From antigravity-awesome-skills
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
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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.
Mandates invoking relevant skills via tools before any response in coding sessions. Covers access, priorities, and adaptations for Claude Code, Copilot CLI, Gemini CLI.
Share bugs, ideas, or general feedback.
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.
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="./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)
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
| 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+ |
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;
Componentes do system prompt Auri:
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)
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)
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}
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"]
},
]
| 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 |