ai写作修改
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董宏凯 committed
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+64
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+64
-20
@@ -83,11 +83,16 @@ _SYSTEM_TEMPLATE = """你是一名写作助手。
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_USER_TEMPLATE = """用户写作意图：{user_intent}
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请分析并输出以下 JSON（如果意图中未明确某字段，给出合理默认值）：
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用户在表单中已指定写作配置（以下三项必须原样输出，不要自行修改）：
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- 文章类型（article_type）：{preconfigured_article_type}
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- 目标读者（target_audience）：{preconfigured_audience}
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- 目标字数（word_count_target）：{preconfigured_word_count}
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请输出以下 JSON，article_type / target_audience / word_count_target 必须与上述一致，只需根据用户意图精炼 refined_intent：
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{{{{
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"article_type": "{type_options} 之一",
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"target_audience": "目标读者描述",
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"word_count_target": 800,
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"article_type": "{preconfigured_article_type}",
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"target_audience": "{preconfigured_audience}",
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"word_count_target": {preconfigured_word_count},
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"refined_intent": "精炼后的写作意图（一句话）"
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}}}}"""
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@@ -124,6 +129,44 @@ def _coerce_word_count(raw) -> int | None:
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return value if value > 0 else None
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def _resolve_intent_fields(
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state: AIWritingState,
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result: dict,
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fallback_result: dict,
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valid_keys: list[str],
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default_key: str,
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) -> tuple[str, str, int, str]:
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"""合并 LLM 解析结果与用户表单预填字段；预填字段优先（见 AI_WRITING_BACKEND_DEV_ZH §intent_parser）。"""
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fallback_type = fallback_result["article_type"]
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if fallback_type not in valid_keys:
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fallback_type = default_key
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raw_type = result.get("article_type", "")
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llm_type = raw_type if raw_type in valid_keys else fallback_type
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preconfigured_type = (state.get("article_type") or "").strip()
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if preconfigured_type in valid_keys:
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final_article_type = preconfigured_type
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else:
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final_article_type = llm_type
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preconfigured_audience = (state.get("target_audience") or "").strip()
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if preconfigured_audience:
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final_target_audience = preconfigured_audience
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else:
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final_target_audience = result.get("target_audience") or fallback_result["target_audience"]
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preconfigured_wc = state.get("word_count_target")
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if isinstance(preconfigured_wc, int) and preconfigured_wc > 0:
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final_word_count = preconfigured_wc
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else:
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word_count = _coerce_word_count(result.get("word_count_target"))
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final_word_count = word_count if word_count is not None else fallback_result["word_count_target"]
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final_refined_intent = result.get("refined_intent") or state["user_intent"]
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return final_article_type, final_target_audience, final_word_count, final_refined_intent
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async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> dict:
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# 从 configurable 读取动态类型列表，未配置时退回默认值
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configurable = (config or {}).get("configurable", {})
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@@ -137,10 +180,6 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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type_options = _build_type_options(valid_article_types)
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system = _SYSTEM_TEMPLATE.format(type_options=type_options)
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user = _USER_TEMPLATE.format(
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user_intent=state["user_intent"],
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type_options=" | ".join(valid_keys),
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)
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fallback_type = state.get("article_type", default_key)
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if fallback_type not in valid_keys:
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@@ -153,6 +192,17 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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"refined_intent": state["user_intent"],
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}
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preconfigured_wc = fallback_result["word_count_target"]
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if not isinstance(preconfigured_wc, int) or preconfigured_wc <= 0:
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preconfigured_wc = 800
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user = _USER_TEMPLATE.format(
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user_intent=state["user_intent"],
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preconfigured_article_type=fallback_type,
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preconfigured_audience=fallback_result["target_audience"],
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preconfigured_word_count=preconfigured_wc,
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)
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# 流式调用：与 writer_outline / writer_draft 同套机制 —— LLM 逐字吐出 JSON，
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# 通过 ``intent_chunk`` 推给前端打字机；完成后 ``parse_json`` 解析结构化结果，
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# 再通过 ``intent_ready`` 推一份结构化字段给前端结果卡片。
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@@ -193,13 +243,9 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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"detail": f"意图解析失败，已按默认配置兜底。原因：{llm_error}",
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})
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# 校验 LLM 返回的 article_type 是否在有效范围内，不合法时回退到默认值
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raw_type = result.get("article_type", "")
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final_article_type = raw_type if raw_type in valid_keys else fallback_type
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final_target_audience = result.get("target_audience") or fallback_result["target_audience"]
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word_count = _coerce_word_count(result.get("word_count_target"))
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final_word_count = word_count if word_count is not None else fallback_result["word_count_target"]
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final_refined_intent = result.get("refined_intent") or state["user_intent"]
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final_article_type, final_target_audience, final_word_count, final_refined_intent = _resolve_intent_fields(
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state, result, fallback_result, valid_keys, default_key
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)
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# 推送结构化结果给前端 —— 结果卡片显示文章类型 / 目标读者 / 字数 / 精炼意图
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writer({
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@@ -214,11 +260,9 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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updates: dict = {"status": "researching", "progress_events": progress_events}
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updates["article_type"] = final_article_type
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if result.get("target_audience"):
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updates["target_audience"] = final_target_audience
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if word_count is not None:
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updates["word_count_target"] = final_word_count
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if result.get("refined_intent"):
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updates["target_audience"] = final_target_audience
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updates["word_count_target"] = final_word_count
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if final_refined_intent != state["user_intent"]:
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updates["user_intent"] = final_refined_intent
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return updates
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