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
This commit is contained in:
wdm
2026-03-29 16:49:05 +08:00
parent 79509fd1ba
commit ed0e3a9c7d
6 changed files with 194 additions and 14 deletions
@@ -0,0 +1,41 @@
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。
要求:
- 基于文章完整正文(article.plain_text)进行分析
- 所有输出字段使用中文
- 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 []
- 输出必须是单个合法 JSON 对象,不要加任何解释文字
输出 JSON schema:
{
"title": "文章标题(与原文一致)",
"url": "文章 URL(与原文一致)",
"core_conclusion": "作者最核心的结论,1-2 句,精准概括",
"main_argument": "文章的主要论点或主张,可展开,允许多句",
"key_methods": [
"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"
],
"important_details": [
"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"
],
"reusable_insights": [
"可复用于其他场景的启发或观点,2-4 条"
],
"keywords": [
"具体实体、工具名、方法名,5-8 个"
],
"topics": [
"更高层的主题标签,3-5 个"
],
"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",
"worth_keeping": true,
"reason": "沉淀理由,一句话说明为什么值得长期保留"
}
注意:
- keywords 和 topics 不能有重叠
- keywords 侧重具体实体(工具名、框架名、人名、产品名)
- topics 侧重抽象主题(如「知识管理」「系统设计」「AI Agent」)
- core_conclusion 必须是作者的核心观点,不是文章描述
- 只输出 JSON,不要输出任何其他内容
@@ -0,0 +1,40 @@
# 规划:单篇文章总结 Prompt 独立化
## 背景
单篇精读总结(article_summary.py)目前复用日报摘要的同一份 prompt,
导致输出是 2-3 句短摘要卡片,没有体现正文深度。
目标是让单篇精读总结彻底独立,输出知识沉淀笔记。
## 已确认决策
- Validator 方案:方案 B,新增 ArticleSummaryResult 模型 + 专用校验函数
- 输出语言:中文
- 无技术内容时 key_methods / important_details 返回空数组 []
- 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
## 涉及文件
- outputs/prompts/article-summary-prompt.txt 新建
- src/summary_mcp/models/article_summary_result.py 新建
- src/summary_mcp/validators/article_summary.py 新建
- src/summary_mcp/core/summary_loop.py 修改
- src/summary_mcp/workflows/article_summary.py 修改
## 新增字段(ArticleSummaryResult)
- core_conclusion:核心结论,1-2 句
- main_argument:主要论点
- key_methods:关键方法,无内容返回 []
- important_details:重要细节,无内容返回 []
- reusable_insights:可复用启发
- keywords / topics / category / worth_keeping / reason
## Markdown 输出格式
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
## 约束
- freshrss_pipeline.py 不变
- summary_loop.py 只加参数,默认行为不变
+5 -2
View File
@@ -7,7 +7,7 @@ import json
import os
import re
from pathlib import Path
from typing import Any
from typing import Any, Callable
import httpx
@@ -183,7 +183,10 @@ def run_loop_payload(
model: str | None,
api_url: str | None,
output_path: Path | None = None,
validator: Callable[[dict[str, Any], dict[str, Any] | None], ValidationReport] | None = None,
) -> tuple[int, dict[str, Any] | None, ValidationReport | None]:
# 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
resolved_validator = validator if validator is not None else validate_llm_result_payload
prompt_template = load_text(prompt_path)
if output_path is not None:
output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -223,7 +226,7 @@ def run_loop_payload(
if output_path is not None:
save_json(output_path, result_payload)
report = validate_llm_result_payload(result_payload, extracted_payload)
report = resolved_validator(result_payload, extracted_payload)
_save_attempt_artifact(
output_path,
f"attempt-{attempt}.validation.json",
@@ -0,0 +1,25 @@
from __future__ import annotations
# 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。
from typing import Literal
from pydantic import BaseModel, HttpUrl
ArticleSummaryCategory = Literal["资讯", "方法论", "工具实践", "观点评论"]
class ArticleSummaryResult(BaseModel):
title: str
url: HttpUrl
core_conclusion: str
main_argument: str
key_methods: list[str] = []
important_details: list[str] = []
reusable_insights: list[str] = []
keywords: list[str] = []
topics: list[str] = []
category: ArticleSummaryCategory
worth_keeping: bool
reason: str
@@ -0,0 +1,40 @@
from __future__ import annotations
# 单篇文章深度沉淀的校验器,与日报摘要 validator 完全独立。
# 只校验 ArticleSummaryResult schema,不做日报摘要的业务规则检查。
from typing import Any
from pydantic import ValidationError
from summary_mcp.models.article_summary_result import ArticleSummaryResult
from summary_mcp.validators.llm_result import ValidationReport
def validate_article_summary_payload(
result_payload: dict[str, Any],
extracted_payload: dict[str, Any] | None = None,
) -> ValidationReport:
# 校验 LLM 输出是否符合 ArticleSummaryResult schema
# extracted_payload 暂未使用,保留参数与 run_loop_payload validator 签名一致
try:
parsed = ArticleSummaryResult.model_validate(result_payload)
except ValidationError as exc:
errors = [
".".join(str(part) for part in error["loc"]) + ": " + error["msg"]
for error in exc.errors()
]
return ValidationReport(valid=False, errors=errors)
# keywords 和 topics 不能重叠
keyword_overlap = set(parsed.keywords) & set(parsed.topics)
errors: list[str] = []
if keyword_overlap:
errors.append(f"`keywords` and `topics` must not overlap: {sorted(keyword_overlap)}")
return ValidationReport(
valid=not errors,
errors=errors,
warnings=[],
normalized_result=parsed.model_dump(mode="json"),
)
+43 -12
View File
@@ -5,12 +5,13 @@ from pathlib import Path
from typing import Iterable, Mapping, Sequence
from summary_mcp.core.summary_loop import run_loop_payload
from summary_mcp.validators.article_summary import validate_article_summary_payload
REPO_ROOT = Path(__file__).resolve().parents[3]
OUTPUT_ROOT = REPO_ROOT / "outputs"
FRESHRSS_OUTPUT_ROOT = OUTPUT_ROOT / "freshrss"
DEFAULT_PROMPT_PATH = OUTPUT_ROOT / "prompts" / "llm-summary-prompt.txt"
DEFAULT_PROMPT_PATH = OUTPUT_ROOT / "prompts" / "article-summary-prompt.txt"
@dataclass
@@ -181,28 +182,58 @@ def summarize_selected_articles(
model=resolved_model,
api_url=resolved_api_url,
output_path=None,
validator=validate_article_summary_payload,
)
if summary_exit_code != 0 or summary_payload is None:
continue
# Render a simple Markdown file summarizing the article.
# Render a Markdown knowledge note using the new article summary schema.
title = article.get("title") or summary_payload.get("title") or item_id
url = article.get("url")
summary_text = summary_payload.get("summary") or ""
highlights = summary_payload.get("highlights") or []
category = summary_payload.get("category") or ""
core_conclusion = summary_payload.get("core_conclusion") or ""
main_argument = summary_payload.get("main_argument") or ""
key_methods = summary_payload.get("key_methods") or []
important_details = summary_payload.get("important_details") or []
reusable_insights = summary_payload.get("reusable_insights") or []
keywords = summary_payload.get("keywords") or []
topics = summary_payload.get("topics") or []
lines: list[str] = []
lines.append(f"# {title}")
if url:
lines.append("")
lines.append(f"Source: {url}")
lines.append("")
if summary_text:
lines.append(summary_text)
if url:
lines.append(f"Source: {url}")
if category:
lines.append(f"Category: {category}")
lines.append("")
if core_conclusion:
lines.append("## 核心结论")
lines.append(core_conclusion)
lines.append("")
if highlights:
lines.append("## Highlights")
lines.extend(f"- {h}" for h in highlights)
if main_argument:
lines.append("## 主要论点")
lines.append(main_argument)
lines.append("")
if key_methods:
lines.append("## 关键方法 / 机制")
lines.extend(f"- {m}" for m in key_methods)
lines.append("")
if important_details:
lines.append("## 重要细节")
lines.extend(f"- {d}" for d in important_details)
lines.append("")
if reusable_insights:
lines.append("## 可复用启发")
lines.extend(f"- {i}" for i in reusable_insights)
lines.append("")
if keywords:
lines.append("## 关键词")
lines.append("、".join(keywords))
lines.append("")
if topics:
lines.append("## 主题")
lines.append("、".join(topics))
lines.append("")
safe_title = "-".join(