llm-ops

Topics in the source

• LLM-OPS -- IA de Producao
• Overview
• When to Use This Skill
• Do Not Use This Skill When
• How It Works
• Arquitetura Rag Completa
• Pipeline De Indexacao
• Pipeline De Query Com Rag

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 you need specialized assistance with this domain 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 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. -- [Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB] | [Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks] | [LLM + Context] -> [Resposta] from anthropic import Anthropic import chromadb client = Anthropic() chroma = chromadb.PersistentClient(path="./chromadb") def chunktext(text, chunksize=500, overlap=50): words = text.split() chunks = [] for i in range(0, len(words), chunksize - overlap): chunk = " ".join(words[i:i + chunksize]) if chunk: chunks.append(chunk) return chu

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