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.
2026-07-12 21:03:48 -06:00
parent 49a5b617c0
commit af33f6c338
4 changed files with 311 additions and 14 deletions
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""Reference launcher for running SkillOpt-Sleep against an OpenAI-compatible
+endpoint (DeepSeek shown here), plus an Antigravity `session-end` hook.
+
+This is a *sanitized example*, not a supported entry point. Adapt the paths and
+provider details to your environment. No API keys are hardcoded — the key is read
+from an .env file or the process environment.
+
+Usage:
+    python runner.py run           # run a full sleep cycle against DeepSeek
+    python runner.py dry-run       # harvest + replay, report only
+    python runner.py session-end   # Antigravity Stop-hook: append rollout evidence
+"""
+import os
+import re
+import sys
+import json
+import subprocess
+import datetime
+from pathlib import Path
+
+# --- Configure these for your environment -----------------------------------
+# Path to a file containing your provider key as `sk-...` (kept out of source).
+PROVIDER_ENV_FILE = Path(os.environ.get("SKILLOPT_PROVIDER_ENV_FILE", "provider.env"))
+# Endpoint + model for the OpenAI-compatible provider.
+PROVIDER_ENDPOINT = os.environ.get("SKILLOPT_PROVIDER_ENDPOINT", "https://api.deepseek.com")
+PROVIDER_MODEL = os.environ.get("SKILLOPT_PROVIDER_MODEL", "deepseek-v4-pro")
+# Project whose SKILL.md files the sleep cycle should evolve.
+PROJECT_DIR = os.environ.get("SKILLOPT_PROJECT_DIR", os.getcwd())
+# Where the session-end hook appends rollout evidence.
+ROLLOUT_LOG = Path(os.environ.get("SKILLOPT_ROLLOUT_LOG", "brain/rollout-evidence.jsonl"))
+# ----------------------------------------------------------------------------
+
+
+def load_provider_key(env: dict) -> None:
+    """Ensure DEEPSEEK_API_KEY is set, reading it from PROVIDER_ENV_FILE if needed."""
+    if env.get("DEEPSEEK_API_KEY"):
+        return
+    try:
+        text = PROVIDER_ENV_FILE.read_text(encoding="utf-8")
+    except OSError:
+        return
+    m = re.search(r"sk-[A-Za-z0-9]+", text)
+    if m:
+        env["DEEPSEEK_API_KEY"] = m.group(0)
+
+
+def main() -> None:
+    if len(sys.argv) < 2:
+        print("Usage: runner.py [dry-run|run|status|adopt|session-end]")
+        sys.exit(1)
+
+    command = sys.argv[1]
+
+    # Antigravity Stop-hook: enrich future nights with task-outcome metadata.
+    if command == "session-end":
+        ROLLOUT_LOG.parent.mkdir(parents=True, exist_ok=True)
+        outcome = {
+            "timestamp": datetime.datetime.now().isoformat(),
+            "event": "SessionEnd",
+            "metadata": "Appended task outcome metadata",
+        }
+        with open(ROLLOUT_LOG, "a", encoding="utf-8") as f:
+            f.write(json.dumps(outcome) + "\n")
+        print("Rollout evidence metadata appended.")
+        return
+
+    env = os.environ.copy()
+    load_provider_key(env)
+
+    if env.get("DEEPSEEK_API_KEY"):
+        # OpenAI-compatible path — see docs/sleep/openai-compatible-endpoints.md
+        backend = "azure_openai"
+        env["PYTHONIOENCODING"] = "utf-8"
+        for prefix in ("", "OPTIMIZER_", "TARGET_"):
+            env[f"{prefix}AZURE_OPENAI_AUTH_MODE"] = "openai_compatible"
+            env[f"{prefix}AZURE_OPENAI_ENDPOINT"] = PROVIDER_ENDPOINT
+            env[f"{prefix}AZURE_OPENAI_API_KEY"] = env["DEEPSEEK_API_KEY"]
+        env["TARGET_DEPLOYMENT"] = PROVIDER_MODEL
+        env["OPTIMIZER_DEPLOYMENT"] = PROVIDER_MODEL
+    else:
+        # OPTIONAL, UNVERIFIED fallback: route the `claude` CLI backend through a
+        # local Anthropic-compatible proxy (e.g. LiteLLM) to reach Gemini. There
+        # is no native Gemini backend; this path was not validated. See the doc.
+        backend = "claude"
+        if "ANTHROPIC_API_KEY" not in env and "GEMINI_API_KEY" in env:
+            env["ANTHROPIC_API_KEY"] = env["GEMINI_API_KEY"]
+        env.setdefault("ANTHROPIC_BASE_URL", "http://127.0.0.1:4000")
+
+    args = ["skillopt-sleep", command]
+    if command in ("run", "dry-run"):
+        args = ["skillopt-sleep", command, "--backend", backend,
+                "--model", PROVIDER_MODEL, "--project", PROJECT_DIR]
+
+    print(f"Running: {' '.join(args)}")
+    subprocess.run(args, env=env, check=False)
+
+
+if __name__ == "__main__":
+    main()