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

chapter10/parallel-web-research/llm.py

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+"""Evidence-grounded profile extraction with real configured LLM APIs."""
+
+from __future__ import annotations
+
+import json
+import os
+import time
+from typing import Callable, Dict, Optional
+
+
+ReceiptSink = Optional[Callable[[Dict[str, object]], None]]
+
+
+def _backends():
+    from openai import AsyncOpenAI
+
+    out = []
+    if os.getenv("ARK_API_KEY"):
+        out.append((AsyncOpenAI(api_key=os.environ["ARK_API_KEY"], base_url="https://ark.cn-beijing.volces.com/api/v3"), os.getenv("ARK_MODEL", "doubao-seed-1-6-250615"), "ark"))
+    if os.getenv("MOONSHOT_API_KEY"):
+        out.append((AsyncOpenAI(api_key=os.environ["MOONSHOT_API_KEY"], base_url="https://api.moonshot.cn/v1"), os.getenv("MOONSHOT_MODEL", "kimi-k3"), "moonshot"))
+    if os.getenv("OPENAI_API_KEY"):
+        out.append((AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"], base_url=os.getenv("OPENAI_BASE_URL") or None), os.getenv("OPENAI_MODEL", "gpt-4.1-mini"), "openai"))
+    if os.getenv("OPENROUTER_API_KEY"):
+        raw = os.getenv("OPENAI_MODEL", "gpt-4.1-mini")
+        out.append((AsyncOpenAI(api_key=os.environ["OPENROUTER_API_KEY"], base_url="https://openrouter.ai/api/v1"), raw if "/" in raw else f"openai/{raw}", "openrouter"))
+    if not out:
+        raise RuntimeError("真实网页内容抽取需要 ARK/MOONSHOT/OPENAI/OPENROUTER 任一 API Key")
+    return out
+
+
+async def extract_profile(
+    target: str,
+    college: str,
+    url: str,
+    text: str,
+    receipt_sink: ReceiptSink = None,
+    call_context: Optional[Dict[str, object]] = None,
+) -> Dict[str, object]:
+    """Extract only facts visible in the browser observation.
+
+    The deterministic name-presence gate prevents a model from supplying a profile
+    from parametric memory when the page did not actually contain the target.
+    """
+    if target.casefold() not in text.casefold():
+        return {"found": False, "reason": "target name absent from rendered page"}
+    clipped = text[:45_000]
+    prompt = {
+        "target": target,
+        "site_college": college,
+        "url": url,
+        "rendered_page_text": clipped,
+        "instruction": (
+            "Use only rendered_page_text. Decide whether it contains this exact person's faculty profile. "
+            "Return JSON keys found, name, college, position, research, evidence. If the name is only a link/listing, "
+            "found may be true but leave unsupported fields empty. evidence must be a short verbatim excerpt."
+        ),
+    }
+    last = None
+    for client, model, provider in _backends():
+        started = time.monotonic()
+        try:
+            kwargs = dict(
+                model=model,
+                messages=[{"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}],
+                response_format={"type": "json_object"},
+            )
+            if "kimi-k3" in model:
+                kwargs.update(temperature=1, max_tokens=2048)
+            response = await client.chat.completions.create(**kwargs)
+            raw_response = response.model_dump(mode="json")
+            if receipt_sink:
+                receipt_sink({
+                    "kind": "llm_chat_completion",
+                    "context": dict(call_context or {}),
+                    "provider": provider,
+                    "request": kwargs,
+                    "response": raw_response,
+                    "response_id": response.id,
+                    "response_model": response.model,
+                    "usage": response.usage.model_dump(mode="json") if response.usage else None,
+                    "duration_seconds": round(time.monotonic() - started, 3),
+                })
+            content = response.choices[0].message.content or ""
+            if not content.strip():
+                raise ValueError("empty model response")
+            result = json.loads(content)
+            result["provider"] = provider
+            result["url"] = url
+            return result
+        except Exception as exc:
+            last = exc
+            if receipt_sink:
+                receipt_sink({
+                    "kind": "llm_chat_completion_error",
+                    "context": dict(call_context or {}),
+                    "provider": provider,
+                    "model": model,
+                    "error_type": type(exc).__name__,
+                    "duration_seconds": round(time.monotonic() - started, 3),
+                })
+            print(f"  [extract] {provider} failed: {type(exc).__name__}; trying next endpoint")
+    raise RuntimeError("all configured LLM extraction endpoints failed") from last