Add deep mode for full literature notes

README.md

@@ -43,6 +43,10 @@ config/config.local.json
 使用 MYwrite skill，为这个 Zotero 条目生成一条 AI 阅读笔记：SXAIQUJT。
 ```
 
+```text
+使用 MYwrite skill，为这个 Zotero 条目生成满血版深度阅读笔记：SXAIQUJT。不要跳过已有笔记，生成一条新的更详细 Zotero 子笔记。
+```
+
 ```text
 使用 MYwrite skill，给这些 Zotero 条目批量生成 AI 阅读笔记：SXAIQUJT、X7GJZ627、ZCZXGRAM。
 ```
@@ -65,6 +69,8 @@ config/config.local.json
 
 更稳妥的方式是每次处理 20-30 篇文献，并要求 AI 跳过已经生成过笔记的条目。这样即使中途失败，也可以继续执行而不会重复写入。
 
+如果需要和手动 AwesomeGPT 生成结果接近的详细笔记，请明确告诉 AI 使用“满血版”或“深度精读”模式。该模式会读取更多全文内容，并要求模型输出更完整的结构化笔记。
+
 ## 仓库内容
 
 ```text

SKILL.md

@@ -48,6 +48,14 @@ Do not print the config contents. When checking setup, report only whether each
 py "$env:USERPROFILE\.codex\skills\MYwrite\scripts\generate_zotero_ai_note.py" --vault "C:\Users\qyh15\Documents\Obsidian Vault" --item-key SXAIQUJT --skip-existing
 ```
 
+### Deep full-detail item
+
+Use this when the user asks for `满血版本`, `详细版`, `深度精读`, or says the batch note is not as detailed as manually generated AwesomeGPT notes. Do not use `--skip-existing` when the user wants to regenerate a fuller note; create a new Zotero child note unless the user explicitly asks to delete old notes.
+
+```powershell
+py "$env:USERPROFILE\.codex\skills\MYwrite\scripts\generate_zotero_ai_note.py" --vault "C:\Users\qyh15\Documents\Obsidian Vault" --item-key SXAIQUJT --mode deep --fulltext-chars 80000 --max-tokens 12000
+```
+
 ### Multiple items
 
 ```powershell
@@ -92,7 +100,7 @@ Default vault organization:
 
 - For live Zotero writes, state that Zotero child notes will be created.
 - Use `--dry-run` for first-time validation or template changes.
-- Use `--fulltext-chars 4000` for cheap, lightweight notes; increase only when the user asks for deeper notes.
+- Use `--fulltext-chars 4000` for cheap, lightweight notes; use `--mode deep --fulltext-chars 80000 --max-tokens 12000` when the user asks for a full-detail note.
 - Do not add visible machine markers to note bodies. Use the Zotero item link for duplicate detection.
 - Do not delete Zotero duplicate notes unless the user explicitly requests cleanup.
 - For Obsidian cleanup, delete only files that are empty after removing frontmatter, separators, whitespace, and empty HTML placeholders.

scripts/generate_zotero_ai_note.py

@@ -406,7 +406,7 @@ def creators_text(creators: list[dict[str, Any]]) -> str:
     return "; ".join(names)
 
 
-def build_prompt(item: dict[str, Any], bibtex: str, fulltext: str, vault: Path) -> str:
+def build_prompt(item: dict[str, Any], bibtex: str, fulltext: str, vault: Path, mode: str) -> str:
     data = item.get("data") or {}
     prompt_path, research_template_path, review_template_path = template_paths(vault)
     prompt = prompt_path.read_text(encoding="utf-8")
@@ -428,8 +428,7 @@ def build_prompt(item: dict[str, Any], bibtex: str, fulltext: str, vault: Path)
         "bibtex": bibtex,
         "indexedFullTextExcerpt": fulltext,
     }
-    return "\n\n".join(
-        [
+    instructions = [
         prompt,
         "请先判断文献类型：综述型文献或研究型文献。",
         "若为研究型文献，请严格填充下面的研究型模板：",
@@ -437,14 +436,29 @@ def build_prompt(item: dict[str, Any], bibtex: str, fulltext: str, vault: Path)
         "若为综述型文献，请严格填充下面的综述型模板：",
         review_template,
         "请将模板中的 ${topItem.getField('title')} 替换为真实题名，将 ${topItem.key} 替换为 Zotero key。",
+    ]
+    if mode == "deep":
+        instructions.extend(
+            [
+                "这是满血精读模式。请优先追求完整、具体、可复用的文献阅读笔记，而不是短摘要。",
+                "每个模板栏目都要充分展开；如果原文提供了材料配比、制备参数、测试条件、器件结构、性能数据、机理解释或对照实验，必须具体写出。",
+                "核心数据必须尽量保留数值和单位；不要用'显著提高'、'性能优异'这类空泛表达替代具体结果。",
+                "机制部分要写清因果链条：材料/结构设计如何影响微观结构、界面、导电网络、离子/电子传输、力学响应或器件输出。",
+                "局限与启发部分要给出可用于后续课题设计的具体启发，而不是泛泛评价。",
+                "如果全文摘录中缺少某项信息，请明确写'原文摘录中未提供'，不要编造。",
+            ]
+        )
+    instructions.extend(
+        [
             "只输出最终 Markdown 笔记，不要输出解释、判断过程或代码围栏。",
             "文献材料如下：",
             json.dumps(source, ensure_ascii=False, indent=2),
         ]
     )
+    return "\n\n".join(instructions)
 
 
-def call_llm(prompt: str) -> str:
+def call_llm(prompt: str, max_tokens: int | None = None) -> str:
     api_key = os.environ.get("AWESOMEGPT_API_KEY")
     base_url = (os.environ.get("AWESOMEGPT_BASE_URL") or "").rstrip("/")
     model = os.environ.get("AWESOMEGPT_MODEL")
@@ -460,6 +474,8 @@ def call_llm(prompt: str) -> str:
         ],
         "temperature": 0.3,
     }
+    if max_tokens:
+        payload["max_tokens"] = max_tokens
     response = http_json(
         base_url + "/chat/completions",
         method="POST",
@@ -524,6 +540,8 @@ def main() -> None:
     parser.add_argument("--all", action="store_true", help="Process all top-level Zotero items")
     parser.add_argument("--limit", type=int, default=1, help="Maximum number of items to process")
     parser.add_argument("--fulltext-chars", type=int, default=12000)
+    parser.add_argument("--mode", choices=["quick", "deep"], default="quick", help="quick for batch notes; deep for full-detail notes")
+    parser.add_argument("--max-tokens", type=int, default=0, help="Optional LLM output token limit; deep mode defaults to 12000")
     parser.add_argument("--skip-existing", action="store_true", help="Skip items that already have a generated AI child note")
     parser.add_argument("--dry-run", action="store_true", help="generate but do not write Zotero note")
     parser.add_argument("--vault", default=str(DEFAULT_VAULT), help="Obsidian vault containing 00 Templater")
@@ -557,9 +575,15 @@ def main() -> None:
                 continue
             print(f"[{index}/{len(items)}] generating {key}: {title}", file=sys.stderr)
             bibtex = export_bibtex(key)
-            fulltext = local_fulltext(key, args.fulltext_chars)
-            prompt = build_prompt(item, bibtex, fulltext, vault)
-            markdown = call_llm(prompt)
+            fulltext_chars = args.fulltext_chars
+            if args.mode == "deep" and fulltext_chars == 12000:
+                fulltext_chars = 80000
+            max_tokens = args.max_tokens
+            if args.mode == "deep" and not max_tokens:
+                max_tokens = 12000
+            fulltext = local_fulltext(key, fulltext_chars)
+            prompt = build_prompt(item, bibtex, fulltext, vault, args.mode)
+            markdown = call_llm(prompt, max_tokens or None)
             result = create_child_note(user_id, key, markdown, args.dry_run)
             results.append({"index": index, "itemKey": key, "title": title, "status": "ok", "result": result})
         except SystemExit as exc: