夜雨聆风🚀 Day 13: 成本勘算 —— 真实环境下的 API Token 成本提取
今日目标：由于不同大模型厂商对 Token 消耗量的回传格式完全不同（有的在 usage.total_tokens，有的分 input 和 output，有的甚至塞在 HTTP Headers 里），我们将编写一个强壮的多态提取函数 (Universal Token Extractor)。确保无论你未来对接哪个大模型，都能将每一次 AI 狩猎的真实资金成本精准记录进 Splunk，实现 100% 的财务可审计化！
💻 架构大纲：今天我们将如何重构“计费引擎”？
1. 废弃粗暴获取：移除之前单纯依赖 response_json.get("usage", {}).get("total_tokens", 0) 的脆弱写法。
2. 多厂商兼容字典遍历：
• OpenAI / 阿里 / DeepSeek 族：提取 usage.total_tokens。
• Anthropic / Claude 族：动态计算 usage.input_tokens + usage.output_tokens。
• 网关/代理族：从 HTTP 响应头（Headers）中提取 x-token-usage 等字段。
1. 安全回退机制 (Graceful Degradation)：即便 API 厂商大改版导致提取失败，使用 try-except 兜底返回 0，绝不允许因为计费失败导致核心安全阻断流程崩溃。
💻 终极实战：Day 13 FinOps 计费版全量代码
请打开 Add-on Builder 的 Define & Test 编辑器，用以下代码覆盖原有代码。
import os
import sys
import time
import datetime
import json
import uuid
import requests
import splunklib.client as client
import splunklib.results as results
# ==========================================
# HELPER 1: Execute AI Generated SPL
# ==========================================
def execute_ai_spl(helper, service, spl_query):
"""
ExecuteSPL generated by AI and return the raw result data.
"""
spl_query= spl_query.strip()
#Force the 'search' prefix to prevent syntax errors
ifnot spl_query.startswith("search") and not spl_query.startswith("|"):
spl_query= "search " + spl_query
kwargs_oneshot= {"output_mode": "json"}
helper.log_info(f"[AgenticEngine] Executing SPL: {spl_query}")
try:
search_results= service.jobs.oneshot(spl_query, **kwargs_oneshot)
reader= results.JSONResultsReader(search_results)
result_data= [res for res in reader if isinstance(res, dict)]
helper.log_info(f"[AgenticEngine] SUCCESS: Found {len(result_data)} events.")
returnresult_data
exceptException as e:
helper.log_error(f"[AgenticEngine] FAILED execution: {str(e)}")
return[]
# ==========================================
# HELPER 2: Fetch Real Logs (M-ATH Concept)
# ==========================================
def fetch_rare_logs(helper, service, target_index):
"""
Fetchthe most recent rare/anomalous logs from the target index to feed the AI.
"""
helper.log_info("Fetchingreal rare logs for analysis...")
#Fetching fresh data. Use cluster only if CPU permits, otherwise use head.
spl= f"search index={target_index} | head 5 | table _raw"
try:
results_data= execute_ai_spl(helper, service, spl)
ifnot results_data:
returnNone
#Extract the _raw strings and join them into a single text payload
raw_logs= [item.get("_raw", "") for item in results_data if "_raw" in item]
payload= "n".join(raw_logs)
#=========================================================================
#Context Distillation (Payload Truncation)
#Prevents massive Splunk logs from blowing up the LLM Context Window
#=========================================================================
MAX_CHARS= 6000 # Roughly equals 1500 Tokens
iflen(payload) > MAX_CHARS:
helper.log_info(f"Payloadtoo large ({len(payload)} chars). Truncating to {MAX_CHARS}...")
#Slice the string and append a clear signal for the LLM
payload= payload[:MAX_CHARS] + "nn...[TRUNCATED DUE TO CONTEXT LIMITS. ANALYZE AVAILABLE DATA ONLY.]..."
#=========================================================================
returnpayload
exceptException as e:
helper.log_error(f"Failedto fetch rare logs: {str(e)}")
returnNone
# =========================================================================
# [DAY 13 NEW]: Universal Token Extractor (FinOps Cost Tracking)
# =========================================================================
def extract_token_usage(helper, response_json, response_headers):
"""
Robustlyextract token usage across different LLM providers and API gateways.
EnsuresFinOps tracking never crashes the main thread.
"""
try:
#Strategy 1: OpenAI / DeepSeek / DashScope standard format
if"usage" in response_json:
usage= response_json["usage"]
if"total_tokens" in usage:
returnint(usage["total_tokens"])
#Strategy 2: Anthropic-style or granular input/output split
elif"prompt_tokens" in usage and "completion_tokens" in usage:
returnint(usage["prompt_tokens"]) + int(usage["completion_tokens"])
elif"input_tokens" in usage and "output_tokens" in usage:
returnint(usage["input_tokens"]) + int(usage["output_tokens"])
#Strategy 3: API Gateway headers (e.g., Azure, Cloudflare AI Gateway)
header_keys= [k.lower() for k in response_headers.keys()]
forkey in header_keys:
if"token-usage" in key or "x-ratelimit-usage" in key:
returnint(response_headers.get(key, 0))
exceptException as e:
helper.log_error(f"[FinOpsWarning] Failed to parse token usage correctly: {str(e)}")
#Graceful degradation: Return 0 if extraction fails, ensuring pipeline survival
return0
# ==========================================
# HELPER 3: The LLM API Connector
# ==========================================
# Added dynamic 'max_tokens' parameter to function signature
def call_llm_api(helper, api_key, base_url, model, system_prompt, user_prompt, max_tokens):
"""
Establishreal HTTP connection to the LLM API and return the JSON response.
"""
headers= {
"Authorization":f"Bearer {api_key}",
"Content-Type":"application/json"
}
payload= {
"model":model,
"messages":[
{"role":"system", "content": system_prompt},
{"role":"user", "content": user_prompt}
],
#Mandatory flag for modern LLMs to strictly output JSON
"response_format":{"type": "json_object"},
#Hardware-level output boundary (Token Circuit Breaker)
"max_tokens":max_tokens
}
#Ensure URL formatting is correct
endpoint= base_url if base_url.endswith("/chat/completions") else f"{base_url.rstrip('/')}/chat/completions"
try:
helper.log_info(f"Initiatingnetwork request to LLM API: {endpoint} (Max Tokens: {max_tokens})")
#120s timeout ensures deep-thinking models (CoT) have enough time
response= requests.post(endpoint, headers=headers, json=payload, timeout=120)
response.raise_for_status()
response_json= response.json()
llm_content= response_json["choices"][0]["message"]["content"]
#=========================================================================
#[DAY 13 MODIFIED]: Call the Universal Token Extractor
#=========================================================================
total_tokens= extract_token_usage(helper, response_json, response.headers)
helper.log_info(f"APICall Success. FinOps Tracked: {total_tokens} tokens consumed.")
returnllm_content, total_tokens
exceptrequests.exceptions.RequestException as e:
helper.log_error(f"Networkerror during API call: {str(e)}")
raise
# ==========================================
# MAIN WORKFLOW: The Autonomous Agent
# ==========================================
def collect_events(helper, ew):
"""
TheUltimate Live Workflow.
Features:Real API Integration, Unix Epoch Time injection, Anti-Hallucination, Truncation, and FinOps Tracking.
"""
helper.log_info("PEAKAI Hunter: LIVE MODE INITIALIZED.")
cycle_start_time= time.time()
#Generate a unique Session ID to stitch the flattened logs together
hunt_session_id= str(uuid.uuid4())
try:
#1. Acquire Splunk Service Session
session_key= getattr(helper, 'session_key', None) or getattr(helper._input_definition, 'metadata', {}).get('session_key')
ifnot session_key:
raiseValueError("Failed to acquire session_key.")
service= client.Service(token=session_key)
#2. Acquire Global Setup Configurations (API credentials)
api_key= helper.get_global_setting("api_key")
base_url= helper.get_global_setting("base_url")
model_name= helper.get_global_setting("model_name")
target_index= helper.get_output_index() or "main"
ifnot api_key or not base_url:
raiseValueError("API Key or Base URL is missing in Global Settings.")
#==========================================
#PHASE 1: PREPARE (Real LLM Call for Blueprint)
#==========================================
rare_logs_payload= fetch_rare_logs(helper, service, target_index)
ifnot rare_logs_payload:
helper.log_info("Noanomalous logs found to analyze. Terminating cycle early gracefully.")
return
#Prompt Distillation - Forcing extreme conciseness
sys_prompt_prepare= "You are a Senior Threat Hunter. You MUST reply in JSON format. Be extremely concise. No pleasantries. Schema requires: 'analysis' (string) and 'hypotheses' (array of objects). Each hypothesis must have 'hypothesis_id', 'ABLE' (Actor, Behavior, Location, Evidence), 'spl_round_1_validation', and 'spl_round_2_drilldown'."
#ANTI-HALLUCINATION FIX: Forcing the LLM to strictly use {target_index} parameter
usr_prompt_prepare= f"Analyze these real, rare logs from our environment:n{rare_logs_payload}nnGenerate exactly 2 hunting hypotheses. CRITICAL: For 'spl_round_1_validation' and 'spl_round_2_drilldown', you MUST strictly start your queries with 'search index={{target_index}}'. Do NOT guess or use real index names! Output ONLY JSON format."
helper.log_info("TriggeringLLM for Prepare Phase...")
#Pass max_tokens=1500 for generating SPLs
blueprint_text,prep_tokens = call_llm_api(helper, api_key, base_url, model_name, sys_prompt_prepare, usr_prompt_prepare, max_tokens=1500)
ai_hunting_plan= json.loads(blueprint_text.strip())
hypotheses= ai_hunting_plan.get("hypotheses", [])
#Write Plan to Splunk IMMEDIATELY (Injecting dynamic Unix Time)
ew.write_event(helper.new_event(
source=helper.get_input_type(),index=target_index, sourcetype="_json",
time=time.time(),# THE ULTIMATE TIMEZONE FIX
data=json.dumps({
"session_id":hunt_session_id,
"event_type":"PEAK_Plan",
"timestamp":round(time.time(), 3),
"content":ai_hunting_plan
},ensure_ascii=False)
))
#==========================================
#PHASE 2: EXECUTE (Agentic Splunk Query Loop)
#==========================================
all_hunt_evidence= []
fori, hyp in enumerate(hypotheses):
hyp_start= time.time()
spl_r1= hyp.get("spl_round_1_validation", "").replace("{target_index}", target_index)
spl_r2= hyp.get("spl_round_2_drilldown", "").replace("{target_index}", target_index)
r1_hits= len(execute_ai_spl(helper, service, spl_r1))
r2_hits= len(execute_ai_spl(helper, service, spl_r2))
all_hunt_evidence.append({
"hypothesis_id":hyp.get("hypothesis_id", i+1),
"threat_behavior":hyp.get('ABLE', {}).get('Behavior', 'Unknown'),
"round_1_hit_count":r1_hits,
"round_2_hit_count":r2_hits,
"execution_duration_sec":round(time.time() - hyp_start, 2)
})
#Write Evidence to Splunk IMMEDIATELY (Injecting dynamic Unix Time)
ew.write_event(helper.new_event(
source=helper.get_input_type(),index=target_index, sourcetype="_json",
time=time.time(),# THE ULTIMATE TIMEZONE FIX
data=json.dumps({
"session_id":hunt_session_id,
"event_type":"PEAK_Evidence",
"timestamp":round(time.time(), 3),
"content":all_hunt_evidence
},ensure_ascii=False)
))
#==========================================
#PHASE 3: ACT (Real LLM Call for Final Report)
#==========================================
#Concise prompt for Act Phase (Limits summary length)
sys_prompt_act= "You are a Security Director. Output ONLY valid JSON. Keep summaries under 30 words. Keys: 'executive_summary', 'threat_qualification' (Benign/Suspicious/Confirmed), 'risk_score' (0-100), 'recommended_alert_spl'."
usr_prompt_act= f"Here is the quantitative execution evidence collected by our agent:n{json.dumps(all_hunt_evidence)}nnBased on these hit counts, qualify the threat, assign a risk score, and generate an alert SPL. Reply in JSON format."
helper.log_info("TriggeringLLM for Act Phase...")
#Pass max_tokens=800 since this is just a short summary
report_text,act_tokens = call_llm_api(helper, api_key, base_url, model_name, sys_prompt_act, usr_prompt_act, max_tokens=800)
try:
final_report= json.loads(report_text.strip())
exceptjson.JSONDecodeError as e:
helper.log_error("JSONTruncation in Act Phase. Engaging fallback.")
final_report= {"executive_summary": "LLM output truncated.", "risk_score": -1, "raw": report_text}
#Write Final Report to Splunk (Injecting dynamic Unix Time)
ew.write_event(helper.new_event(
source=helper.get_input_type(),index=target_index, sourcetype="_json",
time=time.time(),# THE ULTIMATE TIMEZONE FIX
data=json.dumps({
"session_id":hunt_session_id,
"event_type":"PEAK_Final_Report",
"timestamp":round(time.time(), 3),
"total_tokens_used":prep_tokens + act_tokens,
"content":final_report
},ensure_ascii=False)
))
helper.log_info(f"LIVECYCLE COMPLETE. Time: {round(time.time() - cycle_start_time, 2)}s. Session ID: {hunt_session_id}")
exceptException as e:
helper.log_error(f"FATALPipeline Crash: {str(e)}")
💵 极客验证：将 Token 转化为真金白银
代码写好了，大模型不再是“糊涂账”了。现在，让我们在 Splunk 中体验一把精算师的快感！
1. 在 AOB 中保存代码并点击 Test 运行一次完整的流程。
2. 回到 Splunk 的 Search 界面。这一次，我们要在面板里引入一个惊艳的动态运算——把 Token 直接折算成美元成本（Cost USD）！ (假设我们以 GPT-4o-mini 或 Qwen 的平均价格，约为 $0.002 每 1000 个 Token 进行估算)
执行以下带有财务视角的终极 Dashboard SPL：
index=main sourcetype="_json" event_type="PEAK_Plan" OR event_type="PEAK_Evidence" OR event_type="PEAK_Final_Report"
| spath
| stats
min(timestamp)as Start_Time_Epoch,
max(timestamp)as End_Time_Epoch,
latest(content.risk_score)as Risk_Score,
latest(content.executive_summary)as Summary,
sum(content{}.round_1_hit_count)as Total_R1_Hits,
sum(content{}.round_2_hit_count)as Total_R2_Hits,
sum(total_tokens_used)as Total_Tokens
bysession_id
| eval Execution_Time_Sec = round(End_Time_Epoch - Start_Time_Epoch, 2)
| eval Start_Time = strftime(Start_Time_Epoch, "%Y-%m-%d %H:%M:%S")
| eval Cost_USD = "$" . tostring(round((Total_Tokens / 1000) * 0.002, 6))
| sort - Start_Time_Epoch
| table Start_Time, session_id, Risk_Score, Total_R1_Hits, Total_R2_Hits, Execution_Time_Sec, Total_Tokens, Cost_USD, Summary
🎯 你的验收时刻： 看一眼表格的倒数第二列！Cost_USD 会以 $ 开头的形式，无比清晰地告诉你：刚刚这几秒钟大模型的思考，到底花了公司几厘钱！ 有了这套极为强壮的兼容逻辑，不管你们安全团队以后切换成哪个厂商的大模型，这份成本监控表将永远精准跳动。这就是企业级开发的尽头：业务要闭环，财务要透传！
👇 全套教程多平台同步更新 👇
https://blog.csdn.net/thewindrider/category_13144991.html
https://gitcode.com/Chang_feng_Po/Splunk-AI-PEAK-Tutorial