@@ -0,0 +1,452 @@
# -*- coding: utf-8 -*-
import json
import math
import re
from collections import Counter
from datetime import datetime
from typing import Dict , List , Optional
import requests
from loguru import logger
from db . connection import DBConnectionManager
from db . contacts_db import ContactsDBOperator
from db . member_context_db import MemberContextDBOperator
from db . message_storage import MessageStorageDB
from utils . robot_cmd . robot_command import Feature , GroupBotManager , PermissionStatus
class MemberContextService :
""" 成员交互摘要插件内部服务 """
FEATURE_KEY = " MEMBER_CONTEXT_CAPABILITY "
STOPWORDS = {
" 这个 " , " 那个 " , " 就是 " , " 然后 " , " 怎么 " , " 什么 " , " 你们 " , " 我们 " , " 他们 " , " 是不是 " , " 可以 " ,
" 一下 " , " 一个 " , " 已经 " , " 还有 " , " 没有 " , " 因为 " , " 所以 " , " 如果 " , " 但是 " , " 还是 " , " 今天 " ,
" 昨天 " , " 现在 " , " 时候 " , " 感觉 " , " 真的 " , " 应该 " , " 知道 " , " 觉得 " , " 问题 " , " 老师 " , " 老板 " ,
" 群里 " , " 大家 " , " 一下子 " , " 自己 " , " 东西 " , " 这里 " , " 那里 " , " 进行 " , " 需要 " , " 关于 "
}
def __init__ ( self , db_manager : DBConnectionManager , plugin_config : Optional [ Dict ] = None ) :
self . db_manager = db_manager
self . contacts_db = ContactsDBOperator ( self . db_manager )
self . message_db = MessageStorageDB ( self . db_manager )
self . member_context_db = MemberContextDBOperator ( self . db_manager )
self . LOG = logger
self . plugin_config = plugin_config or { }
api_config = self . plugin_config . get ( " api " , { } )
profile_config = self . plugin_config . get ( " profile " , { } )
self . ai_enabled = bool ( api_config . get ( " enabled " , False ) )
self . ai_base_url = ( api_config . get ( " base_url " ) or " " ) . rstrip ( " / " )
self . ai_api_key = api_config . get ( " api_key " , " " )
self . ai_endpoint = str ( api_config . get ( " endpoint " , " completion-messages " ) ) . lstrip ( " / " )
self . ai_timeout = int ( api_config . get ( " request_timeout " , 60 ) )
self . sample_days = int ( profile_config . get ( " sample_days " , 30 ) )
self . ai_sample_limit = int ( profile_config . get ( " sample_message_limit " , 80 ) )
self . refresh_limit_per_member = int ( profile_config . get ( " refresh_limit_per_member " , 200 ) )
schedule_config = self . plugin_config . get ( " schedule " , { } )
self . only_recent_active_groups = bool ( schedule_config . get ( " only_recent_active_groups " , False ) )
self . active_hours = int ( schedule_config . get ( " active_hours " , 72 ) )
self . min_group_messages = int ( schedule_config . get ( " min_group_messages " , 20 ) )
def build_member_context ( self , chatroom_id : str , wxid : str , days : Optional [ int ] = None ,
limit : Optional [ int ] = None ) - > Dict :
days = days or self . sample_days
limit = limit or self . refresh_limit_per_member
member = self . contacts_db . get_chatroom_member_info ( chatroom_id , wxid ) or { }
messages = self . message_db . get_member_recent_messages ( chatroom_id , wxid , days = days , limit = limit )
recent_messages = self . message_db . get_member_recent_messages ( chatroom_id , wxid , days = min ( days , 7 ) , limit = 100 )
display_name = member . get ( " display_name " ) or member . get ( " nick_name " ) or wxid
activity_level = self . _calc_activity_level ( len ( messages ) , days )
message_pattern = self . _build_message_pattern ( messages )
response_style_hint = self . _build_response_style_hint ( messages )
topics = self . _extract_keywords ( messages , limit = 5 )
recent_focus = self . _extract_keywords ( recent_messages , limit = 4 )
confidence = self . _calc_confidence ( len ( messages ) )
context = {
" chatroom_id " : chatroom_id ,
" wxid " : wxid ,
" display_name " : display_name ,
" activity_level " : activity_level ,
" message_pattern " : message_pattern ,
" interaction_style " : self . _build_interaction_style ( messages ) ,
" response_style_hint " : response_style_hint ,
" topics_of_interest " : topics ,
" recent_focus " : recent_focus ,
" summary_text " : self . _build_summary_text ( activity_level , message_pattern , response_style_hint , topics , recent_focus ) ,
" confidence " : confidence ,
" source_message_count " : len ( messages ) ,
" source_days " : days ,
" last_profiled_at " : datetime . now ( ) . strftime ( " % Y- % m- %d % H: % M: % S " ) ,
" meta " : self . _build_meta ( messages , recent_messages ) ,
}
ai_context = self . _generate_ai_context ( chatroom_id , wxid , display_name , context , messages )
if ai_context :
context . update ( {
" activity_level " : ai_context . get ( " activity_level " ) or context [ " activity_level " ] ,
" message_pattern " : ai_context . get ( " message_pattern " ) or context [ " message_pattern " ] ,
" interaction_style " : ai_context . get ( " interaction_style " ) or context [ " interaction_style " ] ,
" response_style_hint " : ai_context . get ( " response_style_hint " ) or context [ " response_style_hint " ] ,
" topics_of_interest " : ai_context . get ( " topics_of_interest " ) or context [ " topics_of_interest " ] ,
" recent_focus " : ai_context . get ( " recent_focus " ) or context [ " recent_focus " ] ,
" summary_text " : ai_context . get ( " summary_text " ) or context [ " summary_text " ] ,
" confidence " : ai_context . get ( " confidence " , context [ " confidence " ] ) ,
} )
context [ " meta " ] . update ( ai_context . get ( " meta " , { } ) )
return context
def refresh_member_context ( self , chatroom_id : str , wxid : str , days : Optional [ int ] = None ,
limit : Optional [ int ] = None ) - > 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 " )
context = self . build_member_context ( chatroom_id , wxid , days = days , limit = limit )
self . member_context_db . save_member_context ( context )
return context
def refresh_group_contexts ( self , chatroom_id : str , days : Optional [ int ] = None ,
limit_per_member : Optional [ int ] = None ) - > Dict :
days = days or self . sample_days
limit_per_member = limit_per_member or self . refresh_limit_per_member
if not self . is_group_enabled ( chatroom_id ) :
self . LOG . info ( f " 群 { chatroom_id } 未启用成员交互摘要功能，跳过刷新 " )
return { " refreshed " : 0 , " skipped " : 0 , " disabled " : True }
members = self . contacts_db . get_chatroom_member_list ( chatroom_id ) or [ ]
refreshed = 0
skipped = 0
for member in members :
if member . get ( " status " , 1 ) != 1 :
continue
wxid = member . get ( " wxid " )
if not wxid :
continue
context = self . build_member_context ( chatroom_id , wxid , days = days , limit = limit_per_member )
if context [ " source_message_count " ] < = 0 :
skipped + = 1
continue
self . member_context_db . save_member_context ( context )
refreshed + = 1
return { " refreshed " : refreshed , " skipped " : skipped }
def refresh_all_chatrooms ( self , days : Optional [ int ] = None , limit_per_member : Optional [ int ] = None ) - > Dict :
days = days or self . sample_days
limit_per_member = limit_per_member or self . refresh_limit_per_member
groups = self . contacts_db . get_chatroom_list ( ) or [ ]
active_group_ids = self . _get_recent_active_chatrooms ( ) if self . only_recent_active_groups else None
group_count = 0
member_count = 0
skipped = 0
disabled = 0
inactive = 0
for group in groups :
chatroom_id = group . get ( " chatroom_id " )
if not chatroom_id :
continue
if active_group_ids is not None and chatroom_id not in active_group_ids :
inactive + = 1
continue
result = self . refresh_group_contexts ( chatroom_id , days = days , limit_per_member = limit_per_member )
if result . get ( " disabled " ) :
disabled + = 1
continue
group_count + = 1
member_count + = result [ " refreshed " ]
skipped + = result [ " skipped " ]
self . LOG . info ( f " 成员交互摘要刷新完成: 启用活跃群= { group_count } , 成员= { member_count } , 跳过= { skipped } , 未启用群= { disabled } , 非活跃群= { inactive } " )
return { " groups " : group_count , " members " : member_count , " skipped " : skipped , " disabled_groups " : disabled , " inactive_groups " : inactive }
def is_group_enabled ( self , chatroom_id : str ) - > bool :
feature = Feature . get_feature ( self . FEATURE_KEY )
if feature is None :
return True
return GroupBotManager . get_group_permission ( chatroom_id , feature ) == PermissionStatus . ENABLED
def _calc_activity_level ( self , message_count : int , days : int ) - > str :
daily_avg = message_count / max ( days , 1 )
if message_count > = 80 or daily_avg > = 3 :
return " 高活跃 "
if message_count > = 25 or daily_avg > = 1 :
return " 中活跃 "
if message_count > 0 :
return " 低活跃 "
return " 观察中 "
def _build_message_pattern ( self , messages : List [ Dict ] ) - > str :
if not messages :
return " 样本较少，暂不做明显模式判断 "
contents = [ m . get ( " content " , " " ) for m in messages if m . get ( " content " ) ]
if not contents :
return " 样本较少，暂不做明显模式判断 "
avg_len = sum ( len ( c ) for c in contents ) / len ( contents )
question_ratio = sum ( 1 for c in contents if " ? " in c or " ？ " in c ) / len ( contents )
link_ratio = sum ( 1 for c in contents if " http:// " in c or " https:// " in c ) / len ( contents )
traits = [ ]
if avg_len < = 12 :
traits . append ( " 短句居多 " )
elif avg_len > = 35 :
traits . append ( " 表达较完整 " )
else :
traits . append ( " 表达中等长度 " )
if question_ratio > = 0.35 :
traits . append ( " 问题导向明显 " )
elif question_ratio > = 0.15 :
traits . append ( " 偶尔连续追问 " )
if link_ratio > = 0.15 :
traits . append ( " 常分享链接或资料 " )
if not traits :
traits . append ( " 发言较平稳 " )
return " ， " . join ( traits )
def _build_response_style_hint ( self , messages : List [ Dict ] ) - > str :
if not messages :
return " 样本不足时保持中性、简洁、避免过度熟络 "
contents = [ m . get ( " content " , " " ) for m in messages if m . get ( " content " ) ]
avg_len = sum ( len ( c ) for c in contents ) / max ( len ( contents ) , 1 )
question_ratio = sum ( 1 for c in contents if " ? " in c or " ？ " in c ) / max ( len ( contents ) , 1 )
if question_ratio > = 0.35 :
return " 优先给明确结论，再补充步骤或依据，避免空泛回应 "
if avg_len < = 12 :
return " 回复尽量简洁直接，先回答核心点，减少铺垫 "
if avg_len > = 35 :
return " 可以给稍完整的解释，但保持结构清楚，避免冗长 "
return " 保持自然口语化，结论和解释尽量平衡 "
def _build_interaction_style ( self , messages : List [ Dict ] ) - > str :
if not messages :
return " 互动样本较少 "
contents = [ m . get ( " content " , " " ) for m in messages if m . get ( " content " ) ]
question_ratio = sum ( 1 for c in contents if " ? " in c or " ？ " in c ) / max ( len ( contents ) , 1 )
emoji_ratio = sum ( 1 for c in contents if re . search ( r " [ \ U0001F300- \ U0001FAFF \ u2600- \ u27BF] " , c ) ) / max ( len ( contents ) , 1 )
mention_ratio = sum ( 1 for c in contents if " @ " in c ) / max ( len ( contents ) , 1 )
parts = [ ]
if question_ratio > = 0.3 :
parts . append ( " 偏提问推进 " )
if emoji_ratio > = 0.15 :
parts . append ( " 表情互动感较强 " )
if mention_ratio > = 0.1 :
parts . append ( " 会主动点名互动 " )
if not parts :
parts . append ( " 自然跟随式互动 " )
return " ， " . join ( parts )
def _extract_keywords ( self , messages : List [ Dict ] , limit : int = 5 ) - > List [ str ] :
counter = Counter ( )
for message in messages :
content = message . get ( " content " , " " )
for token in self . _tokenize ( content ) :
if token in self . STOPWORDS :
continue
counter [ token ] + = 1
return [ word for word , _ in counter . most_common ( limit ) ]
def _tokenize ( self , text : str ) - > List [ str ] :
chinese_words = re . findall ( r " [ \ u4e00- \ u9fff] { 2,6} " , text )
english_words = re . findall ( r " [A-Za-z][A-Za-z0-9_-] { 2,20} " , text )
return chinese_words + [ word . lower ( ) for word in english_words ]
def _calc_confidence ( self , message_count : int ) - > float :
return round ( min ( 0.95 , math . log ( message_count + 1 , 10 ) ) , 2 ) if message_count > 0 else 0.1
def _build_summary_text ( self , activity_level : str , message_pattern : str ,
response_style_hint : str , topics : List [ str ] , recent_focus : List [ str ] ) - > str :
parts = [
f " 近期互动强度： { activity_level } " ,
f " 表达特征： { message_pattern } " ,
f " 回复建议： { response_style_hint } " ,
]
if topics :
parts . append ( f " 长期关注： { ' 、 ' . join ( topics ) } " )
if recent_focus :
parts . append ( f " 近期话题： { ' 、 ' . join ( recent_focus ) } " )
return " ； " . join ( parts )
def _build_meta ( self , messages : List [ Dict ] , recent_messages : List [ Dict ] ) - > Dict :
latest_time = None
if recent_messages :
latest = recent_messages [ - 1 ] . get ( " timestamp " )
if isinstance ( latest , datetime ) :
latest_time = latest . strftime ( " % Y- % m- %d % H: % M: % S " )
elif latest :
latest_time = str ( latest )
return {
" message_count_30d " : len ( messages ) ,
" message_count_7d " : len ( recent_messages ) ,
" latest_message_time " : latest_time ,
}
def _get_recent_active_chatrooms ( self ) - > set :
sql = """
SELECT group_id, COUNT(*) AS msg_count
FROM messages
WHERE group_id LIKE %s
AND timestamp >= DATE_SUB(NOW(), INTERVAL %s HOUR)
GROUP BY group_id
HAVING COUNT(*) >= %s
"""
rows = self . message_db . execute_query ( sql , ( " % @chatroom " , self . active_hours , self . min_group_messages ) ) or [ ]
return { row . get ( " group_id " ) for row in rows if row . get ( " group_id " ) }
def _generate_ai_context ( self , chatroom_id : str , wxid : str , display_name : str ,
base_context : Dict , messages : List [ Dict ] ) - > Optional [ Dict ] :
if not self . ai_enabled or not self . ai_base_url or not self . ai_api_key :
return None
if len ( messages ) < 8 :
return None
prompt = self . _build_ai_prompt ( chatroom_id , wxid , display_name , base_context , messages [ - self . ai_sample_limit : ] )
headers = {
" Authorization " : f " Bearer { self . ai_api_key } " ,
" Content-Type " : " application/json " ,
}
payload = {
" inputs " : { " query " : prompt } ,
" response_mode " : " blocking " ,
" user " : f " member-context: { chatroom_id } : { wxid } " ,
}
url = f " { self . ai_base_url } / { self . ai_endpoint } "
try :
response = requests . post ( url , headers = headers , json = payload , timeout = self . ai_timeout )
response . raise_for_status ( )
response_data = response . json ( )
parsed = self . _parse_ai_answer ( response_data . get ( " answer " , " " ) )
if not parsed :
return None
usage = ( response_data . get ( " metadata " ) or { } ) . get ( " usage " , { } ) or { }
parsed [ " meta " ] = {
" ai_provider " : " dify " ,
" ai_mode " : " completion " ,
" ai_tokens " : usage . get ( " total_tokens " ) ,
" ai_latency " : usage . get ( " latency " ) ,
}
return parsed
except Exception as e :
self . LOG . warning ( f " 成员交互摘要 AI 生成失败，回退到本地摘要: chatroom= { chatroom_id } , wxid= { wxid } , error= { e } " )
return None
def _build_ai_prompt ( self , chatroom_id : str , wxid : str , display_name : str ,
base_context : Dict , messages : List [ Dict ] ) - > str :
message_lines = [ ]
for msg in messages [ - 40 : ] :
ts = msg . get ( " timestamp " )
if isinstance ( ts , datetime ) :
ts = ts . strftime ( " % m- %d % H: % M " )
content = ( msg . get ( " content " ) or " " ) . replace ( " \n " , " " ) . strip ( )
content = content [ : 160 ]
if content :
message_lines . append ( f " [ { ts } ] { content } " )
topics = " 、 " . join ( base_context . get ( " topics_of_interest " , [ ] ) ) or " 无明显长期话题 "
recent_focus = " 、 " . join ( base_context . get ( " recent_focus " , [ ] ) ) or " 无明显近期话题 "
return (
" 你是一个微信群运营后台的成员交互摘要提取器。 \n "
" 你的任务不是做人设分析，也不是性格判断，而是基于公开聊天记录，提取对后续回复策略有帮助的“交互特征摘要”。 \n "
" 你只能依据给定聊天样本输出保守结论，不能脑补，不能做敏感推断，不能写负面标签，不能输出隐私猜测。 \n "
" 请根据以下成员近30天公开发言，输出一个严格 JSON 对象，不要 markdown，不要解释，不要代码块。 \n "
" JSON schema: \n "
" { "
" \" activity_level \" : \" 高活跃|中活跃|低活跃|观察中 \" , "
" \" message_pattern \" : \" 一句中文，描述表达特点 \" , "
" \" interaction_style \" : \" 一句中文，描述他在群里如何与人互动 \" , "
" \" response_style_hint \" : \" 一句中文，描述适合怎样回应 \" , "
" \" topics_of_interest \" :[ \" 主题1 \" , \" 主题2 \" ], "
" \" recent_focus \" :[ \" 近期主题1 \" , \" 近期主题2 \" ], "
" \" summary_text \" : \" 一段不超过120字的后台交互摘要 \" , "
" \" confidence \" :0.0, "
" \" engagement_traits \" :[ \" 特征1 \" , \" 特征2 \" ], "
" \" reply_taboos \" :[ \" 避坑1 \" , \" 避坑2 \" ] "
" } \n "
" 要求： \n "
" 1. 只总结群内公开行为特征，不要输出性格诊断、负面标签或敏感结论。 \n "
" 2. topics_of_interest 表示相对稳定的话题偏好，最多5个；recent_focus 表示近期频繁提及的话题，最多4个。 \n "
" 3. message_pattern 只能描述可观察到的表达方式，例如：短句居多、问题导向、爱发链接、解释较完整、常接梗互动。 \n "
" 4. interaction_style 要描述他在群里的参与方式，例如：偏围观后插话、喜欢接梗、会连续追问、偏一对一回应。 \n "
" 5. response_style_hint 只能写对回复策略有帮助的建议，例如：先给结论再补步骤、保持简洁直接、可以适度接梗；不要写成评价语。 \n "
" 6. engagement_traits 最多4个，写成中性的短标签，例如：节奏快、爱追问细节、接梗自然、偏结果导向。 \n "
" 7. reply_taboos 最多3个，只写回复时应避免的方式，例如：避免长篇铺垫、避免过度说教、避免太官方。 \n "
" 8. summary_text 要像后台备注，客观、中性、克制，不要让人一眼看出是在给用户贴标签。 \n "
" 9. confidence 取值 0 到 1；如果样本较少或不稳定，必须降低 confidence。 \n "
" 10. 如果证据不足，宁可输出更弱、更泛化的结论，也不要瞎猜。 \n \n "
" 下面是正反例参考。 \n "
" 坏例子：这个人情绪化、爱抬杠、虚荣、玻璃心。 \n "
" 好例子：常用短句直接表达观点；遇到问题时更适合先给明确结论，再补充解释。 \n \n "
f " 成员标识: { display_name } ( { wxid } ) \n "
f " 群ID: { chatroom_id } \n "
f " 样本消息数: { base_context . get ( ' source_message_count ' , 0 ) } \n "
f " 本地活跃度估计: { base_context . get ( ' activity_level ' , ' ' ) } \n "
f " 本地表达特征: { base_context . get ( ' message_pattern ' , ' ' ) } \n "
f " 本地互动风格: { base_context . get ( ' interaction_style ' , ' ' ) } \n "
f " 本地回复建议: { base_context . get ( ' response_style_hint ' , ' ' ) } \n "
f " 本地长期关注: { topics } \n "
f " 本地近期话题: { recent_focus } \n "
" 最近消息样本: \n "
+ " \n " . join ( message_lines )
)
def _parse_ai_answer ( self , answer : str ) - > Optional [ Dict ] :
if not answer :
return None
text = answer . strip ( )
match = re . search ( r " \ { .* \ } " , text , re . S )
if match :
text = match . group ( 0 )
try :
data = json . loads ( text )
except Exception :
return None
topics = data . get ( " topics_of_interest " ) or [ ]
recent_focus = data . get ( " recent_focus " ) or [ ]
engagement_traits = data . get ( " engagement_traits " ) or [ ]
reply_taboos = data . get ( " reply_taboos " ) or [ ]
if not isinstance ( topics , list ) :
topics = [ ]
if not isinstance ( recent_focus , list ) :
recent_focus = [ ]
if not isinstance ( engagement_traits , list ) :
engagement_traits = [ ]
if not isinstance ( reply_taboos , list ) :
reply_taboos = [ ]
try :
confidence = float ( data . get ( " confidence " , 0 ) )
except Exception :
confidence = 0.0
return {
" activity_level " : str ( data . get ( " activity_level " , " " ) ) . strip ( ) ,
" message_pattern " : str ( data . get ( " message_pattern " , " " ) ) . strip ( ) ,
" interaction_style " : str ( data . get ( " interaction_style " , " " ) ) . strip ( ) ,
" response_style_hint " : str ( data . get ( " response_style_hint " , " " ) ) . strip ( ) ,
" topics_of_interest " : [ str ( item ) . strip ( ) for item in topics [ : 5 ] if str ( item ) . strip ( ) ] ,
" recent_focus " : [ str ( item ) . strip ( ) for item in recent_focus [ : 4 ] if str ( item ) . strip ( ) ] ,
" summary_text " : str ( data . get ( " summary_text " , " " ) ) . strip ( ) ,
" confidence " : max ( 0.0 , min ( 1.0 , confidence ) ) ,
" meta " : {
" engagement_traits " : [ str ( item ) . strip ( ) for item in engagement_traits [ : 4 ] if str ( item ) . strip ( ) ] ,
" reply_taboos " : [ str ( item ) . strip ( ) for item in reply_taboos [ : 3 ] if str ( item ) . strip ( ) ] ,
}
}
liuwei