Gemini Batch API now supports Embeddings and OpenAI Compatibility

# Create a JSONL that contains these lines:
# {"key": "request_1", "request": {"output_dimensionality": 3, "content": {"parts": [{"text": "Explain GenAI"}]}}}
# {"key": "request_2", "request": {"output_dimensionality": 4, "content": {"parts": [{"text": "Explain quantum computing"}]}}}

uploaded_batch_requests = client.files.upload(file='embedding_requests.json')

batch_job = client.batches.create_embeddings(
    model="gemini-embedding-001",
    src={"file_name": uploaded_batch_requests.name}
)

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

# Wait for up to 24 hours

if batch_job.state.name == 'JOB_STATE_SUCCEEDED':
    result_file_name = batch_job.dest.file_name
    file_content_bytes = client.files.download(file=result_file_name)
    file_content = file_content_bytes.decode('utf-8')

    for line in file_content.splitlines():
        print(line)

from openai import OpenAI

openai_client = OpenAI(
    api_key="GEMINI_API_KEY",
    base_url="https://generativelanguage.googleapis.com/v1alphav1beta/openai/"
)

# Upload JSONL file in OpenAI batch input format...

# Create batch
batch = openai_client.batches.create(
    input_file_id=batch_input_file_id,
    endpoint="/v1/chat/completions",
    completion_window="24h"
)

# Wait for up to 24 hours & poll for status
batch = openai_client.batches.retrieve(batch.id)

if batch.status == "completed":
    # Download results...