aiconfig-custom-metrics

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Custom Metrics for AI Configs

AI Configs自定义指标

Full lifecycle management of custom business metrics: create metric definitions via API, track events via SDK, retrieve metric data, and manage metrics programmatically.

自定义业务指标的全生命周期管理：通过API创建指标定义，通过SDK跟踪事件，检索指标数据，并以编程方式管理指标。

Prerequisites

前置条件

LaunchDarkly SDK initialized (see
```
aiconfig-sdk
```
)
LaunchDarkly API token with
```
writer
```
role for metric management
Understanding of built-in AI metrics (see
```
aiconfig-ai-metrics
```
)

已初始化LaunchDarkly SDK（详见
```
aiconfig-sdk
```
）
拥有
```
writer
```
权限的LaunchDarkly API令牌，用于指标管理
了解内置AI指标（详见
```
aiconfig-ai-metrics
```
）

API Key Detection

API密钥检测

Before prompting the user for an API key, try to detect it automatically:

Check Claude MCP config - Read

~/.claude/config.json

and look for

mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY

Check environment variables - Look for

LAUNCHDARKLY_API_KEY

LAUNCHDARKLY_API_TOKEN

, or

LD_API_KEY

Prompt user - Only if detection fails, ask the user for their API key

python

import os
import json
from pathlib import Path

def get_launchdarkly_api_key():
    """Auto-detect LaunchDarkly API key from Claude config or environment."""
    # 1. Check Claude MCP config
    claude_config = Path.home() / ".claude" / "config.json"
    if claude_config.exists():
        try:
            config = json.load(open(claude_config))
            api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
            if api_key:
                return api_key
        except (json.JSONDecodeError, IOError):
            pass

    # 2. Check environment variables
    for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
        if os.environ.get(var):
            return os.environ[var]

    return None

在提示用户输入API密钥之前，先尝试自动检测：

检查Claude MCP配置 - 读取

~/.claude/config.json

，查找

mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY

检查环境变量 - 查找

LAUNCHDARKLY_API_KEY

、

LAUNCHDARKLY_API_TOKEN

或

LD_API_KEY

提示用户 - 仅当检测失败时，才向用户询问其API密钥

python

import os
import json
from pathlib import Path

def get_launchdarkly_api_key():
    """Auto-detect LaunchDarkly API key from Claude config or environment."""
    # 1. Check Claude MCP config
    claude_config = Path.home() / ".claude" / "config.json"
    if claude_config.exists():
        try:
            config = json.load(open(claude_config))
            api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
            if api_key:
                return api_key
        except (json.JSONDecodeError, IOError):
            pass

    # 2. Check environment variables
    for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
        if os.environ.get(var):
            return os.environ[var]

    return None

Metrics Lifecycle Overview

指标生命周期概述

Step	Method	Purpose
1. Create	API	Define metric in LaunchDarkly
2. Track	SDK	Send events to the metric
3. Get	API	Retrieve metric definition/data
4. Update	API	Modify metric properties
5. Delete	API	Remove metric

步骤	方式	用途
1. 创建	API	在LaunchDarkly中定义指标
2. 跟踪	SDK	向指标发送事件
3. 获取	API	检索指标定义/数据
4. 更新	API	修改指标属性
5. 删除	API	删除指标

1. Create Metric (API)

1. 创建指标（API）

Required fields for numeric custom metrics:

successCriteria

- Must be one of:

"HigherThanBaseline"

"LowerThanBaseline"

```
unit
```
- e.g.,
```
"count"
```
,
```
"percent"
```
,
```
"milliseconds"
```

The API will return

400 Bad Request

if these are missing for numeric metrics.

python

import requests
import os

def create_metric(
    project_key: str,
    metric_key: str,
    name: str,
    kind: str = "custom",
    is_numeric: bool = True,
    unit: str = "count",
    success_criteria: str = "HigherThanBaseline",
    event_key: str = None,
    description: str = None
):
    """Create a new metric definition in LaunchDarkly."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    payload = {
        "key": metric_key,
        "name": name,
        "kind": kind,
        "isNumeric": is_numeric,
        "eventKey": event_key or metric_key
    }

    # Unit and successCriteria are required for numeric custom metrics
    if is_numeric and kind == "custom":
        payload["unit"] = unit
        payload["successCriteria"] = success_criteria

    if description:
        payload["description"] = description

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.post(url, json=payload, headers=headers)

    if response.status_code == 201:
        print(f"[OK] Created metric: {metric_key}")
        return response.json()
    elif response.status_code == 409:
        print(f"[INFO] Metric already exists: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to create metric: {response.status_code}")
        print(f"        {response.text}")
        return None

Metric Kinds:

```
custom
```
- Track any event (most common for AI metrics)
```
pageview
```
- Track page views
```
click
```
- Track click events

Success Criteria (for numeric metrics):

```
HigherThanBaseline
```
- Higher values are better (e.g., revenue, satisfaction)
```
LowerThanBaseline
```
- Lower values are better (e.g., errors, latency)

Common Units:

```
count
```
- Generic count
```
milliseconds
```
- Time duration
```
percent
```
- Percentage values
```
dollars
```
- Currency

数值型自定义指标必填字段：

successCriteria

- 必须为以下值之一：

"HigherThanBaseline"

、

"LowerThanBaseline"

```
unit
```
- 例如：
```
"count"
```
、
```
"percent"
```
、
```
"milliseconds"
```

如果数值型指标缺少这些字段，API将返回

400 Bad Request

错误。

python

import requests
import os

def create_metric(
    project_key: str,
    metric_key: str,
    name: str,
    kind: str = "custom",
    is_numeric: bool = True,
    unit: str = "count",
    success_criteria: str = "HigherThanBaseline",
    event_key: str = None,
    description: str = None
):
    """Create a new metric definition in LaunchDarkly."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    payload = {
        "key": metric_key,
        "name": name,
        "kind": kind,
        "isNumeric": is_numeric,
        "eventKey": event_key or metric_key
    }

    # Unit and successCriteria are required for numeric custom metrics
    if is_numeric and kind == "custom":
        payload["unit"] = unit
        payload["successCriteria"] = success_criteria

    if description:
        payload["description"] = description

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.post(url, json=payload, headers=headers)

    if response.status_code == 201:
        print(f"[OK] Created metric: {metric_key}")
        return response.json()
    elif response.status_code == 409:
        print(f"[INFO] Metric already exists: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to create metric: {response.status_code}")
        print(f"        {response.text}")
        return None

指标类型：

```
custom
```
- 跟踪任意事件（AI指标最常用类型）
```
pageview
```
- 跟踪页面浏览量
```
click
```
- 跟踪点击事件

成功判定标准（针对数值型指标）：

```
HigherThanBaseline
```
- 值越高越好（例如：收入、满意度）
```
LowerThanBaseline
```
- 值越低越好（例如：错误数、延迟）

常用单位：

```
count
```
- 通用计数单位
```
milliseconds
```
- 时间时长单位
```
percent
```
- 百分比单位
```
dollars
```
- 货币单位

2. Track Events (SDK)

2. 跟踪事件（SDK）

Once the metric is created, track events using the SDK:

python

from ldclient import Context
from ldclient.config import Config
import ldclient

创建指标后，使用SDK跟踪事件：

python

from ldclient import Context
from ldclient.config import Config
import ldclient

Initialize (see aiconfig-sdk for details)

ldclient.set_config(Config("your-sdk-key")) ld_client = ldclient.get()

def track_metric(ld_client, user_id: str, metric_key: str, value: float, data: dict = None): """Track an event to a metric.""" context = Context.builder(user_id).build()

ld_client.track(
    metric_key,
    context,
    data=data,
    metric_value=value
)

undefined

ldclient.set_config(Config("your-sdk-key")) ld_client = ldclient.get()

def track_metric(ld_client, user_id: str, metric_key: str, value: float, data: dict = None): """Track an event to a metric.""" context = Context.builder(user_id).build()

ld_client.track(
    metric_key,
    context,
    data=data,
    metric_value=value
)

undefined

Common Tracking Patterns

常见跟踪模式

python

def track_conversion(ld_client, user_id: str, amount: float, config_key: str):
    """Track a conversion event with revenue."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "business.conversion",
        context,
        data={"configKey": config_key, "category": "electronics"},
        metric_value=amount
    )

def track_task_success(ld_client, user_id: str, task_type: str, success: bool):
    """Track task completion success/failure."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "task.success_rate",
        context,
        data={"taskType": task_type},
        metric_value=1.0 if success else 0.0
    )

def track_satisfaction(ld_client, user_id: str, score: float, feedback_type: str):
    """Track user satisfaction (0-100 scale)."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "user.satisfaction",
        context,
        data={"feedbackType": feedback_type},
        metric_value=score
    )

    # Track negative feedback separately for alerts
    if score < 50:
        ld_client.track(
            "user.negative_feedback",
            context,
            metric_value=1.0
        )

def track_revenue(ld_client, user_id: str, revenue: float, source: str):
    """Track revenue generated after AI interaction."""
    context = Context.builder(user_id).set("tier", "premium").build()

    if revenue > 0:
        ld_client.track(
            "revenue.impact",
            context,
            data={"source": source},
            metric_value=revenue
        )

python

def track_conversion(ld_client, user_id: str, amount: float, config_key: str):
    """Track a conversion event with revenue."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "business.conversion",
        context,
        data={"configKey": config_key, "category": "electronics"},
        metric_value=amount
    )

def track_task_success(ld_client, user_id: str, task_type: str, success: bool):
    """Track task completion success/failure."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "task.success_rate",
        context,
        data={"taskType": task_type},
        metric_value=1.0 if success else 0.0
    )

def track_satisfaction(ld_client, user_id: str, score: float, feedback_type: str):
    """Track user satisfaction (0-100 scale)."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "user.satisfaction",
        context,
        data={"feedbackType": feedback_type},
        metric_value=score
    )

    # Track negative feedback separately for alerts
    if score < 50:
        ld_client.track(
            "user.negative_feedback",
            context,
            metric_value=1.0
        )

def track_revenue(ld_client, user_id: str, revenue: float, source: str):
    """Track revenue generated after AI interaction."""
    context = Context.builder(user_id).set("tier", "premium").build()

    if revenue > 0:
        ld_client.track(
            "revenue.impact",
            context,
            data={"source": source},
            metric_value=revenue
        )

3. Get Metrics (API)

3. 获取指标（API）

Get Single Metric

获取单个指标

python

def get_metric(project_key: str, metric_key: str):
    """Get a single metric definition."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        metric = response.json()
        print(f"[OK] Metric: {metric['key']}")
        print(f"     Name: {metric.get('name', 'N/A')}")
        print(f"     Kind: {metric.get('kind', 'N/A')}")
        print(f"     Numeric: {metric.get('isNumeric', False)}")
        print(f"     Event Key: {metric.get('eventKey', 'N/A')}")
        return metric
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to get metric: {response.status_code}")
        return None

python

def get_metric(project_key: str, metric_key: str):
    """Get a single metric definition."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        metric = response.json()
        print(f"[OK] Metric: {metric['key']}")
        print(f"     Name: {metric.get('name', 'N/A')}")
        print(f"     Kind: {metric.get('kind', 'N/A')}")
        print(f"     Numeric: {metric.get('isNumeric', False)}")
        print(f"     Event Key: {metric.get('eventKey', 'N/A')}")
        return metric
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to get metric: {response.status_code}")
        return None

List All Metrics

列出所有指标

python

def list_metrics(project_key: str, limit: int = 20):
    """List all metrics in a project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    headers = {"Authorization": API_TOKEN}
    params = {"limit": limit}

    response = requests.get(url, headers=headers, params=params)

    if response.status_code == 200:
        data = response.json()
        metrics = data.get("items", [])
        print(f"[OK] Found {len(metrics)} metrics:")
        for metric in metrics:
            numeric = "numeric" if metric.get("isNumeric") else "non-numeric"
            print(f"     - {metric['key']} ({metric.get('kind', 'custom')}, {numeric})")
        return metrics
    else:
        print(f"[ERROR] Failed to list metrics: {response.status_code}")
        return None

python

def list_metrics(project_key: str, limit: int = 20):
    """List all metrics in a project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    headers = {"Authorization": API_TOKEN}
    params = {"limit": limit}

    response = requests.get(url, headers=headers, params=params)

    if response.status_code == 200:
        data = response.json()
        metrics = data.get("items", [])
        print(f"[OK] Found {len(metrics)} metrics:")
        for metric in metrics:
            numeric = "numeric" if metric.get("isNumeric") else "non-numeric"
            print(f"     - {metric['key']} ({metric.get('kind', 'custom')}, {numeric})")
        return metrics
    else:
        print(f"[ERROR] Failed to list metrics: {response.status_code}")
        return None

4. Update Metric (API)

4. 更新指标（API）

python

def update_metric(project_key: str, metric_key: str, updates: list):
    """
    Update a metric using JSON Patch operations.

    Args:
        updates: List of patch operations, e.g.:
            [{"op": "replace", "path": "/name", "value": "New Name"}]
    """
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.patch(url, json=updates, headers=headers)

    if response.status_code == 200:
        print(f"[OK] Updated metric: {metric_key}")
        return response.json()
    elif response.status_code == 404:
        print(f"[ERROR] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to update metric: {response.status_code}")
        print(f"        {response.text}")
        return None

python

def update_metric(project_key: str, metric_key: str, updates: list):
    """
    Update a metric using JSON Patch operations.

    Args:
        updates: List of patch operations, e.g.:
            [{"op": "replace", "path": "/name", "value": "New Name"}]
    """
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.patch(url, json=updates, headers=headers)

    if response.status_code == 200:
        print(f"[OK] Updated metric: {metric_key}")
        return response.json()
    elif response.status_code == 404:
        print(f"[ERROR] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to update metric: {response.status_code}")
        print(f"        {response.text}")
        return None

Example: Update metric name and description

def rename_metric(project_key: str, metric_key: str, new_name: str, new_description: str = None): """Rename a metric and optionally update description.""" updates = [ {"op": "replace", "path": "/name", "value": new_name} ] if new_description: updates.append({"op": "replace", "path": "/description", "value": new_description})

return update_metric(project_key, metric_key, updates)

undefined

return update_metric(project_key, metric_key, updates)

undefined

5. Delete Metric (API)

5. 删除指标（API）

python

def delete_metric(project_key: str, metric_key: str):
    """Delete a metric from the project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.delete(url, headers=headers)

    if response.status_code == 204:
        print(f"[OK] Deleted metric: {metric_key}")
        return True
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return False
    else:
        print(f"[ERROR] Failed to delete metric: {response.status_code}")
        return False

python

def delete_metric(project_key: str, metric_key: str):
    """Delete a metric from the project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.delete(url, headers=headers)

    if response.status_code == 204:
        print(f"[OK] Deleted metric: {metric_key}")
        return True
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return False
    else:
        print(f"[ERROR] Failed to delete metric: {response.status_code}")
        return False

Complete Workflow Example

完整工作流示例

python

import os
import requests
from ldclient import Context
from ldclient.config import Config
import ldclient

python

import os
import requests
from ldclient import Context
from ldclient.config import Config
import ldclient

Setup

API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN") SDK_KEY = os.environ.get("LAUNCHDARKLY_SDK_KEY") PROJECT_KEY = "support-ai"

ldclient.set_config(Config(SDK_KEY)) ld_client = ldclient.get()

API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN") SDK_KEY = os.environ.get("LAUNCHDARKLY_SDK_KEY") PROJECT_KEY = "support-ai"

ldclient.set_config(Config(SDK_KEY)) ld_client = ldclient.get()

1. Create metric

create_metric( PROJECT_KEY, "ai.task.completion", name="AI Task Completion Rate", kind="custom", is_numeric=True, description="Tracks successful AI task completions" )

2. Track events

context = Context.builder("user-123").build() ld_client.track("ai.task.completion", context, metric_value=1.0) ld_client.track("ai.task.completion", context, metric_value=1.0) ld_client.track("ai.task.completion", context, metric_value=0.0) # failure ld_client.flush()

3. Get metric definition

metric = get_metric(PROJECT_KEY, "ai.task.completion")

4. Update metric name

rename_metric(PROJECT_KEY, "ai.task.completion", "AI Task Success Rate")

5. List all metrics

list_metrics(PROJECT_KEY)

6. Delete metric (when no longer needed)

delete_metric(PROJECT_KEY, "ai.task.completion")

undefined

undefined

Session Metrics Tracker

会话指标跟踪器

python

import time
from ldclient import Context

class SessionMetricsTracker:
    """Track metrics across an entire user session."""

    def __init__(self, ld_client):
        self.ld_client = ld_client
        self.session_data = {}

    def start_session(self, user_id: str, session_id: str):
        """Initialize session tracking."""
        self.session_data[session_id] = {
            "user_id": user_id,
            "start_time": time.time(),
            "interactions": 0,
            "successful_tasks": 0
        }

    def track_interaction(self, session_id: str, success: bool):
        """Track individual interaction within session."""
        if session_id not in self.session_data:
            return
        session = self.session_data[session_id]
        session["interactions"] += 1
        if success:
            session["successful_tasks"] += 1

    def end_session(self, session_id: str):
        """Finalize and track session metrics."""
        if session_id not in self.session_data:
            return None

        session = self.session_data[session_id]
        duration = time.time() - session["start_time"]

        context = Context.builder(session["user_id"]).build()

        # Track session duration
        self.ld_client.track(
            "session.duration",
            context,
            data={"interactions": session["interactions"]},
            metric_value=duration
        )

        # Track session success rate
        if session["interactions"] > 0:
            success_rate = session["successful_tasks"] / session["interactions"]
            self.ld_client.track(
                "session.success_rate",
                context,
                metric_value=success_rate * 100
            )

        result = dict(session)
        result["duration"] = duration
        del self.session_data[session_id]
        return result

python

import time
from ldclient import Context

class SessionMetricsTracker:
    """Track metrics across an entire user session."""

    def __init__(self, ld_client):
        self.ld_client = ld_client
        self.session_data = {}

    def start_session(self, user_id: str, session_id: str):
        """Initialize session tracking."""
        self.session_data[session_id] = {
            "user_id": user_id,
            "start_time": time.time(),
            "interactions": 0,
            "successful_tasks": 0
        }

    def track_interaction(self, session_id: str, success: bool):
        """Track individual interaction within session."""
        if session_id not in self.session_data:
            return
        session = self.session_data[session_id]
        session["interactions"] += 1
        if success:
            session["successful_tasks"] += 1

    def end_session(self, session_id: str):
        """Finalize and track session metrics."""
        if session_id not in self.session_data:
            return None

        session = self.session_data[session_id]
        duration = time.time() - session["start_time"]

        context = Context.builder(session["user_id"]).build()

        # Track session duration
        self.ld_client.track(
            "session.duration",
            context,
            data={"interactions": session["interactions"]},
            metric_value=duration
        )

        # Track session success rate
        if session["interactions"] > 0:
            success_rate = session["successful_tasks"] / session["interactions"]
            self.ld_client.track(
                "session.success_rate",
                context,
                metric_value=success_rate * 100
            )

        result = dict(session)
        result["duration"] = duration
        del self.session_data[session_id]
        return result

Naming Conventions

命名规范

python

undefined

python

undefined

Use dot notation for hierarchy

"quality.accuracy" "quality.relevance" "user.satisfaction" "user.engagement" "revenue.conversion" "task.success_rate" "session.duration" "ai.task.completion" "ai.recommendation.conversion"

undefined

"quality.accuracy" "quality.relevance" "user.satisfaction" "user.engagement" "revenue.conversion" "task.success_rate" "session.duration" "ai.task.completion" "ai.recommendation.conversion"

undefined

Best Practices

最佳实践

Create Before Track - Metric must exist before tracking events
Use Numeric Metrics - Set
```
isNumeric=True
```
for aggregation
Consistent Keys - Use same key in
```
create_metric()
```
and
```
ld_client.track()
```
Flush in Serverless - Call
```
ld_client.flush()
```
before Lambda terminates
Rate Limit - Don't track on every keystroke

先创建再跟踪 - 跟踪事件前必须先创建指标
使用数值型指标 - 设置
```
isNumeric=True
```
以支持聚合
保持密钥一致 - 在
```
create_metric()
```
和
```
ld_client.track()
```
中使用相同的密钥
无服务器环境下刷新 - 在Lambda终止前调用
```
ld_client.flush()
```
限制跟踪频率 - 不要对每次按键都进行跟踪

Viewing Metrics

查看指标

Custom metrics appear in:

Metrics page in LaunchDarkly UI
Monitoring tab of your AI Config
Via API using
```
get_metric()
```
or
```
list_metrics()
```

自定义指标可在以下位置查看：

LaunchDarkly UI中的指标页面
AI Config的监控标签页
通过API调用
```
get_metric()
```
或
```
list_metrics()
```
查看

aiconfig-custom-metrics

Original

Translation

Custom Metrics for AI Configs

AI Configs自定义指标

Prerequisites

前置条件

API Key Detection

API密钥检测

Metrics Lifecycle Overview

指标生命周期概述

1. Create Metric (API)

1. 创建指标（API）

2. Track Events (SDK)

2. 跟踪事件（SDK）

Initialize (see aiconfig-sdk for details)

Initialize (see aiconfig-sdk for details)

Common Tracking Patterns

常见跟踪模式

3. Get Metrics (API)

3. 获取指标（API）

Get Single Metric

获取单个指标

List All Metrics

列出所有指标

4. Update Metric (API)

4. 更新指标（API）

Example: Update metric name and description

Example: Update metric name and description

5. Delete Metric (API)

5. 删除指标（API）

Complete Workflow Example

完整工作流示例

Setup

Setup

1. Create metric

1. Create metric

2. Track events

2. Track events

3. Get metric definition

3. Get metric definition

4. Update metric name

4. Update metric name

5. List all metrics

5. List all metrics

6. Delete metric (when no longer needed)

6. Delete metric (when no longer needed)

delete_metric(PROJECT_KEY, "ai.task.completion")

delete_metric(PROJECT_KEY, "ai.task.completion")

Session Metrics Tracker

会话指标跟踪器

Naming Conventions

命名规范

Use dot notation for hierarchy

Use dot notation for hierarchy

Best Practices

最佳实践

Viewing Metrics

查看指标

Related Skills

相关技能

References

参考资料