add multi-model concurrent extraction with per-model output files

--config and --model now accept comma-separated lists to run several
models on one document. Runs execute concurrently via a thread pool
(--max-workers controls parallelism; 1 forces sequential), with logs
prefixed by the config/model label.

Each model writes to its own YAML/cost/debug files by inserting a
sanitized label into the --out name (or the input name when --out is
omitted), and cost is computed per-model from its own profile.
Single-model behavior, including stdout output, is unchanged.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

hk_case_extractor.py

@@ -68,6 +68,7 @@ import json
 import re
 import sys
 import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any
@@ -1319,8 +1320,9 @@ def run_pipeline(text: str, model: str, base_url: str, api_key: str,
                  entities_window: int = 400,
                  entities_max: int = 6500,
                  analysis_window: int = 500,
-                 analysis_max: int = 6500) -> tuple[dict, OpenAICompatibleClient]:
+                 analysis_max: int = 6500,
-    log = lambda m: print(m, file=sys.stderr)
+                 log_prefix: str = "") -> tuple[dict, OpenAICompatibleClient]:
+    log = lambda m: print(f"{log_prefix}{m}", file=sys.stderr)
 
     log("[0/7] 檢測語言...")
     lang = detect_language(text)
@@ -1467,6 +1469,93 @@ def to_yaml(result: dict) -> str:
                      default_flow_style=False, width=100)
 
 
+# =============================================================================
+# 7. 多模型運行：解析運行規格 + 輸出路徑推導
+# =============================================================================
+
+@dataclass
+class RunSpec:
+    """單個模型的運行規格（標籤 + 連接參數 + 計費配置）"""
+    label: str                 # 用於日誌前綴與輸出文件名（配置名或模型名）
+    model: str
+    base_url: str
+    api_key: str
+    profile: dict | None = None  # models.json 配置（用於成本計算），無則 None
+
+
+def _safe_label(name: str) -> str:
+    """把配置/模型名轉成可安全用於文件名的標籤（如 anthropic/claude → anthropic_claude）"""
+    safe = re.sub(r"[^0-9A-Za-z._-]+", "_", name).strip("_")
+    return safe or "model"
+
+
+def parse_run_specs(args) -> list[RunSpec]:
+    """解析 --config / --model（均支持逗號分隔）為一組運行規格。
+
+    優先級：
+    - 有 --config：逐個從 models.json 加載配置（自帶 model/base_url/api_key/價格）
+    - 否則：用 --model（可逗號分隔多個），共用 --base-url / --api-key
+    """
+    specs: list[RunSpec] = []
+    if args.config:
+        names = [n.strip() for n in args.config.split(",") if n.strip()]
+        for name in names:
+            profile = load_model_profile(name, args.models_file)
+            specs.append(RunSpec(
+                label=name,
+                model=profile.get("model") or args.model,
+                base_url=profile.get("BaseApiUrl") or args.base_url,
+                api_key=profile.get("ApiKey") or args.api_key,
+                profile=profile,
+            ))
+    else:
+        names = [n.strip() for n in args.model.split(",") if n.strip()] or [args.model]
+        for name in names:
+            specs.append(RunSpec(
+                label=name,
+                model=name,
+                base_url=args.base_url,
+                api_key=args.api_key,
+                profile=None,
+            ))
+    return specs
+
+
+def build_out_path(args, input_path: Path, label: str, multi: bool) -> Path | None:
+    """推導某個模型的 YAML 輸出路徑。
+
+    - 單模型：沿用原行為（--out 指定則用之，否則 None 表示輸出到 stdout）
+    - 多模型：在文件名中插入標籤；未給 --out 時用「輸入名_標籤.yaml」
+    """
+    if not multi:
+        return Path(args.out) if args.out else None
+    safe = _safe_label(label)
+    if args.out:
+        base = Path(args.out)
+        return base.with_name(f"{base.stem}_{safe}{base.suffix or '.yaml'}")
+    return input_path.with_name(f"{input_path.stem}_{safe}.yaml")
+
+
+def build_cost_path(out_path: Path | None, input_path: Path,
+                    label: str, multi: bool) -> Path:
+    """成本文件路徑：有輸出文件時用「輸出名_cost.json」，否則回退到輸入名。"""
+    if out_path is not None:
+        return out_path.with_name(out_path.stem + "_cost.json")
+    if multi:
+        return input_path.with_name(f"{input_path.stem}_{_safe_label(label)}_cost.json")
+    return input_path.with_name(input_path.stem + "_cost.json")
+
+
+def build_debug_path(args, label: str, multi: bool) -> Path | None:
+    """debug-dump 路徑：多模型時在文件名中插入標籤。"""
+    if not args.debug_dump:
+        return None
+    base = Path(args.debug_dump)
+    if multi:
+        return base.with_name(f"{base.stem}_{_safe_label(label)}{base.suffix or '.json'}")
+    return base
+
+
 # =============================================================================
 # CLI
 # =============================================================================
@@ -1495,6 +1584,17 @@ def main() -> None:
     --model gpt-4 \\
     --api-key your-api-key
   
+  # 同時跑多個模型（逗號分隔），並發執行，分別輸出到不同文件
+  python hk_case_extractor.py case.txt \\
+    --config openrouter-claude-sonnet,openrouter-gpt4o,ollama-qwen \\
+    --out result.yaml --cost
+  # 生成 result_openrouter-claude-sonnet.yaml / result_openrouter-gpt4o.yaml / ...
+  #      及各自的 *_cost.json
+
+  # 多個本地 Ollama 模型共用同一端點
+  python hk_case_extractor.py case.txt \\
+    --model qwen2.5:7b-instruct,llama3.1:8b --out result.yaml
+
   # 調整截取長度
   python hk_case_extractor.py case.txt \\
     --head-length 8000 \\
@@ -1506,21 +1606,24 @@ def main() -> None:
     )
     ap.add_argument("input", help="判決書文本路徑（.txt 或 .json）")
     ap.add_argument("--config", default=None,
-                    help="models.json 中的配置名稱（Name），"
+                    help="models.json 中的配置名稱（Name），可逗號分隔多個以同時運行多個模型，"
                          "使用後可省略 --model/--base-url/--api-key")
     ap.add_argument("--models-file", default=DEFAULT_MODELS_FILE,
                     help=f"模型配置文件路徑（默認：{DEFAULT_MODELS_FILE}）")
     ap.add_argument("--model", default=DEFAULT_MODEL,
-                    help=f"模型名稱（默認：{DEFAULT_MODEL}）")
+                    help=f"模型名稱（默認：{DEFAULT_MODEL}），可逗號分隔多個（共用 --base-url/--api-key）")
     ap.add_argument("--base-url", default=DEFAULT_BASE_URL,
                     help=f"API base URL（默認：{DEFAULT_BASE_URL}）")
     ap.add_argument("--api-key", default=DEFAULT_API_KEY,
                     help="API key（Ollama 可忽略）")
-    ap.add_argument("--out", default=None, help="輸出 YAML 路徑（默認 stdout）")
+    ap.add_argument("--out", default=None,
+                    help="輸出 YAML 路徑（單模型默認 stdout）；多模型時在文件名中插入標籤")
+    ap.add_argument("--max-workers", type=int, default=0,
+                    help="多模型時的並發數（默認 0 = 模型數量；設為 1 則順序執行）")
     ap.add_argument("--cost", action="store_true",
                     help="輸出成本統計到 {輸出文件名}_cost.json（默認不輸出）")
     ap.add_argument("--debug-dump", default=None,
-                    help="額外輸出原始 JSON 結果到該路徑（便於 diff）")
+                    help="額外輸出原始 JSON 結果到該路徑（多模型時在文件名中插入標籤）")
     
     # 截取長度控制參數
     ap.add_argument("--head-length", type=int, default=5000,
@@ -1538,24 +1641,17 @@ def main() -> None:
     
     args = ap.parse_args()
 
-    # 解析模型配置：--config 優先，未命中的字段回退到命令行/默認值
+    # 解析運行規格（--config / --model 均支持逗號分隔多個）
-    profile: dict | None = None
+    specs = parse_run_specs(args)
-    if args.config:
+    multi = len(specs) > 1
-        profile = load_model_profile(args.config, args.models_file)
+    for spec in specs:
-        model = profile.get("model") or args.model
+        if spec.profile is not None:
-        base_url = profile.get("BaseApiUrl") or args.base_url
+            print(f"使用配置 '{spec.label}'：model={spec.model}, base_url={spec.base_url}",
-        api_key = profile.get("ApiKey") or args.api_key
+                  file=sys.stderr)
-        print(f"使用配置 '{args.config}'：model={model}, base_url={base_url}",
-              file=sys.stderr)
-    else:
-        model = args.model
-        base_url = args.base_url
-        api_key = args.api_key
 
     # 支持从 .json 文件的 content 字段读取
     input_path = Path(args.input)
     if input_path.suffix.lower() == '.json':
-        import json
         data = json.loads(input_path.read_text(encoding="utf-8"))
         text = data.get("content", "")
         if not text:
@@ -1563,44 +1659,77 @@ def main() -> None:
             sys.exit(1)
     else:
         text = input_path.read_text(encoding="utf-8")
-    
-    start = time.perf_counter()
-    result, client = run_pipeline(text, model, base_url, api_key,
-                                  args.head_length, args.tail_length,
-                                  args.entities_window, args.entities_max,
-                                  args.analysis_window, args.analysis_max)
-    elapsed = time.perf_counter() - start
 
-    # 成本統計：僅在 --cost 時輸出到 {summary_file_name}_cost.json
+    def run_and_emit(spec: RunSpec) -> str:
-    if args.cost:
+        """跑單個模型並寫出其 YAML / cost / debug 文件，返回輸出路徑描述。"""
-        cost = compute_cost(client, elapsed, profile)
+        log_prefix = f"[{spec.label}] " if multi else ""
-        if args.out:
+        start = time.perf_counter()
-            cost_path = Path(args.out).with_name(Path(args.out).stem + "_cost.json")
+        result, client = run_pipeline(
-        else:
+            text, spec.model, spec.base_url, spec.api_key,
-            cost_path = input_path.with_name(input_path.stem + "_cost.json")
+            args.head_length, args.tail_length,
-        cost_path.parent.mkdir(parents=True, exist_ok=True)
+            args.entities_window, args.entities_max,
-        cost_path.write_text(json.dumps(cost, ensure_ascii=False, indent=2),
+            args.analysis_window, args.analysis_max,
-                             encoding="utf-8")
+            log_prefix=log_prefix)
-        print(f"💰 成本統計已寫入 {cost_path}：耗時 {cost['elapsed_seconds']}s，"
+        elapsed = time.perf_counter() - start
-              f"input={cost['input_tokens']} output={cost['output_tokens']} "
-              f"total_cost={cost['total_cost']} {cost['price_unit'] or ''}",
-              file=sys.stderr)
 
-    if args.debug_dump:
+        out_path = build_out_path(args, input_path, spec.label, multi)
-        debug_path = Path(args.debug_dump)
-        debug_path.parent.mkdir(parents=True, exist_ok=True)
-        debug_path.write_text(
-            json.dumps(result, ensure_ascii=False, indent=2),
-            encoding="utf-8")
 
-    yaml_str = to_yaml(result)
+        # 成本統計：僅在 --cost 時輸出到 {輸出文件名}_cost.json
-    if args.out:
+        if args.cost:
-        out_path = Path(args.out)
+            cost = compute_cost(client, elapsed, spec.profile)
-        out_path.parent.mkdir(parents=True, exist_ok=True)
+            cost_path = build_cost_path(out_path, input_path, spec.label, multi)
-        out_path.write_text(yaml_str, encoding="utf-8")
+            cost_path.parent.mkdir(parents=True, exist_ok=True)
-        print(f"\n✅ 已寫入 {args.out}", file=sys.stderr)
+            cost_path.write_text(json.dumps(cost, ensure_ascii=False, indent=2),
-    else:
+                                 encoding="utf-8")
+            print(f"{log_prefix}💰 成本統計已寫入 {cost_path}：耗時 {cost['elapsed_seconds']}s，"
+                  f"input={cost['input_tokens']} output={cost['output_tokens']} "
+                  f"total_cost={cost['total_cost']} {cost['price_unit'] or ''}",
+                  file=sys.stderr)
+
+        # debug dump（原始 JSON）
+        debug_path = build_debug_path(args, spec.label, multi)
+        if debug_path is not None:
+            debug_path.parent.mkdir(parents=True, exist_ok=True)
+            debug_path.write_text(json.dumps(result, ensure_ascii=False, indent=2),
+                                  encoding="utf-8")
+
+        # YAML 輸出
+        yaml_str = to_yaml(result)
+        if out_path is not None:
+            out_path.parent.mkdir(parents=True, exist_ok=True)
+            out_path.write_text(yaml_str, encoding="utf-8")
+            print(f"{log_prefix}✅ 已寫入 {out_path}", file=sys.stderr)
+            return str(out_path)
         print(yaml_str)
+        return "(stdout)"
+
+    if multi and args.max_workers != 1:
+        # 並發執行：每個模型一個線程，日誌以 [標籤] 前綴區分
+        workers = args.max_workers if args.max_workers > 0 else len(specs)
+        print(f"⏳ 同時運行 {len(specs)} 個模型（並發 {workers}）："
+              f"{[s.label for s in specs]}", file=sys.stderr)
+        summary: list[tuple[str, str]] = []
+        with ThreadPoolExecutor(max_workers=workers) as ex:
+            futures = {ex.submit(run_and_emit, spec): spec for spec in specs}
+            for fut in as_completed(futures):
+                spec = futures[fut]
+                try:
+                    summary.append((spec.label, fut.result()))
+                except Exception as e:
+                    print(f"[{spec.label}] ❌ 運行失敗：{e}", file=sys.stderr)
+                    summary.append((spec.label, f"FAILED: {e}"))
+        print("\n=== 多模型運行結果 ===", file=sys.stderr)
+        for label, out in sorted(summary):
+            print(f"  {label:30s} → {out}", file=sys.stderr)
+    else:
+        # 單模型，或多模型但顯式 --max-workers 1 順序執行
+        for spec in specs:
+            try:
+                run_and_emit(spec)
+            except Exception as e:
+                if not multi:
+                    raise
+                print(f"[{spec.label}] ❌ 運行失敗：{e}", file=sys.stderr)
 
 
 if __name__ == "__main__":