llm-ops

# LLM-OPS -- IA de Producao ## Overview LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao. Ativar para: implementar RAG, criar pipeline de embeddings, Pinecone/Chroma/pgvector, fine-tuning, prompt engineering, reducao de custos de LLM, evals, cache semantico, streaming, agents. ## When to Use This Skill - When you need specialized assistance with this domain ## Do Not Use This Skill When - The task is unrelated to llm ops - A simpler, more specific tool can handle the request - The user needs general-purpose assistance without domain expertise ## How It Works > A diferenca entre um prototipo de IA e um produto de IA e operabilidade. > LLM-Ops e a engenharia que torna IA confiavel, escalavel e economica. --- ## Arquitetura Rag Completa [Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB] | [Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks] | [LLM + Context] -> [Resposta] ## Pipeline De Indexacao from anthropic import Anthropic import chromadb client = Anthropic() chroma = chromadb.PersistentClient(path="./chroma_db") def chunk_text(text, chunk_size=500, overlap=50): words = text.split() chunks = [] for i in range(0, len(words), chunk_siz...