webui | 129,975 |
| 2 | ColossalAI | 37,911 |
| 3 | Open-Assistant | 36,647 |
| 4 | MockingBird | 33,862 |
| 5 | spaCy | 28,751 |
| 6 | pytorch-lightning | 26,952 |
| 7 | chatgpt-on-wechat | 24,945 |
| 8 | MindsDB | 21,312 |
| 9 | MLflow | 17,284 |
| 10 | frigate | 14,840 |
| 11 | SuperAGI | 14,491 |
| 12 | FaceFusion | 14,408 |
| 13 | DocsGPT | 14,169 |
| 14 | haystack | 13,711 |
| 15 | dvc | 13,139 |
| 16 | h2ogpt | 10,458 |
| 17 | awesome-chatgpt-zh | 9,924 |
| 18 | embedchain | 8,479 |
| 19 | nebuly | 8,363 |
| 20 | unsloth | 8,282 |
| 21 | RobustVideoMatting | 8,176 |
| 22 | promptflow | 8,154 |
| 23 | deeplake | 7,708 |
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