LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para produ
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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.
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 |