Files and Batch API

Overview

DeepintShield supports the Anthropic Files API and Batch API (via the beta namespace) with cross-provider routing. This means you can use the Anthropic SDK to manage files and batch jobs across multiple providers including Anthropic, OpenAI, and Gemini.

The provider is specified using the x-model-provider header in default_headers.

Note

Bedrock Limitation: Bedrock batch operations require file-based input with S3 storage, which is not supported via the Anthropic SDK’s inline batch API. For Bedrock batch operations, use the Bedrock SDK directly.

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Client Setup

Note

In API Key section, you can either send virtual key or a dummy key to escape client side validation.

Anthropic Provider (Default)

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

Cross-Provider Client

To route requests to a different provider, set the x-model-provider header:

OpenAI Provider

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

Bedrock Provider

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "bedrock"}
)

Caution

Bedrock can be used for chat completions via the Anthropic SDK, but batch operations are not supported. Bedrock requires file-based batch input with S3 storage. Use the Bedrock SDK for batch operations.

Gemini Provider

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "gemini"}
)

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Files API

The Files API is accessed through the beta.files namespace. Note that file support varies by provider.

Upload a File

Anthropic Provider

Upload a text file for use with Anthropic:

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

# Upload a text file
text_content = b"This is a test file for Files API integration."

response = client.beta.files.upload(
    file=("test_upload.txt", text_content, "text/plain"),
)

print(f"File ID: {response.id}")
print(f"Filename: {response.filename}")

OpenAI Provider

Upload a JSONL file for OpenAI batch processing:

import anthropic

# Client configured for OpenAI provider
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

# Create JSONL content in OpenAI batch format
jsonl_content = b'''{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 100}}
{"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}}'''

response = client.beta.files.upload(
    file=("batch_input.jsonl", jsonl_content, "application/jsonl"),
)

print(f"File ID: {response.id}")

List Files

Anthropic Provider

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

# List all files
response = client.beta.files.list()

for file in response.data:
    print(f"File ID: {file.id}")
    print(f"Filename: {file.filename}")
    print(f"Size: {file.size} bytes")
    print("---")

OpenAI Provider

import anthropic

# Client configured for OpenAI provider
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

# List all files from OpenAI
response = client.beta.files.list()

for file in response.data:
    print(f"File ID: {file.id}, Name: {file.filename}")

Delete a File

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}  # or omit for anthropic
)

# Delete a file
file_id = "file-abc123"
response = client.beta.files.delete(file_id)

print(f"Deleted file: {file_id}")

Download File Content

Note: Anthropic only allows downloading files created by certain tools (like code execution). OpenAI allows downloading batch output files.

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

# Download file content
file_id = "file-abc123"
response = client.beta.files.download(file_id)

content = response.text()
print(f"File content:\n{content}")

---

Batch API

The Anthropic Batch API is accessed through beta.messages.batches. Anthropic’s batch API uses inline requests rather than file uploads.

Create a Batch with Inline Requests

Anthropic Provider

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

# Create batch with inline requests
batch_requests = [
    {
        "custom_id": "request-1",
        "params": {
            "model": "claude-3-sonnet-20240229",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is 2+2?"}
            ]
        }
    },
    {
        "custom_id": "request-2",
        "params": {
            "model": "claude-3-sonnet-20240229",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is the capital of France?"}
            ]
        }
    }
]

batch = client.beta.messages.batches.create(requests=batch_requests)

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")

OpenAI Provider

When routing to OpenAI, use OpenAI-compatible models:

import anthropic

# Client configured for OpenAI provider
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

# Create batch with inline requests (using OpenAI models)
batch_requests = [
    {
        "custom_id": "request-1",
        "params": {
            "model": "gpt-4o-mini",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is 2+2?"}
            ]
        }
    },
    {
        "custom_id": "request-2",
        "params": {
            "model": "gpt-4o-mini",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is the capital of France?"}
            ]
        }
    }
]

batch = client.beta.messages.batches.create(requests=batch_requests)

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")

Gemini Provider

When routing to Gemini:

import anthropic

# Client configured for Gemini provider
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "gemini"}
)

# Create batch with inline requests (using Gemini models)
batch_requests = [
    {
        "custom_id": "request-1",
        "params": {
            "model": "gemini-1.5-flash",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is 2+2?"}
            ]
        }
    },
    {
        "custom_id": "request-2",
        "params": {
            "model": "gemini-1.5-flash",
            "max_tokens": 100,
            "messages": [
                {"role": "user", "content": "What is the capital of France?"}
            ]
        }
    }
]

batch = client.beta.messages.batches.create(requests=batch_requests)

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")

Note

Bedrock Note: Bedrock requires file-based batch creation with S3 storage. When routing to Bedrock from the Anthropic SDK, you’ll need to use the Bedrock SDK directly for batch operations. See the Bedrock SDK documentation for details.

List Batches

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "anthropic"}  # or "openai", "gemini"
)

# List batches
response = client.beta.messages.batches.list(limit=10)

for batch in response.data:
    print(f"Batch ID: {batch.id}")
    print(f"Status: {batch.processing_status}")
    if batch.request_counts:
        print(f"Processing: {batch.request_counts.processing}")
        print(f"Succeeded: {batch.request_counts.succeeded}")
        print(f"Errored: {batch.request_counts.errored}")
    print("---")

Retrieve Batch Status

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "anthropic"}  # or "openai", "gemini"
)

# Retrieve batch status
batch_id = "batch-abc123"
batch = client.beta.messages.batches.retrieve(batch_id)

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")

if batch.request_counts:
    print(f"Processing: {batch.request_counts.processing}")
    print(f"Succeeded: {batch.request_counts.succeeded}")
    print(f"Errored: {batch.request_counts.errored}")

Cancel a Batch

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "anthropic"}  # or "openai", "gemini"
)

# Cancel batch
batch_id = "batch-abc123"
batch = client.beta.messages.batches.cancel(batch_id)

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")  # "canceling" or "ended"

Get Batch Results

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

# Get batch results (only available after batch is completed)
batch_id = "batch-abc123"
results = client.beta.messages.batches.results(batch_id)

# Iterate over results
for result in results:
    print(f"Custom ID: {result.custom_id}")
    if result.result.type == "succeeded":
        message = result.result.message
        print(f"Response: {message.content[0].text}")
    elif result.result.type == "errored":
        print(f"Error: {result.result.error}")
    print("---")

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End-to-End Workflows

Anthropic Batch Workflow

import time
import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key"
)

# Step 1: Create batch with inline requests
print("Step 1: Creating batch...")
batch_requests = [
    {
        "custom_id": "math-question",
        "params": {
            "model": "claude-3-sonnet-20240229",
            "max_tokens": 100,
            "messages": [{"role": "user", "content": "What is 15 * 7?"}]
        }
    },
    {
        "custom_id": "geography-question",
        "params": {
            "model": "claude-3-sonnet-20240229",
            "max_tokens": 100,
            "messages": [{"role": "user", "content": "What is the largest ocean?"}]
        }
    }
]

batch = client.beta.messages.batches.create(requests=batch_requests)
print(f"  Created batch: {batch.id}, status: {batch.processing_status}")

# Step 2: Poll for completion
print("Step 2: Polling batch status...")
for i in range(20):
    batch = client.beta.messages.batches.retrieve(batch.id)
    print(f"  Poll {i+1}: status = {batch.processing_status}")

    if batch.processing_status == "ended":
        print("  Batch completed!")
        break

    if batch.request_counts:
        print(f"    Processing: {batch.request_counts.processing}")
        print(f"    Succeeded: {batch.request_counts.succeeded}")

    time.sleep(5)

# Step 3: Verify batch is in list
print("Step 3: Verifying batch in list...")
batch_list = client.beta.messages.batches.list(limit=20)
batch_ids = [b.id for b in batch_list.data]
assert batch.id in batch_ids, f"Batch {batch.id} should be in list"
print(f"  Verified batch {batch.id} is in list")

# Step 4: Get results (if completed)
if batch.processing_status == "ended":
    print("Step 4: Getting results...")
    try:
        results = client.beta.messages.batches.results(batch.id)
        for result in results:
            print(f"  {result.custom_id}: ", end="")
            if result.result.type == "succeeded":
                print(result.result.message.content[0].text[:50] + "...")
            else:
                print(f"Error: {result.result.error}")
    except Exception as e:
        print(f"  Results not yet available: {e}")

print(f"\nSuccess! Batch {batch.id} workflow completed.")

Cross-Provider Batch Workflow (OpenAI via Anthropic SDK)

import time
import anthropic

# Create client with OpenAI provider header
client = anthropic.Anthropic(
    base_url="http://localhost:8080/anthropic",
    api_key="virtual-key-or-dummy-key",
    default_headers={"x-model-provider": "openai"}
)

# Step 1: Create batch with OpenAI models
print("Step 1: Creating batch for OpenAI provider...")
batch_requests = [
    {
        "custom_id": "openai-request-1",
        "params": {
            "model": "gpt-4o-mini",
            "max_tokens": 100,
            "messages": [{"role": "user", "content": "Explain AI in one sentence."}]
        }
    },
    {
        "custom_id": "openai-request-2",
        "params": {
            "model": "gpt-4o-mini",
            "max_tokens": 100,
            "messages": [{"role": "user", "content": "What is machine learning?"}]
        }
    }
]

batch = client.beta.messages.batches.create(requests=batch_requests)
print(f"  Created batch: {batch.id}, status: {batch.processing_status}")

# Step 2: Poll for completion
print("Step 2: Polling batch status...")
for i in range(10):
    batch = client.beta.messages.batches.retrieve(batch.id)
    print(f"  Poll {i+1}: status = {batch.processing_status}")

    if batch.processing_status in ["ended", "completed"]:
        break

    time.sleep(5)

print(f"\nSuccess! Cross-provider batch {batch.id} completed via Anthropic SDK.")

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Provider-Specific Notes

Provider	Header Value	File Upload	Batch Type	Models
Anthropic	anthropic or omit	✅ Beta API	Inline requests	claude-3-*
OpenAI	openai	✅ Beta API	Inline requests	gpt-4o-*, gpt-4-*
Gemini	gemini	✅ Beta API	Inline requests	gemini-1.5-*
Bedrock	bedrock	❌ Use Bedrock SDK	File-based (S3)	anthropic.claude-*

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Next Steps

• Overview - Anthropic SDK integration basics
• Configuration - DeepintShield setup and configuration
• Core Features - Governance, semantic caching, and more