Interfejs API do przetwarzania zbiorczego

from google import genai
from google.genai import types

client = genai.Client()

# A list of dictionaries, where each is a GenerateContentRequest
inline_requests = [
    {
        'contents': [{
            'parts': [{'text': 'Tell me a one-sentence joke.'}],
            'role': 'user'
        }]
    },
    {
        'contents': [{
            'parts': [{'text': 'Why is the sky blue?'}],
            'role': 'user'
        }]
    }
]

inline_batch_job = client.batches.create(
    model="models/gemini-2.5-flash",
    src=inline_requests,
    config={
        'display_name': "inlined-requests-job-1",
    },
)

print(f"Created batch job: {inline_batch_job.name}")

import {GoogleGenAI} from '@google/genai';
const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY});

const inlinedRequests = [
    {
        contents: [{
            parts: [{text: 'Tell me a one-sentence joke.'}],
            role: 'user'
        }]
    },
    {
        contents: [{
            parts: [{'text': 'Why is the sky blue?'}],
            role: 'user'
        }]
    }
]

const response = await ai.batches.create({
    model: 'gemini-2.5-flash',
    src: inlinedRequests,
    config: {
        displayName: 'inlined-requests-job-1',
    }
});

console.log(response);

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:batchGenerateContent \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-X POST \
-H "Content-Type:application/json" \
-d '{
    "batch": {
        "display_name": "my-batch-requests",
        "input_config": {
            "requests": {
                "requests": [
                    {
                        "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]},
                        "metadata": {
                            "key": "request-1"
                        }
                    },
                    {
                        "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]},
                        "metadata": {
                            "key": "request-2"
                        }
                    }
                ]
            }
        }
    }
}'

from google import genai
from google.genai import types

client = genai.Client()

# Create a sample JSONL file
with open("my-batch-requests.jsonl", "w") as f:
    requests = [
        {"key": "request-1", "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]}},
        {"key": "request-2", "request": {"contents": [{"parts": [{"text": "What are the main ingredients in a Margherita pizza?"}]}]}}
    ]
    for req in requests:
        f.write(json.dumps(req) + "\n")

# Upload the file to the File API
uploaded_file = client.files.upload(
    file='my-batch-requests.jsonl',
    config=types.UploadFileConfig(display_name='my-batch-requests', mime_type='jsonl')
)

print(f"Uploaded file: {uploaded_file.name}")

import {GoogleGenAI} from '@google/genai';
import * as fs from "fs";
import * as path from "path";
import { fileURLToPath } from 'url';

const GEMINI_API_KEY = process.env.GEMINI_API_KEY;
const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY});
const fileName = "my-batch-requests.jsonl";

// Define the requests
const requests = [
    { "key": "request-1", "request": { "contents": [{ "parts": [{ "text": "Describe the process of photosynthesis." }] }] } },
    { "key": "request-2", "request": { "contents": [{ "parts": [{ "text": "What are the main ingredients in a Margherita pizza?" }] }] } }
];

// Construct the full path to file
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const filePath = path.join(__dirname, fileName); // __dirname is the directory of the current script

async function writeBatchRequestsToFile(requests, filePath) {
    try {
        // Use a writable stream for efficiency, especially with larger files.
        const writeStream = fs.createWriteStream(filePath, { flags: 'w' });

        writeStream.on('error', (err) => {
            console.error(`Error writing to file ${filePath}:`, err);
        });

        for (const req of requests) {
            writeStream.write(JSON.stringify(req) + '\n');
        }

        writeStream.end();

        console.log(`Successfully wrote batch requests to ${filePath}`);

    } catch (error) {
        // This catch block is for errors that might occur before stream setup,
        // stream errors are handled by the 'error' event.
        console.error(`An unexpected error occurred:`, error);
    }
}

// Write to a file.
writeBatchRequestsToFile(requests, filePath);

// Upload the file to the File API.
const uploadedFile = await ai.files.upload({file: 'my-batch-requests.jsonl', config: {
    mimeType: 'jsonl',
}});
console.log(uploadedFile.name);

tmp_batch_input_file=batch_input.tmp
echo -e '{"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}], "generationConfig": {"temperature": 0.7}}\n{"contents": [{"parts": [{"text": "What are the main ingredients in a Margherita pizza?"}]}]}' > batch_input.tmp
MIME_TYPE=$(file -b --mime-type "${tmp_batch_input_file}")
NUM_BYTES=$(wc -c < "${tmp_batch_input_file}")
DISPLAY_NAME=BatchInput

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
-D "${tmp_header_file}" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "X-Goog-Upload-Protocol: resumable" \
-H "X-Goog-Upload-Command: start" \
-H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
-H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
-H "Content-Type: application/jsonl" \
-d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
-H "Content-Length: ${NUM_BYTES}" \
-H "X-Goog-Upload-Offset: 0" \
-H "X-Goog-Upload-Command: upload, finalize" \
--data-binary "@${tmp_batch_input_file}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)

# Assumes `uploaded_file` is the file object from the previous step
file_batch_job = client.batches.create(
    model="gemini-2.5-flash",
    src=uploaded_file.name,
    config={
        'display_name': "file-upload-job-1",
    },
)

print(f"Created batch job: {file_batch_job.name}")

// Assumes `uploadedFile` is the file object from the previous step
const fileBatchJob = await ai.batches.create({
    model: 'gemini-2.5-flash',
    src: uploadedFile.name,
    config: {
        displayName: 'file-upload-job-1',
    }
});

console.log(fileBatchJob);

# Set the File ID taken from the upload response.
BATCH_INPUT_FILE='files/123456'
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:batchGenerateContent \
-X POST \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" \
-d "{
    'batch': {
        'display_name': 'my-batch-requests',
        'input_config': {
            'file_name': '${BATCH_INPUT_FILE}'
        }
    }
}"

# Creating an embeddings batch job with an input file request:
file_job = client.batches.create_embeddings(
    model="gemini-embedding-001",
    src={'file_name': uploaded_batch_requests.name},
    config={'display_name': "Input embeddings batch"},
)

# Creating an embeddings batch job with an inline request:
batch_job = client.batches.create_embeddings(
    model="gemini-embedding-001",
    # For a predefined list of requests `inlined_requests`
    src={'inlined_requests': inlined_requests},
    config={'display_name': "Inlined embeddings batch"},
)

// Creating an embeddings batch job with an input file request:
let fileJob;
fileJob = await client.batches.createEmbeddings({
    model: 'gemini-embedding-001',
    src: {fileName: uploadedBatchRequests.name},
    config: {displayName: 'Input embeddings batch'},
});
console.log(`Created batch job: ${fileJob.name}`);

// Creating an embeddings batch job with an inline request:
let batchJob;
batchJob = await client.batches.createEmbeddings({
    model: 'gemini-embedding-001',
    // For a predefined a list of requests `inlinedRequests`
    src: {inlinedRequests: inlinedRequests},
    config: {displayName: 'Inlined embeddings batch'},
});
console.log(`Created batch job: ${batchJob.name}`);

inline_requests_list = [
    {'contents': [{'parts': [{'text': 'Write a short poem about a cloud.'}]}]},
    {'contents': [{'parts': [{'text': 'Write a short poem about a cat.'}]}], 'system_instructions': {'parts': [{'text': 'You are a cat. Your name is Neko.'}]}}
]

inlineRequestsList = [
    {contents: [{parts: [{text: 'Write a short poem about a cloud.'}]}]},
    {contents: [{parts: [{text: 'Write a short poem about a cat.'}]}], systemInstructions: {parts: [{text: 'You are a cat. Your name is Neko.'}]}}
]

inline_requests_list = [
    {'contents': [{'parts': [{'text': 'Who won the euro 1998?'}]}]},
    {'contents': [{'parts': [{'text': 'Who won the euro 2025?'}]}], 'tools': [{'google_search ': {}}]}
]

inlineRequestsList = [
    {contents: [{parts: [{text: 'Who won the euro 1998?'}]}]},
    {contents: [{parts: [{text: 'Who won the euro 2025?'}]}], tools: [{googleSearch: {}}]}
]

from google import genai
from pydantic import BaseModel, TypeAdapter

class Recipe(BaseModel):
    recipe_name: str
    ingredients: list[str]

client = genai.Client()

# A list of dictionaries, where each is a GenerateContentRequest
inline_requests = [
    {
        'contents': [{
            'parts': [{'text': 'List a few popular cookie recipes, and include the amounts of ingredients.'}],
            'role': 'user'
        }],
        'config': {
            'response_mime_type': 'application/json',
            'response_schema': list[Recipe]
        }
    },
    {
        'contents': [{
            'parts': [{'text': 'List a few popular gluten free cookie recipes, and include the amounts of ingredients.'}],
            'role': 'user'
        }],
        'config': {
            'response_mime_type': 'application/json',
            'response_schema': list[Recipe]
        }
    }
]

inline_batch_job = client.batches.create(
    model="models/gemini-2.5-flash",
    src=inline_requests,
    config={
        'display_name': "structured-output-job-1"
    },
)

# wait for the job to finish
job_name = inline_batch_job.name
print(f"Polling status for job: {job_name}")

while True:
    batch_job_inline = client.batches.get(name=job_name)
    if batch_job_inline.state.name in ('JOB_STATE_SUCCEEDED', 'JOB_STATE_FAILED', 'JOB_STATE_CANCELLED', 'JOB_STATE_EXPIRED'):
        break
    print(f"Job not finished. Current state: {batch_job_inline.state.name}. Waiting 30 seconds...")
    time.sleep(30)

print(f"Job finished with state: {batch_job_inline.state.name}")

# print the response
for i, inline_response in enumerate(batch_job_inline.dest.inlined_responses, start=1):
    print(f"\n--- Response {i} ---")

    # Check for a successful response
    if inline_response.response:
        # The .text property is a shortcut to the generated text.
        print(inline_response.response.text)

import {GoogleGenAI, Type} from '@google/genai';
const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY});

const inlinedRequests = [
    {
        contents: [{
            parts: [{text: 'List a few popular cookie recipes, and include the amounts of ingredients.'}],
            role: 'user'
        }],
        config: {
            responseMimeType: 'application/json',
            responseSchema: {
            type: Type.ARRAY,
            items: {
                type: Type.OBJECT,
                properties: {
                'recipeName': {
                    type: Type.STRING,
                    description: 'Name of the recipe',
                    nullable: false,
                },
                'ingredients': {
                    type: Type.ARRAY,
                    items: {
                    type: Type.STRING,
                    description: 'Ingredients of the recipe',
                    nullable: false,
                    },
                },
                },
                required: ['recipeName'],
            },
            },
        }
    },
    {
        contents: [{
            parts: [{text: 'List a few popular gluten free cookie recipes, and include the amounts of ingredients.'}],
            role: 'user'
        }],
        config: {
            responseMimeType: 'application/json',
            responseSchema: {
            type: Type.ARRAY,
            items: {
                type: Type.OBJECT,
                properties: {
                'recipeName': {
                    type: Type.STRING,
                    description: 'Name of the recipe',
                    nullable: false,
                },
                'ingredients': {
                    type: Type.ARRAY,
                    items: {
                    type: Type.STRING,
                    description: 'Ingredients of the recipe',
                    nullable: false,
                    },
                },
                },
                required: ['recipeName'],
            },
            },
        }
    }
]

const inlinedBatchJob = await ai.batches.create({
    model: 'gemini-2.5-flash',
    src: inlinedRequests,
    config: {
        displayName: 'inlined-requests-job-1',
    }
});

# Use the name of the job you want to check
# e.g., inline_batch_job.name from the previous step
job_name = "YOUR_BATCH_JOB_NAME"  # (e.g. 'batches/your-batch-id')
batch_job = client.batches.get(name=job_name)

completed_states = set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_EXPIRED',
])

print(f"Polling status for job: {job_name}")
batch_job = client.batches.get(name=job_name) # Initial get
while batch_job.state.name not in completed_states:
  print(f"Current state: {batch_job.state.name}")
  time.sleep(30) # Wait for 30 seconds before polling again
  batch_job = client.batches.get(name=job_name)

print(f"Job finished with state: {batch_job.state.name}")
if batch_job.state.name == 'JOB_STATE_FAILED':
    print(f"Error: {batch_job.error}")

// Use the name of the job you want to check
// e.g., inlinedBatchJob.name from the previous step
let batchJob;
const completedStates = new Set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_EXPIRED',
]);

try {
    batchJob = await ai.batches.get({name: inlinedBatchJob.name});
    while (!completedStates.has(batchJob.state)) {
        console.log(`Current state: ${batchJob.state}`);
        // Wait for 30 seconds before polling again
        await new Promise(resolve => setTimeout(resolve, 30000));
        batchJob = await client.batches.get({ name: batchJob.name });
    }
    console.log(`Job finished with state: ${batchJob.state}`);
    if (batchJob.state === 'JOB_STATE_FAILED') {
        // The exact structure of `error` might vary depending on the SDK
        // This assumes `error` is an object with a `message` property.
        console.error(`Error: ${batchJob.state}`);
    }
} catch (error) {
    console.error(`An error occurred while polling job ${batchJob.name}:`, error);
}

import json

# Use the name of the job you want to check
# e.g., inline_batch_job.name from the previous step
job_name = "YOUR_BATCH_JOB_NAME"
batch_job = client.batches.get(name=job_name)

if batch_job.state.name == 'JOB_STATE_SUCCEEDED':

    # If batch job was created with a file
    if batch_job.dest and batch_job.dest.file_name:
        # Results are in a file
        result_file_name = batch_job.dest.file_name
        print(f"Results are in file: {result_file_name}")

        print("Downloading result file content...")
        file_content = client.files.download(file=result_file_name)
        # Process file_content (bytes) as needed
        print(file_content.decode('utf-8'))

    # If batch job was created with inline request
    # (for embeddings, use batch_job.dest.inlined_embed_content_responses)
    elif batch_job.dest and batch_job.dest.inlined_responses:
        # Results are inline
        print("Results are inline:")
        for i, inline_response in enumerate(batch_job.dest.inlined_responses):
            print(f"Response {i+1}:")
            if inline_response.response:
                # Accessing response, structure may vary.
                try:
                    print(inline_response.response.text)
                except AttributeError:
                    print(inline_response.response) # Fallback
            elif inline_response.error:
                print(f"Error: {inline_response.error}")
    else:
        print("No results found (neither file nor inline).")
else:
    print(f"Job did not succeed. Final state: {batch_job.state.name}")
    if batch_job.error:
        print(f"Error: {batch_job.error}")

// Use the name of the job you want to check
// e.g., inlinedBatchJob.name from the previous step
const jobName = "YOUR_BATCH_JOB_NAME";

try {
    const batchJob = await ai.batches.get({ name: jobName });

    if (batchJob.state === 'JOB_STATE_SUCCEEDED') {
        console.log('Found completed batch:', batchJob.displayName);
        console.log(batchJob);

        // If batch job was created with a file destination
        if (batchJob.dest?.fileName) {
            const resultFileName = batchJob.dest.fileName;
            console.log(`Results are in file: ${resultFileName}`);

            console.log("Downloading result file content...");
            const fileContentBuffer = await ai.files.download({ file: resultFileName });

            // Process fileContentBuffer (Buffer) as needed
            console.log(fileContentBuffer.toString('utf-8'));
        }

        // If batch job was created with inline responses
        else if (batchJob.dest?.inlinedResponses) {
            console.log("Results are inline:");
            for (let i = 0; i < batchJob.dest.inlinedResponses.length; i++) {
                const inlineResponse = batchJob.dest.inlinedResponses[i];
                console.log(`Response ${i + 1}:`);
                if (inlineResponse.response) {
                    // Accessing response, structure may vary.
                    if (inlineResponse.response.text !== undefined) {
                        console.log(inlineResponse.response.text);
                    } else {
                        console.log(inlineResponse.response); // Fallback
                    }
                } else if (inlineResponse.error) {
                    console.error(`Error: ${inlineResponse.error}`);
                }
            }
        }

        // If batch job was an embedding batch with inline responses
        else if (batchJob.dest?.inlinedEmbedContentResponses) {
            console.log("Embedding results found inline:");
            for (let i = 0; i < batchJob.dest.inlinedEmbedContentResponses.length; i++) {
                const inlineResponse = batchJob.dest.inlinedEmbedContentResponses[i];
                console.log(`Response ${i + 1}:`);
                if (inlineResponse.response) {
                    console.log(inlineResponse.response);
                } else if (inlineResponse.error) {
                    console.error(`Error: ${inlineResponse.error}`);
                }
            }
        } else {
            console.log("No results found (neither file nor inline).");
        }
    } else {
        console.log(`Job did not succeed. Final state: ${batchJob.state}`);
        if (batchJob.error) {
            console.error(`Error: ${typeof batchJob.error === 'string' ? batchJob.error : batchJob.error.message || JSON.stringify(batchJob.error)}`);
        }
    }
} catch (error) {
    console.error(`An error occurred while processing job ${jobName}:`, error);
}

BATCH_NAME="batches/123456" # Your batch job name

curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" 2> /dev/null > batch_status.json

if jq -r '.done' batch_status.json | grep -q "false"; then
    echo "Batch has not finished processing"
fi

batch_state=$(jq -r '.metadata.state' batch_status.json)
if [[ $batch_state = "JOB_STATE_SUCCEEDED" ]]; then
    if [[ $(jq '.response | has("inlinedResponses")' batch_status.json) = "true" ]]; then
        jq -r '.response.inlinedResponses' batch_status.json
        exit
    fi
    responses_file_name=$(jq -r '.response.responsesFile' batch_status.json)
    curl https://generativelanguage.googleapis.com/download/v1beta/$responses_file_name:download?alt=media \
    -H "x-goog-api-key: $GEMINI_API_KEY" 2> /dev/null
elif [[ $batch_state = "JOB_STATE_FAILED" ]]; then
    jq '.error' batch_status.json
elif [[ $batch_state == "JOB_STATE_CANCELLED" ]]; then
    echo "Batch was cancelled by the user"
elif [[ $batch_state == "JOB_STATE_EXPIRED" ]]; then
    echo "Batch expired after 48 hours"
fi

# Cancel a batch job
client.batches.cancel(name=batch_job_to_cancel.name)

await ai.batches.cancel({name: batchJobToCancel.name});

BATCH_NAME="batches/123456" # Your batch job name

# Cancel the batch
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME:cancel \
-H "x-goog-api-key: $GEMINI_API_KEY" \

# Confirm that the status of the batch after cancellation is JOB_STATE_CANCELLED
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" 2> /dev/null | jq -r '.metadata.state'

# Delete a batch job
client.batches.delete(name=batch_job_to_delete.name)

await ai.batches.delete({name: batchJobToDelete.name});

BATCH_NAME="batches/123456" # Your batch job name

# Delete the batch job
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME:delete \
-H "x-goog-api-key: $GEMINI_API_KEY"