refactor: centralize llm provider transport

app/backend/features/screener/compiler.py

@@ -1,11 +1,9 @@
 from __future__ import annotations
 
 import json
-import time
-import urllib.error
-import urllib.request
 from typing import Any
 
+from backend.llm import transport as llm_transport
 from screener import FACTOR_FIELDS, REGIMES
 
 
@@ -21,39 +19,25 @@ def test_llm_connection(
 ) -> dict[str, Any]:
     if not api_key or not model:
         raise LLMCompilerError("API Key 或模型未配置。")
-    endpoint = f"{base_url.rstrip('/')}/chat/completions"
-    payload = json.dumps(
-        {
-            "model": model,
-            "messages": [{"role": "user", "content": "只回复 OK"}],
-            "stream": False,
-        },
-        ensure_ascii=False,
-    ).encode("utf-8")
-    request = urllib.request.Request(
-        endpoint,
-        data=payload,
-        headers={
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}",
-            "User-Agent": "XiaobaiReviewWeb/0.5",
-        },
-        method="POST",
-    )
-    started = time.perf_counter()
     try:
-        with urllib.request.urlopen(request, timeout=timeout) as response:
-            result = json.loads(response.read().decode("utf-8"))
-        reply = str(result["choices"][0]["message"]["content"]).strip()
-    except urllib.error.HTTPError as exc:
-        raise LLMCompilerError(_http_error_message(exc)) from exc
-    except (urllib.error.URLError, TimeoutError, json.JSONDecodeError, KeyError, IndexError) as exc:
+        result = llm_transport.chat_completion(
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            messages=[{"role": "user", "content": "只回复 OK"}],
+            timeout=timeout,
+            user_agent="XiaobaiReviewWeb/0.5",
+        )
+        reply = str(result.content).strip()
+    except llm_transport.OpenAIHTTPError as exc:
+        raise LLMCompilerError(exc.describe("模型连接测试失败")) from exc
+    except llm_transport.OpenAITransportError as exc:
         raise LLMCompilerError(f"模型连接测试失败：{exc}") from exc
     return {
         "ok": True,
         "model": model,
         "reply": reply[:100],
-        "latency_ms": round((time.perf_counter() - started) * 1000),
+        "latency_ms": result.latency_ms,
     }
 
 
@@ -67,7 +51,6 @@ def compile_strategy_with_llm(
 ) -> dict[str, Any]:
     if not api_key or not model:
         raise LLMCompilerError("尚未配置 LLM API Key 或模型。")
-    endpoint = f"{base_url.rstrip('/')}/chat/completions"
     schema = {
         "name": "策略名称",
         "description": "策略说明",
@@ -90,57 +73,28 @@ def compile_strategy_with_llm(
         "退潮和冰点策略必须提高门槛并允许结果为空。"
         f"严格遵循以下结构：{json.dumps(schema, ensure_ascii=False)}"
     )
-    payload = json.dumps(
-        {
-            "model": model,
-            "messages": [
+    try:
+        result = llm_transport.chat_completion(
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": prompt[:3000]},
             ],
-            "stream": False,
-        },
-        ensure_ascii=False,
-    ).encode("utf-8")
-    request = urllib.request.Request(
-        endpoint,
-        data=payload,
-        headers={
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}",
-            "User-Agent": "XiaobaiReviewWeb/0.4",
-        },
-        method="POST",
-    )
-    try:
-        with urllib.request.urlopen(request, timeout=timeout) as response:
-            result = json.loads(response.read().decode("utf-8"))
-        content = result["choices"][0]["message"]["content"].strip()
+            timeout=timeout,
+            user_agent="XiaobaiReviewWeb/0.4",
+        )
+        content = result.content.strip()
         if content.startswith("```"):
             content = content.strip("`")
             if content.startswith("json"):
                 content = content[4:].strip()
         compiled = json.loads(content)
-    except urllib.error.HTTPError as exc:
-        raise LLMCompilerError(_http_error_message(exc).replace("模型连接测试", "LLM 策略编译")) from exc
-    except (urllib.error.URLError, TimeoutError, json.JSONDecodeError, KeyError, IndexError) as exc:
+    except llm_transport.OpenAIHTTPError as exc:
+        raise LLMCompilerError(exc.describe("LLM 策略编译失败")) from exc
+    except (llm_transport.OpenAITransportError, json.JSONDecodeError) as exc:
         raise LLMCompilerError(f"LLM 策略编译失败：{exc}") from exc
     compiled["compiler"] = "llm"
     compiled["model"] = model
     return compiled
-
-
-def _http_error_message(exc: urllib.error.HTTPError) -> str:
-    detail = ""
-    try:
-        payload = json.loads(exc.read().decode("utf-8", errors="replace"))
-        error = payload.get("error")
-        if isinstance(error, dict):
-            detail = str(error.get("message") or error.get("code") or "")
-        elif error:
-            detail = str(error)
-        elif payload.get("message"):
-            detail = str(payload["message"])
-    except (json.JSONDecodeError, OSError):
-        detail = ""
-    suffix = f"：{detail[:300]}" if detail else ""
-    return f"模型连接测试失败（HTTP {exc.code}）{suffix}"