setup models

dmb/llm.py

@@ -0,0 +1,91 @@
+"""Cliente mínimo do OCI Generative AI (chat on-demand) para geração e anotação de dados.
+
+Autenticação: resource principal (jobs/notebooks), instance principal (VM, DMB_OCI_AUTH=instance_principal)
+ou a chave de API em .secrets/ (máquina local).
+"""
+from __future__ import annotations
+
+import json
+import os
+import re
+import sys
+import time
+from functools import lru_cache
+from pathlib import Path
+
+GENAI_REGION = os.environ.get("DMB_GENAI_REGION", "us-chicago-1")
+ROOT = Path(__file__).resolve().parents[1]
+
+
+@lru_cache(maxsize=None)
+def _client_and_compartment():
+    import oci
+
+    endpoint = f"https://inference.generativeai.{GENAI_REGION}.oci.oraclecloud.com"
+    retry = oci.retry.DEFAULT_RETRY_STRATEGY
+    if os.environ.get("OCI_RESOURCE_PRINCIPAL_VERSION"):
+        signer = oci.auth.signers.get_resource_principals_signer()
+        client = oci.generative_ai_inference.GenerativeAiInferenceClient(
+            {}, signer=signer, service_endpoint=endpoint, retry_strategy=retry, timeout=(10, 300))
+    elif os.environ.get("DMB_OCI_AUTH") == "instance_principal":
+        signer = oci.auth.signers.InstancePrincipalsSecurityTokenSigner()
+        client = oci.generative_ai_inference.GenerativeAiInferenceClient(
+            {}, signer=signer, service_endpoint=endpoint, retry_strategy=retry, timeout=(10, 300))
+    else:
+        sys.path.insert(0, str(ROOT / "infra" / "oci"))
+        from session import load_config
+        client = oci.generative_ai_inference.GenerativeAiInferenceClient(
+            load_config(region=GENAI_REGION), service_endpoint=endpoint, retry_strategy=retry, timeout=(10, 300))
+    compartment = os.environ.get("DMB_COMPARTMENT_OCID") or os.environ.get("JOB_COMPARTMENT_OCID")
+    if not compartment:
+        raise RuntimeError("defina DMB_COMPARTMENT_OCID")
+    return client, compartment
+
+
+def chat(model: str, system: str, user: str, temperature: float = 0.7, max_tokens: int = 4000,
+         attempts: int = 4) -> str:
+    import oci
+    from oci.generative_ai_inference import models as m
+
+    client, compartment = _client_and_compartment()
+    if model.startswith("cohere."):
+        request = m.CohereChatRequest(preamble_override=system, message=user, temperature=temperature,
+                                      max_tokens=max_tokens)
+    else:
+        msgs = [m.SystemMessage(content=[m.TextContent(text=system)]),
+                m.UserMessage(content=[m.TextContent(text=user)])]
+        kwargs = {"messages": msgs, "temperature": temperature}
+        # modelos de raciocínio da OpenAI só aceitam max_completion_tokens e temperatura padrão
+        if model.startswith("openai.gpt-5") or model.startswith("openai.o"):
+            kwargs.pop("temperature")
+            kwargs["max_completion_tokens"] = max_tokens
+        else:
+            kwargs["max_tokens"] = max_tokens
+        request = m.GenericChatRequest(**kwargs)
+    details = m.ChatDetails(compartment_id=compartment, serving_mode=m.OnDemandServingMode(model_id=model),
+                            chat_request=request)
+    for attempt in range(attempts):
+        try:
+            resp = client.chat(details).data.chat_response
+            if model.startswith("cohere."):
+                return resp.text
+            return "".join(c.text for c in resp.choices[0].message.content if getattr(c, "text", None))
+        except oci.exceptions.ServiceError as error:
+            if error.status in (429, 500, 502, 503, 504) and attempt < attempts - 1:
+                time.sleep(5 * 2 ** attempt)
+                continue
+            raise
+
+
+def extract_json(text: str):
+    """Primeiro objeto ou lista JSON do texto (tolera cercas ```json)."""
+    text = re.sub(r"^```(?:json)?|```$", "", text.strip(), flags=re.M).strip()
+    for opener, closer in (("[", "]"), ("{", "}")):
+        start = text.find(opener)
+        end = text.rfind(closer)
+        if start != -1 and end > start:
+            try:
+                return json.loads(text[start:end + 1])
+            except json.JSONDecodeError:
+                continue
+    raise ValueError(f"resposta sem JSON válido: {text[:200]!r}")