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bedrock_app_custom_dashboard.py
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"""
Create a custom dashboard in CloudWatch with
relevant metrics associated with Bedrock app.
Note: the `InvocationSummary by User` component of
this dashboard assumes you have invocation logs
enabled under Bedrock settings. If you don't enable
invocation logs, you will see no data for this
component.
"""
import json
import boto3
def knowledge_base_id_to_oss_collection(knowledge_base_id):
bedrock_agent_client = boto3.client("bedrock-agent")
response = bedrock_agent_client.get_knowledge_base(
knowledgeBaseId=knowledge_base_id
)
collection_id = response["knowledgeBase"]["storageConfiguration"][
"opensearchServerlessConfiguration"
]["collectionArn"].split("/")[-1]
return collection_id
def knowledge_base_name_to_id(knowledge_base_name):
bedrock_agent_client = boto3.client("bedrock-agent")
response = bedrock_agent_client.list_knowledge_bases()
for knowledge_base in response["knowledgeBaseSummaries"]:
if knowledge_base["name"] == knowledge_base_name:
return knowledge_base["knowledgeBaseId"]
try:
next_token = response["nextToken"]
except Exception as e:
next_token = None
while next_token:
response = bedrock_agent_client.list_knowledge_bases(nextToken=next_token)
for knowledge_base in response["knowledgeBaseSummaries"]:
if knowledge_base["name"] == knowledge_base_name:
return knowledge_base["knowledgeBaseId"]
try:
next_token = response["NextToken"]
except Exception as e:
next_token = None
return None
def generate_dashboard_json(region, knowledge_base_name, invoke_model_id):
# Get account ID
sts_client = boto3.client("sts")
caller_identity = sts_client.get_caller_identity()
account_id = caller_identity["Account"]
# Get OpenSearch Serverless info
knowledge_base_id = knowledge_base_name_to_id(knowledge_base_name)
collection_id = knowledge_base_id_to_oss_collection(knowledge_base_id)
oss_client = boto3.client("opensearchserverless")
collection_name = oss_client.batch_get_collection(
ids=[collection_id],
)[
"collectionDetails"
][0]["name"]
# Embedding model ID
bedrock_agent_client = boto3.client("bedrock-agent")
response = bedrock_agent_client.get_knowledge_base(
knowledgeBaseId=knowledge_base_id
)
embed_model_id = response["knowledgeBase"]["knowledgeBaseConfiguration"][
"vectorKnowledgeBaseConfiguration"
]["embeddingModelArn"].split("/")[-1]
dashboard_def = {
"variables": [],
"widgets": [
{
"height": 6,
"width": 7,
"y": 0,
"x": 8,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/Bedrock",
"Invocations",
"ModelId",
f"arn:aws:bedrock:{region}::foundation-model/{invoke_model_id}",
{"region": region},
],
[
"...",
f"arn:aws:bedrock:{region}::foundation-model/{embed_model_id}",
{"region": region},
],
],
"view": "timeSeries",
"stacked": True,
"region": region,
"period": 60,
"stat": "Sum",
"yAxis": {"left": {"label": "Count", "showUnits": False}},
},
},
{
"height": 6,
"width": 8,
"y": 0,
"x": 0,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/Bedrock",
"InvocationLatency",
"ModelId",
"ai21.j2-mid-v1",
{"region": region, "yAxis": "right", "visible": False},
],
[
"...",
"anthropic.claude-v2",
{
"region": region,
"yAxis": "left",
"period": 60,
"visible": False,
},
],
[
"...",
"stability.stable-diffusion-xl-v0",
{"region": region, "yAxis": "right", "visible": False},
],
[
"...",
"ai21.j2-ultra-v1",
{"region": region, "visible": False},
],
[
"...",
"amazon.titan-image-generator-v1",
{"region": region, "visible": False},
],
[
"...",
"amazon.titan-text-express-v1",
{"region": region, "visible": False},
],
[
"...",
"anthropic.claude-instant-v1",
{
"region": region,
"yAxis": "left",
"period": 60,
"visible": False,
},
],
[
"...",
"anthropic.claude-v1",
{"region": region, "visible": False},
],
[
"...",
"anthropic.claude-v2:1",
{"region": region, "visible": False},
],
[
"...",
f"arn:aws:bedrock:{region}::foundation-model/{embed_model_id}",
{"region": region, "period": 60},
],
[
"...",
f"arn:aws:bedrock:{region}::foundation-model/{invoke_model_id}",
{"region": region, "period": 60},
],
[
"...",
"meta.llama2-13b-chat-v1",
{"region": region, "visible": False},
],
[
"...",
"meta.llama2-70b-chat-v1",
{"region": region, "visible": False},
],
[
"...",
"stability.stable-diffusion-xl-v1",
{"region": region, "visible": False},
],
],
"view": "timeSeries",
"stacked": False,
"region": region,
"period": 300,
"stat": "Average",
"yAxis": {"right": {"showUnits": False}},
},
},
{
"height": 5,
"width": 12,
"y": 6,
"x": 0,
"type": "metric",
"properties": {
"metrics": [
["AWS/Bedrock", "InputTokenCount", {"region": region}],
[".", "OutputTokenCount", {"region": region}],
],
"sparkline": False,
"view": "gauge",
"region": region,
"yAxis": {"left": {"min": 20, "max": 10000}},
"period": 60,
"stat": "Sum",
"annotations": {
"horizontal": [
{
"color": "#d62728",
"label": "Above forecasts",
"value": 7000,
"fill": "above",
},
{
"color": "#2ca02c",
"label": "Untitled annotation",
"value": 5000,
"fill": "below",
},
[
{
"color": "#ff7f0e",
"label": "Untitled annotation",
"value": 5001,
},
{"value": 6999, "label": "Untitled annotation"},
],
]
},
},
},
{
"height": 5,
"width": 12,
"y": 6,
"x": 12,
"type": "log",
"properties": {
"query": "SOURCE 'textGeneration/Bedrock' | fields @timestamp, identity.arn, input.inputTokenCount, output.outputTokenCount\n| stats sum(input.inputTokenCount) as totalInputTokens, sum(output.outputTokenCount) as totalOutputTokens, count(*) as invocationCount by identity.arn",
"region": region,
"stacked": False,
"title": "InvocationSummary by User",
"view": "table",
},
},
{
"height": 6,
"width": 6,
"y": 17,
"x": 0,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/AOSS",
"IndexingOCU",
"ClientId",
account_id,
{"region": region},
],
[".", "SearchOCU", ".", ".", {"region": region}],
],
"view": "timeSeries",
"stacked": True,
"region": region,
"legend": {"position": "bottom"},
"title": "VectorDB - IndexingOCU, SearchOCU",
"period": 60,
"stat": "Average",
},
},
{
"height": 6,
"width": 6,
"y": 17,
"x": 6,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/AOSS",
"IndexingOCU",
"ClientId",
account_id,
{"visible": False, "region": region},
],
[
".",
"SearchOCU",
".",
".",
{"visible": False, "region": region},
],
[
".",
"SearchableDocuments",
"CollectionName",
collection_name,
"CollectionId",
collection_id,
"ClientId",
account_id,
{"visible": False, "region": region},
],
[
".",
"SearchRequestRate",
".",
".",
".",
".",
".",
".",
{"region": region, "period": 60},
],
[
".",
"SearchRequestErrors",
".",
".",
".",
".",
".",
".",
{"visible": False, "region": region},
],
[
".",
"2xx",
".",
".",
".",
".",
".",
".",
{"region": region, "period": 60},
],
],
"view": "timeSeries",
"stacked": False,
"region": region,
"legend": {"position": "bottom"},
"stat": "Average",
"period": 300,
"title": "VectorDB - 2xx, SearchRequestRate",
},
},
{
"height": 6,
"width": 6,
"y": 17,
"x": 18,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/AOSS",
"IndexingOCU",
"ClientId",
account_id,
{"visible": False, "region": region},
],
[
".",
"SearchOCU",
".",
".",
{"visible": False, "region": region},
],
[
".",
"SearchableDocuments",
"CollectionName",
collection_name,
"CollectionId",
collection_id,
"ClientId",
account_id,
{"visible": False, "region": region},
],
[
".",
"SearchRequestRate",
".",
".",
".",
".",
".",
".",
{"visible": False, "region": region},
],
[
".",
"SearchRequestErrors",
".",
".",
".",
".",
".",
".",
{"region": region, "period": 60},
],
[
".",
"2xx",
".",
".",
".",
".",
".",
".",
{"visible": False, "region": region},
],
],
"view": "timeSeries",
"stacked": False,
"region": region,
"legend": {"position": "bottom"},
"stat": "Average",
"period": 300,
"title": "SearchRequestErrors",
},
},
{
"height": 6,
"width": 6,
"y": 17,
"x": 12,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/AOSS",
"IndexingOCU",
"ClientId",
account_id,
{"visible": False, "region": region},
],
[
".",
"SearchOCU",
".",
".",
{"visible": False, "region": region},
],
[
".",
"SearchRequestLatency",
"CollectionName",
collection_name,
"CollectionId",
collection_id,
"ClientId",
account_id,
{"region": region},
],
[
".",
"IngestionRequestLatency",
".",
".",
".",
".",
".",
".",
{"region": region},
],
],
"view": "timeSeries",
"stacked": False,
"region": region,
"legend": {"position": "bottom"},
"stat": "Average",
"period": 300,
"title": "RetrievalLatency",
},
},
{
"height": 6,
"width": 8,
"y": 11,
"x": 16,
"type": "metric",
"properties": {
"metrics": [
["AWS/Lambda", "Invocations", "FunctionName", lambda_name],
[".", "Errors", ".", "."],
[".", "Throttles", ".", "."],
],
"view": "timeSeries",
"stacked": True,
"region": region,
"title": "Lambda Performance",
"period": 60,
"stat": "Sum",
},
},
{
"height": 6,
"width": 9,
"y": 0,
"x": 15,
"type": "metric",
"properties": {
"metrics": [
[
"AWS/Bedrock",
"OutputTokenCount",
"ModelId",
f"arn:aws:bedrock:{region}::foundation-model/{embed_model_id}",
{"region": region},
],
[
"AWS/Bedrock",
"OutputTokenCount",
"ModelId",
f"arn:aws:bedrock:{region}::foundation-model/{invoke_model_id}",
{"region": region},
],
[
"AWS/Bedrock",
"InputTokenCount",
"ModelId",
f"arn:aws:bedrock:{region}::foundation-model/{invoke_model_id}",
{"region": region},
],
[
"AWS/Bedrock",
"InputTokenCount",
"ModelId",
f"arn:aws:bedrock:{region}::foundation-model/{embed_model_id}",
{"region": region},
],
],
"view": "timeSeries",
"stacked": True,
"region": region,
"title": "Token usage",
"period": 300,
"stat": "Average",
},
},
],
}
return json.dumps(dashboard_def)
def create_dashboard(dashboard_name, dashboard_json):
# Create log group
try:
logs_client = boto3.client("logs")
logs_client.create_log_group(logGroupName="textGeneration/Bedrock")
except Exception as e:
# Log group already exists
pass
# Create custom dashboard
cw_client = boto3.client("cloudwatch")
cw_client.put_dashboard(
DashboardName=dashboard_name,
DashboardBody=dashboard_json,
)
# Parameters
region = "us-east-1"
lambda_name = "InvokeKnowledgeBase"
knowledge_base_name = "bedrock-sample-knowledge-base-111"
invoke_model_id = "anthropic.claude-instant-v1"
dashboard_name = "Contextual-Chatbot-Dashboard"
# Create custom dashboard for your Bedrock app
dashboard_json = generate_dashboard_json(region, knowledge_base_name, invoke_model_id)
create_dashboard(dashboard_name, dashboard_json)