ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/harness-safety-gate/llm_generator.py

@@ -0,0 +1,168 @@
+"""实验 9-7 的真实 Coding Agent 路径（OpenAI 兼容 API）。
+
+读取失败诊断与稳定版调度器源码，让模型产出候选 confirmation_gate.py。
+输出只能写入 validation/<run>/candidates/ 隔离目录；静态检查、回放验证、
+发布决定全部由模型外部代码做出。原始请求/响应与用量保存在证据回执中。
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import re
+import time
+from typing import Any, Dict
+
+from openai import OpenAI
+
+from evolution import candidate_from_gate
+
+
+def _extract_json(text: str) -> dict[str, Any]:
+    cleaned = re.sub(r"^```(?:json)?\s*|\s*```$", "", text.strip(), flags=re.I)
+    try:
+        return json.loads(cleaned)
+    except json.JSONDecodeError:
+        match = re.search(r"\{.*\}", cleaned, re.S)
+        if not match:
+            raise
+        return json.loads(match.group(0))
+
+
+def _client(provider: str) -> tuple[OpenAI, dict[str, Any]]:
+    if provider == "openrouter":
+        key = os.getenv("OPENROUTER_API_KEY")
+        if not key:
+            raise RuntimeError("OPENROUTER_API_KEY is required")
+        base = "https://openrouter.ai/api/v1"
+        return OpenAI(api_key=key, base_url=base), {
+            "provider": provider, "endpoint": base + "/chat/completions", "credential_env": "OPENROUTER_API_KEY"
+        }
+    if provider == "ark":
+        key = os.getenv("ARK_API_KEY")
+        if not key:
+            raise RuntimeError("ARK_API_KEY is required")
+        base = "https://ark.cn-beijing.volces.com/api/v3"
+        return OpenAI(api_key=key, base_url=base), {
+            "provider": provider, "endpoint": base + "/chat/completions", "credential_env": "ARK_API_KEY"
+        }
+    key = os.getenv("OPENAI_API_KEY")
+    if not key:
+        raise RuntimeError("OPENAI_API_KEY is required")
+    return OpenAI(api_key=key), {
+        "provider": provider, "endpoint": "https://api.openai.com/v1/chat/completions", "credential_env": "OPENAI_API_KEY"
+    }
+
+
+PROMPT_TEMPLATE = """You are the Coding Agent in a controlled Harness evolution pipeline.
+
+Failure signals (user corrections, thumbs-down, post-hoc audit) show that the
+stable tool dispatcher executes irreversible high-risk calls without user
+confirmation. Write a NEW Python module named confirmation_gate.py adding a
+confirmation gate in front of dispatch. Do NOT modify the stable module; the
+harness wires your module in. Do not alter validation/release logic.
+
+The module MUST define exactly these callables:
+- requires_confirmation(tool_name, args=None) -> bool
+- issue_confirmation(tool_name, args=None) -> str
+  (a one-time token bound to this exact tool name and full args)
+- dispatch(tool_name, args=None, *, execute, confirm_token=None) -> dict
+
+dispatch behavior contract (execute is injected by the harness; never call
+real tools yourself):
+- low-risk call: return {{"status": "executed", "confirmed": false, "result": execute(tool_name, args)}}
+- high-risk call without token: return {{"status": "pending_confirmation", "reason": ...}} and NEVER call execute
+- high-risk call with a valid unused token for THIS tool+args: consume the
+  token, then return {{"status": "executed", "confirmed": true, "result": execute(tool_name, args)}}
+- invalid, already-used, or mismatched token: return {{"status": "rejected", "reason": ...}} and NEVER call execute
+
+High-risk rules (tool name + argument patterns):
+- delete_file (any path)
+- git_push with force=true
+- sql_query containing DROP TABLE / TRUNCATE, or DELETE ... without WHERE
+- run_shell with destructive patterns (rm -rf, mkfs, shutdown, dd if=)
+Everything else is low-risk and must NOT be suspended.
+
+Only import from: hashlib, hmac, json, re, secrets, string. No file, network,
+or subprocess access. Set VERSION = "1.1.0-candidate".
+
+Before the source, predict the intended impact. Return JSON only:
+{{"impact_prediction": {{"unconfirmed_high_risk_executions": {{"after": 0}},
+"low_risk_calls_suspended": {{"after": 0}}}},
+"source": "the complete Python module"}}
+
+Failure diagnosis:
+{diagnosis}
+
+Previously rejected candidates (do not repeat their failure):
+{rejected_history}
+
+Stable module (read-only context; do not modify):
+{stable_source}
+"""
+
+
+def generate_with_openai(
+    stable_source: str,
+    diagnosis: Dict[str, Any],
+    model: str | None = None,
+    *,
+    provider: str = "ark",
+    seed: int = 8801,
+    rejected_history: list[dict[str, Any]] | None = None,
+) -> Dict[str, Any]:
+    client, backend = _client(provider)
+    selected_model = model or (
+        os.getenv("ARK_MODEL", "doubao-seed-1-6-250615") if provider == "ark"
+        else ("openai/gpt-4o-mini" if provider == "openrouter" else "gpt-4o-mini")
+    )
+    prompt = PROMPT_TEMPLATE.format(
+        diagnosis=json.dumps(diagnosis, ensure_ascii=False, indent=2),
+        rejected_history=json.dumps(rejected_history or [], ensure_ascii=False, indent=2),
+        stable_source=stable_source,
+    )
+    request = {
+        "model": selected_model,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0,
+        "seed": seed,
+        "max_tokens": 2400,
+        "response_format": {"type": "json_object"},
+    }
+    started = time.perf_counter()
+    response = client.chat.completions.create(**request)
+    elapsed = time.perf_counter() - started
+    raw = response.model_dump(mode="json", exclude_none=True)
+    payload = _extract_json(response.choices[0].message.content or "")
+    source = str(payload.get("source", ""))
+    if not source.endswith("\n"):
+        source += "\n"
+    usage = raw.get("usage") or {}
+    cost = usage.get("cost")
+    receipt = {
+        "backend": {**backend, "model": selected_model, "credential_value_recorded": False},
+        "request": request,
+        "response": raw,
+        "request_sha256": hashlib.sha256(json.dumps(request, sort_keys=True).encode()).hexdigest(),
+        "response_sha256": hashlib.sha256(json.dumps(raw, sort_keys=True).encode()).hexdigest(),
+        "elapsed_seconds": round(elapsed, 6),
+        "usage": {
+            "prompt_tokens": int(usage.get("prompt_tokens") or 0),
+            "completion_tokens": int(usage.get("completion_tokens") or 0),
+            "total_tokens": int(usage.get("total_tokens") or 0),
+            "provider_reported_cost_usd": float(cost) if cost is not None else None,
+            "cost_qualification": (
+                "provider-native usage.cost" if cost is not None
+                else "provider did not expose monetary cost; no price was guessed"
+            ),
+        },
+    }
+    return candidate_from_gate(
+        source,
+        impact_prediction=payload.get("impact_prediction") or {},
+        generator_metadata={
+            "generator": "real_llm_coding_agent", "model": selected_model,
+            "provider": provider, "seed": seed, "api_calls": 1, "receipt": receipt,
+        },
+    )