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member_context: split daily weekly monthly digest scheduling

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liuwei
2026-04-14 10:53:46 +08:00
parent 1800ae585a
commit 28e038a25c
7 changed files with 1339 additions and 240 deletions
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2
plugins/member_context/config.toml
View File

@@ -38,6 +38,8 @@ stable_ready_days = 180
[schedule]
refresh_times = ["04:20"]
weekly_refresh_time = "04:40"
monthly_refresh_time = "04:50"
only_recent_active_groups = true
active_hours = 72
min_group_messages = 20

153
plugins/member_context/digest_service.py
View File

@@ -11,7 +11,6 @@ from db.contacts_db import ContactsDBOperator
from db.member_digest_db import MemberDigestDBOperator
from db.message_storage import MessageStorageDB
from plugins.member_context.dify_client import DifyClient
from plugins.member_context.prompt_builder import MemberContextPromptBuilder
from utils.compress_chat_data import compress_chat_data
@@ -107,43 +106,94 @@ class MemberDigestService:
self.LOG.warning(f"[成员交互摘要] 检查群初始化状态失败，按增量处理: group={chatroom_id}, error={e}")
return False
def ensure_member_digest_pipeline(self, chatroom_id: str, wxid: str, force: bool = False) -> Dict:
def ensure_member_digest_pipeline(self, chatroom_id: str, wxid: str, force: bool = False,
enable_weekly: bool = True, enable_monthly: bool = True) -> Dict:
member = self.contacts_db.get_chatroom_member_info(chatroom_id, wxid) or {}
display_name = member.get("display_name") or member.get("nick_name") or wxid
daily_digests = self.digest_db.list_digests(chatroom_id, wxid, "daily", limit=400)
if not daily_digests:
all_daily_digests = self.digest_db.list_digests(chatroom_id, wxid, "daily", limit=400)
if not all_daily_digests:
return {
"display_name": display_name,
"daily_digests": [],
"weekly_digests": [],
"monthly_digests": [],
"all_daily_digests": [],
"all_weekly_digests": [],
"all_monthly_digests": [],
"stats": {"daily": 0, "weekly": 0, "monthly": 0, "active_days": 0, "built_daily": 0},
}
built_weekly = self._ensure_weekly_digests(chatroom_id, wxid, display_name, force=force)
built_monthly = self._ensure_monthly_digests(chatroom_id, wxid, display_name, force=force)
latest_daily_date = self._extract_latest_daily_date(all_daily_digests)
built_weekly = 0
built_monthly = 0
if enable_weekly and (force or self._should_run_weekly(latest_daily_date)):
built_weekly = self._ensure_weekly_digests(chatroom_id, wxid, display_name, force=force)
elif enable_weekly:
self.LOG.debug(
f"[成员交互摘要][周摘要] 本次跳过(未到周处理窗口): "
f"group={chatroom_id}, wxid={wxid}, latest_daily_date={latest_daily_date}"
)
daily_digests = self.digest_db.list_digests(chatroom_id, wxid, "daily", limit=self.final_daily_limit)
weekly_digests = self.digest_db.list_digests(chatroom_id, wxid, "weekly", limit=self.final_weekly_limit)
monthly_digests = self.digest_db.list_digests(chatroom_id, wxid, "monthly", limit=self.final_monthly_limit)
if enable_monthly and (force or self._should_run_monthly(latest_daily_date)):
built_monthly = self._ensure_monthly_digests(chatroom_id, wxid, display_name, force=force)
elif enable_monthly:
self.LOG.debug(
f"[成员交互摘要][月摘要] 本次跳过(未到月处理窗口): "
f"group={chatroom_id}, wxid={wxid}, latest_daily_date={latest_daily_date}"
)
all_weekly_digests = self.digest_db.list_digests(chatroom_id, wxid, "weekly", limit=200)
all_monthly_digests = self.digest_db.list_digests(chatroom_id, wxid, "monthly", limit=120)
daily_digests = all_daily_digests[:self.final_daily_limit]
weekly_digests = all_weekly_digests[:self.final_weekly_limit]
monthly_digests = all_monthly_digests[:self.final_monthly_limit]
return {
"display_name": display_name,
"daily_digests": daily_digests,
"weekly_digests": weekly_digests,
"monthly_digests": monthly_digests,
"all_daily_digests": all_daily_digests,
"all_weekly_digests": all_weekly_digests,
"all_monthly_digests": all_monthly_digests,
"stats": {
"daily": len(daily_digests),
"weekly": len(weekly_digests),
"monthly": len(monthly_digests),
"active_days": len(self.digest_db.list_digest_keys(chatroom_id, wxid, "daily")),
"daily": len(all_daily_digests),
"weekly": len(all_weekly_digests),
"monthly": len(all_monthly_digests),
"active_days": len(all_daily_digests),
"built_daily": 0,
"built_weekly": built_weekly,
"built_monthly": built_monthly,
},
}
def _extract_latest_daily_date(self, daily_digests: List[Dict]) -> Optional[datetime]:
if not daily_digests:
return None
latest_key = daily_digests[0].get("period_key") or daily_digests[0].get("period_end")
return self._parse_period_date(latest_key)
@staticmethod
def _parse_period_date(value: Optional[str]) -> Optional[datetime]:
if not value:
return None
try:
return datetime.strptime(str(value)[:10], "%Y-%m-%d")
except Exception:
return None
def _should_run_weekly(self, latest_daily_date: Optional[datetime]) -> bool:
if not latest_daily_date:
return False
return latest_daily_date.weekday() == 6
def _should_run_monthly(self, latest_daily_date: Optional[datetime]) -> bool:
if not latest_daily_date:
return False
return (latest_daily_date + timedelta(days=1)).day == 1
@staticmethod
def _normalize_profile_item(item: Dict) -> Dict:
normalized = {}
@@ -312,17 +362,24 @@ class MemberDigestService:
def _build_period_digest(self, digest_type: str, chatroom_id: str, wxid: str, display_name: str,
period_key: str, period_start: str, period_end: str,
items: List[Dict]) -> Optional[Dict]:
prompt = MemberContextPromptBuilder.build_period_digest_prompt(
digest_type, chatroom_id, wxid, display_name, period_key, items
parsed = self._request_period_json(
digest_type=digest_type,
chatroom_id=chatroom_id,
wxid=wxid,
display_name=display_name,
period_key=period_key,
items=items,
)
parsed = self._request_ai_json(prompt, tag=f"{digest_type}:{period_key}", chatroom_id=chatroom_id, wxid=wxid)
if not parsed:
self.LOG.warning(
f"[成员交互摘要][{digest_type}] 跳过周期摘要(未提取到有效结果): "
f"group={chatroom_id}, wxid={wxid}, period={period_key}, source_count={len(items)}"
f"group={chatroom_id}, wxid={wxid}, period={period_key}, source_count={len(items)}, "
f"last_error={self.dify_client.last_error}"
)
return None
parsed = self._normalize_profile_item(parsed)
return {
"chatroom_id": chatroom_id,
"wxid": wxid,
@@ -355,18 +412,42 @@ class MemberDigestService:
parsed["ai_usage"] = response.get("usage", {}) or {}
return parsed
def _request_period_json(self, digest_type: str, chatroom_id: str, wxid: str,
display_name: str, period_key: str, items: List[Dict]) -> Optional[Dict]:
if not self.dify_client.is_available():
return None
inputs = {
"digest_type": digest_type,
"chatroom_id": chatroom_id,
"wxid": wxid,
"display_name": display_name,
"period_key": period_key,
"source_items_json": json.dumps(self._build_period_source_items(items), ensure_ascii=False),
"source_item_count": str(len(items)),
}
response = self.dify_client.run(
prompt="",
user=f"member-digest:{chatroom_id}:{wxid}:{digest_type}:{period_key}",
inputs=inputs,
tag=f"{digest_type}:{period_key}",
)
if not response:
return None
parsed = self._parse_ai_answer(response.get("text", ""))
if parsed:
parsed["ai_usage"] = response.get("usage", {}) or {}
return parsed
def _request_group_daily_json(self, chatroom_id: str, digest_date: str,
member_labels: List[str], compressed_chat: str) -> List[Dict]:
if not self.dify_client.is_available():
return []
prompt = MemberContextPromptBuilder.build_group_daily_digest_prompt(
chatroom_id, digest_date, member_labels, compressed_chat
)
response = self.dify_client.run(
prompt=prompt,
prompt="",
user=f"member-digest:{chatroom_id}:group-daily:{digest_date}",
inputs={
"query": prompt,
"digest_type": "daily",
"chatroom_id": chatroom_id,
"digest_date": digest_date,
"member_labels": "\n".join(member_labels),
@@ -379,6 +460,34 @@ class MemberDigestService:
parsed = self._parse_group_daily_answer(response.get("text", ""))
return parsed
@staticmethod
def _build_period_source_items(items: List[Dict]) -> List[Dict]:
source_items = []
for item in items:
structured = item.get("structured", {}) or {}
source_items.append({
"period_key": item.get("period_key"),
"summary_text": item.get("summary_text", ""),
"topics": structured.get("topics") or structured.get("stable_topics") or structured.get("long_term_topics") or [],
"discussion_scenarios": structured.get("discussion_scenarios") or structured.get("common_scenarios") or [],
"identity_clues": structured.get("identity_clues") or structured.get("identity_traits") or [],
"skill_signals": structured.get("skill_signals") or structured.get("skill_profile") or [],
"problem_solving_signals": structured.get("problem_solving_signals") or structured.get("problem_solving_profile") or [],
"family_signals": structured.get("family_signals") or structured.get("family_profile") or [],
"life_stage_signals": structured.get("life_stage_signals") or structured.get("life_stage_profile") or [],
"value_preferences": structured.get("value_preferences") or structured.get("value_profile") or [],
"habit_signals": structured.get("habit_signals") or structured.get("habit_patterns") or [],
"expression_markers": structured.get("expression_markers") or structured.get("expression_profile") or [],
"engagement_traits": structured.get("engagement_traits") or structured.get("stable_traits") or [],
"reply_entry_points": structured.get("reply_entry_points") or structured.get("reply_entry_profile") or [],
"reply_preferences": structured.get("reply_preferences") or structured.get("long_term_reply_preferences") or [],
"social_role": structured.get("social_role") or structured.get("group_role") or "",
"decision_style": structured.get("decision_style") or structured.get("decision_profile") or "",
"temperament_signal": structured.get("temperament_signal") or structured.get("temperament_tendency") or "",
"recent_state": structured.get("recent_state") or structured.get("phase_state") or [],
})
return source_items
def _parse_ai_answer(self, answer: str) -> Optional[Dict]:
if not answer:
return None

33
plugins/member_context/main.py
View File

@@ -50,13 +50,32 @@ class MemberContextPlugin(MessagePluginInterface):
self.LOG.debug(f"正在初始化 {self.name} 插件...")
self.service = MemberContextService(context["db_manager"], self._config)
refresh_times = self._config.get("schedule", {}).get("refresh_times", [])
if refresh_times and not self._job_registered:
@async_job.at_times(refresh_times)
async def refresh_member_context_job():
if self.service:
self.LOG.info("开始刷新成员交互摘要")
self.service.refresh_all_chatrooms()
self.LOG.info("成员交互摘要刷新完成")
weekly_refresh_time = self._config.get("schedule", {}).get("weekly_refresh_time", "")
monthly_refresh_time = self._config.get("schedule", {}).get("monthly_refresh_time", "")
if not self._job_registered:
if refresh_times:
@async_job.at_times(refresh_times)
async def refresh_member_context_job():
if self.service:
self.LOG.info("开始刷新成员交互摘要(日任务)")
self.service.refresh_all_chatrooms(enable_weekly_digest=False, enable_monthly_digest=False)
self.LOG.info("成员交互摘要刷新完成(日任务)")
if weekly_refresh_time:
@async_job.every_week_time(weekday=6, time_str=weekly_refresh_time)
async def refresh_member_context_weekly_job():
if self.service:
self.LOG.info("开始刷新成员交互摘要(周任务)")
self.service.refresh_all_chatrooms(enable_weekly_digest=True, enable_monthly_digest=False)
self.LOG.info("成员交互摘要刷新完成(周任务)")
if monthly_refresh_time:
@async_job.every_month_last_day_time(monthly_refresh_time)
async def refresh_member_context_monthly_job():
if self.service:
self.LOG.info("开始刷新成员交互摘要(月任务)")
self.service.refresh_all_chatrooms(enable_weekly_digest=False, enable_monthly_digest=True)
self.LOG.info("成员交互摘要刷新完成(月任务)")
self._job_registered = True
self.LOG.debug(f"{self.name} 插件初始化完成")
return True

702
plugins/member_context/member_context.yml
View File

@@ -1,5 +1,5 @@
app:
description: 按群和日期提取群成员日画像，输出严格 JSON，供 member_context 插件直接消费
description: 支持日/周/月成员摘要生成的统一 workflow，按 digest_type 分支输出严格 JSON
icon: 🤖
icon_background: '#E0F2FE'
mode: workflow
@@ -12,7 +12,7 @@ dependencies:
marketplace_plugin_unique_identifier: langgenius/openai_api_compatible:0.0.27@f9ce3ff5e28f09931a3a7fca59add2d09590408f7e9a3d701b10c77a60249719
version: null
kind: app
version: 0.5.0
version: 0.6.0
workflow:
conversation_variables: []
environment_variables: []
@@ -62,48 +62,116 @@ workflow:
isInIteration: false
isInLoop: false
sourceType: start
targetType: llm
id: start-source-llm-target
targetType: if-else
id: start-source-router-target
selected: false
source: start_node
sourceHandle: source
target: llm_node
target: digest_router
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: llm-source-end-target
selected: false
source: llm_node
sourceHandle: source
target: end_node
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
sourceType: if-else
targetType: llm
id: llm_node-fail-branch-1775115372864-target
source: llm_node
sourceHandle: fail-branch
target: '1775115372864'
id: digest_router-daily-daily_llm-target
selected: false
source: digest_router
sourceHandle: daily_case
target: daily_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-weekly-weekly_llm-target
selected: false
source: digest_router
sourceHandle: weekly_case
target: weekly_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-monthly-monthly_llm-target
selected: false
source: digest_router
sourceHandle: monthly_case
target: monthly_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-false-invalid_llm-target
selected: false
source: digest_router
sourceHandle: 'false'
target: invalid_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: 1775115372864-source-end_node-target
source: '1775115372864'
id: daily_llm-source-end_daily-target
selected: false
source: daily_llm
sourceHandle: source
target: end_node
target: end_daily
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: weekly_llm-source-end_weekly-target
selected: false
source: weekly_llm
sourceHandle: source
target: end_weekly
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: monthly_llm-source-end_monthly-target
selected: false
source: monthly_llm
sourceHandle: source
target: end_monthly
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: invalid_llm-source-end_invalid-target
selected: false
source: invalid_llm
sourceHandle: source
target: end_invalid
targetHandle: target
type: custom
zIndex: 0
@@ -114,6 +182,12 @@ workflow:
title: 开始
type: start
variables:
- label: digest_type
max_length: 32
options: []
required: true
type: text-input
variable: digest_type
- label: query
max_length: 120000
options: []
@@ -126,32 +200,114 @@ workflow:
required: true
type: text-input
variable: chatroom_id
- label: wxid
max_length: 128
options: []
required: false
type: text-input
variable: wxid
- label: display_name
max_length: 128
options: []
required: false
type: text-input
variable: display_name
- label: digest_date
max_length: 32
options: []
required: true
required: false
type: text-input
variable: digest_date
- label: period_key
max_length: 64
options: []
required: false
type: text-input
variable: period_key
- label: member_labels
max_length: 50000
options: []
required: true
required: false
type: paragraph
variable: member_labels
- label: compressed_chat
max_length: 200000
options: []
required: true
required: false
type: paragraph
variable: compressed_chat
height: 213
- label: source_items_json
max_length: 200000
options: []
required: false
type: paragraph
variable: source_items_json
- label: source_item_count
max_length: 32
options: []
required: false
type: text-input
variable: source_item_count
height: 388
id: start_node
position:
x: 0
y: 0
y: 110
positionAbsolute:
x: 0
y: 0
y: 110
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
cases:
- case_id: daily_case
conditions:
- comparison_operator: contains
id: daily_case_cond
value: daily
varType: string
variable_selector:
- start_node
- digest_type
id: daily_case
logical_operator: and
- case_id: weekly_case
conditions:
- comparison_operator: contains
id: weekly_case_cond
value: weekly
varType: string
variable_selector:
- start_node
- digest_type
id: weekly_case
logical_operator: and
- case_id: monthly_case
conditions:
- comparison_operator: contains
id: monthly_case_cond
value: monthly
varType: string
variable_selector:
- start_node
- digest_type
id: monthly_case
logical_operator: and
desc: ''
selected: false
title: 摘要类型分支
type: if-else
height: 222
id: digest_router
position:
x: 312
y: 162
positionAbsolute:
x: 312
y: 162
selected: false
sourcePosition: right
targetPosition: left
@@ -162,7 +318,7 @@ workflow:
enabled: false
variable_selector: []
desc: ''
error_strategy: fail-branch
error_strategy: default-value
model:
completion_params:
temperature: 0.2
@@ -170,94 +326,289 @@ workflow:
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: system_prompt_member_context
- id: daily_system_prompt
role: system
text: "你是微信群后台的成员日行为证据提取器。\n\n任务：\n根据给定的一天群聊记录，只按 wxid 识别成员，输出每个成员当天的结构化行为观察。\n\
\n关键规则：\n1. wxid 是唯一标识。display_name 仅用于展示，不用于身份判定。\n2. 每个 wxid 最终只能输出一条记录，严禁重复输出同一个\
\ wxid。\n3. 请先按 wxid 汇总该成员全天发言，再提取结果。\n4. 即使成员发言以短句为主，只要样本量足够，也必须尽量提炼：\n\
\ - topics\n - discussion_scenarios\n - skill_signals\n - problem_solving_signals\n\
\ - value_preferences\n - habit_signals\n - expression_markers\n\
\ - engagement_traits\n - reply_entry_points\n - social_role\n\
\ - temperament_signal\n - summary_text\n5. identity_clues、family_signals、life_stage_signals\
\ 没有明确公开证据时允许为空。\n6. 不允许因为“短句较多”就统一输出空数组和通用摘要。\n7. 不做心理诊断、不做隐私猜测、不把玩笑当事实。\n\
8. 只能输出候选成员列表中的 wxid。\n9. topics 更偏向反复出现的关注方向；discussion_scenarios 更偏向什么情境下会发言；skill_signals\
\ 更偏向能力表现；problem_solving_signals 更偏向怎么处理问题；value_preferences 更偏向判断偏好；social_role 更偏向当天在群里的实际作用。\n\
10. expression_markers 记录可观察的表达标记，如常用句式、口头禅、是否爱列步骤/贴日志；reply_entry_points 记录什么样的接话方式最容易接住他。\n\
11. 输出前自行去重，同一个 wxid 只保留一条最完整结果。\n\n输出要求：\n- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。\n\
- 输出格式：\n{\n \"members\": [\n {\n \"wxid\": \"成员wxid\",\n \
\ \"display_name\": \"显示名\",\n \"topics\": [\"主题1\"],\n \"discussion_scenarios\"\
: [\"场景1\"],\n \"identity_clues\": [\"身份线索1\"],\n \"skill_signals\"\
: [\"技能信号1\"],\n \"problem_solving_signals\": [\"处理方式1\"],\n \
\ \"family_signals\": [\"家庭线索1\"],\n \"life_stage_signals\": [\"\
阶段线索1\"],\n \"value_preferences\": [\"价值偏好1\"],\n \"interaction_style\"\
: \"一句中文\",\n \"message_pattern\": \"一句中文\",\n \"response_style_hint\"\
: \"一句中文\",\n \"habit_signals\": [\"习惯1\"],\n \"expression_markers\"\
: [\"表达标记1\"],\n \"engagement_traits\": [\"参与特征1\"],\n \"reply_entry_points\"\
: [\"接话点1\"],\n \"decision_style\": \"一句中文\",\n \"social_role\"\
: \"一句中文\",\n \"reply_taboos\": [\"避坑1\"],\n \"temperament_signal\"\
: \"一句中文\",\n \"summary_text\": \"不超过100字\",\n \"representative_messages\"\
: [\"原话1\", \"原话2\"],\n \"confidence\": 0.95\n }\n ]\n}\n\n字段约束：\n\
- topics、discussion_scenarios、skill_signals、problem_solving_signals、value_preferences、habit_signals、expression_markers、engagement_traits、reply_entry_points\
\ 最多 4 个\n- identity_clues、family_signals、life_stage_signals 最多 3 个\n\
- reply_taboos 最多 3 个\n- representative_messages 最多 3 条\n- 如果某成员样本明显不足，可以不输出该成员\n"
- id: user_prompt_member_context
role: user
text: '群ID: {{#start_node.chatroom_id#}}
text: |
你是微信群后台的成员日行为证据提取器。
任务：
根据给定的一天群聊记录，只按 wxid 识别成员，输出每个成员当天的结构化行为观察。
关键规则：
1. wxid 是唯一标识。display_name 仅用于展示，不用于身份判定。
2. 每个 wxid 最终只能输出一条记录，严禁重复输出同一个 wxid。
3. 请先按 wxid 汇总该成员全天发言，再提取结果。
4. 即使成员发言以短句为主，只要样本量足够，也必须尽量提炼：
- topics
- discussion_scenarios
- skill_signals
- problem_solving_signals
- value_preferences
- habit_signals
- expression_markers
- engagement_traits
- reply_entry_points
- social_role
- temperament_signal
- summary_text
5. identity_clues、family_signals、life_stage_signals 没有明确公开证据时允许为空。
6. 不允许因为“短句较多”就统一输出空数组和通用摘要。
7. 不做心理诊断、不做隐私猜测、不把玩笑当事实。
8. 只能输出候选成员列表中的 wxid。
9. topics 更偏向反复出现的关注方向；discussion_scenarios 更偏向什么情境下会发言；skill_signals 更偏向能力表现；problem_solving_signals 更偏向怎么处理问题；value_preferences 更偏向判断偏好；social_role 更偏向当天在群里的实际作用。
10. expression_markers 记录可观察的表达标记，如常用句式、口头禅、是否爱列步骤/贴日志；reply_entry_points 记录什么样的接话方式最容易接住他。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
- 输出格式：
{
"members": [
{
"wxid": "成员wxid",
"display_name": "显示名",
"topics": ["主题1"],
"discussion_scenarios": ["场景1"],
"identity_clues": ["身份线索1"],
"skill_signals": ["技能信号1"],
"problem_solving_signals": ["处理方式1"],
"family_signals": ["家庭线索1"],
"life_stage_signals": ["阶段线索1"],
"value_preferences": ["价值偏好1"],
"interaction_style": "一句中文",
"message_pattern": "一句中文",
"response_style_hint": "一句中文",
"habit_signals": ["习惯1"],
"expression_markers": ["表达标记1"],
"engagement_traits": ["参与特征1"],
"reply_entry_points": ["接话点1"],
"decision_style": "一句中文",
"social_role": "一句中文",
"reply_taboos": ["避坑1"],
"temperament_signal": "一句中文",
"summary_text": "不超过100字",
"representative_messages": ["原话1", "原话2"],
"confidence": 0.95
}
]
}
字段约束：
- topics、discussion_scenarios、skill_signals、problem_solving_signals、value_preferences、habit_signals、expression_markers、engagement_traits、reply_entry_points 最多 4 个
- identity_clues、family_signals、life_stage_signals 最多 3 个
- reply_taboos 最多 3 个
- representative_messages 最多 3 条
- 如果某成员样本明显不足，可以不输出该成员
- id: daily_user_prompt
role: user
text: |
摘要类型: daily
群ID: {{#start_node.chatroom_id#}}
日期: {{#start_node.digest_date#}}
候选成员:
{{#start_node.member_labels#}}
压缩后的群聊记录:
{{#start_node.compressed_chat#}}
'
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 成员画像提取
title: 日摘要生成
type: llm
variables: []
vision:
enabled: false
height: 154
id: llm_node
id: daily_llm
position:
x: 342
y: 30
x: 652
y: 18
positionAbsolute:
x: 342
y: 30
x: 652
y: 18
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
context:
enabled: false
variable_selector: []
desc: ''
outputs:
- value_selector:
- llm_node
- text
variable: text
error_strategy: default-value
model:
completion_params:
temperature: 0.2
mode: chat
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: weekly_system_prompt
role: system
text: |
你是微信群后台的成员周摘要生成器。
任务：
根据同一成员的多个日摘要，提炼本周重复出现的模式，过滤单日噪音，输出严格 JSON。
关键规则：
1. 只根据输入中的下级摘要生成，不可编造新增事实。
2. 只有多个日摘要反复出现的特征，才允许进入 stable_topics、stable_traits、habit_patterns、reply_preferences。
3. recent_state 只描述当前阶段状态，不要把短期现象写成长期人格。
4. identity_traits、family_profile、life_stage_profile 只能保留反复出现的公开线索。
5. common_scenarios、problem_solving_profile、expression_profile、reply_entry_profile 要尽量写成具体行为模式。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
- 输出格式：
{
"stable_topics": ["主题1"],
"common_scenarios": ["场景1"],
"identity_traits": ["身份特征1"],
"skill_profile": ["技能画像1"],
"problem_solving_profile": ["处理方式1"],
"family_profile": ["家庭线索1"],
"life_stage_profile": ["阶段线索1"],
"value_profile": ["价值偏好1"],
"stable_traits": ["特征1"],
"habit_patterns": ["习惯1"],
"expression_profile": ["表达标记1"],
"reply_entry_profile": ["接话点1"],
"reply_preferences": ["偏好1"],
"group_role": "一句中文",
"decision_profile": "一句中文",
"recent_state": ["状态1"],
"temperament_tendency": "一句中文",
"summary_text": "一段不超过120字的周摘要",
"confidence": 0.0
}
- id: weekly_user_prompt
role: user
text: |
摘要类型: weekly
群ID: {{#start_node.chatroom_id#}}
成员: {{#start_node.display_name#}} ({{#start_node.wxid#}})
周期: {{#start_node.period_key#}}
下级摘要数量: {{#start_node.source_item_count#}}
下级摘要 JSON:
{{#start_node.source_items_json#}}
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 结束
type: end
height: 88
id: end_node
title: 周摘要生成
type: llm
variables: []
vision:
enabled: false
height: 154
id: weekly_llm
position:
x: 1066
y: 52
x: 652
y: 196
positionAbsolute:
x: 1066
y: 52
x: 652
y: 196
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
context:
enabled: false
variable_selector: []
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.2
mode: chat
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: monthly_system_prompt
role: system
text: |
你是微信群后台的成员月摘要生成器。
任务：
根据同一成员的多个周摘要，提炼阶段性稳定特征，只有反复出现的模式才能进入长期层，输出严格 JSON。
关键规则：
1. 只根据输入中的下级摘要生成，不可编造新增事实。
2. 只有多个周摘要反复出现的特征，才允许进入 long_term_topics、stable_traits、habit_patterns、long_term_reply_preferences。
3. phase_state 只描述当前阶段状态，不要冒充长期人格。
4. identity_traits、family_profile、life_stage_profile 只能保留反复出现的公开线索。
5. group_role、decision_profile、problem_solving_profile、expression_profile 要总结阶段性稳定模式。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
- 输出格式：
{
"long_term_topics": ["主题1"],
"common_scenarios": ["场景1"],
"identity_traits": ["身份特征1"],
"skill_profile": ["技能画像1"],
"problem_solving_profile": ["处理方式1"],
"family_profile": ["家庭线索1"],
"life_stage_profile": ["阶段线索1"],
"value_profile": ["价值偏好1"],
"stable_traits": ["特征1"],
"habit_patterns": ["习惯1"],
"expression_profile": ["表达标记1"],
"reply_entry_profile": ["接话点1"],
"long_term_reply_preferences": ["偏好1"],
"group_role": "一句中文",
"decision_profile": "一句中文",
"phase_state": ["状态1"],
"temperament_tendency": "一句中文",
"summary_text": "一段不超过140字的月摘要",
"confidence": 0.0
}
- id: monthly_user_prompt
role: user
text: |
摘要类型: monthly
群ID: {{#start_node.chatroom_id#}}
成员: {{#start_node.display_name#}} ({{#start_node.wxid#}})
周期: {{#start_node.period_key#}}
下级摘要数量: {{#start_node.source_item_count#}}
下级摘要 JSON:
{{#start_node.source_items_json#}}
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 月摘要生成
type: llm
variables: []
vision:
enabled: false
height: 154
id: monthly_llm
position:
x: 652
y: 374
positionAbsolute:
x: 652
y: 374
selected: false
sourcePosition: right
targetPosition: left
@@ -270,82 +621,139 @@ workflow:
default_value:
- key: text
type: string
value: '{"members": []}'
value: '{"error":"unsupported_digest_type"}'
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.7
temperature: 0.1
mode: chat
name: grok-4-fast
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: c5ee983e-d6e0-4790-ac3b-f4e097013b70
- id: invalid_system_prompt
role: system
text: "你是微信群后台的成员日行为证据提取器。\n\n任务：\n根据给定的一天群聊记录，只按 wxid 识别成员，输出每个成员当天的结构化行为观察。\n\
\n关键规则：\n1. wxid 是唯一标识。display_name 仅用于展示，不用于身份判定。\n2. 每个 wxid 最终只能输出一条记录，严禁重复输出同一个\
\ wxid。\n3. 请先按 wxid 汇总该成员全天发言，再提取结果。\n4. 即使成员发言以短句为主，只要样本量足够，也必须尽量提炼：\n\
\ - topics\n - discussion_scenarios\n - skill_signals\n - problem_solving_signals\n\
\ - value_preferences\n - habit_signals\n - expression_markers\n\
\ - engagement_traits\n - reply_entry_points\n - social_role\n\
\ - temperament_signal\n - summary_text\n5. identity_clues、family_signals、life_stage_signals\
\ 没有明确公开证据时允许为空。\n6. 不允许因为“短句较多”就统一输出空数组和通用摘要。\n7. 不做心理诊断、不做隐私猜测、不把玩笑当事实。\n\
8. 只能输出候选成员列表中的 wxid。\n9. topics 更偏向反复出现的关注方向；discussion_scenarios 更偏向什么情境下会发言；skill_signals\
\ 更偏向能力表现；problem_solving_signals 更偏向怎么处理问题；value_preferences 更偏向判断偏好；social_role 更偏向当天在群里的实际作用。\n\
10. expression_markers 记录可观察的表达标记，如常用句式、口头禅、是否爱列步骤/贴日志；reply_entry_points 记录什么样的接话方式最容易接住他。\n\
11. 输出前自行去重，同一个 wxid 只保留一条最完整结果。\n\n输出要求：\n- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。\n\
- 输出格式：\n{\n \"members\": [\n {\n \"wxid\": \"成员wxid\",\n \
\ \"display_name\": \"显示名\",\n \"topics\": [\"主题1\"],\n \"discussion_scenarios\"\
: [\"场景1\"],\n \"identity_clues\": [\"身份线索1\"],\n \"skill_signals\"\
: [\"技能信号1\"],\n \"problem_solving_signals\": [\"处理方式1\"],\n \
\ \"family_signals\": [\"家庭线索1\"],\n \"life_stage_signals\": [\"\
阶段线索1\"],\n \"value_preferences\": [\"价值偏好1\"],\n \"interaction_style\"\
: \"一句中文\",\n \"message_pattern\": \"一句中文\",\n \"response_style_hint\"\
: \"一句中文\",\n \"habit_signals\": [\"习惯1\"],\n \"expression_markers\"\
: [\"表达标记1\"],\n \"engagement_traits\": [\"参与特征1\"],\n \"reply_entry_points\"\
: [\"接话点1\"],\n \"decision_style\": \"一句中文\",\n \"social_role\"\
: \"一句中文\",\n \"reply_taboos\": [\"避坑1\"],\n \"temperament_signal\"\
: \"一句中文\",\n \"summary_text\": \"不超过100字\",\n \"representative_messages\"\
: [\"原话1\", \"原话2\"],\n \"confidence\": 0.95\n }\n ]\n}\n\n字段约束：\n\
- topics、discussion_scenarios、skill_signals、problem_solving_signals、value_preferences、habit_signals、expression_markers、engagement_traits、reply_entry_points\
\ 最多 4 个\n- identity_clues、family_signals、life_stage_signals 最多 3 个\n\
- reply_taboos 最多 3 个\n- representative_messages 最多 3 条\n- 如果某成员样本明显不足，可以不输出该成员\n"
- id: 2df6d4ce-4e40-42a9-85f3-184075da24c7
text: |
你是一个严格返回 JSON 的错误响应器。
当摘要类型不受支持时，只返回一个紧凑 JSON。
- id: invalid_user_prompt
role: user
text: '群ID: {{#start_node.chatroom_id#}}
日期: {{#start_node.digest_date#}}
候选成员:
{{#start_node.member_labels#}}
压缩后的群聊记录:
{{#start_node.compressed_chat#}}
'
selected: true
title: 异常分支
text: |
输入的 digest_type 是: {{#start_node.digest_type#}}
请只返回：
{"error":"unsupported_digest_type","digest_type":"原值"}
selected: false
title: 非法类型处理
type: llm
vision:
enabled: false
height: 124
id: '1775115372864'
id: invalid_llm
position:
x: 704
y: 132
x: 652
y: 552
positionAbsolute:
x: 704
y: 132
selected: true
x: 652
y: 552
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- daily_llm
- text
variable: text
selected: false
title: 结束-日
type: end
height: 88
id: end_daily
position:
x: 1016
y: 54
positionAbsolute:
x: 1016
y: 54
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- weekly_llm
- text
variable: text
selected: false
title: 结束-周
type: end
height: 88
id: end_weekly
position:
x: 1016
y: 232
positionAbsolute:
x: 1016
y: 232
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- monthly_llm
- text
variable: text
selected: false
title: 结束-月
type: end
height: 88
id: end_monthly
position:
x: 1016
y: 410
positionAbsolute:
x: 1016
y: 410
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- invalid_llm
- text
variable: text
selected: false
title: 结束-异常
type: end
height: 88
id: end_invalid
position:
x: 1016
y: 588
positionAbsolute:
x: 1016
y: 588
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
viewport:
x: 210
y: 340
x: 60
y: 120
zoom: 0.7
rag_pipeline_variables: []

579
plugins/member_context/member_context_workflow.yml
View File

@@ -1,6 +1,6 @@
app:
description: 按群和日期提取群成员日画像，输出严格 JSON，供 member_context 插件直接消费
icon: 🧠
description: 支持日/周/月成员摘要生成的统一 workflow，按 digest_type 分支输出严格 JSON
icon: 🤖
icon_background: '#E0F2FE'
mode: workflow
name: member_context
@@ -9,9 +9,10 @@ dependencies:
- current_identifier: null
type: marketplace
value:
marketplace_plugin_unique_identifier: langgenius/volcengine_maas:0.0.13@d402dc32a505b1b4f27588f10e729209bf413ec263467635774d96c4345bd197
marketplace_plugin_unique_identifier: langgenius/openai_api_compatible:0.0.27@f9ce3ff5e28f09931a3a7fca59add2d09590408f7e9a3d701b10c77a60249719
version: null
kind: app
version: 0.3.0
version: 0.6.0
workflow:
conversation_variables: []
environment_variables: []
@@ -29,7 +30,9 @@ workflow:
audio_file_size_limit: 50
batch_count_limit: 5
file_size_limit: 15
image_file_batch_limit: 10
image_file_size_limit: 10
single_chunk_attachment_limit: 10
video_file_size_limit: 100
workflow_file_upload_limit: 10
image:
@@ -59,12 +62,64 @@ workflow:
isInIteration: false
isInLoop: false
sourceType: start
targetType: llm
id: start-source-llm-target
targetType: if-else
id: start-source-router-target
selected: false
source: 'start_node'
source: start_node
sourceHandle: source
target: 'llm_node'
target: digest_router
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-daily-daily_llm-target
selected: false
source: digest_router
sourceHandle: daily_case
target: daily_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-weekly-weekly_llm-target
selected: false
source: digest_router
sourceHandle: weekly_case
target: weekly_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-monthly-monthly_llm-target
selected: false
source: digest_router
sourceHandle: monthly_case
target: monthly_llm
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: if-else
targetType: llm
id: digest_router-false-invalid_llm-target
selected: false
source: digest_router
sourceHandle: 'false'
target: invalid_llm
targetHandle: target
type: custom
zIndex: 0
@@ -73,11 +128,50 @@ workflow:
isInLoop: false
sourceType: llm
targetType: end
id: llm-source-end-target
id: daily_llm-source-end_daily-target
selected: false
source: 'llm_node'
source: daily_llm
sourceHandle: source
target: 'end_node'
target: end_daily
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: weekly_llm-source-end_weekly-target
selected: false
source: weekly_llm
sourceHandle: source
target: end_weekly
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: monthly_llm-source-end_monthly-target
selected: false
source: monthly_llm
sourceHandle: source
target: end_monthly
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: llm
targetType: end
id: invalid_llm-source-end_invalid-target
selected: false
source: invalid_llm
sourceHandle: source
target: end_invalid
targetHandle: target
type: custom
zIndex: 0
@@ -88,6 +182,12 @@ workflow:
title: 开始
type: start
variables:
- label: digest_type
max_length: 32
options: []
required: true
type: text-input
variable: digest_type
- label: query
max_length: 120000
options: []
@@ -100,55 +200,133 @@ workflow:
required: true
type: text-input
variable: chatroom_id
- label: wxid
max_length: 128
options: []
required: false
type: text-input
variable: wxid
- label: display_name
max_length: 128
options: []
required: false
type: text-input
variable: display_name
- label: digest_date
max_length: 32
options: []
required: true
required: false
type: text-input
variable: digest_date
- label: period_key
max_length: 64
options: []
required: false
type: text-input
variable: period_key
- label: member_labels
max_length: 50000
options: []
required: true
required: false
type: paragraph
variable: member_labels
- label: compressed_chat
max_length: 200000
options: []
required: true
required: false
type: paragraph
variable: compressed_chat
height: 194
id: 'start_node'
- label: source_items_json
max_length: 200000
options: []
required: false
type: paragraph
variable: source_items_json
- label: source_item_count
max_length: 32
options: []
required: false
type: text-input
variable: source_item_count
height: 388
id: start_node
position:
x: -420
y: 120
x: 0
y: 110
positionAbsolute:
x: -420
y: 120
x: 0
y: 110
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
width: 242
- data:
cases:
- case_id: daily_case
conditions:
- comparison_operator: contains
id: daily_case_cond
value: daily
varType: string
variable_selector:
- start_node
- digest_type
id: daily_case
logical_operator: and
- case_id: weekly_case
conditions:
- comparison_operator: contains
id: weekly_case_cond
value: weekly
varType: string
variable_selector:
- start_node
- digest_type
id: weekly_case
logical_operator: and
- case_id: monthly_case
conditions:
- comparison_operator: contains
id: monthly_case_cond
value: monthly
varType: string
variable_selector:
- start_node
- digest_type
id: monthly_case
logical_operator: and
desc: ''
selected: false
title: 摘要类型分支
type: if-else
height: 222
id: digest_router
position:
x: 312
y: 162
positionAbsolute:
x: 312
y: 162
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
context:
enabled: false
variable_selector: []
default_value:
- key: text
type: string
value: '{"members":[]}'
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.2
mode: chat
name: Doubao-1.5-pro-256k
provider: langgenius/volcengine_maas/volcengine_maas
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: system_prompt_member_context
- id: daily_system_prompt
role: system
text: |
你是微信群后台的成员日行为证据提取器。
@@ -179,7 +357,6 @@ workflow:
8. 只能输出候选成员列表中的 wxid。
9. topics 更偏向反复出现的关注方向；discussion_scenarios 更偏向什么情境下会发言；skill_signals 更偏向能力表现；problem_solving_signals 更偏向怎么处理问题；value_preferences 更偏向判断偏好；social_role 更偏向当天在群里的实际作用。
10. expression_markers 记录可观察的表达标记，如常用句式、口头禅、是否爱列步骤/贴日志；reply_entry_points 记录什么样的接话方式最容易接住他。
11. 输出前自行去重，同一个 wxid 只保留一条最完整结果。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
@@ -221,9 +398,10 @@ workflow:
- reply_taboos 最多 3 个
- representative_messages 最多 3 条
- 如果某成员样本明显不足，可以不输出该成员
- id: user_prompt_member_context
- id: daily_user_prompt
role: user
text: |
摘要类型: daily
群ID: {{#start_node.chatroom_id#}}
日期: {{#start_node.digest_date#}}
@@ -232,49 +410,350 @@ workflow:
压缩后的群聊记录:
{{#start_node.compressed_chat#}}
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 成员画像提取
title: 日摘要生成
type: llm
variables: []
vision:
enabled: false
height: 98
id: 'llm_node'
height: 154
id: daily_llm
position:
x: 10
y: 140
x: 652
y: 18
positionAbsolute:
x: 10
y: 140
x: 652
y: 18
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
width: 242
- data:
context:
enabled: false
variable_selector: []
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.2
mode: chat
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: weekly_system_prompt
role: system
text: |
你是微信群后台的成员周摘要生成器。
任务：
根据同一成员的多个日摘要，提炼本周重复出现的模式，过滤单日噪音，输出严格 JSON。
关键规则：
1. 只根据输入中的下级摘要生成，不可编造新增事实。
2. 只有多个日摘要反复出现的特征，才允许进入 stable_topics、stable_traits、habit_patterns、reply_preferences。
3. recent_state 只描述当前阶段状态，不要把短期现象写成长期人格。
4. identity_traits、family_profile、life_stage_profile 只能保留反复出现的公开线索。
5. common_scenarios、problem_solving_profile、expression_profile、reply_entry_profile 要尽量写成具体行为模式。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
- 输出格式：
{
"stable_topics": ["主题1"],
"common_scenarios": ["场景1"],
"identity_traits": ["身份特征1"],
"skill_profile": ["技能画像1"],
"problem_solving_profile": ["处理方式1"],
"family_profile": ["家庭线索1"],
"life_stage_profile": ["阶段线索1"],
"value_profile": ["价值偏好1"],
"stable_traits": ["特征1"],
"habit_patterns": ["习惯1"],
"expression_profile": ["表达标记1"],
"reply_entry_profile": ["接话点1"],
"reply_preferences": ["偏好1"],
"group_role": "一句中文",
"decision_profile": "一句中文",
"recent_state": ["状态1"],
"temperament_tendency": "一句中文",
"summary_text": "一段不超过120字的周摘要",
"confidence": 0.0
}
- id: weekly_user_prompt
role: user
text: |
摘要类型: weekly
群ID: {{#start_node.chatroom_id#}}
成员: {{#start_node.display_name#}} ({{#start_node.wxid#}})
周期: {{#start_node.period_key#}}
下级摘要数量: {{#start_node.source_item_count#}}
下级摘要 JSON:
{{#start_node.source_items_json#}}
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 周摘要生成
type: llm
variables: []
vision:
enabled: false
height: 154
id: weekly_llm
position:
x: 652
y: 196
positionAbsolute:
x: 652
y: 196
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
context:
enabled: false
variable_selector: []
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.2
mode: chat
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: monthly_system_prompt
role: system
text: |
你是微信群后台的成员月摘要生成器。
任务：
根据同一成员的多个周摘要，提炼阶段性稳定特征，只有反复出现的模式才能进入长期层，输出严格 JSON。
关键规则：
1. 只根据输入中的下级摘要生成，不可编造新增事实。
2. 只有多个周摘要反复出现的特征，才允许进入 long_term_topics、stable_traits、habit_patterns、long_term_reply_preferences。
3. phase_state 只描述当前阶段状态，不要冒充长期人格。
4. identity_traits、family_profile、life_stage_profile 只能保留反复出现的公开线索。
5. group_role、decision_profile、problem_solving_profile、expression_profile 要总结阶段性稳定模式。
输出要求：
- 只输出严格 JSON，不要 markdown，不要解释，不要前后缀。
- 输出格式：
{
"long_term_topics": ["主题1"],
"common_scenarios": ["场景1"],
"identity_traits": ["身份特征1"],
"skill_profile": ["技能画像1"],
"problem_solving_profile": ["处理方式1"],
"family_profile": ["家庭线索1"],
"life_stage_profile": ["阶段线索1"],
"value_profile": ["价值偏好1"],
"stable_traits": ["特征1"],
"habit_patterns": ["习惯1"],
"expression_profile": ["表达标记1"],
"reply_entry_profile": ["接话点1"],
"long_term_reply_preferences": ["偏好1"],
"group_role": "一句中文",
"decision_profile": "一句中文",
"phase_state": ["状态1"],
"temperament_tendency": "一句中文",
"summary_text": "一段不超过140字的月摘要",
"confidence": 0.0
}
- id: monthly_user_prompt
role: user
text: |
摘要类型: monthly
群ID: {{#start_node.chatroom_id#}}
成员: {{#start_node.display_name#}} ({{#start_node.wxid#}})
周期: {{#start_node.period_key#}}
下级摘要数量: {{#start_node.source_item_count#}}
下级摘要 JSON:
{{#start_node.source_items_json#}}
补充上下文:
{{#start_node.query#}}
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 1000
selected: false
title: 月摘要生成
type: llm
variables: []
vision:
enabled: false
height: 154
id: monthly_llm
position:
x: 652
y: 374
positionAbsolute:
x: 652
y: 374
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
context:
enabled: false
variable_selector: []
default_value:
- key: text
type: string
value: '{"error":"unsupported_digest_type"}'
desc: ''
error_strategy: default-value
model:
completion_params:
temperature: 0.1
mode: chat
name: gpt-5.4-mini
provider: langgenius/openai_api_compatible/openai_api_compatible
prompt_template:
- id: invalid_system_prompt
role: system
text: |
你是一个严格返回 JSON 的错误响应器。
当摘要类型不受支持时，只返回一个紧凑 JSON。
- id: invalid_user_prompt
role: user
text: |
输入的 digest_type 是: {{#start_node.digest_type#}}
请只返回：
{"error":"unsupported_digest_type","digest_type":"原值"}
selected: false
title: 非法类型处理
type: llm
vision:
enabled: false
height: 124
id: invalid_llm
position:
x: 652
y: 552
positionAbsolute:
x: 652
y: 552
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- 'llm_node'
- daily_llm
- text
variable: text
selected: false
title: 结束
title: 结束-日
type: end
height: 90
id: 'end_node'
height: 88
id: end_daily
position:
x: 430
y: 140
x: 1016
y: 54
positionAbsolute:
x: 430
y: 140
x: 1016
y: 54
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
width: 242
- data:
desc: ''
outputs:
- value_selector:
- weekly_llm
- text
variable: text
selected: false
title: 结束-周
type: end
height: 88
id: end_weekly
position:
x: 1016
y: 232
positionAbsolute:
x: 1016
y: 232
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- monthly_llm
- text
variable: text
selected: false
title: 结束-月
type: end
height: 88
id: end_monthly
position:
x: 1016
y: 410
positionAbsolute:
x: 1016
y: 410
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
- data:
desc: ''
outputs:
- value_selector:
- invalid_llm
- text
variable: text
selected: false
title: 结束-异常
type: end
height: 88
id: end_invalid
position:
x: 1016
y: 588
positionAbsolute:
x: 1016
y: 588
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 242
viewport:
x: 120
y: 180
zoom: 0.9
x: 60
y: 120
zoom: 0.7
rag_pipeline_variables: []

46
plugins/member_context/service.py
View File

@@ -69,7 +69,8 @@ class MemberContextService:
def build_member_context(self, chatroom_id: str, wxid: str, days: Optional[int] = None,
limit: Optional[int] = None, force_digest_rebuild: bool = False,
ensure_group_daily: bool = True) -> Dict:
ensure_group_daily: bool = True, enable_weekly_digest: bool = True,
enable_monthly_digest: bool = True) -> Dict:
days = days or self.sample_days
limit = limit or self.refresh_limit_per_member
@@ -83,11 +84,15 @@ class MemberContextService:
chatroom_id, force=force_digest_rebuild
)
digest_snapshot = self.digest_service.ensure_member_digest_pipeline(
chatroom_id, wxid, force=force_digest_rebuild
chatroom_id, wxid, force=force_digest_rebuild,
enable_weekly=enable_weekly_digest, enable_monthly=enable_monthly_digest
)
daily_digests = digest_snapshot.get("daily_digests", [])
weekly_digests = digest_snapshot.get("weekly_digests", [])
monthly_digests = digest_snapshot.get("monthly_digests", [])
all_daily_digests = digest_snapshot.get("all_daily_digests", daily_digests)
all_weekly_digests = digest_snapshot.get("all_weekly_digests", weekly_digests)
all_monthly_digests = digest_snapshot.get("all_monthly_digests", monthly_digests)
recent_messages = self.message_db.get_member_recent_messages(
chatroom_id,
@@ -100,7 +105,7 @@ class MemberContextService:
weekly_structured = [item.get("structured", {}) or {} for item in weekly_digests]
daily_structured = [item.get("structured", {}) or {} for item in daily_digests]
observation_days = self._calc_observation_days(daily_digests)
observation_days = self._calc_observation_days(all_daily_digests)
activity_level = self._calc_activity_level(len(recent_messages), max(min(days, 7), 1))
context = {
"chatroom_id": chatroom_id,
@@ -121,7 +126,7 @@ class MemberContextService:
),
"recent_focus": self._extract_scored_items(daily_structured, ["topics"], limit=4),
"summary_text": "",
"confidence": self._calc_digest_confidence(monthly_digests, weekly_digests, daily_digests),
"confidence": self._calc_digest_confidence(all_monthly_digests, all_weekly_digests, all_daily_digests),
"source_message_count": len(recent_messages),
"source_days": days,
"last_profiled_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
@@ -194,10 +199,10 @@ class MemberContextService:
"observation_days": observation_days,
"stable_ready": observation_days >= self.stable_ready_days,
"profile_iterations": int(((existing_context or {}).get("meta", {}) or {}).get("profile_iterations", 0)) + 1,
"history_message_count": self._sum_digest_source_count(daily_digests),
"digest_daily_count": len(daily_digests),
"digest_weekly_count": len(weekly_digests),
"digest_monthly_count": len(monthly_digests),
"history_message_count": self._sum_digest_source_count(all_daily_digests),
"digest_daily_count": len(all_daily_digests),
"digest_weekly_count": len(all_weekly_digests),
"digest_monthly_count": len(all_monthly_digests),
"last_daily_digest_at": daily_digests[0].get("last_generated_at") if daily_digests else "",
"last_weekly_digest_at": weekly_digests[0].get("last_generated_at") if weekly_digests else "",
"last_monthly_digest_at": monthly_digests[0].get("last_generated_at") if monthly_digests else "",
@@ -226,11 +231,15 @@ class MemberContextService:
return context
def refresh_member_context(self, chatroom_id: str, wxid: str, days: Optional[int] = None,
limit: Optional[int] = None) -> Dict:
limit: Optional[int] = None, enable_weekly_digest: bool = True,
enable_monthly_digest: bool = True) -> Dict:
if not self.is_group_enabled(chatroom_id):
raise ValueError(f"群 {chatroom_id} 未启用成员交互摘要功能")
self.LOG.info(f"[成员交互摘要] 开始刷新单个成员: group={chatroom_id}, wxid={wxid}")
context = self.build_member_context(chatroom_id, wxid, days=days, limit=limit)
context = self.build_member_context(
chatroom_id, wxid, days=days, limit=limit,
enable_weekly_digest=enable_weekly_digest, enable_monthly_digest=enable_monthly_digest
)
self.member_context_db.save_member_context(context)
self.LOG.info(
f"[成员交互摘要] 单个成员刷新完成: group={chatroom_id}, wxid={wxid}, "
@@ -244,7 +253,8 @@ class MemberContextService:
return context
def refresh_group_contexts(self, chatroom_id: str, days: Optional[int] = None,
limit_per_member: Optional[int] = None) -> Dict:
limit_per_member: Optional[int] = None, enable_weekly_digest: bool = True,
enable_monthly_digest: bool = True) -> Dict:
days = days or self.sample_days
limit_per_member = limit_per_member or self.refresh_limit_per_member
@@ -321,7 +331,8 @@ class MemberContextService:
)
continue
context = self.build_member_context(
chatroom_id, wxid, days=days, limit=limit_per_member, ensure_group_daily=False
chatroom_id, wxid, days=days, limit=limit_per_member, ensure_group_daily=False,
enable_weekly_digest=enable_weekly_digest, enable_monthly_digest=enable_monthly_digest
)
if context["source_message_count"] <= 0 and context.get("meta", {}).get("digest_daily_count", 0) <= 0:
skipped += 1
@@ -350,7 +361,8 @@ class MemberContextService:
)
return {"refreshed": refreshed, "skipped": skipped, "active_candidates": len(active_members)}
def refresh_all_chatrooms(self, days: Optional[int] = None, limit_per_member: Optional[int] = None) -> Dict:
def refresh_all_chatrooms(self, days: Optional[int] = None, limit_per_member: Optional[int] = None,
enable_weekly_digest: bool = True, enable_monthly_digest: bool = True) -> Dict:
days = days or self.sample_days
limit_per_member = limit_per_member or self.refresh_limit_per_member
@@ -386,7 +398,13 @@ class MemberContextService:
self.LOG.info(
f"[成员交互摘要] 批量刷新进度: group_index={processed_groups}/{total_groups}, group={chatroom_id}"
)
result = self.refresh_group_contexts(chatroom_id, days=days, limit_per_member=limit_per_member)
result = self.refresh_group_contexts(
chatroom_id,
days=days,
limit_per_member=limit_per_member,
enable_weekly_digest=enable_weekly_digest,
enable_monthly_digest=enable_monthly_digest,
)
if result.get("disabled"):
disabled += 1
continue

64
utils/decorator/async_job.py
View File

@@ -71,6 +71,70 @@ class AsyncJob:
return decorator
def every_week_time(self, weekday: int, time_str: str):
"""
每周 weekday（0=周一，6=周日） 的 time_str 时间执行
"""
def decorator(func: Callable):
async def wrapper():
while True:
now = datetime.now()
target_time = datetime.strptime(time_str, "%H:%M").time()
days_ahead = (weekday - now.weekday() + 7) % 7
target_date = now.date() + timedelta(days=days_ahead)
target_dt = datetime.combine(target_date, target_time)
if target_dt <= now:
target_dt += timedelta(days=7)
sleep_secs = (target_dt - now).total_seconds()
await asyncio.sleep(sleep_secs)
await func()
self.tasks.append(wrapper)
return func
return decorator
def every_month_last_day_time(self, time_str: str):
"""
每月最后一天的 time_str 时间执行
"""
def decorator(func: Callable):
async def wrapper():
while True:
now = datetime.now()
target_time = datetime.strptime(time_str, "%H:%M").time()
if now.month == 12:
next_month = datetime(now.year + 1, 1, 1)
else:
next_month = datetime(now.year, now.month + 1, 1)
last_day = next_month - timedelta(days=1)
target_dt = datetime.combine(last_day.date(), target_time)
if target_dt <= now:
if now.month == 12:
next_month = datetime(now.year + 1, 2, 1)
elif now.month == 11:
next_month = datetime(now.year + 1, 1, 1)
else:
next_month = datetime(now.year, now.month + 2, 1)
last_day = next_month - timedelta(days=1)
target_dt = datetime.combine(last_day.date(), target_time)
sleep_secs = (target_dt - now).total_seconds()
await asyncio.sleep(sleep_secs)
await func()
self.tasks.append(wrapper)
return func
return decorator
async def run_all(self):
await asyncio.gather(*(task() for task in self.tasks))