> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.
# Gemini
> Connect your Gemini-powered application to Latitude Telemetry for feature-level observability and evaluations.
## Overview
This guide shows you how to integrate **Latitude Telemetry** into an existing application that uses the official **Gemini SDK** (`google-genai`).
After completing these steps:
* Every Gemini call (e.g. `generate_content`) can be captured as a log in Latitude.
* Logs are grouped under a **prompt**, identified by a `path`, inside a Latitude **project**.
* You can inspect inputs/outputs, measure latency, and debug your Gemini-powered features from the Latitude dashboard.
You'll keep calling Gemini exactly as you do today — Telemetry simply observes
and enriches those calls.
***
## Requirements
Before you start, make sure you have:
* A **Latitude account** and **API key**
* A **Latitude project ID**
* A Node.js or Python-based project that uses the **Gemini SDK**
That's it — prompts do **not** need to be created ahead of time.
***
## Steps
Add the Latitude Telemetry package to your project:
```bash npm theme={null}
npm add @latitude-data/telemetry
```
```bash pnpm theme={null}
pnpm add @latitude-data/telemetry
```
```bash yarn theme={null}
yarn add @latitude-data/telemetry
```
```bash bun theme={null}
bun add @latitude-data/telemetry
```
```bash pip theme={null}
pip install latitude-telemetry
```
```bash uv theme={null}
uv add latitude-telemetry
```
```bash poetry theme={null}
poetry add latitude-telemetry
```
Since Gemini doesn't have automatic instrumentation in TypeScript, you need to manually create spans to track your Gemini calls.
```ts theme={null}
import { LatitudeTelemetry } from '@latitude-data/telemetry'
import { GoogleGenAI } from '@google/genai'
const telemetry = new LatitudeTelemetry(process.env.LATITUDE_API_KEY)
async function generateSupportReply(input: string) {
return telemetry.capture(
{
projectId: 123, // The ID of your project in Latitude
path: 'generate-support-reply', // Add a path to identify this prompt in Latitude
},
async () => {
const model = 'gemini-2.0-flash'
// 1) Start the completion span
const span = telemetry.span.completion({
model,
input: [{ role: 'user', content: input }]
})
try {
// 2) Call Gemini as usual
const google = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY })
const response = await google.models.generateContent({
model,
contents: input,
})
const text = response.text
// 3) End the span (attach output + useful metadata)
span.end({
output: [{ role: 'assistant', content: text }],
})
return text
} catch (error) {
// Make sure to close the span even on errors
span.fail(error)
throw error
}
}
)
}
```
Python has automatic instrumentation for Gemini. You can use the `capture` method as a decorator (recommended) or as a context manager:
```python Using decorator (recommended) theme={null}
import os
import google.generativeai as genai
from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions
telemetry = Telemetry(
os.environ["LATITUDE_API_KEY"],
TelemetryOptions(instrumentors=[Instrumentors.GoogleGenAI]),
)
@telemetry.capture(
project_id=123, # The ID of your project in Latitude
path="generate-support-reply", # Add a path to identify this prompt in Latitude
)
def generate_support_reply(input: str) -> str:
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content(input)
return response.text
```
```python Using context manager theme={null}
import os
import google.generativeai as genai
from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions
telemetry = Telemetry(
os.environ["LATITUDE_API_KEY"],
TelemetryOptions(instrumentors=[Instrumentors.GoogleGenAI]),
)
def generate_support_reply(input: str) -> str:
with telemetry.capture(
project_id=123, # The ID of your project in Latitude
path="generate-support-reply", # Add a path to identify this prompt in Latitude
):
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content(input)
return response.text
```
The `path`:
* Identifies the prompt in Latitude
* Can be new or existing
* Should not contain spaces or special characters (use letters, numbers, `- _ / .`)
***
## Streaming responses
When using streaming, consume the stream inside your capture block so the span covers the entire operation.
**Consume the stream inside** your `capture()` callback:
```typescript theme={null}
async function streamSupportReply(input: string, res: Response) {
await telemetry.capture(
{ projectId: 123, path: 'generate-support-reply' },
async () => {
const google = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY })
const stream = await google.models.generateContentStream({
model: 'gemini-2.0-flash',
contents: input,
})
for await (const chunk of stream) {
const text = chunk.text()
if (text) {
res.write(text)
}
}
res.end()
}
)
}
```
**Use a generator function** with the decorator:
```python theme={null}
@telemetry.capture(project_id=123, path="generate-support-reply")
async def stream_support_reply(input: str):
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content(input, stream=True)
for chunk in response:
yield chunk.text
```
***
## Seeing your logs in Latitude
Once your feature is wrapped, logs will appear automatically.
1. Open the **prompt** in your Latitude dashboard (identified by `path`)
2. Go to the **Traces** section
3. Each execution will show:
* Input and output messages
* Model and token usage
* Latency and errors
* One trace per feature invocation
Each Gemini call appears as a child span under the captured prompt execution, giving you a full, end-to-end view of what happened.
***
## That's it
No changes to your Gemini calls, no special return values, and no extra plumbing — just wrap the feature you want to observe.