soenmie

AppLogV2_CL
| where TimeGenerated >= ago(7d)
| where location_s has "call_decision_helpers.py" and Level == "ERROR"
| summarize ErrorCount = count() by bin(TimeGenerated, 1h)
| order by TimeGenerated asc
| render columnchart with (title="Error Count - Past 7 Days", xtitle="Time", ytitle="Error Count")

soenmie

let FilteredData = LiteLLMLog_CL
      | where TimeGenerated >= datetime(2025-07-25 00:00:00)
      | where model_s !in (
          'text-embedding-3-large',
          'openai/text-moderation-stable',
          'deepseek-r1-distill-llama-70b-specdec',
          'gemini-1.5-flash'
      )
      | where caller_tag_s in (
          'data_visualization',

soenmie

#!/usr/bin/env python3
"""
Sample 10 random Python tool calls from evaluation results
"""

import json
import random
from pathlib import Path
from typing import Dict, List, Any

soenmie

#!/bin/bash

# Batch evaluate tool usage for all evaluation prompt files with parallel processing
# Usage: ./batch_evaluate_tool_usage.sh [num_parallel_jobs] [limit]

# Number of parallel jobs (default: 4)
PARALLEL_JOBS=${1:-4}

# Optional limit on number of files to process (default: all)
LIMIT=${2:-0}

soenmie

#!/bin/bash

# Generate evaluation prompts for all weighted project JSON files
# Usage: ./generate_all_evaluation_prompts.sh [num_parallel_jobs]

# Number of parallel jobs (default: 4)
PARALLEL_JOBS=${1:-4}

echo "🚀 Starting batch evaluation prompt generation..."
echo "Parallel jobs: $PARALLEL_JOBS"

soenmie

{% set messages = data.session_state.messages -%}
# Conversation
{%- for message in messages %}

{%- if message.role == "user" %}
## User
{%- if message.content is string %}
{{ message.content }}
{%- elif message.content is iterable %}
{%- for item in message.content %}

soenmie

#!/usr/bin/env python3
"""
从 weighted_sampling_projects.csv 中获取所有项目的JSON数据
"""

import asyncio
import csv
import json
import os
import sys

soenmie

#!/usr/bin/env python3
"""
按照频次加权随机采样分析BLOCKING_SAVE日志
"""

import sys
import random
from pathlib import Path

soenmie

import pandas as pd

# 读取数据
df = pd.read_csv('query_data.csv')

# 只分析jupyter_code_executor的时间趋势
jupyter_executor = df[df['caller_tag_s'] == 'jupyter_code_executor'].copy()
jupyter_executor['date'] = pd.to_datetime(jupyter_executor['TimeGenerated [UTC]'])
jupyter_executor = jupyter_executor.sort_values('date')

soenmie

#!/usr/bin/env python3
"""
分析AI过度确认问题，提取带时间戳的原始对话
找出餐厅说"请稍等"后AI仍然打扰的案例
"""

import json
from typing import Dict, List, Optional
from datetime import datetime