feat: make article summary prompt independent from daily digest
- Add outputs/prompts/article-summary-prompt.txt with deep knowledge note schema (core_conclusion, main_argument, key_methods, etc.) - Add ArticleSummaryResult Pydantic model for the new schema - Add validate_article_summary_payload() validator - Add optional validator param to run_loop_payload(); defaults to existing validate_llm_result_payload so daily digest is unaffected - Update article_summary.py: new prompt path, new validator, new Markdown output with Chinese section headers
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你是一个专业的知识沉淀助手。你的任务是对提供的文章正文做深度分析,输出一份结构化的知识沉淀笔记,而不是简短的摘要卡片。
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要求:
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- 基于文章完整正文(article.plain_text)进行分析
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- 所有输出字段使用中文
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- 如果文章没有相关内容(如无技术方法、无具体细节),对应数组字段返回空数组 []
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- 输出必须是单个合法 JSON 对象,不要加任何解释文字
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输出 JSON schema:
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{
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"title": "文章标题(与原文一致)",
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"url": "文章 URL(与原文一致)",
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"core_conclusion": "作者最核心的结论,1-2 句,精准概括",
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"main_argument": "文章的主要论点或主张,可展开,允许多句",
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"key_methods": [
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"关键方法、机制或技术手段,每条一句,3-6 条;无相关内容时返回空数组"
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],
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"important_details": [
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"值得记录的细节、数据或案例,每条一句,3-6 条;无相关内容时返回空数组"
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],
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"reusable_insights": [
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"可复用于其他场景的启发或观点,2-4 条"
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],
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"keywords": [
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"具体实体、工具名、方法名,5-8 个"
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],
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"topics": [
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"更高层的主题标签,3-5 个"
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],
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"category": "内容分类,从以下选项中选择一个:资讯 / 方法论 / 工具实践 / 观点评论",
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"worth_keeping": true,
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"reason": "沉淀理由,一句话说明为什么值得长期保留"
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}
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注意:
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- keywords 和 topics 不能有重叠
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- keywords 侧重具体实体(工具名、框架名、人名、产品名)
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- topics 侧重抽象主题(如「知识管理」「系统设计」「AI Agent」)
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- core_conclusion 必须是作者的核心观点,不是文章描述
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- 只输出 JSON,不要输出任何其他内容
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# 规划:单篇文章总结 Prompt 独立化
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## 背景
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单篇精读总结(article_summary.py)目前复用日报摘要的同一份 prompt,
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导致输出是 2-3 句短摘要卡片,没有体现正文深度。
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目标是让单篇精读总结彻底独立,输出知识沉淀笔记。
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## 已确认决策
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- Validator 方案:方案 B,新增 ArticleSummaryResult 模型 + 专用校验函数
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- 输出语言:中文
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- 无技术内容时 key_methods / important_details 返回空数组 []
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- run_loop_payload 新增可选 validator 参数,None 时保持原有行为,日报链路不受影响
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## 涉及文件
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- outputs/prompts/article-summary-prompt.txt 新建
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- src/summary_mcp/models/article_summary_result.py 新建
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- src/summary_mcp/validators/article_summary.py 新建
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- src/summary_mcp/core/summary_loop.py 修改
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- src/summary_mcp/workflows/article_summary.py 修改
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## 新增字段(ArticleSummaryResult)
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- core_conclusion:核心结论,1-2 句
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- main_argument:主要论点
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- key_methods:关键方法,无内容返回 []
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- important_details:重要细节,无内容返回 []
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- reusable_insights:可复用启发
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- keywords / topics / category / worth_keeping / reason
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## Markdown 输出格式
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核心结论 / 主要论点 / 关键方法 / 重要细节 / 可复用启发 / 关键词 / 主题
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## 约束
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- freshrss_pipeline.py 不变
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- summary_loop.py 只加参数,默认行为不变
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@@ -7,7 +7,7 @@ import json
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import os
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import os
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import re
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import re
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from pathlib import Path
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from pathlib import Path
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from typing import Any
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from typing import Any, Callable
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import httpx
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import httpx
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@@ -183,7 +183,10 @@ def run_loop_payload(
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model: str | None,
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model: str | None,
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api_url: str | None,
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api_url: str | None,
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output_path: Path | None = None,
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output_path: Path | None = None,
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validator: Callable[[dict[str, Any], dict[str, Any] | None], ValidationReport] | None = None,
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) -> tuple[int, dict[str, Any] | None, ValidationReport | None]:
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) -> tuple[int, dict[str, Any] | None, ValidationReport | None]:
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# validator 为 None 时使用日报摘要默认校验器;传入自定义 validator 时使用传入的
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resolved_validator = validator if validator is not None else validate_llm_result_payload
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prompt_template = load_text(prompt_path)
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prompt_template = load_text(prompt_path)
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if output_path is not None:
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if output_path is not None:
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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@@ -223,7 +226,7 @@ def run_loop_payload(
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if output_path is not None:
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if output_path is not None:
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save_json(output_path, result_payload)
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save_json(output_path, result_payload)
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report = validate_llm_result_payload(result_payload, extracted_payload)
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report = resolved_validator(result_payload, extracted_payload)
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_save_attempt_artifact(
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_save_attempt_artifact(
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output_path,
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output_path,
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f"attempt-{attempt}.validation.json",
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f"attempt-{attempt}.validation.json",
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from __future__ import annotations
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# 单篇文章深度沉淀的结构化输出模型,与日报摘要的 LlmSummaryResult 完全独立。
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from typing import Literal
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from pydantic import BaseModel, HttpUrl
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ArticleSummaryCategory = Literal["资讯", "方法论", "工具实践", "观点评论"]
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class ArticleSummaryResult(BaseModel):
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title: str
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url: HttpUrl
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core_conclusion: str
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main_argument: str
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key_methods: list[str] = []
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important_details: list[str] = []
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reusable_insights: list[str] = []
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keywords: list[str] = []
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topics: list[str] = []
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category: ArticleSummaryCategory
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worth_keeping: bool
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reason: str
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from __future__ import annotations
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# 单篇文章深度沉淀的校验器,与日报摘要 validator 完全独立。
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# 只校验 ArticleSummaryResult schema,不做日报摘要的业务规则检查。
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from typing import Any
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from pydantic import ValidationError
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from summary_mcp.models.article_summary_result import ArticleSummaryResult
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from summary_mcp.validators.llm_result import ValidationReport
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def validate_article_summary_payload(
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result_payload: dict[str, Any],
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extracted_payload: dict[str, Any] | None = None,
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) -> ValidationReport:
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# 校验 LLM 输出是否符合 ArticleSummaryResult schema
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# extracted_payload 暂未使用,保留参数与 run_loop_payload validator 签名一致
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try:
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parsed = ArticleSummaryResult.model_validate(result_payload)
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except ValidationError as exc:
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errors = [
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".".join(str(part) for part in error["loc"]) + ": " + error["msg"]
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for error in exc.errors()
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]
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return ValidationReport(valid=False, errors=errors)
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# keywords 和 topics 不能重叠
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keyword_overlap = set(parsed.keywords) & set(parsed.topics)
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errors: list[str] = []
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if keyword_overlap:
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errors.append(f"`keywords` and `topics` must not overlap: {sorted(keyword_overlap)}")
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return ValidationReport(
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valid=not errors,
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errors=errors,
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warnings=[],
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normalized_result=parsed.model_dump(mode="json"),
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)
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@@ -5,12 +5,13 @@ from pathlib import Path
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from typing import Iterable, Mapping, Sequence
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from typing import Iterable, Mapping, Sequence
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from summary_mcp.core.summary_loop import run_loop_payload
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from summary_mcp.core.summary_loop import run_loop_payload
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from summary_mcp.validators.article_summary import validate_article_summary_payload
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REPO_ROOT = Path(__file__).resolve().parents[3]
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REPO_ROOT = Path(__file__).resolve().parents[3]
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OUTPUT_ROOT = REPO_ROOT / "outputs"
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OUTPUT_ROOT = REPO_ROOT / "outputs"
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FRESHRSS_OUTPUT_ROOT = OUTPUT_ROOT / "freshrss"
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FRESHRSS_OUTPUT_ROOT = OUTPUT_ROOT / "freshrss"
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DEFAULT_PROMPT_PATH = OUTPUT_ROOT / "prompts" / "llm-summary-prompt.txt"
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DEFAULT_PROMPT_PATH = OUTPUT_ROOT / "prompts" / "article-summary-prompt.txt"
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@dataclass
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@dataclass
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@@ -181,28 +182,58 @@ def summarize_selected_articles(
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model=resolved_model,
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model=resolved_model,
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api_url=resolved_api_url,
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api_url=resolved_api_url,
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output_path=None,
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output_path=None,
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validator=validate_article_summary_payload,
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)
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)
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if summary_exit_code != 0 or summary_payload is None:
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if summary_exit_code != 0 or summary_payload is None:
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continue
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continue
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# Render a simple Markdown file summarizing the article.
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# Render a Markdown knowledge note using the new article summary schema.
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title = article.get("title") or summary_payload.get("title") or item_id
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title = article.get("title") or summary_payload.get("title") or item_id
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url = article.get("url")
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url = article.get("url")
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summary_text = summary_payload.get("summary") or ""
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category = summary_payload.get("category") or ""
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highlights = summary_payload.get("highlights") or []
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core_conclusion = summary_payload.get("core_conclusion") or ""
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main_argument = summary_payload.get("main_argument") or ""
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key_methods = summary_payload.get("key_methods") or []
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important_details = summary_payload.get("important_details") or []
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reusable_insights = summary_payload.get("reusable_insights") or []
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keywords = summary_payload.get("keywords") or []
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topics = summary_payload.get("topics") or []
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lines: list[str] = []
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lines: list[str] = []
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lines.append(f"# {title}")
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lines.append(f"# {title}")
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if url:
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lines.append("")
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lines.append(f"Source: {url}")
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lines.append("")
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lines.append("")
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if summary_text:
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if url:
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lines.append(summary_text)
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lines.append(f"Source: {url}")
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if category:
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lines.append(f"Category: {category}")
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lines.append("")
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if core_conclusion:
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lines.append("## 核心结论")
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lines.append(core_conclusion)
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lines.append("")
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lines.append("")
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if highlights:
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if main_argument:
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lines.append("## Highlights")
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lines.append("## 主要论点")
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lines.extend(f"- {h}" for h in highlights)
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lines.append(main_argument)
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lines.append("")
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if key_methods:
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lines.append("## 关键方法 / 机制")
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lines.extend(f"- {m}" for m in key_methods)
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lines.append("")
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if important_details:
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lines.append("## 重要细节")
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lines.extend(f"- {d}" for d in important_details)
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lines.append("")
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if reusable_insights:
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lines.append("## 可复用启发")
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lines.extend(f"- {i}" for i in reusable_insights)
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lines.append("")
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if keywords:
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lines.append("## 关键词")
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lines.append("、".join(keywords))
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lines.append("")
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if topics:
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lines.append("## 主题")
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lines.append("、".join(topics))
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lines.append("")
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lines.append("")
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safe_title = "-".join(
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safe_title = "-".join(
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