--- name: llm-ops-2bb71e9c5ea0 description: "LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao." risk: safe source: community date_added: '2026-03-06' author: renat tags: - llm - rag - embeddings - vector-db - fine-tuning tools: - claude-code - antigravity - cursor - gemini-cli - codex-cli --- # 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_size - overlap): chunk = " ".join(words[i:i + chunk_size]) if chunk: chunks.append(chunk) return chunks def index_document(doc_id, content_text, metadata=None): chunks = chunk_text(content_text) ids = [f"{doc_id}_chunk_{i}" for i in range(len(chunks))] collection.upsert(ids=ids, documents=chunks) return len(chunks) ## Pipeline De Query Com Rag def rag_query(query, top_k=5, system=None): results = collection.query( query_texts=[query], n_results=top_k, include=["documents", "metadatas", "distances"]) context_parts = [] for doc, meta, dist in zip(results["documents"][0], results["metadatas"][0], results["distances"][0]): if dist < 1.5: src = meta.get("source", "doc") context_parts.append(f"[Fonte: {src}] {doc}") context = " --- ".join(context_parts) response = client.messages.create( model="claude-opus-4-20250805", max_tokens=1024, system=system or "Responda baseado no contexto.", messages=[{"role": "user", "content": f"Contexto: {context} {query}"}]) return response.content[0].text --- ## Escolha Do Vector Db | DB | Melhor Para | Hosting | Custo | |----|------------|---------|-------| | Chroma | Desenvolvimento, local | Self-hosted | Gratis | | pgvector | Ja usa PostgreSQL | Self/Cloud | Gratis | | Pinecone | Producao gerenciada | Cloud | USD 70+/mes | | Weaviate | Multi-modal | Self/Cloud | Gratis+ | | Qdrant | Alta performance | Self/Cloud | Gratis+ | ## Pgvector CREATE EXTENSION IF NOT EXISTS vector; CREATE TABLE knowledge_embeddings ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), content TEXT NOT NULL, embedding vector(1536), metadata JSONB, created_at TIMESTAMPTZ DEFAULT NOW() ); CREATE INDEX ON knowledge_embeddings USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100); SELECT content, 1 - (embedding <=> QUERY_VECTOR) AS similarity FROM knowledge_embeddings ORDER BY similarity DESC LIMIT 5; --- ## Estrutura De Prompt De Elite Componentes do system prompt Auri: - Identidade: Nome (Auri), Tom (Natural, caloroso, direto), Plataforma (Amazon Alexa) - Regras: Maximo 3 paragrafos curtos, sem markdown, linguagem conversacional - Capacidades: analise de negocios, conselho baseado em dados, criatividade - Limitacoes: sem internet tempo real, sem transacoes financeiras - Personalizacao: {user_name}, {user_preferences}, {relevant_history} ## Chain-Of-Thought def cot_analysis(problem: str) -> str: steps = [ "1. O que exatamente esta sendo pedido?", "2. Que informacoes sao criticas para resolver?", "3. Quais abordagens possiveis existem?", "4. Qual abordagem e melhor e por que?", "5. Quais riscos ou limitacoes existem?", ] prompt = f"Analise passo a passo: PROBLEMA: {problem} " prompt += " ".join(steps) + " Resposta final (concisa, para voz):" return call_claude(prompt) --- ## Cache Semantico class SemanticCache: def __init__(self, similarity_threshold=0.95): self.threshold = similarity_threshold self.cache = {} def get_cached(self, query, embedding): for cached_emb, (response, _) in self.cache.items(): if cosine_similarity(embedding, cached_emb) >= self.threshold: return response return None def set_cache(self, query, embedding, response): self.cache[tuple(embedding)] = (response, query) ## Estimativa De Custos Claude PRICING = { "claude-opus-4-20250805": {"input": 15.00, "output": 75.00}, "claude-sonnet-4-5": {"input": 3.00, "output": 15.00}, "claude-haiku-3-5": {"input": 0.80, "output": 4.00}, } def estimate_monthly_cost(model, avg_input, avg_output, req_per_day): p = PRICING[model] daily = (avg_input + avg_output) * req_per_day / 1e6 monthly = daily * p["input"] * 30 return {"model": model, "monthly_cost": "USD %.2f" % monthly} --- ## Framework De Avaliacao from anthropic import Anthropic client = Anthropic() def evaluate_response(question, expected, actual, criteria): criteria_text = " ".join(f"- {c}" for c in criteria) eval_prompt = ( f"Avalie a resposta do assistente de IA. " f"PERGUNTA: {question} RESPOSTA ESPERADA: {expected} " f"RESPOSTA ATUAL: {actual} Criterios: {criteria_text} " "Nota 0-10 e justificativa para cada criterio. Formato JSON." ) response = client.messages.create( model="claude-haiku-3-5", max_tokens=1024, messages=[{"role": "user", "content": eval_prompt}] ) import json return json.loads(response.content[0].text) AURI_EVALS = [ { "question": "Quais sao os principais riscos de abrir startup agora?", "criteria": ["precisao_factual", "relevancia", "clareza_para_voz"] }, ] --- ## 6. Comandos | Comando | Acao | |---------|------| | /rag-setup | Configura pipeline RAG completo | | /embed-docs | Indexa documentos no vector DB | | /prompt-optimize | Otimiza prompt para qualidade e custo | | /cost-estimate | Estima custo mensal do LLM | | /eval-run | Roda suite de evals de qualidade | | /cache-setup | Configura cache semantico | | /model-select | Escolhe modelo ideal para o caso de uso | ## Best Practices - Provide clear, specific context about your project and requirements - Review all suggestions before applying them to production code - Combine with other complementary skills for comprehensive analysis ## Common Pitfalls - Using this skill for tasks outside its domain expertise - Applying recommendations without understanding your specific context - Not providing enough project context for accurate analysis ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing. --- Source attribution: ASoudy Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills-claude/skills/llm-ops/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Antigravity User Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: ASoudy Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills-claude/skills/llm-ops/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Data Engineer, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API, Backend, Database Tools: Anthropic, Chromadb, Pgvector, Pinecone Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c867d4a7ceeb7fee96da952fbfc44756fc2c809104a9a08ff97a1758f800fc45