Quando crei un'interazione, puoi impostare stream: true per trasmettere in streaming la risposta in modo incrementale utilizzando gli eventi inviati dal server (SSE).
Python
from google import genai
client = genai.Client()
stream = client.interactions.create(
model="gemini-3.8-flash",
input="Count from 1 to 25.",
stream=True,
)
for event in stream:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const stream = await client.interactions.create({
model: "gemini-3.8-flash",
input: "Count from 1 to 25.",
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
Client client = new Client();
CreateModelInteraction params =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.8-flash"))
.input(InteractionsInput.of("Count from 1 to 25."))
.stream(true)
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
try (EventStream<InteractionSSEStreamEvent> events = response.events()) {
for (InteractionSSEStreamEvent streamEvent : events) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepDelta stepDelta) {
StepDeltaData data = stepDelta.delta().orElse(null);
if (data instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
}
}
}
}
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.8-flash"),
Input: interactions.NewInteractionsInput("Explain quantum computing in simple terms."),
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
}
if err := stream.Err(); err != nil {
log.Fatal(err)
}
}
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.8-flash",
"input": "Count from 1 to 25.",
"stream": true
}'
event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3.8-flash"},"event_type":"interaction.created"}
event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}
event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}
event: step.stop
data: {"index":0,"event_type":"step.stop"}
event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}
event: step.delta
data: {"index":1,"delta":{"text":"1, 2, 3, 4, 5, 6, ","type":"text"},"event_type":"step.delta"}
event: step.delta
data: {"index":1,"delta":{"text":"7, 8, 9, 10, 11, 12, 13,","type":"text"},"event_type":"step.delta"}
...
event: step.stop
data: {"index":1,"event_type":"step.stop"}
event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":346,"total_input_tokens":11,"input_tokens_by_modality":[{"modality":"text","tokens":11}],"total_cached_tokens":0,"total_output_tokens":90,"total_tool_use_tokens":0,"total_thought_tokens":245},"created":"2026-05-12T18:44:51Z","updated":"2026-05-12T18:44:51Z","service_tier":"standard","object":"interaction","model":"gemini-3.8-flash"},"event_type":"interaction.completed"}
event: done
data: [DONE]
Tipi di evento
Ogni evento inviato dal server include un event_type denominato e dati JSON associati. L'API Interactions utilizza un modello di streaming simmetrico in cui tutti i contenuti (testo, chiamate di strumenti, pensiero) scorrono attraverso un evento basato su passaggi coerente.
Ogni stream segue questo flusso di eventi:
interaction.created: l'interazione viene creata e include i metadati (ID, modello, stato).- Una serie di passaggi, ciascuno composto da:
- Un evento
step.start, che indica il tipo di passaggio (ad es.model_output,thought,function_call). - Uno o più eventi
step.deltacon dati incrementali per questo passaggio. - Un evento
step.stopche contrassegna il passaggio come completato.
- Un evento
- Un evento
interaction.completedcon statisticheusagefinali.
Quando imposti stream: false, l'API restituisce un singolo oggetto interaction con un array steps. Ogni elemento in steps è la versione completamente assemblata di un ciclo step.start → step.delta → step.stop.
interaction.created
Inviato al momento della creazione dell'interazione. Contiene l'ID interazione, il modello e lo stato iniziale.
event: interaction.created
data: {"interaction": {"id": "...", "model": "gemini-3.8-flash", "status": "in_progress", "object": "interaction"}, "event_type": "interaction.created"}
interaction.status_update
Indica una transizione di stato a livello di interazione. Potrebbe essere visualizzato tra i passaggi.
event: interaction.status_update
data: {"interaction_id": "...", "status": "in_progress", "event_type": "interaction.status_update"}
step.start
Indica l'inizio di un nuovo passaggio. Contiene i passaggi type e index. Il tipo di passaggio determina quali tipi di delta prevedere e come viene visualizzato il passaggio in una risposta non in streaming:
| Tipo di passaggio | Tipi di delta previsti | Descrizione |
|---|---|---|
model_output |
text, image e audio |
Il contenuto della risposta finale del modello. |
thought |
thought_signature, thought_summary |
Ragionamento Chain-of-Thought. summary è presente solo quando thinking_summaries è abilitato. |
function_call |
arguments_delta |
Una richiesta al client di eseguire una funzione. Imposta lo stato dell'interazione su requires_action. |
| Strumenti lato server | Varia in base allo strumento | Strumenti eseguiti dall'API (ad es. google_search_call, google_search_result, code_execution_call, code_execution_result). |
Per l'elenco completo, consulta il riferimento all'API Interactions.
event: step.start
data: {"index": 0, "step": {"type": "model_output"}, "event_type": "step.start"}
Per le chiamate di funzioni, il passaggio include il nome della funzione, l'ID e gli argomenti vuoti {}.
event: step.start
data: {"index": 0, "step": {"type": "function_call", "id":"un6k8t18", "name": "get_weather", "arguments":{}}, "event_type": "step.start"}
step.delta
Dati incrementali per il passaggio attuale. L'oggetto delta contiene un campo type che ne determina la forma.
Esempi:
text: token di testo incrementale di un passaggio model_output:
event: step.delta
data: {"index": 0, "delta": {"type": "text", "text": "Hello, my name is Phil"}, "event_type": "step.delta"}
event: step.delta
data: {"index": 0, "delta": {"type": "text", "text": ", and I live in Germany." }, "event_type": "step.delta"}
image: dati immagine con codifica Base64 da un passaggio model_output:
event: step.delta
data: {"index": 0, "delta": {"type": "image", "mime_type": "image/jpeg", "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoHBwgHBgoICAgLCg..."}, "event_type": "step.delta"}
thought_summary: contenuti di riepilogo del pensiero di un passaggio thought:
event: step.delta
data: {"index": 0, "delta": {"type": "thought_summary", "content": {"type": "text", "text": "I need to find the GCD..."}}, "event_type": "step.delta"}
arguments_delta: stringa JSON (parziale) per gli argomenti della chiamata di funzione. Devono essere accumulati in tutti i delta:
event: step.delta
data: {"index": 0, "delta": {"type": "arguments_delta", "arguments": "{\"location\": \"San Francisco, CA\"}"}, "event_type": "step.delta"}
Questi sono alcuni dei tipi di delta più comuni. Per l'elenco completo di tutti i tipi di delta, consulta il riferimento API Interactions.
step.stop
Indica la fine di un passaggio. Contiene il passaggio index.
event: step.stop
data: {"index": 0, "event_type": "step.stop"}
Quando utilizzi l'agente antigravità, l'evento
step.stop può includere anche statistiche sull'utilizzo:
usage: l'utilizzo accumulato (totale parziale) dall'inizio dell'interazione.step_usage: l'utilizzo di questo passaggio specifico.
event: step.stop
data: {"index": 2, "event_type": "step.stop", "usage": {"total_tokens": 4650, "total_input_tokens": 3577, "total_output_tokens": 305, "total_cached_tokens": 0}, "step_usage": {"total_tokens": 303, "total_input_tokens": 31, "total_output_tokens": 3, "total_cached_tokens": 0}}
interaction.completed
Inviato al termine dell'interazione. Contiene l'oggetto dell'interazione finale con le statistiche usage. In modalità non streaming, questo è l'oggetto di risposta di primo livello. Non include steps nella risposta.
event: interaction.completed
data: {"interaction": {"id": "v1_abc123", "status": "completed", "usage": {"total_input_tokens": 7, "total_output_tokens": 12, "total_tokens": 19}}, "event_type": "interaction.completed"}
error
Inviato quando si verifica un errore durante l'interazione. Contiene un oggetto di errore con un messaggio e un codice.
event: error
data: {"error":{"message":"Deadline expired before operation could complete.","code":"gateway_timeout"},"event_type":"error"}
Streaming con gli strumenti
L'API Interactions supporta lo streaming con strumenti lato client (chiamata di funzione) e lato server (Ricerca Google, esecuzione di codice e così via) in un'unica richiesta. Durante lo streaming, le chiamate agli strumenti vengono visualizzate come passaggi digitati nello stream di eventi. Per le chiamate di funzione, l'evento step.start fornisce il nome della funzione e gli eventi step.delta trasmettono gli argomenti come stringhe JSON (arguments_delta). Devi accumulare questi delta per ottenere gli argomenti completi.
Gli strumenti lato server come la Ricerca Google vengono eseguiti automaticamente dall'API, producendo i passaggi google_search_call e google_search_result.
Streaming con la chiamata di funzione
Per eseguire la chiamata di funzione con lo streaming, il client deve gestire una conversazione multiturno:
- Turno 1 (richiesta di funzione): chiama
interactions.createconstream: truee il tuotoolsdefinito. L'API trasmetterà in streaming un passaggiofunction_call. Devi accumulare le stringhe JSON degli argomenti incrementali (arguments_delta) dagli eventistep.deltafinché l'interazione non viene completata con lo statorequires_action. - Turno 2 (invio del risultato): chiama di nuovo
interactions.create, passandoprevious_interaction_id(corrispondente all'ID della prima interazione) e inviando un bloccofunction_resultall'interno dell'arrayinput. In questo modo, il flusso viene ripreso e il modello può generare la risposta finale.
Python
from google import genai
client = genai.Client()
weather_tool = {
"type": "function",
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
# Turn 1: Request function call
stream = client.interactions.create(
model="gemini-3.8-flash",
tools=[weather_tool],
input="What is the weather in Paris right now?",
stream=True,
)
first_interaction_id = None
func_call_id = None
func_call_name = None
func_args_accumulated = ""
for event in stream:
if event.event_type == "interaction.created":
first_interaction_id = event.interaction.id
elif event.event_type == "step.start":
step = event.step
if step.type == "function_call":
func_call_id = step.id
func_call_name = step.name
elif event.event_type == "step.delta":
if event.delta.type == "arguments_delta":
func_args_accumulated += event.delta.arguments
# Turn 2: Execute tool and send the result back to resume stream
if func_call_id:
# Execute weather_tool using accumulated arguments
dummy_result = {
"content": [{"type": "text", "text": '{"weather": "Sunny and 22°C"}'}]
}
stream2 = client.interactions.create(
model="gemini-3.8-flash",
previous_interaction_id=first_interaction_id,
input=[{
"type": "function_result",
"name": func_call_name,
"call_id": func_call_id,
"result": dummy_result
}],
stream=True,
)
for event in stream2:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const weatherTool = {
type: "function",
name: "get_weather",
description: "Get the current weather in a given location",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "The city and state, e.g. San Francisco, CA"
}
},
required: ["location"]
}
};
// Turn 1: Request function call
const stream = await client.interactions.create({
model: "gemini-3.8-flash",
tools: [weatherTool],
input: "What is the weather in Paris right now?",
stream: true,
});
let firstInteractionId = null;
let funcCallId = null;
let funcCallName = null;
let funcArgsAccumulated = "";
for await (const event of stream) {
if (event.event_type === "interaction.created") {
firstInteractionId = event.interaction.id;
} else if (event.event_type === "step.start") {
const step = event.step;
if (step.type === "function_call") {
funcCallId = step.id;
funcCallName = step.name;
}
} else if (event.event_type === "step.delta") {
if (event.delta.type === "arguments_delta") {
funcArgsAccumulated += event.delta.arguments;
}
}
}
// Turn 2: Execute tool and send the result back to resume stream
if (funcCallId && firstInteractionId && funcCallName) {
const dummyResult = {
content: [{ type: "text", text: '{"weather": "Sunny and 22°C"}' }]
};
const stream2 = await client.interactions.create({
model: "gemini-3.8-flash",
previous_interaction_id: firstInteractionId,
input: [{
type: "function_result",
name: funcCallName,
call_id: funcCallId,
result: dummyResult
}],
stream: true,
});
for await (const event of stream2) {
if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
}
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.ArgumentsDelta;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Function;
import com.google.genai.gaos.models.interactions.FunctionCallStep;
import com.google.genai.gaos.models.interactions.FunctionResultStep;
import com.google.genai.gaos.models.interactions.FunctionResultStepResultUnion;
import com.google.genai.gaos.models.interactions.InteractionCreatedEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionSseEventInteraction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.StepStart;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
Client client = new Client();
Map<String, Object> locationProp = new HashMap<>();
locationProp.put("type", "string");
locationProp.put("description", "The city and state, e.g. San Francisco, CA");
Map<String, Object> properties = new HashMap<>();
properties.put("location", locationProp);
Map<String, Object> parameters = new HashMap<>();
parameters.put("type", "object");
parameters.put("properties", properties);
parameters.put("required", Arrays.asList("location"));
Function weatherTool =
Function.builder()
.name("get_weather")
.description("Get the current weather in a given location")
.parameters(parameters)
.build();
// Turn 1: Request function call
CreateModelInteraction params =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.8-flash"))
.tools(Arrays.asList(weatherTool))
.input(InteractionsInput.of("What is the weather in Paris right now?"))
.stream(true)
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
String firstInteractionId = null;
String funcCallId = null;
String funcCallName = null;
StringBuilder funcArgsAccumulated = new StringBuilder();
try (EventStream<InteractionSSEStreamEvent> stream = response.events()) {
for (InteractionSSEStreamEvent streamEvent : stream) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof InteractionCreatedEvent createdEvent) {
firstInteractionId =
createdEvent.interaction().flatMap(InteractionSseEventInteraction::id).orElse(null);
} else if (event instanceof StepStart stepStart) {
Step step = stepStart.step().orElse(null);
if (step instanceof FunctionCallStep fcStep) {
funcCallId = fcStep.id().orElse(null);
funcCallName = fcStep.name().orElse(null);
}
} else if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof ArgumentsDelta argsDelta) {
funcArgsAccumulated.append(argsDelta.arguments().orElse(""));
}
}
}
}
// Turn 2: Execute tool and send the result back to resume stream
if (funcCallId != null && firstInteractionId != null && funcCallName != null) {
FunctionResultStep resultStep =
FunctionResultStep.builder()
.name(funcCallName)
.callId(funcCallId)
.result(
FunctionResultStepResultUnion.of(
Arrays.asList(TextContent.builder().text("{\"weather\": \"Sunny and 22°C\"}").build())))
.build();
CreateModelInteraction params2 =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.8-flash"))
.previousInteractionId(firstInteractionId)
.input(InteractionsInput.ofStep(Arrays.asList(resultStep)))
.stream(true)
.build();
CreateInteractionResponse response2 =
client.interactions.create(CreateInteractionRequestBody.of(params2));
try (EventStream<InteractionSSEStreamEvent> stream2 = response2.events()) {
for (InteractionSSEStreamEvent streamEvent : stream2) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
}
}
}
}
}
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.8-pro"),
Input: interactions.NewInteractionsInput("Solve the Monty Hall problem step-by-step."),
GenerationConfig: &interactions.GenerationConfig{
ThinkingLevel: interactions.ThinkingLevelHigh.ToPointer(),
ThinkingSummaries: interactions.ThinkingSummariesAuto.ToPointer(),
},
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if thoughtDelta := stepDelta.GetDeltaThoughtSummary(); thoughtDelta != nil {
if textContent := thoughtDelta.GetContentText(); textContent != nil {
fmt.Printf("[Thought] %s\n", textContent.Text)
}
}
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
}
if err := stream.Err(); err != nil {
log.Fatal(err)
}
}
REST
Turno 1: richiesta di chiamata di funzione
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.8-flash",
"input": "What is the weather in Paris right now?",
"stream": true,
"tools": [
{
"type": "function",
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
]
}'
Turno 2: invia il risultato della funzione utilizzando previous_interaction_id e call_id del Turno 1
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.8-flash",
"previous_interaction_id": "v1_ChdGUVFJYXBXVUdLVEF4TjhQ...",
"stream": true,
"input": [
{
"type": "function_result",
"name": "get_weather",
"call_id": "CALL_ID",
"result": {
"content": [
{
"type": "text",
"text": "{\"weather\": \"Sunny and 22°C\"}"
}
]
}
}
]
}'
Streaming con più strumenti
L'esempio seguente utilizza sia uno strumento function sia google_search in una sola richiesta:
Python
from google import genai
client = genai.Client()
tools = [
{"type": "google_search"},
{
"type": "function",
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
]
stream = client.interactions.create(
model="gemini-3.8-flash",
tools=tools,
input="Search what is the largest mountain in Europe and what the weather is there right now?",
stream=True,
)
for event in stream:
if event.event_type == "step.start":
step = event.step
print(f"\n--- Step {event.index}: {step.type} ---")
# Show details for tool steps
if step.type == "google_search_call":
print(f" Search ID: {step.id}")
elif step.type == "google_search_result":
print(f" Result for: {step.call_id}")
elif step.type == "function_call":
print(f" Function: {step.name}({step.arguments})")
elif event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
elif event.delta.type == "google_search_call":
print(f" Queries: {event.delta.arguments}")
elif event.delta.type == "arguments_delta":
print(f" Args chunk: {event.delta.arguments}", end="", flush=True)
elif event.event_type == "interaction.completed":
print(f"\n\nStatus: {event.interaction.status}")
if event.interaction.status == "requires_action":
print("Action required: provide function call results to continue.")
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const tools = [
{ type: "google_search" },
{
type: "function",
name: "get_weather",
description: "Get the current weather in a given location",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "The city and state, e.g. San Francisco, CA"
}
},
required: ["location"]
}
}
];
const stream = await client.interactions.create({
model: "gemini-3.8-flash",
tools: tools,
input: "Search what is the largest mountain in Europe and what the weather is there right now?",
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.start") {
const step = event.step;
console.log(`\n--- Step ${event.index}: ${step.type} ---`);
// Show details for tool steps
if (step.type === "google_search_call") {
console.log(` Search ID: ${step.id}`);
} else if (step.type === "google_search_result") {
console.log(` Result for: ${step.call_id}`);
} else if (step.type === "function_call") {
console.log(` Function: ${step.name}(${JSON.stringify(step.arguments)})`);
}
} else if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
} else if (event.delta.type === "google_search_call") {
console.log(` Queries: ${JSON.stringify(event.delta.arguments?.queries)}`);
} else if (event.delta.type === "arguments_delta") {
process.stdout.write(` Args chunk: ${event.delta.arguments}`);
}
} else if (event.event_type === "interaction.completed") {
console.log(`\n\nStatus: ${event.interaction.status}`);
if (event.interaction.status === "requires_action") {
console.log("Action required: provide function call results to continue.");
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.ArgumentsDelta;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Function;
import com.google.genai.gaos.models.interactions.FunctionCallStep;
import com.google.genai.gaos.models.interactions.GoogleSearch;
import com.google.genai.gaos.models.interactions.GoogleSearchCallDelta;
import com.google.genai.gaos.models.interactions.GoogleSearchCallStep;
import com.google.genai.gaos.models.interactions.GoogleSearchResultStep;
import com.google.genai.gaos.models.interactions.InteractionCompletedEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionSseEventInteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.StepStart;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.Tool;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
Client client = new Client();
Map<String, Object> locationProp = new HashMap<>();
locationProp.put("type", "string");
locationProp.put("description", "The city and state, e.g. San Francisco, CA");
Map<String, Object> properties = new HashMap<>();
properties.put("location", locationProp);
Map<String, Object> parameters = new HashMap<>();
parameters.put("type", "object");
parameters.put("properties", properties);
parameters.put("required", Arrays.asList("location"));
List<Tool> tools =
Arrays.asList(
new GoogleSearch(),
Function.builder()
.name("get_weather")
.description("Get the current weather in a given location")
.parameters(parameters)
.build());
CreateModelInteraction params =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.8-flash"))
.tools(tools)
.input(
InteractionsInput.of(
"Search what is the largest mountain in Europe and what the weather is there right now?"))
.stream(true)
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
try (EventStream<InteractionSSEStreamEvent> stream = response.events()) {
for (InteractionSSEStreamEvent streamEvent : stream) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepStart stepStart) {
Step step = stepStart.step().orElse(null);
if (step != null) {
System.out.printf("%n--- Step %d: %s ---%n", stepStart.index().orElse(0), step.type());
if (step instanceof GoogleSearchCallStep searchCall) {
System.out.println(" Search ID: " + searchCall.id().orElse(""));
} else if (step instanceof GoogleSearchResultStep searchResult) {
System.out.println(" Result for: " + searchResult.callId().orElse(""));
} else if (step instanceof FunctionCallStep fcStep) {
System.out.printf(
" Function: %s(%s)%n",
fcStep.name().orElse(""), fcStep.arguments().orElse(Collections.emptyMap()));
}
}
} else if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
} else if (delta instanceof GoogleSearchCallDelta searchDelta) {
System.out.println(" Queries: " + searchDelta.arguments().orElse(null));
} else if (delta instanceof ArgumentsDelta argsDelta) {
System.out.print(" Args chunk: " + argsDelta.arguments().orElse(""));
}
} else if (event instanceof InteractionCompletedEvent completedEvent) {
completedEvent
.interaction()
.ifPresent(
interaction -> {
String status =
interaction
.status()
.map(InteractionSseEventInteractionStatus::value)
.orElse("");
System.out.println("\n\nStatus: " + status);
if ("requires_action".equals(status)) {
System.out.println("Action required: provide function call results to continue.");
}
});
}
}
}
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
weatherTool := interactions.NewTool(interactions.Function{
Name: genai.Ptr("get_weather"),
Description: genai.Ptr("Gets the current weather for a given location."),
Parameters: map[string]any{
"type": "object",
"properties": map[string]any{
"location": map[string]any{"type": "string"},
},
"required": []string{"location"},
},
})
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.8-flash"),
Input: interactions.NewInteractionsInput("What is the weather in Tokyo and Paris?"),
Tools: []interactions.Tool{weatherTool},
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if stepStart := event.GetDataStepStart(); stepStart != nil {
if call := stepStart.GetStepFunctionCall(); call != nil {
fmt.Printf("\n[Function Call Started] %s (id: %s)\n", call.Name, call.ID)
}
}
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if argsDelta := stepDelta.GetDeltaArgumentsDelta(); argsDelta != nil && argsDelta.Arguments != nil {
fmt.Printf("[Args Delta] %s\n", *argsDelta.Arguments)
}
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
if completed := event.GetDataInteractionCompleted(); completed != nil {
interaction := completed.Interaction
if interaction.Status == interactions.InteractionSseEventInteractionStatusRequiresAction {
fmt.Printf("\nStream paused: Waiting for tool outputs for interaction %s\n", interaction.ID)
}
}
}
if err := stream.Err(); err != nil {
log.Fatal(err)
}
}
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.8-flash",
"input": "Search what is the largest mountain in Europe and what the weather is there right now?",
"stream": true,
"tools": [
{ "type": "google_search" },
{
"type": "function",
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
]
}'
event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3.8-flash"},"event_type":"interaction.created"}
event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}
event: step.start
data: {"index":0,"step":{"id":"mkutnkgn","signature":"","type":"google_search_call"},"event_type":"step.start"}
event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"google_search_call","arguments":{"queries":["largest mountain in Europe"]}},"event_type":"step.delta"}
event: step.stop
data: {"index":0,"event_type":"step.stop"}
event: step.start
data: {"index":1,"step":{"call_id":"mkutnkgn","signature":"","type":"google_search_result"},"event_type":"step.start"}
event: step.delta
data: {"index":1,"delta":{"signature":"...","type":"google_search_result","is_error":false},"event_type":"step.delta"}
event: step.stop
data: {"index":1,"event_type":"step.stop"}
event: step.start
data: {"index":2,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":2,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}
event: step.stop
data: {"index":2,"event_type":"step.stop"}
event: step.start
data: {"index":3,"step":{"id":"ktr5aysg","type":"function_call","name":"get_weather","arguments":{}},"event_type":"step.start"}
event: step.delta
data: {"index":3,"delta":{"arguments":"{\"location\":\"Mount Elbrus, Russia\"}","type":"arguments_delta"},"event_type":"step.delta"}
event: step.stop
data: {"index":3,"event_type":"step.stop"}
event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"requires_action","usage":{"total_tokens":299,"total_input_tokens":138,"input_tokens_by_modality":[{"modality":"text","tokens":138}],"total_cached_tokens":0,"total_output_tokens":20,"total_tool_use_tokens":0,"total_thought_tokens":141},"created":"2026-05-12T17:24:26Z","updated":"2026-05-12T17:24:26Z","service_tier":"standard","object":"interaction","model":"gemini-3.8-flash"},"event_type":"interaction.completed"}
event: done
data: [DONE]
Streaming con pensiero
Quando il modello utilizza la funzionalità di pensiero, riceverai thought passaggi con due tipi di delta distinti: thought_summary (contenuti di riepilogo incrementali di testo o immagine) e thought_signature (una rappresentazione criptata del ragionamento interno del modello, inviata come ultimo delta prima di step.stop). Se thinking_summaries è abilitato, i delta thought_summary trasmettono in streaming un riepilogo del ragionamento del modello. Per maggiori dettagli sul pensiero, consulta la guida al pensiero.
Python
from google import genai
client = genai.Client()
stream = client.interactions.create(
model="gemini-3.8-flash",
input="What is the greatest common divisor of 1071 and 462?",
generation_config={
"thinking_summaries": "auto"
},
stream=True,
)
for event in stream:
if event.event_type == "step.start":
print(f"\n--- Step: {event.step.type} ---")
elif event.event_type == "step.delta":
if event.delta.type == "thought_summary":
if event.delta.content.type == "text":
print(event.delta.content.text, end="", flush=True)
elif event.delta.type == "text":
print(event.delta.text, end="", flush=True)
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const stream = await client.interactions.create({
model: "gemini-3.8-flash",
input: "What is the greatest common divisor of 1071 and 462?",
generation_config: {
thinking_summaries: "auto",
},
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.start") {
console.log(`\n--- Step: ${event.step.type} ---`);
} else if (event.event_type === "step.delta") {
if (event.delta.type === "thought_summary") {
if (event.delta.content.type === "text") {
process.stdout.write(event.delta.content.text);
}
} else if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.GenerationConfig;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.StepStart;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.interactions.ThoughtSummaryDelta;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
Client client = new Client();
CreateModelInteraction params =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.8-flash"))
.input(InteractionsInput.of("What is the greatest common divisor of 1071 and 462?"))
.generationConfig(
GenerationConfig.builder().thinkingSummaries(ThinkingSummaries.AUTO).build())
.stream(true)
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
try (EventStream<InteractionSSEStreamEvent> stream = response.events()) {
for (InteractionSSEStreamEvent streamEvent : stream) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepStart stepStart) {
Step step = stepStart.step().orElse(null);
if (step != null) {
System.out.printf("%n--- Step: %s ---%n", step.type());
}
} else if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof ThoughtSummaryDelta thoughtDelta) {
Content content = thoughtDelta.content().orElse(null);
if (content instanceof TextContent textContent) {
textContent.text().ifPresent(System.out::print);
}
} else if (delta instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
}
}
}
}
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.8-flash"),
Input: interactions.NewInteractionsInput("What are the top news stories in AI today, and calculate 2^64 - 1?"),
Tools: []interactions.Tool{
interactions.NewTool(interactions.GoogleSearch{}),
interactions.NewTool(interactions.CodeExecution{}),
},
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if stepStart := event.GetDataStepStart(); stepStart != nil {
step := stepStart.Step
if searchCall := step.GoogleSearchCallStep; searchCall != nil {
fmt.Printf("[Google Search Started] id: %s\n", searchCall.ID)
} else if searchRes := step.GoogleSearchResultStep; searchRes != nil {
fmt.Printf("[Google Search Results Received] for call_id: %s\n", searchRes.CallID)
} else if codeCall := step.CodeExecutionCallStep; codeCall != nil {
fmt.Printf("[Code Execution Started] id: %s\n", codeCall.ID)
} else if codeRes := step.CodeExecutionResultStep; codeRes != nil {
fmt.Printf("[Code Execution Finished] output: %s\n", codeRes.Result)
}
}
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if searchDelta := stepDelta.GetDeltaGoogleSearchCall(); searchDelta != nil {
fmt.Printf("[Search Queries] %v\n", searchDelta.Arguments.Queries)
}
if codeDelta := stepDelta.GetDeltaCodeExecutionCall(); codeDelta != nil {
fmt.Printf("[Code Delta] %s\n", codeDelta.Arguments.Code)
}
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
}
if err := stream.Err(); err != nil {
log.Fatal(err)
}
}
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.8-flash",
"input": "What is the greatest common divisor of 1071 and 462?",
"stream": true,
"generation_config": {
"thinking_summaries": "auto"
}
}'
event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3.8-flash"},"event_type":"interaction.created"}
event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}
event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":0,"delta":{"content":{"text":"**Implementing Euclidean Algorithm**\n\nI've just worked through a detailed example applying the Euclidean algorithm to find the GCD of 1071 and 462, confirming its step-by-step nature. The calculations went smoothly, tracking the remainders until zero. My focus is now solidifying the implementation logic, ensuring accuracy and considering potential edge cases. I'll translate this example into code.\n\n\n","type":"text"},"type":"thought_summary"},"event_type":"step.delta"}
event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}
event: step.stop
data: {"index":0,"event_type":"step.stop"}
event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}
...
Streaming con gli agenti
L'API Interactions supporta agenti come Deep Research. Gli agenti utilizzano background=True e restituiscono i risultati in modo asincrono, ma puoi anche trasmettere in streaming le interazioni degli agenti per ricevere aggiornamenti sui progressi e passaggi intermedi man mano che si verificano. Per maggiori dettagli, consulta la guida all'esecuzione in background e la guida a Deep Research.
Python
from google import genai
client = genai.Client()
stream = client.interactions.create(
agent="deep-research-preview-04-2026",
input="Research the latest advances in quantum computing.",
stream=True,
background=True,
agent_config={
"type": "deep-research",
"thinking_summaries": "auto"
}
)
for event in stream:
if event.event_type == "step.start":
print(f"\n--- Step: {event.step.type} ---")
elif event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
elif event.delta.type == "thought_summary":
if event.delta.content.type == "text":
print(event.delta.content.text, end="", flush=True)
elif event.event_type == "interaction.completed":
print(f"\n\nTotal Tokens: {event.interaction.usage.total_tokens}")
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const stream = await client.interactions.create({
agent: "deep-research-preview-04-2026",
input: "Research the latest advances in quantum computing.",
stream: true,
background: true,
agent_config: {
type: "deep-research",
thinking_summaries: "auto"
}
});
for await (const event of stream) {
if (event.event_type === "step.start") {
console.log(`\n--- Step: ${event.step.type} ---`);
} else if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
} else if (event.delta.type === "thought_summary") {
if (event.delta.content.type === "text") {
process.stdout.write(event.delta.content.text);
}
}
} else if (event.event_type === "interaction.completed") {
console.log(`\n\nTotal Tokens: ${event.interaction.usage.total_tokens}`);
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.InteractionCompletedEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionSseEventInteraction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.StepStart;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.interactions.ThoughtSummaryDelta;
import com.google.genai.gaos.models.interactions.Usage;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
Client client = new Client();
CreateAgentInteraction params =
CreateAgentInteraction.builder()
.agent("deep-research-preview-04-2026")
.input(InteractionsInput.of("Research the latest advances in quantum computing."))
.stream(true)
.background(true)
.agentConfig(
DeepResearchAgentConfig.builder()
.thinkingSummaries(ThinkingSummaries.AUTO)
.build())
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
try (EventStream<InteractionSSEStreamEvent> stream = response.events()) {
for (InteractionSSEStreamEvent streamEvent : stream) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepStart stepStart) {
Step step = stepStart.step().orElse(null);
if (step != null) {
System.out.printf("%n--- Step: %s ---%n", step.type());
}
} else if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
} else if (delta instanceof ThoughtSummaryDelta thoughtDelta) {
Content content = thoughtDelta.content().orElse(null);
if (content instanceof TextContent textContent) {
textContent.text().ifPresent(System.out::print);
}
}
} else if (event instanceof InteractionCompletedEvent completedEvent) {
completedEvent
.interaction()
.flatMap(InteractionSseEventInteraction::usage)
.flatMap(Usage::totalTokens)
.ifPresent(tokens -> System.out.println("\n\nTotal Tokens: " + tokens));
}
}
}
Go
package main
import (
"context"
"encoding/base64"
"fmt"
"log"
"os"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.1-flash-image-preview"),
Input: interactions.NewInteractionsInput("Generate a watercolor illustration of a lighthouse at sunset and describe the scene."),
ResponseFormat: genai.Ptr(interactions.NewCreateModelInteractionResponseFormat([]interactions.ResponseFormat{
interactions.NewResponseFormat(interactions.TextResponseFormat{}),
interactions.NewResponseFormat(interactions.ImageResponseFormat{}),
})),
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
if imgDelta := stepDelta.GetDeltaImage(); imgDelta != nil && imgDelta.Data != nil {
imageBytes, err := base64.StdEncoding.DecodeString(*imgDelta.Data)
if err != nil {
log.Fatal(err)
}
if err := os.WriteFile("lighthouse.png", imageBytes, 0644); err != nil {
log.Fatal(err)
}
fmt.Println("\n[Saved lighthouse.png]")
}
}
if completed := event.GetDataInteractionCompleted(); completed != nil {
// You can also access the final image using interaction.GetOutputImage() on a non-streamed or retrieved interaction
fmt.Println("\nGeneration complete!")
}
}
if err := stream.Err(); err != nil {
log.Fatal(err)
}
}
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"agent": "deep-research-preview-04-2026",
"input": "Research the latest advances in quantum computing.",
"stream": true,
"background": true,
"agent_config": {
"type": "deep-research",
"thinking_summaries": "auto"
}
}'
event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","agent":"deep-research-preview-04-2026"},"event_type":"interaction.created"}
event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}
event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":0,"delta":{"content":{"text":"***Generating research plan***\n\nTo best answer your request, I'm starting by constructing a comprehensive research plan. This will outline the key areas I need to investigate and the strategy I'll use to connect them."},"type":"thought_summary"},"event_type":"step.delta"}
... (additional thought steps) ...
event: step.stop
data: {"index":0,"event_type":"step.stop"}
event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}
event: step.delta
data: {"index":1,"delta":{"text":"# The Quantum Inflection Point: Exhaustive Analysis of Hardware, Algorithms, and Market Dynamics in 2026\n\n## Executive Summary\n\n..."},"event_type":"step.delta"}
event: step.stop
data: {"index":1,"event_type":"step.stop"}
event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":1117031,"total_input_tokens":428865,"total_output_tokens":22294,"total_thought_tokens":26213},"created":"2026-05-12T17:24:27Z","updated":"2026-05-12T17:24:27Z","object":"interaction","agent":"deep-research-preview-04-2026"},"event_type":"interaction.completed"}
event: done
data: [DONE]
Generazione di immagini in streaming
L'API Interactions supporta lo streaming simultaneo di più modalità di output. Se richiedi sia text sia image in response_format, puoi ricevere testo alternato e immagini generate nello stesso stream.
L'esempio seguente utilizza gemini-3.1-flash-image (Nano Banana 2) per cercare informazioni e generare una storia con illustrazioni intercalate.
Python
from google import genai
client = genai.Client()
stream = client.interactions.create(
model="gemini-3.1-flash-image",
tools=[{"type": "google_search", "search_types": ["web_search", "image_search"]}],
input="Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
response_format=[
{"type": "text"},
{"type": "image"}
],
stream=True,
)
for event in stream:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="", flush=True)
elif event.delta.type == "image":
print(f"\n[Image chunk: {len(event.delta.data)} bytes]", end="", flush=True)
JavaScript
import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const stream = await client.interactions.create({
model: "gemini-3.1-flash-image",
tools: [{ type: "google_search", search_types: ["web_search", "image_search"] }],
input: "Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
response_format: [
{ type: "text" },
{ type: "image" }
],
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
} else if (event.delta.type === "image") {
console.log(`\n[Image chunk: ${event.delta.data.length} bytes]`);
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.GoogleSearch;
import com.google.genai.gaos.models.interactions.GoogleSearchSearchType;
import com.google.genai.gaos.models.interactions.ImageDelta;
import com.google.genai.gaos.models.interactions.ImageResponseFormat;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.StepDeltaData;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.CreateInteractionResponse;
import com.google.genai.gaos.utils.EventStream;
import java.util.Arrays;
Client client = new Client();
CreateModelInteraction params =
CreateModelInteraction.builder()
.model(Model.of("gemini-3.1-flash-image"))
.tools(
Arrays.asList(
GoogleSearch.builder()
.searchTypes(
Arrays.asList(
GoogleSearchSearchType.of("web_search"),
GoogleSearchSearchType.of("image_search")))
.build()))
.input(
InteractionsInput.of(
"Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images."))
.responseFormat(
CreateModelInteractionResponseFormat.of(
Arrays.asList(
ResponseFormat.of(TextResponseFormat.builder().build()),
ResponseFormat.of(ImageResponseFormat.builder().build()))))
.stream(true)
.build();
CreateInteractionResponse response =
client.interactions.create(CreateInteractionRequestBody.of(params));
try (EventStream<InteractionSSEStreamEvent> stream = response.events()) {
for (InteractionSSEStreamEvent streamEvent : stream) {
InteractionSSEEvent event = streamEvent.data().orElse(null);
if (event instanceof StepDelta stepDelta) {
StepDeltaData delta = stepDelta.delta().orElse(null);
if (delta instanceof TextDelta textDelta) {
textDelta.text().ifPresent(System.out::print);
} else if (delta instanceof ImageDelta imageDelta) {
imageDelta
.data()
.ifPresent(data -> System.out.printf("%n[Image chunk: %d bytes]", data.length()));
}
}
}
}
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
"google.golang.org/genai/interactions/models/interactions"
"google.golang.org/genai/interactions/models/operations"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
var interactionID string
var lastEventID *string
res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
Model: interactions.Model("gemini-3.8-pro"),
Input: interactions.NewInteractionsInput("Write a detailed 5-section guide to distributed consensus algorithms."),
Stream: genai.Ptr(true),
}),
})
if err != nil {
log.Fatal(err)
}
stream := res.InteractionSSEStreamEvent
defer stream.Close()
for stream.Next() {
event := stream.Value()
if created := event.GetDataInteractionCreated(); created != nil {
interactionID = created.Interaction.ID
if created.EventID != nil {
lastEventID = created.EventID
}
}
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if stepDelta.EventID != nil {
lastEventID = stepDelta.EventID
}
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
}
if err := stream.Err(); err != nil {
fmt.Printf("\nStream interrupted (%v). Resuming...\n", err)
if interactionID != "" && lastEventID != nil {
resumedRes, err := client.Interactions.Get(ctx, operations.GetInteractionByIDRequest{
ID: interactionID,
Stream: genai.Ptr(true),
LastEventID: lastEventID,
})
if err != nil {
log.Fatal(err)
}
resumedStream := resumedRes.InteractionSSEStreamEvent
defer resumedStream.Close()
for resumedStream.Next() {
event := resumedStream.Value()
if stepDelta := event.GetDataStepDelta(); stepDelta != nil {
if textDelta := stepDelta.GetDeltaText(); textDelta != nil {
fmt.Print(textDelta.GetText())
}
}
}
if err := resumedStream.Err(); err != nil {
log.Fatal(err)
}
}
}
}
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
"model": "gemini-3.1-flash-image",
"input": "Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
"stream": true,
"tools": [
{ "type": "google_search",
"search_types": ["web_search", "image_search"]
}
],
"generation_config": {
"thinking_summaries": "auto"
},
"response_format": [
{ "type": "text" }, { "type": "image"}
]
}'
event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3.1-flash-image"},"event_type":"interaction.created"}
event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}
event: step.start
data: {"index":0,"step":{"type":"model_output"},"event_type":"step.start"}
event: step.delta
data: {"index":0,"delta":{"text":"Here is a short illustrated story about the Colosseum...\n\n### Part 1: The New Flavian Amphitheater\n\n...","type":"text"},"event_type":"step.delta"}
...
event: step.stop
data: {"index":0,"event_type":"step.stop"}
event: step.start
data: {"index":1,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":1,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}
event: step.stop
data: {"index":1,"event_type":"step.stop"}
event: step.start
data: {"index":2,"step":{"type":"model_output"},"event_type":"step.start"}
event: step.delta
data: {"index":2,"delta":{"mime_type":"image/jpeg","data":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoHBwgHBgoICAgLCg...","type":"image"},"event_type":"step.delta"}
event: step.delta
data: {"index":2,"delta":{"text":"### Part 2: The Hypogeum and the Wait\n\n...","type":"text"},"event_type":"step.delta"}
...
event: step.stop
data: {"index":2,"event_type":"step.stop"}
event: step.start
data: {"index":3,"step":{"type":"thought"},"event_type":"step.start"}
event: step.delta
data: {"index":3,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}
event: step.stop
data: {"index":3,"event_type":"step.stop"}
event: step.start
data: {"index":4,"step":{"type":"model_output"},"event_type":"step.start"}
event: step.delta
data: {"index":4,"delta":{"mime_type":"image/jpeg","data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...","type":"image"},"event_type":"step.delta"}
event: step.delta
data: {"index":4,"delta":{"text":"### Part 3: The Moment of Spectacle\n\n...","type":"text"},"event_type":"step.delta"}
...
event: step.stop
data: {"index":4,"event_type":"step.stop"}
event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":6128,"total_input_tokens":29,"total_output_tokens":6099,"output_tokens_by_modality":[{"modality":"image","tokens":4480}]}},"event_type":"interaction.completed"}
event: done
data: [DONE]
Gestione di eventi sconosciuti
In conformità con le norme di controllo delle versioni dell'API, nel tempo potrebbero essere aggiunti nuovi tipi di eventi e tipi di delta. Il codice deve gestire i tipi di eventi sconosciuti in modo controllato: registra e salta gli eventi che non riconosci anziché generare un errore.
Passaggi successivi
- Scopri di più sull'API Interactions.
- Esplora la chiamata di funzione con gli strumenti.
- Scopri di più sulla funzione Pensa per un ragionamento avanzato.
- Prova l'agente Deep Research per le attività di lunga durata.
- Per tutti i tipi di eventi e delta, consulta il riferimento API Interactions.