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feat: tag-aware daily digest - tags in AI prompt, grouping, frontmatter, cross-day continuity

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Beast
2026-06-15 11:27:28 +08:00
parent 01d0aef23f
commit 75586f1fac
2 changed files with 186 additions and 36 deletions
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136
analyzers/daily_digest.py
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@ -5,6 +5,7 @@ import logging
import os
import sys
import subprocess
from collections import Counter
from datetime import datetime, timezone, timedelta
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@ -33,7 +34,6 @@ def utc_to_beijing(ts_str):
def read_previous_digest(date):
"""Read the most recent previous daily digest (if exists) for continuity."""
daily_root = get_output_dir("daily")
# Try yesterday first, then go back up to 7 days
for days_back in range(1, 8):
prev_date = date - timedelta(days=days_back)
prev_dir = os.path.join(daily_root, prev_date.strftime("%Y-%m-%d"))
@ -45,10 +45,8 @@ def read_previous_digest(date):
prev_file = os.path.join(prev_dir, files[0])
with open(prev_file, "r", encoding="utf-8") as f:
content = f.read()
# Extract just the AI analysis section, skip frontmatter
ai_section_start = content.find("## AI 分析")
if ai_section_start != -1:
# Find the "我的批注" section boundary
annotation_start = content.find("## 我的批注", ai_section_start)
if annotation_start != -1:
ai_section = content[ai_section_start:annotation_start].strip()
@ -60,7 +58,6 @@ def read_previous_digest(date):
)
return prev_date, ai_section
else:
# No AI section found, return whole file
logger.info(
"Found previous digest from %s (no AI section found, using full)",
prev_date.strftime("%Y-%m-%d")
@ -71,19 +68,28 @@ def read_previous_digest(date):
# ============================================================
# System prompt for daily analysis
# System prompt for daily analysis (v2 - tag-aware)
# ============================================================
DAILY_SYSTEM_PROMPT = """你是一个私人思考伙伴。你的任务是认真阅读用户今天的每一条灵感记录，结合之前的分析，写一篇有深度的分析文章。
注意：所有时间戳都是**北京时间**（UTC+8），不是 UTC。用户在中国，记录和活动时间均以北京时间为准。
## 关于标签
用户的每条记录可能带有标签（如 #培训 #交易 #生活 等）。标签是用户自己对记录的分类和上下文标注，对理解记录非常重要：
1. **标签揭示场景**：同一条灵感，标注 #培训 和标注 #交易 的解读角度完全不同。标签告诉你用户在什么情境下产生的这个想法。
2. **标签揭示延续性**：如果多天都有 #90后四级副职培训班 标签，说明这是一个持续性的活动/项目，分析时应体现跨日的连续思考。
3. **同标签分组**：当多条记录共享同一标签时，它们通常是同一主题下的不同侧面，应该融会贯通地分析，而不是孤立看待。
4. **不要忽略无标签记录**：没有标签的记录可能是随想、零散灵感，但也有其价值。
核心原则：不做缩写，不做分类表，不写空话。
具体要求：
1. **引用原文**：分析每一条灵感时，必须先引用（或精炼复述）用户的原文，让用户一眼就知道"哦我在说这个"。引用要压缩但不失原意，不能断章取义。
2. **融会贯通，而非逐条罗列**：不要"第一条...第二条...第三条..."机械堆砌。要把所有灵感当作一个整体来思考——哪几条在说同一个主题？哪几条看似无关实则互补？把它们串起来写。文章是流淌的整体，不是并列的零件。
2. **融会贯通，而非逐条罗列**：不要"第一条...第二条...第三条..."机械堆砌。要把所有灵感当作一个整体来思考——哪几条在说同一个主题？哪几条看似无关实则互补？把它们串起来写。文章是流淌的整体，不是并列的零件。**注意利用标签来识别主题聚类**。
3. **承接延续之前的分析**：你会看到上一次的分析内容。今天的分析不能只写"今天的事"，要把今天的灵感和昨天的分析结合起来——昨天讨论了什么？今天有什么进展？哪些问题有了答案？哪些问题还在延续？要让文章有跨日的时间纵深感。
@ -110,13 +116,61 @@ DAILY_SYSTEM_PROMPT = """你是一个私人思考伙伴。你的任务是认真
---
注意：即便只有1条灵感，也要写出深度。宁可写长，不可简略。"""
注意：即便只有1条灵感，也要写出深度。篇幅不设上限，内容完整即可。"""
def build_user_prompt(memos, date):
"""Build user prompt for AI, with tag-aware formatting and grouping.
Memos are grouped by shared tags so the AI can see thematic clusters.
Each memo includes its tags in the text block.
"""
# Step 1: Collect all tags across today's memos
tag_freq = MemosClient.aggregate_tags(memos)
groups = MemosClient.group_memos_by_tag(memos)
prompt_parts = []
# Header: date + tag overview
date_str = date.strftime("%Y-%m-%d")
prompt_parts.append(f"以下是我 {date_str} 的灵感记录（共 {len(memos)} 条）。")
if tag_freq:
tag_summary = "、".join(f"#{t}（{c}条）" for t, c in tag_freq.items())
prompt_parts.append(f"\n今天的标签分布：{tag_summary}")
prompt_parts.append("\n标签说明：用户通过标签自行分类记录。同标签的记录通常属于同一主题或场景，分析时应关注标签揭示的上下文。")
# Step 2: Group memos by tags for thematic presentation
prompt_parts.append("\n---\n")
# Priority: present tagged memos first (grouped by tag), then untagged
sorted_tags = sorted(groups.keys(), key=lambda t: (t == "无标签", -len(groups[t])))
for tag in sorted_tags:
group_memos = groups[tag]
if tag == "无标签":
prompt_parts.append(f"\n## 无标签记录（{len(group_memos)}条）")
else:
prompt_parts.append(f"\n## 标签 #{tag}（{len(group_memos)}条）")
for m in group_memos:
time_bj = utc_to_beijing(m["created_at"])
content = m["content"].strip()
memo_tags = m.get("tags", [])
# Show all tags on this memo (for cross-tagged memos)
tag_line = ""
if memo_tags:
tag_line = " [" + ", ".join("#" + t for t in memo_tags) + "]"
prompt_parts.append(f"\n- **[{time_bj}]**{tag_line}\n{content}")
return "\n".join(prompt_parts)
def git_push(digest_file):
"""Commit and push the digest file to Gitea."""
try:
# git add
result = subprocess.run(
["git", "add", digest_file],
cwd=PROJECT_DIR,
@ -126,7 +180,6 @@ def git_push(digest_file):
logger.warning("git add failed: %s", result.stderr.strip())
return False
# git commit (check for changes first)
result = subprocess.run(
["git", "status", "--porcelain", "ai-insights/"],
cwd=PROJECT_DIR,
@ -146,7 +199,6 @@ def git_push(digest_file):
logger.warning("git commit failed: %s", result.stderr.strip())
return False
# git push
result = subprocess.run(
["git", "push", "origin", "main"],
cwd=PROJECT_DIR,
@ -156,7 +208,6 @@ def git_push(digest_file):
logger.warning("git push failed: %s", result.stderr.strip())
return False
# Extract remote result line
for line in result.stdout.split("\n"):
if "->" in line or "remote:" in line:
logger.info("Gitea push: %s", line.strip())
@ -192,17 +243,11 @@ def run(memos_client, llm_client, date=None):
with open(filepath, "w", encoding="utf-8") as f:
f.write(content)
logger.info("Empty digest written to %s", filepath)
# Still push
git_push(filepath)
return filepath, 0
# Step 2: Prepare user prompt
memo_texts = []
for m in memos:
time_bj = utc_to_beijing(m["created_at"])
memo_texts.append("- **[" + time_bj + "]** " + m["content"].strip())
user_prompt = "以下是我今天的灵感记录：\n\n" + "\n\n".join(memo_texts)
# Step 2: Build tag-aware user prompt
user_prompt = build_user_prompt(memos, date)
# Step 3: Include previous analysis for continuity
prev_date, prev_analysis = read_previous_digest(date)
@ -215,17 +260,58 @@ def run(memos_client, llm_client, date=None):
+ prev_analysis
)
# Step 4: Check for cross-day tag continuity
# If today has tags that appeared in previous digest, note this
tag_freq = MemosClient.aggregate_tags(memos)
today_tags = set(tag_freq.keys())
if prev_date and today_tags:
try:
prev_tags_line = None
# Extract tags from previous digest frontmatter
daily_root = get_output_dir("daily")
prev_dir = os.path.join(daily_root, prev_date.strftime("%Y-%m-%d"))
if os.path.isdir(prev_dir):
prev_files = sorted([f for f in os.listdir(prev_dir) if f.endswith(".md")], reverse=True)
if prev_files:
with open(os.path.join(prev_dir, prev_files[0]), "r", encoding="utf-8") as f:
prev_content = f.read(500)
if "tags:" in prev_content:
prev_tags_line = prev_content.split("tags:")[1].split("\n")[0]
if prev_tags_line:
import re
prev_tags = set(re.findall(r"'([^']+)'", prev_tags_line))
cross_tags = today_tags & prev_tags
if cross_tags:
user_prompt += (
"\n\n---\n\n"
"跨标签延续提示：以下标签与上一次分析（"
+ prev_date.strftime("%Y-%m-%d")
+ "）共享："
+ "、".join("#" + t for t in sorted(cross_tags))
+ "。这些标签代表持续性的主题，请特别关注跨日的思维连续性。"
)
except Exception as e:
logger.debug("Cross-tag check failed: %s", e)
user_prompt += "\n\n---\n\n请基于以上所有素材，写一篇有深度的分析文章。"
# Step 4: Call DeepSeek API
# Step 5: Call DeepSeek API
raw_response = llm_client.ask(
system_prompt=DAILY_SYSTEM_PROMPT,
user_prompt=user_prompt,
temperature=0.5
)
# Step 5: Wrap with frontmatter and write
content = format_daily_digest(date, ai_body=raw_response, tags=["灵感收集器", "每日总结", "AI分析"], doc_type="daily-digest")
# Step 6: Build frontmatter tags (default tags + today's user tags)
default_tags = ["灵感收集器", "每日总结", "AI分析"]
user_tags = sorted(tag_freq.keys())
all_tags = default_tags + user_tags
content = format_daily_digest(
date, ai_body=raw_response,
tags=all_tags, doc_type="daily-digest"
)
date_str = date.strftime("%Y-%m-%d")
daily_dir = os.path.join(get_output_dir("daily"), date_str)
@ -238,11 +324,11 @@ def run(memos_client, llm_client, date=None):
f.write(content)
logger.info(
"Daily digest written to %s | %d memos | %d chars analysis",
filepath, len(memos), len(raw_response)
"Daily digest written to %s | %d memos | %d chars analysis | tags: %s",
filepath, len(memos), len(raw_response), ", ".join(user_tags) if user_tags else "(none)"
)
# Step 6: Auto-push to Gitea
# Step 7: Auto-push to Gitea
git_push(filepath)
return filepath, len(memos)

86
tools/memos_client.py
View File

@ -1,12 +1,14 @@
"""Memos API client - fetch memos using Connect RPC protocol."""
import logging
from collections import Counter
from datetime import datetime, timezone, timedelta
TZ_BEIJING = timezone(timedelta(hours=8))
from re import findall
import requests
TZ_BEIJING = timezone(timedelta(hours=8))
logger = logging.getLogger(__name__)
@ -45,13 +47,11 @@ class MemosClient:
def list_memos(self, days=1, page_size=100):
"""Fetch memos from the last N days (Beijing time reference) via Connect RPC."""
user = self.get_user_id()
# Use Beijing time as reference for "last N days"
now_bj = datetime.now(TZ_BEIJING)
since_bj = now_bj - timedelta(days=days)
since_utc = since_bj.astimezone(timezone.utc)
since = since_utc.isoformat()
# Connect RPC uses POST with JSON body
payload = {
"pageSize": page_size,
"filter": f"creator == '{user}'",
@ -68,7 +68,6 @@ class MemosClient:
memos = data.get("memos", [])
logger.info("Memos API OK | fetched %d memos", len(memos))
# Filter by time client-side (Beijing time reference)
results = []
for m in memos:
created = m.get("createTime", "")
@ -88,9 +87,7 @@ class MemosClient:
"visibility": m.get("visibility", ""),
})
logger.info(
"Filtered to %d memos since %s", len(results), since[:10]
)
logger.info("Filtered to %d memos since %s", len(results), since[:10])
return results
def list_all_memos_from_range(self, start_date, end_date, page_size=200):
@ -110,7 +107,6 @@ class MemosClient:
memos = data.get("memos", [])
logger.info("Memos API OK | fetched %d memos total", len(memos))
# Client-side date range filtering
results = []
for m in memos:
created = m.get("createTime", "")
@ -136,8 +132,76 @@ class MemosClient:
)
return results
def list_memos_by_tag(self, tag, page_size=200):
"""Fetch all memos containing a specific tag.
Useful for retrieving memos across multiple days related to a specific
topic (e.g. a training course that spans a full week).
"""
payload = {"pageSize": page_size}
resp = self.session.post(
f"{self.base_url}/memos.api.v1.MemoService/ListMemos",
json=payload,
timeout=15
)
resp.raise_for_status()
data = resp.json()
memos = data.get("memos", [])
results = []
for m in memos:
content_text = m.get("content", "")
tags = self._extract_tags(content_text)
if tag not in tags:
continue
results.append({
"id": m.get("name", "").split("/")[-1],
"content": content_text,
"created_at": m.get("createTime", ""),
"tags": tags,
"visibility": m.get("visibility", ""),
})
logger.info("Found %d memos with tag #%s", len(results), tag)
return results
@staticmethod
def aggregate_tags(memos):
"""Count tag frequency across a list of memos.
Returns dict of {tag: count}, sorted by frequency descending.
"""
counter = Counter()
for m in memos:
for tag in m.get("tags", []):
counter[tag] += 1
return dict(counter.most_common())
@staticmethod
def group_memos_by_tag(memos):
"""Group memos by their tags.
Returns dict of {tag: [memo_list]}, with "无标签" key for memos
without any tags. A memo can appear under multiple tags.
"""
groups = {}
untagged = []
for m in memos:
tags = m.get("tags", [])
if not tags:
untagged.append(m)
continue
for tag in tags:
if tag not in groups:
groups[tag] = []
groups[tag].append(m)
if untagged:
groups["无标签"] = untagged
return groups
@staticmethod
def _extract_tags(content):
"""Extract #tags from memo content."""
import re
return re.findall(r"#(\w[\w\-]*)", content)
return findall(r"#(\w[\w\-]*)", content)