670 lines
18 KiB
Plaintext
670 lines
18 KiB
Plaintext
---
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title: "Files and Batch API"
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description: "Upload files and create batch jobs for asynchronous processing using the OpenAI SDK through Bifrost across multiple providers."
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tag: "Beta"
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icon: "folder-open"
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---
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## Overview
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Bifrost supports the OpenAI Files API and Batch API with **cross-provider routing**. This means you can use the familiar OpenAI SDK to manage files and batch jobs across multiple providers including OpenAI, Anthropic, Bedrock, and Gemini.
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The provider is specified using `extra_body` (for POST requests) or `extra_query` (for GET requests) parameters.
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---
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## Client Setup
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The base client setup is the same for all providers. The provider is specified per-request:
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key" # Your actual API key
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)
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```
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---
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## Files API
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### Upload a File
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<Note>
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**Bedrock** requires S3 storage configuration. OpenAI and Gemini use their native file storage. Anthropic uses inline requests (no file upload).
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</Note>
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<Tabs group="provider">
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<Tab title="OpenAI Provider">
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-openai-api-key"
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)
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# Create JSONL content for OpenAI batch format
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jsonl_content = '''{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 100}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "How are you?"}], "max_tokens": 100}}'''
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# Upload file (uses OpenAI's native file storage)
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response = client.files.create(
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file=("batch_input.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={"provider": "openai"},
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)
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print(f"Uploaded file ID: {response.id}")
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```
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</Tab>
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<Tab title="Bedrock Provider">
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For Bedrock, you need to provide S3 storage configuration:
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# Create JSONL content using OpenAI-style format (Bifrost converts to Bedrock format internally)
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jsonl_content = '''{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic.claude-3-sonnet-20240229-v1:0", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 100}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic.claude-3-sonnet-20240229-v1:0", "messages": [{"role": "user", "content": "How are you?"}], "max_tokens": 100}}'''
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# Upload file with S3 storage configuration
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response = client.files.create(
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file=("batch_input.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={
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"provider": "bedrock",
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"storage_config": {
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"s3": {
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"bucket": "your-s3-bucket",
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"region": "us-west-2",
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"prefix": "bifrost-batch-output",
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},
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},
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},
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)
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print(f"Uploaded file ID: {response.id}")
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```
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</Tab>
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<Tab title="Anthropic Provider">
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Anthropic uses inline requests for batching (no file upload needed). See the Batch API section below.
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</Tab>
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<Tab title="Gemini Provider">
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# Create JSONL content using OpenAI-style format (Bifrost converts to Gemini format internally)
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jsonl_content = '''{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 100}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash", "messages": [{"role": "user", "content": "How are you?"}], "max_tokens": 100}}'''
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# Upload file (uses Gemini's native file storage)
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response = client.files.create(
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file=("batch_input.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={"provider": "gemini"},
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)
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print(f"Uploaded file ID: {response.id}")
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```
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</Tab>
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</Tabs>
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### List Files
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```python
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# List files for OpenAI or Gemini (no S3 config needed)
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response = client.files.list(
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extra_query={"provider": "openai"} # or "gemini"
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)
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for file in response.data:
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print(f"File ID: {file.id}, Name: {file.filename}")
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# For Bedrock (requires S3 config)
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response = client.files.list(
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extra_query={
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"provider": "bedrock",
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"storage_config": {
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"s3": {
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"bucket": "your-s3-bucket",
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"region": "us-west-2",
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"prefix": "bifrost-batch-output",
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},
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},
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}
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)
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```
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### Retrieve File Metadata
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```python
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# Retrieve file metadata (specify provider)
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file_id = "file-abc123"
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response = client.files.retrieve(
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file_id,
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extra_query={"provider": "bedrock"} # or "openai", "gemini"
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)
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print(f"File ID: {response.id}")
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print(f"Filename: {response.filename}")
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print(f"Purpose: {response.purpose}")
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print(f"Bytes: {response.bytes}")
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```
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### Delete a File
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```python
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# Delete file (specify provider)
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file_id = "file-abc123"
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response = client.files.delete(
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file_id,
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extra_query={"provider": "bedrock"} # or "openai", "gemini"
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)
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print(f"Deleted: {response.deleted}")
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```
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### Download File Content
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```python
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# Download file content (specify provider)
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file_id = "file-abc123"
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response = client.files.content(
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file_id,
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extra_query={"provider": "bedrock"} # or "openai", "gemini"
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)
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# Handle different response types
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if hasattr(response, "read"):
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content = response.read()
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elif hasattr(response, "content"):
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content = response.content
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else:
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content = response
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# Decode bytes to string if needed
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if isinstance(content, bytes):
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content = content.decode("utf-8")
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print(f"File content:\n{content}")
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```
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---
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## Batch API
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### Create a Batch
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<Tabs group="provider">
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<Tab title="OpenAI Provider">
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For native OpenAI batching:
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-openai-api-key"
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)
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# First upload a file (see Files API section)
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# Then create batch using the file ID
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batch = client.batches.create(
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input_file_id="file-abc123",
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={"provider": "openai"},
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}")
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```
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</Tab>
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<Tab title="Bedrock Provider">
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For Bedrock, you need to provide output S3 URI:
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# First upload a file with S3 config (see Files API section)
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# Then create batch using the file ID
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batch = client.batches.create(
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input_file_id="file-abc123",
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={
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"provider": "bedrock",
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"model": "anthropic.claude-3-sonnet-20240229-v1:0",
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"output_s3_uri": "s3://your-bucket/batch-output",
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},
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}")
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```
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</Tab>
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<Tab title="Anthropic Provider">
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Anthropic supports inline requests (no file upload required):
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-anthropic-api-key"
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)
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# Create inline requests for Anthropic
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requests = [
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{
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"custom_id": "request-1",
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"params": {
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"model": "claude-3-sonnet-20240229",
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"max_tokens": 100,
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"messages": [{"role": "user", "content": "Hello!"}]
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}
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},
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{
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"custom_id": "request-2",
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"params": {
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"model": "claude-3-sonnet-20240229",
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"max_tokens": 100,
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"messages": [{"role": "user", "content": "How are you?"}]
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}
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}
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]
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# Create batch with inline requests (no file ID needed)
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batch = client.batches.create(
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input_file_id="", # Empty for inline requests
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={
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"provider": "anthropic",
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"requests": requests,
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},
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}")
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```
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</Tab>
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<Tab title="Gemini Provider">
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# First upload a file with Gemini format (see Files API section)
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# Then create batch using the file ID
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batch = client.batches.create(
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input_file_id="file-abc123",
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={
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"provider": "gemini",
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"model": "gemini-1.5-flash",
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},
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}")
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```
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</Tab>
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</Tabs>
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### List Batches
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```python
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# List batches (specify provider)
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response = client.batches.list(
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limit=10,
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extra_query={
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"provider": "bedrock", # or "openai", "anthropic", "gemini"
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"model": "anthropic.claude-3-sonnet-20240229-v1:0", # Required for bedrock
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}
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)
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for batch in response.data:
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print(f"Batch ID: {batch.id}, Status: {batch.status}")
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```
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### Retrieve Batch Status
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```python
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# Retrieve batch status (specify provider)
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batch_id = "batch-abc123"
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batch = client.batches.retrieve(
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batch_id,
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extra_query={"provider": "bedrock"} # or "openai", "anthropic", "gemini"
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}")
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if batch.request_counts:
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print(f"Total: {batch.request_counts.total}")
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print(f"Completed: {batch.request_counts.completed}")
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print(f"Failed: {batch.request_counts.failed}")
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```
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### Cancel a Batch
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```python
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# Cancel batch (specify provider)
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batch_id = "batch-abc123"
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batch = client.batches.cancel(
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batch_id,
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extra_body={"provider": "bedrock"} # or "openai", "anthropic", "gemini"
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)
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print(f"Batch ID: {batch.id}")
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print(f"Status: {batch.status}") # "cancelling" or "cancelled"
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```
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---
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## End-to-End Workflows
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### OpenAI Batch Workflow
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```python
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import time
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-openai-api-key"
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)
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# Configuration
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provider = "openai"
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# Step 1: Create OpenAI JSONL content
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jsonl_content = '''{"custom_id": "req-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "What is 2+2?"}], "max_tokens": 100}}
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{"custom_id": "req-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 100}}'''
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# Step 2: Upload file (uses OpenAI's native file storage)
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print("Step 1: Uploading batch input file...")
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uploaded_file = client.files.create(
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file=("batch_e2e.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={"provider": provider},
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)
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print(f" Uploaded file: {uploaded_file.id}")
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# Step 3: Create batch
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print("Step 2: Creating batch job...")
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batch = client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={"provider": provider},
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)
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print(f" Created batch: {batch.id}, status: {batch.status}")
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# Step 4: Poll for completion
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print("Step 3: Polling batch status...")
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for i in range(10):
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batch = client.batches.retrieve(batch.id, extra_query={"provider": provider})
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print(f" Poll {i+1}: status = {batch.status}")
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if batch.status in ["completed", "failed", "expired", "cancelled"]:
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break
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if batch.request_counts:
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print(f" Completed: {batch.request_counts.completed}/{batch.request_counts.total}")
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time.sleep(5)
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print(f"\nSuccess! Batch {batch.id} workflow completed.")
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```
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### Bedrock Batch Workflow
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```python
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import time
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# Configuration
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provider = "bedrock"
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s3_bucket = "your-s3-bucket"
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s3_region = "us-west-2"
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model = "anthropic.claude-3-sonnet-20240229-v1:0"
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# Step 1: Create JSONL content using OpenAI-style format (Bifrost converts to Bedrock format internally)
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jsonl_content = '''{"custom_id": "req-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic.claude-3-sonnet-20240229-v1:0", "messages": [{"role": "user", "content": "What is 2+2?"}], "max_tokens": 100}}
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{"custom_id": "req-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic.claude-3-sonnet-20240229-v1:0", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 100}}'''
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# Step 2: Upload file
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print("Step 1: Uploading batch input file...")
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uploaded_file = client.files.create(
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file=("batch_e2e.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={
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"provider": provider,
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"storage_config": {
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"s3": {"bucket": s3_bucket, "region": s3_region, "prefix": "batch-input"},
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},
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},
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)
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print(f" Uploaded file: {uploaded_file.id}")
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# Step 3: Create batch
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print("Step 2: Creating batch job...")
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batch = client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={
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"provider": provider,
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"model": model,
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"output_s3_uri": f"s3://{s3_bucket}/batch-output",
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},
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)
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print(f" Created batch: {batch.id}, status: {batch.status}")
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# Step 4: Poll for completion
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print("Step 3: Polling batch status...")
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for i in range(10):
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batch = client.batches.retrieve(batch.id, extra_query={"provider": provider})
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print(f" Poll {i+1}: status = {batch.status}")
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if batch.status in ["completed", "failed", "expired", "cancelled"]:
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break
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if batch.request_counts:
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print(f" Completed: {batch.request_counts.completed}/{batch.request_counts.total}")
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time.sleep(5)
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print(f"\nSuccess! Batch {batch.id} workflow completed.")
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```
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### Anthropic Inline Batch Workflow
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```python
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import time
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-anthropic-api-key"
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)
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provider = "anthropic"
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# Step 1: Create inline requests
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print("Step 1: Creating inline requests...")
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requests = [
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{
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"custom_id": "math-question",
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"params": {
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"model": "claude-3-sonnet-20240229",
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"max_tokens": 100,
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"messages": [{"role": "user", "content": "What is 15 * 7?"}]
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}
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},
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{
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"custom_id": "geography-question",
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"params": {
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"model": "claude-3-sonnet-20240229",
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"max_tokens": 100,
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"messages": [{"role": "user", "content": "What is the largest ocean?"}]
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}
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}
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]
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print(f" Created {len(requests)} inline requests")
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# Step 2: Create batch
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print("Step 2: Creating batch job...")
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batch = client.batches.create(
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input_file_id="",
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={"provider": provider, "requests": requests},
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)
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print(f" Created batch: {batch.id}, status: {batch.status}")
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# Step 3: Poll for completion
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print("Step 3: Polling batch status...")
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for i in range(10):
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batch = client.batches.retrieve(batch.id, extra_query={"provider": provider})
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print(f" Poll {i+1}: status = {batch.status}")
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if batch.status in ["completed", "failed", "expired", "cancelled", "ended"]:
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break
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time.sleep(5)
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print(f"\nSuccess! Batch {batch.id} workflow completed.")
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```
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### Gemini Batch Workflow
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```python
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import time
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/openai",
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api_key="your-api-key"
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)
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# Configuration
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provider = "gemini"
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model = "gemini-1.5-flash"
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# Step 1: Create JSONL content using OpenAI-style format (Bifrost converts to Gemini format internally)
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jsonl_content = '''{"custom_id": "req-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash", "messages": [{"role": "user", "content": "What is 2+2?"}], "max_tokens": 100}}
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{"custom_id": "req-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 100}}'''
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# Step 2: Upload file (uses Gemini's native file storage)
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print("Step 1: Uploading batch input file...")
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uploaded_file = client.files.create(
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file=("batch_e2e.jsonl", jsonl_content.encode(), "application/jsonl"),
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purpose="batch",
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extra_body={"provider": provider},
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)
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print(f" Uploaded file: {uploaded_file.id}")
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# Step 3: Create batch
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print("Step 2: Creating batch job...")
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batch = client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint="/v1/chat/completions",
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completion_window="24h",
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extra_body={
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"provider": provider,
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"model": model,
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},
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)
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print(f" Created batch: {batch.id}, status: {batch.status}")
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# Step 4: Poll for completion
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print("Step 3: Polling batch status...")
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for i in range(10):
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batch = client.batches.retrieve(batch.id, extra_query={"provider": provider})
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print(f" Poll {i+1}: status = {batch.status}")
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if batch.status in ["completed", "failed", "expired", "cancelled"]:
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break
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if batch.request_counts:
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print(f" Completed: {batch.request_counts.completed}/{batch.request_counts.total}")
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time.sleep(5)
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print(f"\nSuccess! Batch {batch.id} workflow completed.")
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```
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---
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## Provider-Specific Notes
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| Provider | File Upload | Batch Creation | Extra Configuration |
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|----------|-------------|----------------|---------------------|
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| **OpenAI** | ✅ Native storage | ✅ File-based | None |
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| **Bedrock** | ✅ S3-based | ✅ File-based | `storage_config`, `output_s3_uri` |
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| **Anthropic** | ❌ Not supported | ✅ Inline requests | `requests` array in `extra_body` |
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| **Gemini** | ✅ Native storage | ✅ File-based | `model` in `extra_body` |
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<Note>
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- **OpenAI** and **Gemini** use their native file storage - no S3 configuration needed
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- **Bedrock** requires S3 storage configuration (`storage_config`, `output_s3_uri`)
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- **Anthropic** does not support file-based batch operations - use inline requests instead
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</Note>
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---
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## Next Steps
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- **[Overview](./overview)** - OpenAI SDK integration basics
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- **[Configuration](../../quickstart/gateway/provider-configuration)** - Bifrost setup and configuration
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- **[Core Features](../../features/)** - Governance, semantic caching, and more
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