OpenBatch is a lightweight Python utility designed to streamline the creation of JSONL files for the OpenAI Batch API. It provides a type-safe and intuitive interface using Pydantic models to construct requests for the /v1/responses, /v1/chat/completions, and /v1/embeddings endpoints.
For a detailed guide on using OpenBatch, please refer to the OpenBatch Documentation.
The library offers two distinct APIs to fit your workflow:
BatchCollector: A high-level, fluent API that mimics the officialopenaiclient. It's perfect for adding individual, distinct requests to a batch file with minimal setup.BatchJobManager: A lower-level API designed for programmatically generating large batches of requests from templates and lists of inputs. It's ideal for scalable tasks like classification, data extraction, or bulk embeddings.
pip install openbatchThe BatchCollector provides the simplest way to get started. You instantiate it with a file path, and then use its methods to add requests one by one. This example showcases calls to the Responses and Embeddings APIs.
from pydantic import BaseModel, Field
from typing import List
from openbatch import BatchCollector, ReasoningConfig
# Define a Pydantic model for structured JSON output
class LogicalAnalysis(BaseModel):
premise: str
conclusion: str
is_valid: bool = Field(description="Whether the conclusion logically follows from the premise.")
# 1. Initialize the collector with the desired output file path
BATCH_FILE = "responses_api_batch.jsonl"
collector = BatchCollector(batch_file_path=BATCH_FILE)
# 2. Add a standard request to the Responses API
collector.responses.create(
custom_id="request-1-response",
model="gpt-4o",
instructions="You are a historian. Provide a concise summary.",
input="What were the main causes of the French Revolution?",
max_output_tokens=200
)
# 3. Add a structured request using a reasoning model.
# Note: Reasoning models may not support 'temperature', and it is omitted here.
collector.responses.parse(
custom_id="request-2-reasoning",
model="gpt-5-mini", # Hypothetical reasoning model
text_format=LogicalAnalysis,
instructions="Analyze the logical argument provided by the user.",
input="Premise: All birds can fly. A penguin is a bird. Conclusion: Therefore, a penguin can fly.",
reasoning=ReasoningConfig(effort="high") # Configure the reasoning effort
)
# We need to create a separate collector for embeddings since the batch API requires one request type per file
EMBEDDINGS_BATCH_FILE = "embeddings_api_batch.jsonl"
embeddings_collector = BatchCollector(batch_file_path=EMBEDDINGS_BATCH_FILE)
# 4. Add an Embedding request
embeddings_collector.embeddings.create(
custom_id="request-3-embedding",
model="text-embedding-3-small",
inp="OpenBatch simplifies creating batch jobs."
)
print(f"Batch file '{BATCH_FILE}' created successfully.")For more complex or repetitive tasks, the BatchJobManager is the more appropriate tool. It excels at generating thousands of requests from a single template, for any supported API.
Imagine you want to generate marketing copy for 10,000 new products. Instead of creating each request manually, you can use a template with the Responses API.
from openbatch import (
BatchJobManager,
PromptTemplate,
Message,
ResponsesRequest,
PromptTemplateInputInstance
)
# 1. Define a prompt template with placeholders
copywriting_template = PromptTemplate(
messages=[
Message(role="system", content="You are a marketing copywriter. Generate a catchy, two-sentence description."),
Message(role="user", content="Product: {product_name}, Features: {features}")
]
)
# 2. Define the common configuration for all requests
common_request_config = ResponsesRequest(
model="gpt-4o-mini",
temperature=0.8,
max_output_tokens=100
)
# 3. Create a list of input instances
product_instances = [
PromptTemplateInputInstance(
id="prod_001",
prompt_value_mapping={"product_name": "AeroGlide Drone", "features": "4K camera, 30-min flight"}
),
PromptTemplateInputInstance(
id="prod_002",
prompt_value_mapping={"product_name": "HydroPure Bottle", "features": "Self-cleaning, insulated steel"}
),
# ... add up to 9,998 more products
]
# 4. Use the manager to generate the batch file
manager = BatchJobManager()
manager.add_templated_instances(
prompt=copywriting_template,
common_request=common_request_config,
input_instances=product_instances,
save_file_path="copywriting_batch.jsonl"
)Similarly, you can easily create a batch job for generating embeddings for a large number of documents.
from openbatch import BatchJobManager, EmbeddingsRequest, EmbeddingInputInstance
# 1. Define the common configuration for all embedding requests
common_embedding_config = EmbeddingsRequest(
model="text-embedding-3-small",
dimensions=512
)
# 2. Create a list of input instances
documents_to_embed = [
EmbeddingInputInstance(id="doc_1", input="The sky is blue."),
EmbeddingInputInstance(id="doc_2", input="Grass is green."),
# ... add thousands more documents
]
# 3. Use the manager to generate the batch file
manager = BatchJobManager()
manager.add_embedding_requests(
inputs=documents_to_embed,
common_request=common_embedding_config,
save_file_path="embeddings_batch.jsonl"
)The common_request objects (ResponsesRequest, EmbeddingsRequest, etc.) are Pydantic models that expose all available API parameters. You can configure any parameter by passing it to the constructor.
from openbatch import ResponsesRequest, ReasoningConfig
# Example of a more detailed configuration for the Responses API
detailed_config = ResponsesRequest(
model="gpt-4o",
service_tier="flex",
reasoning=ReasoningConfig(effort="minimal"),
max_output_tokens=500
)You can also override any common setting on a per-instance basis by using the instance_request_options field.
OpenBatch helps you create the batch file. The next steps involve using that file with the OpenAI API:
- Upload File: Upload your generated
.jsonlfile to OpenAI. - Create Batch Job: Create a new batch job pointing to your uploaded file.
- Retrieve Results: Monitor the job's status and, once completed, download the output file with the results.
For detailed instructions on these steps, please refer to the Official OpenAI Batch API Documentation.