llm-app

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

56.1k

Stars

+9.5k

Gained

20.3%

Growth

Jupyter Notebook

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View on GitHub → ↑0.2% this week

💡 Why It Matters

The llm-app repository addresses the challenge of integrating live data into AI pipelines, making it easier for ML/AI teams to develop robust applications. With its ready-to-run cloud templates, engineers can quickly implement solutions for retrieval-augmented generation (RAG) and enterprise search, enhancing productivity. This production-ready solution has demonstrated significant growth, gaining 9,473 stars (20.3% growth) in just 96 days, indicating strong community interest and support. However, it may not be the best fit for teams requiring extensive customisation or those working with highly specific data sources that are not supported.

🎯 When to Use

This is a strong choice for teams looking to streamline AI pipeline development and leverage real-time data integration. Teams should consider alternatives if they need a highly tailored solution or have unique compliance requirements.

👥 Team Fit & Use Cases

This open source tool for engineering teams is particularly beneficial for data scientists, ML engineers, and DevOps professionals. It is often integrated into products that require advanced machine learning capabilities, such as chatbots and data analytics platforms.

🎭 Best For

Machine Learning and AI Engineer

🏷️ Topics & Ecosystem

chatbot hugging-face llm llm-local llm-prompting llm-security llmops machine-learning open-ai pathway rag real-time retrieval-augmented-generation vector-database vector-index

📊 Activity

Latest commit: 2026-01-07. Over the past 97 days, this repository gained 9.5k stars (+20.3% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.