feat(sleep): support OpenAI-compatible endpoints in azure_openai backend
Let AzureOpenAIBackend drive any OpenAI-compatible chat-completions server (DeepSeek, self-hosted vLLM/Ollama, ...) alongside native Azure deployments. - __init__ resolves the endpoint as: explicit arg > AZURE_OPENAI_ENDPOINT env > the built-in _AZURE_ENDPOINTS table (previously a non-Azure endpoint could not be supplied at all). - _get_client() builds a plain openai.OpenAI(base_url=...) client when AZURE_OPENAI_AUTH_MODE=openai_compatible, matching the auth mode already supported by the sibling skillopt/model/azure_openai.py. This avoids the AzureOpenAI SDK rewriting request URLs with Azure-only ?api-version= query params and deployment path segments, which non-Azure servers reject with 404. - _call() sends max_tokens + extra_body thinking flag for deepseek* models and records the last exception in self.last_call_error so a failed night is diagnosable instead of collapsing to a silent empty->0 score. Adds docs/sleep/openai-compatible-endpoints.md and sanitized example runner/watchdog scripts documenting an Antigravity + DeepSeek integration. The default managed-identity Azure path is unchanged.
This commit is contained in:
Alphaxalchemy
+48
-14
@@ -1236,7 +1236,15 @@ class AzureOpenAIBackend(CliBackend):
|
||||
api_version: str = "2024-12-01-preview") -> None:
|
||||
super().__init__(model=deployment or "gpt-5.5", timeout=timeout)
|
||||
self.deployment = deployment or "gpt-5.5"
|
||||
self.endpoint = endpoint or self._endpoint_for(self.deployment)
|
||||
# Endpoint resolution order: explicit arg > AZURE_OPENAI_ENDPOINT env >
|
||||
# the built-in Azure endpoint table. Honoring the env var lets callers
|
||||
# point this backend at any OpenAI-compatible server (DeepSeek, a local
|
||||
# vLLM, etc.) without editing the hardcoded _AZURE_ENDPOINTS map.
|
||||
self.endpoint = (
|
||||
endpoint
|
||||
or os.environ.get("AZURE_OPENAI_ENDPOINT", "").strip()
|
||||
or self._endpoint_for(self.deployment)
|
||||
)
|
||||
self.api_version = api_version
|
||||
self.name = f"azure:{self.deployment}"
|
||||
self._client = None
|
||||
@@ -1250,14 +1258,29 @@ class AzureOpenAIBackend(CliBackend):
|
||||
|
||||
def _get_client(self):
|
||||
if self._client is None:
|
||||
from azure.identity import ManagedIdentityCredential, get_bearer_token_provider
|
||||
from openai import AzureOpenAI
|
||||
cred = ManagedIdentityCredential(client_id=_AZURE_MI_CLIENT_ID)
|
||||
tp = get_bearer_token_provider(cred, "https://cognitiveservices.azure.com/.default")
|
||||
self._client = AzureOpenAI(
|
||||
azure_endpoint=self.endpoint, azure_ad_token_provider=tp,
|
||||
api_version=self.api_version, max_retries=4,
|
||||
)
|
||||
# AZURE_OPENAI_AUTH_MODE=openai_compatible (matching the sibling
|
||||
# skillopt.model.azure_openai module) selects a plain OpenAI client
|
||||
# against a raw base_url. This is what makes any OpenAI-compatible
|
||||
# endpoint work: the AzureOpenAI client would otherwise rewrite the
|
||||
# URL with Azure-only query params (?api-version=...) and deployment
|
||||
# path segments, which non-Azure servers reject with a 404.
|
||||
auth_mode = os.environ.get("AZURE_OPENAI_AUTH_MODE", "").strip().lower()
|
||||
if auth_mode in {"openai_compatible", "compat", "openai"}:
|
||||
from openai import OpenAI
|
||||
self._client = OpenAI(
|
||||
base_url=self.endpoint.rstrip("/"),
|
||||
api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
|
||||
max_retries=4,
|
||||
)
|
||||
else:
|
||||
from azure.identity import ManagedIdentityCredential, get_bearer_token_provider
|
||||
from openai import AzureOpenAI
|
||||
cred = ManagedIdentityCredential(client_id=_AZURE_MI_CLIENT_ID)
|
||||
tp = get_bearer_token_provider(cred, "https://cognitiveservices.azure.com/.default")
|
||||
self._client = AzureOpenAI(
|
||||
azure_endpoint=self.endpoint, azure_ad_token_provider=tp,
|
||||
api_version=self.api_version, max_retries=4,
|
||||
)
|
||||
return self._client
|
||||
|
||||
def _call(self, prompt: str, *, max_tokens: int = 1024, retries: int = 5) -> str:
|
||||
@@ -1277,11 +1300,19 @@ class AzureOpenAIBackend(CliBackend):
|
||||
last_exc = None
|
||||
for attempt in range(max(1, retries)):
|
||||
try:
|
||||
resp = client.chat.completions.create(
|
||||
model=self.deployment,
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
max_completion_tokens=16384,
|
||||
)
|
||||
kwargs: Dict[str, Any] = {
|
||||
"model": self.deployment,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
}
|
||||
# DeepSeek's reasoning models expect `max_tokens` and an
|
||||
# extra_body flag to enable the thinking channel; the Azure
|
||||
# gpt-5.x deployments use `max_completion_tokens`.
|
||||
if "deepseek" in self.deployment.lower():
|
||||
kwargs["max_tokens"] = 8192
|
||||
kwargs["extra_body"] = {"thinking": {"type": "enabled"}}
|
||||
else:
|
||||
kwargs["max_completion_tokens"] = 16384
|
||||
resp = client.chat.completions.create(**kwargs)
|
||||
text = (resp.choices[0].message.content or "").strip()
|
||||
try:
|
||||
u = resp.usage
|
||||
@@ -1295,6 +1326,9 @@ class AzureOpenAIBackend(CliBackend):
|
||||
last_exc = "empty-response"
|
||||
except Exception as e: # noqa: BLE001
|
||||
last_exc = e
|
||||
# Surface the error so a 0.0 night is diagnosable (e.g. a 404
|
||||
# from a mis-pointed endpoint) instead of a silent empty->0.
|
||||
self.last_call_error = str(e)[:500]
|
||||
# backoff before next try (skip after the final attempt)
|
||||
if attempt < retries - 1:
|
||||
_t.sleep(min(8.0, (2 ** attempt) * 0.5) + _r.random() * 0.4)
|
||||
|
||||
Reference in New Issue
Block a user