ai写作bug修改

offline-backend-20260512/backend/config.yaml

@@ -14,7 +14,7 @@ models:
     display_name: zai-org/GLM-5-FP8
     use: langchain_openai:ChatOpenAI
     model: zai-org/GLM-5-FP8
-    api_key: Bearer modalresearch_gWu46FDpVLYZWPVEA3b_2fq8Jw2UJiKMgEm_Rxmn1SA
+    api_key: modalresearch_gWu46FDpVLYZWPVEA3b_2fq8Jw2UJiKMgEm_Rxmn1SA
     base_url: https://api.us-west-2.modal.direct/v1
     request_timeout: 600.0
     max_retries: 2
@@ -71,6 +71,16 @@ models:
     temperature: 1.0
     supports_vision: true
     supports_thinking: true
+  - name: glm-5.1
+    display_name: glm-5.1
+    use: langchain_openai:ChatOpenAI
+    model: glm-5.1
+    api_key: 75121e4bed7640d5b71ac95cf8b4310a.RA3GyRBAR3kv7VrR
+    base_url: https://open.bigmodel.cn/api/paas/v4/
+    max_tokens: 4096
+    temperature: 1.0
+    supports_vision: true
+    supports_thinking: true
 
 title:
   enabled: true

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/_utils.py

@@ -74,6 +74,28 @@ def get_model(config: RunnableConfig, model_name: str | None = None):
     return create_chat_model(name=name)
 
 
+def get_writing_model(config: RunnableConfig, state: dict):
+    """大纲 / 写作 / 审核专用：严格使用用户在表单选定的模型，**不回退默认**。
+
+    用户模型在 intent_parser（首次 invocation，configurable.model_name 可用）阶段
+    捕获进 ``state['model_name']``，经 checkpoint 持久化。这三个节点都在
+    「确认素材 / 大纲」resume 之后才运行，而 resume 是新 invocation、其 config 不再
+    带 model_name —— 所以必须从 state 读，不能从 configurable 读（否则回退到 config
+    列表第一个模型，常是推理模型，既影响质量也会让 reasoning_content 走不出流式）。
+
+    缺失视为配置错误直接抛 ValueError —— 由各节点的 try/except 兜底成 status:error，
+    而不是静默回退默认模型。
+    """
+    name = ((state or {}).get("model_name") or "").strip()
+    if not name:
+        # 兜底再看一眼 configurable（首次同 invocation 内直跑到这里的极端情况）
+        name = (config.get("configurable", {}).get("model_name", "") or "").strip()
+    if not name:
+        raise ValueError("未指定写作模型（model_name 为空）：请在写作表单选择模型后重试")
+    from deerflow.models import create_chat_model
+    return create_chat_model(name=name)
+
+
 # 弱模型常把结构性标点写成中文全角符号，仅在常规解析失败后作为修复手段
 _FULLWIDTH_FIX = {
     "“": '"',   # 左双引号 “
@@ -175,6 +197,21 @@ def parse_json(text: str, fallback: dict) -> dict:
     return fallback
 
 
+def reasoning_delta(chunk) -> str:
+    """提取流式 chunk 的增量「推理内容」。
+
+    推理模型（deepseek-reasoner / GLM 等）会把思考过程放在
+    ``additional_kwargs.reasoning_content`` 增量字段里，而 ``content`` 为空 ——
+    只读 content 的流式循环会把这些 chunk 全部跳过，前端就看不到任何实时输出。
+    各写作节点用本函数把推理增量当作 ``is_thinking=True`` 推给前端的思考框。
+
+    兼容不同 provider 的字段名：``reasoning_content``（主流）/ ``reasoning``。
+    """
+    ak = getattr(chunk, "additional_kwargs", None) or {}
+    val = ak.get("reasoning_content") or ak.get("reasoning") or ""
+    return val if isinstance(val, str) else ""
+
+
 def _content_to_text(content) -> str:
     """将 LLM 响应内容统一为字符串（部分模型返回结构化 content 块列表）。"""
     if isinstance(content, str):

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/editor.py

@@ -11,7 +11,7 @@ from ..state import AIWritingState, ReviewResult, ReviewIssue
 from ..prompts.editor_prompts import (
     EDITOR_SYSTEM, FACT_REVIEW_USER, LOGIC_REVIEW_USER, LANGUAGE_REVIEW_USER,
 )
-from ._utils import get_ai_writing_config, get_model, parse_json, _content_to_text, ThinkTagParser, safe_get_stream_writer
+from ._utils import get_ai_writing_config, get_writing_model, parse_json, _content_to_text, reasoning_delta, ThinkTagParser, safe_get_stream_writer
 
 logger = logging.getLogger(__name__)
 
@@ -49,6 +49,10 @@ async def _stream_one_review(
         SystemMessage(content=EDITOR_SYSTEM),
         HumanMessage(content=user_prompt),
     ]):
+        # 推理模型的思考走 reasoning_content（content 为空）：作为思考实时推送
+        reasoning = reasoning_delta(chunk)
+        if reasoning:
+            writer({"type": "review_chunk", "category": category, "text": reasoning, "is_thinking": True})
         delta = _content_to_text(chunk.content)
         if not delta:
             continue
@@ -118,7 +122,7 @@ async def editor_node(state: AIWritingState, config: RunnableConfig) -> dict:
     # 模型创建守卫：模型名配错时 create_chat_model 抛 ValueError，
     # 不拦截会硬崩溃整个图；此处转成可恢复的 status:error。
     try:
-        model = get_model(config)
+        model = get_writing_model(config, state)
     except Exception as e:
         logger.error("editor model creation failed: %s", e, exc_info=True)
         return {

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/intent_parser.py

@@ -14,6 +14,7 @@ from ._utils import (
     _content_to_text,
     get_model,
     parse_json,
+    reasoning_delta,
     safe_get_stream_writer,
 )
 
@@ -215,6 +216,10 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
         async for chunk in model.astream(
             [SystemMessage(content=system), HumanMessage(content=user)]
         ):
+            # 推理模型的思考走 reasoning_content（content 为空）：作为思考实时推送
+            reasoning = reasoning_delta(chunk)
+            if reasoning:
+                writer({"type": "intent_chunk", "text": reasoning, "is_thinking": True})
             delta = _content_to_text(chunk.content)
             if not delta:
                 continue
@@ -259,6 +264,12 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
     progress_events.append({"type": "step_done", "agent_name": "系统", "message": "意图解析完成"})
 
     updates: dict = {"status": "researching", "progress_events": progress_events}
+    # 捕获用户表单选定的主模型进 state（仅本节点能可靠拿到 configurable.model_name —
+    # 它运行在首次 invocation；后续大纲/写作/审核在 resume 后跑，config 已不带它）。
+    # 大纲/写作/审核经 get_writing_model 严格读 state['model_name']，不回退默认。
+    selected_model = (configurable.get("model_name") or "").strip()
+    if selected_model:
+        updates["model_name"] = selected_model
     updates["article_type"] = final_article_type
     updates["target_audience"] = final_target_audience
     updates["word_count_target"] = final_word_count

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/writer_draft.py

@@ -12,7 +12,7 @@ from ..prompts.writer_prompts import (
     DRAFT_SECTION_SYSTEM, DRAFT_SECTION_USER, DRAFT_EXPAND_USER,
     WRITER_MODE_LOOSE, WRITER_MODE_STRICT, SECTION_BLOCKED_MARKER,
 )
-from ._utils import get_model, _content_to_text, count_words, ThinkTagParser, safe_get_stream_writer
+from ._utils import get_writing_model, _content_to_text, count_words, reasoning_delta, ThinkTagParser, safe_get_stream_writer
 
 logger = logging.getLogger(__name__)
 
@@ -197,7 +197,7 @@ async def writer_draft_node(state: AIWritingState, config: RunnableConfig) -> di
     # 模型创建守卫：模型名配错时 create_chat_model 抛 ValueError，
     # 不拦截会硬崩溃整个图；此处转成可恢复的 status:error。
     try:
-        model = get_model(config)
+        model = get_writing_model(config, state)
     except Exception as e:
         logger.error("draft model creation failed: %s", e, exc_info=True)
         return {
@@ -250,6 +250,17 @@ async def writer_draft_node(state: AIWritingState, config: RunnableConfig) -> di
                 SystemMessage(content=DRAFT_SECTION_SYSTEM),
                 HumanMessage(content=user_msg),
             ]):
+                # 推理模型的思考走 reasoning_content（content 为空）：作为思考实时推送
+                reasoning = reasoning_delta(chunk)
+                if reasoning:
+                    writer({
+                        "type": "draft_chunk",
+                        "section_index": i,
+                        "section_title": sec_title,
+                        "text": reasoning,
+                        "is_thinking": True,
+                        "is_section_done": False,
+                    })
                 delta = _content_to_text(chunk.content)
                 if not delta:
                     continue

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/writer_outline.py

@@ -11,7 +11,7 @@ from ..state import AIWritingState, KeyPoint, Outline, OutlineSection
 from ..prompts.writer_prompts import (
     OUTLINE_SYSTEM, OUTLINE_USER, OUTLINE_REVISE_SYSTEM, OUTLINE_REVISE_USER,
 )
-from ._utils import get_model, parse_json, _content_to_text, ThinkTagParser, safe_get_stream_writer
+from ._utils import get_writing_model, parse_json, _content_to_text, reasoning_delta, ThinkTagParser, safe_get_stream_writer
 
 logger = logging.getLogger(__name__)
 
@@ -125,13 +125,17 @@ async def writer_outline_node(state: AIWritingState, config: RunnableConfig) ->
     parser = ThinkTagParser()
 
     try:
-        # get_model 放进 try：模型名配错时 create_chat_model 抛 ValueError，
+        # get_writing_model 放进 try：用户模型缺失 / 模型名配错时抛 ValueError，
         # 留在 try 外会硬崩溃；放进来则统一走下方的 status:error 兜底。
-        model = get_model(config)
+        model = get_writing_model(config, state)
         async for chunk in model.astream([
             SystemMessage(content=system_prompt),
             HumanMessage(content=user_prompt),
         ]):
+            # 推理模型的思考走 reasoning_content（content 为空）：作为思考实时推送
+            reasoning = reasoning_delta(chunk)
+            if reasoning:
+                writer({"type": "outline_chunk", "text": reasoning, "is_thinking": True})
             delta = _content_to_text(chunk.content)
             if not delta:
                 continue

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/search/skill_search.py

@@ -26,6 +26,7 @@ from ..nodes._utils import (
     _content_to_text,
     get_model,
     parse_json,
+    reasoning_delta,
     safe_get_stream_writer,
 )
 
@@ -163,6 +164,10 @@ class SkillPicker:
                     max_select=self._max_select,
                 )),
             ]):
+                # 推理模型的思考走 reasoning_content（content 为空）：作为思考实时推送
+                reasoning = reasoning_delta(chunk)
+                if reasoning:
+                    _emit_chunk(reasoning, True)
                 delta = _content_to_text(chunk.content)
                 if not delta:
                     continue

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/state.py

@@ -132,6 +132,11 @@ class AIWritingState(TypedDict):
     keyword_count: int
     # 写作模式：strict=严格依据素材 / loose=可结合通用知识（默认 loose）
     writing_mode: str
+    # 用户在写作表单选定的主模型。大纲/写作/审核严格使用它（不回退默认）。
+    # 由 intent_parser 在首次 invocation 从 configurable.model_name 捕获进 state，
+    # 经 checkpoint 持久化 —— interrupt/resume 是新 invocation、其 config 不带原
+    # model_name，存进 state 才能让 resume 后才运行的节点拿到用户模型。
+    model_name: str
 
     # Agent A 输出
     material_package: Optional[MaterialPackage]