feat: add bot token usage analyzer

client/app/services/TokensController.ts

@@ -16,4 +16,20 @@ export async function deleteToken(id: string) {
 export async function createToken(data: LLMTokenInsert) {
   const response = await axios.post(`${apiDomain}/api/user/llm_token`, data);
   return response.data;
-}
+}
+
+export async function analyzeTokenUsage() {
+  const response = await axios.get(`${apiDomain}/api/user/llm_token_usages/analyzer`);
+  return response.data;
+}
+
+export async function analyzeTopBots() {
+  const response = await axios.get(`${apiDomain}/api/user/llm_token_usages/top_bots`);
+  return response.data;
+}
+
+export async function analyzeTopUsers() {
+  const response = await axios.get(`${apiDomain}/api/user/llm_token_usages/top_users`);
+  return response.data;
+}
+

client/package.json

@@ -17,6 +17,8 @@
     "node": ">=18"
   },
   "dependencies": {
+    "@ant-design/charts": "^2.2.6",
+    "@antv/g2": "^5.2.10",
     "@auth0/nextjs-auth0": "^3.3.0",
     "@fingerprintjs/fingerprintjs": "^4.3.0",
     "@fullpage/react-fullpage": "^0.1.42",

migrations/supabase/migrations/20241224095717_remote_schema.sql

@@ -0,0 +1,84 @@
+drop function if exists "public"."analyze_user_token_usage"(start_date date, end_date date);
+
+alter table "public"."profiles" add column "is_admin" boolean default false;
+
+set check_function_bodies = off;
+
+CREATE OR REPLACE FUNCTION public.bot_token_usage_rate(start_date date, end_date date)
+ RETURNS TABLE(bot_id text, bot_name text, input_tokens bigint, output_tokens bigint, total_tokens bigint)
+ LANGUAGE plpgsql
+AS $function$
+BEGIN
+    RETURN QUERY
+    SELECT * from (
+SELECT
+        u.bot_id AS bot_id,
+        COALESCE(b.name, '未命名')::text as bot_name,
+        SUM(u.input_token)::BIGINT AS input_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.output_token)::BIGINT AS output_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.total_token)::BIGINT AS total_tokens  -- 将结果转换为 BIGINT
+    FROM user_token_usage u
+    LEFT JOIN bots b
+        ON b.id::text = u.bot_id
+    GROUP BY u.bot_id, b.name
+) bu
+ORDER BY total_tokens DESC
+limit 3;
+END;
+$function$
+;
+
+CREATE OR REPLACE FUNCTION public.user_token_usage_rate(start_date date, end_date date)
+ RETURNS TABLE(user_id text, user_name text, input_tokens bigint, output_tokens bigint, total_tokens bigint)
+ LANGUAGE plpgsql
+AS $function$
+BEGIN
+    RETURN QUERY
+    SELECT * from (
+SELECT
+        u.user_id AS user_id,
+        COALESCE(p.name, '匿名用户')::text as user_name,
+        SUM(u.input_token)::BIGINT AS input_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.output_token)::BIGINT AS output_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.total_token)::BIGINT AS total_tokens  -- 将结果转换为 BIGINT
+    FROM user_token_usage u
+    LEFT JOIN profiles p
+        ON p.id = u.user_id
+    WHERE 
+        u.token_id = 'DEFAULT_TOKEN' AND
+        u.date >= start_date AND
+        u.date <= end_date
+    GROUP BY u.user_id, p.name
+) bu
+ORDER BY total_tokens DESC
+limit 3;
+END;
+$function$
+;
+
+CREATE OR REPLACE FUNCTION public.analyze_user_token_usage(start_date date, end_date date)
+ RETURNS TABLE(bot_id text, bot_name text, usage_date date, input_tokens bigint, output_tokens bigint, total_tokens bigint)
+ LANGUAGE plpgsql
+AS $function$
+BEGIN
+    RETURN QUERY
+    SELECT
+        u.bot_id AS bot_id,
+        COALESCE(b.name, '未命名')::text as bot_name,
+        Date(u.date) AS usage_date,  -- 使用别名来避免歧义
+        SUM(u.input_token)::BIGINT AS input_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.output_token)::BIGINT AS output_tokens,  -- 将结果转换为 BIGINT
+        SUM(u.total_token)::BIGINT AS total_tokens  -- 将结果转换为 BIGINT
+    FROM user_token_usage u
+    LEFT JOIN bots b
+        ON b.id::text = u.bot_id
+    WHERE 
+        u.date >= start_date AND
+        u.date <= end_date
+    GROUP BY u.date, u.bot_id, b.name
+    ORDER by u.date DESC;
+END;
+$function$
+;
+
+

server/core/dao/userTokenUsageDAO.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 from supabase.client import Client
 
-from core.models.user_token_usage import BotTokenUsageStats, UserTokenUsage, UserTokenUsageStats
+from core.models.user_token_usage import BotTokenUsageRate, BotTokenUsageStats, UserTokenUsage, UserTokenUsageRate, UserTokenUsageStats
 from petercat_utils.db.client.supabase import get_client
 from core.dao.BaseDAO import BaseDAO
 
@@ -36,4 +36,21 @@ def analyze(self, start_date: datetime, end_date: datetime):
             "end_date": end_date.strftime("%Y-%m-%d"),
         }).execute()
 
-        return [BotTokenUsageStats(**stats) for stats in resp.data]
+        return [BotTokenUsageStats(**stats) for stats in resp.data]
+
+    def top_bots(self, start_date: datetime, end_date: datetime):
+        resp = self.client.rpc("bot_token_usage_rate", {
+            "start_date": start_date.strftime("%Y-%m-%d"),
+            "end_date": end_date.strftime("%Y-%m-%d"),
+        }).execute()
+
+        return [BotTokenUsageRate(**stats) for stats in resp.data]
+
+    def top_users(self, start_date: datetime, end_date: datetime):
+        resp = self.client.rpc("user_token_usage_rate", {
+            "start_date": start_date.strftime("%Y-%m-%d"),
+            "end_date": end_date.strftime("%Y-%m-%d"),
+        }).execute()
+
+        return [UserTokenUsageRate(**stats) for stats in resp.data]
+

server/core/models/user_token_usage.py

@@ -23,7 +23,22 @@ class UserTokenUsageStats(BaseModel):
 
 class BotTokenUsageStats(BaseModel):
     bot_id: str
+    bot_name: str
     usage_date: datetime
     input_tokens: Optional[int] = 0
     output_tokens: Optional[int] = 0
+    total_tokens: Optional[int] = 0
+
+class BotTokenUsageRate(BaseModel):
+    bot_id: str
+    bot_name: str
+    input_tokens: Optional[int] = 0
+    output_tokens: Optional[int] = 0
+    total_tokens: Optional[int] = 0
+
+class UserTokenUsageRate(BaseModel):
+    user_id: str
+    user_name: str
+    input_tokens: Optional[int] = 0
+    output_tokens: Optional[int] = 0
     total_tokens: Optional[int] = 0

server/core/service/user_token_usage.py

@@ -27,6 +27,12 @@ def usage_stats(self, user_id: str, start_date: datetime.date, end_date: datetim
   def analyze_token_usage(self, start_date: datetime.date, end_date: datetime.date):
     return self.user_token_usage_dao.analyze(start_date=start_date, end_date=end_date)
 
+  def top_bots(self, start_date: datetime.date, end_date: datetime.date):
+    return self.user_token_usage_dao.top_bots(start_date=start_date, end_date=end_date)
+
+  def top_users(self, start_date: datetime.date, end_date: datetime.date):
+    return self.user_token_usage_dao.top_users(start_date=start_date, end_date=end_date)
+
 def get_user_token_usage_service():
   return UserTokenUsageService()
 

server/user/router.py

@@ -69,8 +69,24 @@ def token_usage(
 
 @router.get("/llm_token_usages/analyzer")
 def token_usage_analyze(
-  start_date: datetime = datetime.now() - timedelta(days=7),
+  start_date: datetime = datetime.now() - timedelta(days=30),
   end_date: datetime = datetime.now(),
   user_token_usage_service: Annotated[UserTokenUsageService | None, Depends(get_user_token_usage_service)] = None,
 ):
-  return user_token_usage_service.analyze_token_usage(start_date=start_date, end_date=end_date)
+  return user_token_usage_service.analyze_token_usage(start_date=start_date, end_date=end_date)
+
+@router.get("/llm_token_usages/top_bots")
+def top_used_bots(
+  start_date: datetime = datetime.now() - timedelta(days=30),
+  end_date: datetime = datetime.now(),
+  user_token_usage_service: Annotated[UserTokenUsageService | None, Depends(get_user_token_usage_service)] = None,
+):
+  return user_token_usage_service.top_bots(start_date=start_date, end_date=end_date)
+
+@router.get("/llm_token_usages/top_users")
+def top_used_users(
+  start_date: datetime = datetime.now() - timedelta(days=30),
+  end_date: datetime = datetime.now(),
+  user_token_usage_service: Annotated[UserTokenUsageService | None, Depends(get_user_token_usage_service)] = None,
+):
+  return user_token_usage_service.top_users(start_date=start_date, end_date=end_date)