Pipeline that scrapes ZenML documentation, generates embeddings, and stores them in a vector store (Pinecone, PostgreSQL, or Elasticsearch). Addresses document ingestion and indexing.
Pipeline that queries the vector store using LLMs (GPT-4, GPT-3.5, Claude 3, or Claude Haiku) to provide answers about ZenML. Includes LLM call tracing with Langfuse.
Pipeline that deploys the RAG application to Hugging Face Spaces as a Gradio app, enabling user interaction with the question-answering system.
Pipeline that evaluates the RAG system's performance, assessing retrieval quality and end-to-end response accuracy.
Pipeline that analyzes user-marked responses (good/bad) from the deployed Hugging Face space to evaluate system performance based on real user feedback.
Pipeline that generates synthetic training data using distilabel for embeddings finetuning. Integrates with Argilla for data annotation.
Pipeline that finetunes embedding models using synthetic data and Matryoshka loss function to improve retrieval performance. Uses annotated data from Argilla.
LLM-Complete is a production-ready RAG (Retrieval-Augmented Generation) system that demonstrates how to build scalable chat applications with ZenML. Built with progressive MLOps pipelines, it evolves from basic document retrieval to advanced capabilities including embeddings finetuning, document reranking, and LLM optimization for domain-specific question answering about ZenML.
LLM-Complete transforms ZenML documentation into an intelligent Q&A system while showcasing best practices for LLMOps workflows. It processes documentation through automated pipelines that scrape content, generate embeddings, store them in vector databases, and serve responses via multiple LLMs. The system includes comprehensive evaluation frameworks, synthetic data generation for finetuning, and deployment to Hugging Face Spaces for user interaction.
