ai-ml-pipeline

What problem does it solve?

This Skill provides a comprehensive framework for building, training, and deploying sophisticated AI and Machine Learning pipelines, including LLM integration and vector database management.

Core Features & Use Cases

• LLM Integration: Seamlessly integrate with various LLMs (OpenAI, Anthropic, Google, local) using tools like Vercel AI SDK for text generation, structured output, and multi-step tool use.
• RAG Pipelines: Implement Retrieval-Augmented Generation by embedding documents, storing them in vector databases (Pinecone, Chroma, pgvector), and querying for contextually relevant information to enhance LLM responses.
• Agent Orchestration: Design and manage multi-agent systems for complex task execution and self-correction.
• ML Training & Serving: Utilize Python scripts for configurable ML training pipelines (using libraries like Transformers, PyTorch, scikit-learn) and FastAPI for efficient model serving.
• Use Case: Develop a customer support chatbot that leverages RAG to answer questions based on your company's documentation and can also perform actions by calling external tools.

Quick Start

Use the ai-ml-pipeline skill to set up a RAG pipeline for answering questions based on provided documents.