Documentation Index
Fetch the complete documentation index at: https://docs.latitude.so/llms.txt
Use this file to discover all available pages before exploring further.
Overview
This guide shows you how to integrate Latitude Telemetry into an application that uses Amazon Bedrock.
You’ll keep calling Bedrock exactly as you do today. Telemetry simply
observes and enriches those calls.
Requirements
- A Latitude account and API key
- A Latitude project slug
- A project that uses the AWS Bedrock SDK
Steps
Install
npm install @latitude-data/telemetry
pip install latitude-telemetry
Initialize and use
import { Latitude, capture } from "@latitude-data/telemetry"
import {
BedrockRuntimeClient,
InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime"
import * as BedrockSDK from "@aws-sdk/client-bedrock-runtime"
const latitude = new Latitude({
apiKey: process.env.LATITUDE_API_KEY!,
project: process.env.LATITUDE_PROJECT_SLUG!,
instrumentations: { bedrock: BedrockSDK },
})
await latitude.ready
const client = new BedrockRuntimeClient({ region: "us-east-1" })
await capture("generate-reply", async () => {
const command = new InvokeModelCommand({
modelId: "anthropic.claude-3-haiku-20240307-v1:0",
contentType: "application/json",
body: JSON.stringify({
anthropic_version: "bedrock-2023-05-31",
max_tokens: 1024,
messages: [{ role: "user", content: "Hello" }],
}),
})
const response = await client.send(command)
return JSON.parse(new TextDecoder().decode(response.body))
})
await latitude.shutdown()
import json
import boto3
from latitude_telemetry import Latitude, capture
latitude = Latitude(
api_key="your-api-key",
project="your-project-slug",
instrumentations={"bedrock": boto3},
)
client = boto3.client("bedrock-runtime", region_name="us-east-1")
def generate_reply():
response = client.invoke_model(
modelId="anthropic.claude-3-haiku-20240307-v1:0",
contentType="application/json",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Hello"}],
}),
)
return json.loads(response["body"].read())
capture("generate-reply", generate_reply)
latitude.shutdown()
Streaming
When streaming, consume the stream inside capture() so the span covers the full operation:
import { InvokeModelWithResponseStreamCommand } from "@aws-sdk/client-bedrock-runtime"
await capture("stream-reply", async () => {
const command = new InvokeModelWithResponseStreamCommand({
modelId: "anthropic.claude-3-haiku-20240307-v1:0",
contentType: "application/json",
body: JSON.stringify({
anthropic_version: "bedrock-2023-05-31",
max_tokens: 1024,
messages: [{ role: "user", content: input }],
}),
})
const response = await client.send(command)
for await (const event of response.body!) {
if (event.chunk) {
const data = JSON.parse(new TextDecoder().decode(event.chunk.bytes))
if (data.type === "content_block_delta") {
res.write(data.delta.text)
}
}
}
res.end()
})
def stream_reply():
response = client.invoke_model_with_response_stream(
modelId="anthropic.claude-3-haiku-20240307-v1:0",
contentType="application/json",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"messages": [{"role": "user", "content": input}],
}),
)
for event in response["body"]:
chunk = json.loads(event["chunk"]["bytes"])
if chunk.get("type") == "content_block_delta":
yield chunk["delta"]["text"]
capture("stream-reply", stream_reply)
Seeing Your Traces
Once connected, traces appear automatically in Latitude:
- Open your project in the Latitude dashboard
- Each execution shows input/output messages, model, token usage, latency, and errors
That’s It
No changes to your Bedrock calls: just initialize Latitude and your LLM calls are traced.
