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Access large language models from the command-line
This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.
Xiaomi Home Integration for Home Assistant
Generate Go client and server boilerplate from OpenAPI 3 specifications
quantitative trading with Javascript, Python, C++, PineScript, Blockly, MyLanguage(麦语言)
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
A modular graph-based Retrieval-Augmented Generation (RAG) system
Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting yo…
Data framework for your LLM applications. Focus on server side solution
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.
LangChain + Next.js starter template
Invest Alchemy is a trading assistant focused on ETF portfolios.
🦜🔗 Build context-aware reasoning applications 🦜🔗
A code-searching tool similar to ack, but faster.
This is a repository of RALM surveys containing a summary of state-of-the-art RAG and other technologies
the AI-native open-source embedding database
「硬地骇客 - 两个月 $12000 ARR 实践之路」是由 硬地骇客 团队编著，本书是关于 Podwise 产品历程的忠实记录：内容包含 灵感 - 构建 - 发布 - 增长 - 复盘 五个章节。如果你觉得一个人读不够过瘾，欢迎加入「硬地骇客」官方知识星球与专家们一起讨论！Podwise 的故事才刚刚开始，我们也将在星球持续分享我们的认知，成功可能无法复制，但失败一定可以借鉴。现在就点击下方…
Data Apps & Dashboards for Python. No JavaScript Required.
Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 2, and other large language models.
The interactive graphing library for Python ✨
Robust Speech Recognition via Large-Scale Weak Supervision
The official Python library for the OpenAI API
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.