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

chapter9/self-modifying-agent/llm_generator.py

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+"""Real API Coding Agent used by the Experiment 9-6 campaign."""
+
+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_source
+
+
+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"
+    }
+
+
+def generate_with_openai(
+    stable_source: str,
+    diagnosis: Dict[str, Any],
+    model: str | None = None,
+    *,
+    provider: str = "openrouter",
+    seed: int = 8501,
+    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 = f"""You are the Coding Agent in a controlled self-modification pipeline.
+
+Modify only the supplied retry-policy module. Preserve both public function
+signatures and retry behavior for temporary failures. Permanent failures
+(retryable=false or a listed permanent code) must not be retried and must open
+the circuit on the first occurrence. Update VERSION to a candidate version.
+Do not import modules, access files, or alter validation/release logic.
+
+Before the source, predict the intended impact. Return JSON only:
+{{"impact_prediction": {{"non_retryable_calls": {{"before": "up to 4", "after": 1}},
+"temporary_timeout_recovery_rate": {{"before": 1.0, "after": 1.0}}}},
+"source": "the complete Python module"}}
+
+Failure diagnosis:
+{json.dumps(diagnosis, ensure_ascii=False, indent=2)}
+
+Previously rejected candidates (do not repeat their failure):
+{json.dumps(rejected_history or [], ensure_ascii=False, indent=2)}
+
+Stable module:
+{stable_source}
+"""
+    request = {
+        "model": selected_model,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0,
+        "seed": seed,
+        "max_tokens": 1400,
+        "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_source(
+        stable_source,
+        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,
+        },
+    )