การโต้ตอบในการสตรีม

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3.5-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)

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3.5-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);
        }
    }
}

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-3.5-flash",
    "input": "Count from 1 to 25.",
    "stream": true
  }'

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-3.5-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-3.5-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)

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-3.5-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-3.5-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);
            }
        }
    }
}

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-3.5-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"]
        }
      }
    ]
  }'

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-3.5-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\"}"
            }
          ]
        }
      }
    ]
  }'

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-3.5-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.")

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-3.5-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.");
        }
    }
}

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-3.5-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"]
        }
      }
    ]
  }'

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3.5-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)

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3.5-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);
        }
    }
}

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-3.5-flash",
    "input": "What is the greatest common divisor of 1071 and 462?",
    "stream": true,
    "generation_config": {
      "thinking_summaries": "auto"
    }
  }'

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}")

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}`);
    }
}

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"
    }
  }'

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)

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]`);
        }
    }
}

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"}
    ]
  }'