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