Build end-to-end AI and ML pipelines with LLM integration and RAG systems.
npx skills add https://github.com/tuyenht/Antigravity-Core --skill ai-ml-pipeline
Please help me install this Agent Skill. Skill: ai-ml-pipeline Source: https://github.com/tuyenht/Antigravity-Core/tree/main/.agent/skills/ai-ml-pipeline Command: npx skills add https://github.com/tuyenht/Antigravity-Core --skill ai-ml-pipeline
This Skill provides a comprehensive framework for building, training, and deploying sophisticated AI and Machine Learning pipelines, including LLM integration and vector database management.
Use the ai-ml-pipeline skill to set up a RAG pipeline for answering questions based on provided documents.
High-intent search queries and answers about installing and using this skill.
To build a RAG pipeline, you embed documents and store them in vector databases like Pinecone, Chroma, or pgvector, then query the database for contextually relevant information to enhance LLM responses.
Yes, you can use Python libraries like Transformers, PyTorch, and scikit-learn for configurable ML training pipelines, and FastAPI for efficient model serving, alongside TypeScript/JavaScript for AI integration.
The best way to integrate large language models is using the Vercel AI SDK, which supports text generation, structured output, and multi-step tool use with OpenAI, Anthropic, Google, or local models.
Agent orchestration works by designing and managing multi-agent systems that execute complex tasks and perform self-correction, enabling sophisticated workflows like automated customer support chatbots.
Yes, you need expertise in TypeScript/JavaScript and Python libraries for AI/ML development to effectively utilize the framework for LLM integration, RAG systems, and model deployment.
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