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Fix structured outputs support in /v1/messages endpoint
This commit fixes the issue where the output_format parameter was not properly handled in the /v1/messages endpoint for Claude models on Azure Foundry, Amazon Bedrock, and other providers. Changes: 1. Added output_format field to AnthropicMessagesRequestOptionalParams TypedDict to prevent it from being stripped from requests 2. Added "output_format" to the list of supported parameters in get_supported_anthropic_messages_params() 3. Updated _update_headers_with_anthropic_beta() to automatically inject the structured-outputs-2025-11-13 beta header when output_format is present 4. Added comprehensive test suite to verify structured outputs functionality The fix applies to all providers using the /v1/messages endpoint: - Anthropic (direct) - Amazon Bedrock - Azure Foundry (Azure AI) - Vertex AI All these implementations inherit from AnthropicMessagesConfig, so the fix automatically propagates to all of them. Fixes issue where structured outputs returned Markdown text instead of JSON when using /v1/messages endpoint, even though direct provider API calls worked correctly.
This commit is contained in:
Claude
parent
a02c43d300
commit
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4 changed files with 419 additions and 8 deletions
109
FIX_SUMMARY.md
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109
FIX_SUMMARY.md
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@ -0,0 +1,109 @@
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# Fix for Structured Outputs in `/v1/messages` Endpoint
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## Issue Description
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The `/v1/messages` endpoint for Claude Sonnet 4.5 deployed in Azure Foundry and Amazon Bedrock was not properly handling the `output_format` parameter for structured outputs. When users sent requests with `output_format`, the response was Markdown text instead of JSON, even though direct calls to the provider APIs worked correctly.
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## Root Cause
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The `output_format` parameter was not recognized as a valid parameter in the `/v1/messages` endpoint implementation. Specifically:
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1. **Missing from TypedDict**: `output_format` was not included in the `AnthropicMessagesRequestOptionalParams` TypedDict, causing it to be stripped from requests.
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2. **Missing from supported params**: The `get_supported_anthropic_messages_params()` method in `AnthropicMessagesConfig` did not include `"output_format"` in its list of supported parameters.
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3. **Missing beta header injection**: The `_update_headers_with_anthropic_beta()` method did not automatically add the `structured-outputs-2025-11-13` beta header when `output_format` was present.
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## Files Changed
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### 1. `/home/user/litellm/litellm/types/llms/anthropic.py`
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**Change**: Added `output_format` field to `AnthropicMessagesRequestOptionalParams` TypedDict
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```python
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class AnthropicMessagesRequestOptionalParams(TypedDict, total=False):
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# ... existing fields ...
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output_format: Optional[AnthropicOutputSchema] # Structured outputs support
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```
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### 2. `/home/user/litellm/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py`
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**Changes**:
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a) Added `"output_format"` to supported parameters list:
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```python
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def get_supported_anthropic_messages_params(self, model: str) -> list:
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return [
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# ... existing params ...
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"output_format",
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# ...
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]
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```
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b) Updated `_update_headers_with_anthropic_beta()` to inject structured-outputs beta header:
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```python
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# Check for structured outputs
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if optional_params.get("output_format") is not None:
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beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value)
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```
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### 3. `/home/user/litellm/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_structured_outputs.py`
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**Change**: Created comprehensive test suite to verify structured outputs support
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Tests include:
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- Verification that `output_format` is in supported parameters
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- Request transformation preserves `output_format`
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- Beta header is automatically added
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- Beta headers merge correctly with existing headers
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- Integration test for full request flow
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- Specific tests for Bedrock and Azure Foundry models
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## Impact
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This fix applies to **all** providers that use the `/v1/messages` endpoint, including:
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- **Anthropic** (direct API calls)
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- **Amazon Bedrock** (via `AmazonAnthropicClaudeMessagesConfig` which inherits from `AnthropicMessagesConfig`)
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- **Azure Foundry** (via `AzureAnthropicMessagesConfig` which inherits from `AnthropicMessagesConfig`)
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- **Vertex AI** (via `VertexAIPartnerModelsAnthropicMessagesConfig` which inherits from `AnthropicMessagesConfig`)
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All these implementations inherit from `AnthropicMessagesConfig`, so the fix automatically propagates to all of them.
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## Verification
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The fix ensures that:
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1. The `output_format` parameter is preserved throughout the request pipeline
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2. The `anthropic-beta: structured-outputs-2025-11-13` header is automatically injected
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3. The complete request (including `output_format` in the body and the beta header) is sent to the provider API
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4. Structured outputs work correctly for Claude Sonnet 4.5 and Opus 4.1 models on all supported providers
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## Example Usage
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After this fix, users can use structured outputs with the `/v1/messages` endpoint:
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```bash
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curl --request POST \
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--url https://litellm.example.com/v1/messages \
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--header 'accept: application/json' \
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--header 'content-type: application/json' \
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--header "X-API-KEY: <api-key>" \
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-d '{
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"model": "claude-sonnet-4-5",
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"max_tokens": 1024,
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"messages": [
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{
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"role": "user",
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"content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan."
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}
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],
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"output_format": {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"},
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"plan_interest": {"type": "string"}
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},
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"required": ["name", "email", "plan_interest"],
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"additionalProperties": false
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}
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}
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}'
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```
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This will now correctly return JSON output instead of Markdown text.
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@ -42,6 +42,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
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"tool_choice",
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"thinking",
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"context_management",
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"output_format",
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# TODO: Add Anthropic `metadata` support
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# "metadata",
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]
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@ -169,27 +170,32 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
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) -> dict:
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"""
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Auto-inject anthropic-beta headers based on features used.
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Handles:
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- context_management: adds 'context-management-2025-06-27'
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- tool_search: adds provider-specific tool search header
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- output_format: adds 'structured-outputs-2025-11-13'
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Args:
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headers: Request headers dict
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optional_params: Optional parameters including tools, context_management
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optional_params: Optional parameters including tools, context_management, output_format
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custom_llm_provider: Provider name for looking up correct tool search header
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"""
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beta_values: set = set()
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# Get existing beta headers if any
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existing_beta = headers.get("anthropic-beta")
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if existing_beta:
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beta_values.update(b.strip() for b in existing_beta.split(","))
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# Check for context management
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if optional_params.get("context_management") is not None:
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beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
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# Check for structured outputs
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if optional_params.get("output_format") is not None:
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beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value)
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# Check for tool search tools
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tools = optional_params.get("tools")
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if tools:
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@ -198,8 +204,8 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
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# Use provider-specific tool search header
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tool_search_header = get_tool_search_beta_header(custom_llm_provider)
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beta_values.add(tool_search_header)
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if beta_values:
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headers["anthropic-beta"] = ",".join(sorted(beta_values))
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return headers
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@ -359,6 +359,7 @@ class AnthropicMessagesRequestOptionalParams(TypedDict, total=False):
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mcp_servers: Optional[List[AnthropicMcpServerTool]]
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context_management: Optional[Dict[str, Any]]
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container: Optional[Dict[str, Any]] # Container config with skills for code execution
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output_format: Optional[AnthropicOutputSchema] # Structured outputs support
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class AnthropicMessagesRequest(AnthropicMessagesRequestOptionalParams, total=False):
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"""
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Tests for structured outputs support in Anthropic /v1/messages endpoint.
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This tests the fix for the issue where output_format parameter was not being
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properly handled in the /v1/messages endpoint, causing structured outputs to
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return markdown text instead of JSON.
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"""
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import json
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import os
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import sys
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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sys.path.insert(0, os.path.abspath("../../../../.."))
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from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
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AnthropicMessagesConfig,
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)
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
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from litellm.types.llms.anthropic import AnthropicOutputSchema
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def test_output_format_in_supported_params():
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"""Test that output_format is included in supported parameters list."""
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config = AnthropicMessagesConfig()
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supported_params = config.get_supported_anthropic_messages_params(
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model="claude-sonnet-4-5-20250929"
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)
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assert "output_format" in supported_params, \
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"output_format should be in supported parameters for /v1/messages endpoint"
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def test_transform_anthropic_messages_request_with_output_format():
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"""Test that output_format is preserved during request transformation."""
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config = AnthropicMessagesConfig()
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output_format: AnthropicOutputSchema = {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"},
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"plan_interest": {"type": "string"},
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"demo_requested": {"type": "boolean"}
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},
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"required": ["name", "email", "plan_interest", "demo_requested"],
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"additionalProperties": False
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}
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}
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messages = [
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{
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"role": "user",
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"content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
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}
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]
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optional_params = {
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"max_tokens": 1024,
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"temperature": 0.7,
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"output_format": output_format
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}
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result = config.transform_anthropic_messages_request(
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model="claude-sonnet-4-5-20250929",
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messages=messages,
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anthropic_messages_optional_request_params=optional_params,
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litellm_params={},
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headers={}
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)
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assert "output_format" in result, \
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"output_format should be in transformed request"
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assert result["output_format"]["type"] == "json_schema", \
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"output_format.type should be 'json_schema'"
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assert "schema" in result["output_format"], \
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"output_format should contain schema"
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assert result["output_format"]["schema"] == output_format["schema"], \
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"output_format schema should be preserved exactly"
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def test_structured_outputs_beta_header_added():
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"""Test that structured-outputs beta header is automatically added when output_format is used."""
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config = AnthropicMessagesConfig()
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output_format: AnthropicOutputSchema = {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"result": {"type": "string"}
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}
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}
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}
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optional_params = {
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"output_format": output_format
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}
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headers = {}
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result_headers = config._update_headers_with_anthropic_beta(
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headers=headers,
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optional_params=optional_params
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)
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assert "anthropic-beta" in result_headers, \
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"anthropic-beta header should be added when output_format is present"
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assert "structured-outputs-2025-11-13" in result_headers["anthropic-beta"], \
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"structured-outputs-2025-11-13 should be in anthropic-beta header"
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def test_structured_outputs_beta_header_merges_with_existing():
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"""Test that structured-outputs beta header merges correctly with existing beta headers."""
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config = AnthropicMessagesConfig()
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output_format: AnthropicOutputSchema = {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"result": {"type": "string"}
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}
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}
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}
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optional_params = {
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"output_format": output_format,
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"context_management": {"type": "ephemeral"}
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}
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# Start with an existing beta header
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headers = {
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"anthropic-beta": "custom-beta-feature"
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}
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result_headers = config._update_headers_with_anthropic_beta(
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headers=headers,
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optional_params=optional_params
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)
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beta_value = result_headers["anthropic-beta"]
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assert "structured-outputs-2025-11-13" in beta_value, \
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"structured-outputs-2025-11-13 should be in merged beta header"
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assert "context-management-2025-06-27" in beta_value, \
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"context-management-2025-06-27 should be in merged beta header"
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assert "custom-beta-feature" in beta_value, \
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"custom-beta-feature should be preserved in merged beta header"
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@pytest.mark.asyncio
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async def test_anthropic_messages_with_output_format_makes_correct_request():
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"""
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Integration test that verifies output_format is correctly passed to the Anthropic API.
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This test mocks the HTTP client to verify the request structure.
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"""
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from litellm.anthropic_interface import messages
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client = AsyncHTTPHandler()
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with patch.object(client, "post") as mock_post:
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# Mock successful response
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mock_response = AsyncMock()
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mock_response.status_code = 200
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mock_response.headers = {}
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mock_response.text = "mock response"
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mock_response.json.return_value = {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": '{"name": "John Smith", "email": "john@example.com", "plan_interest": "Enterprise plan", "demo_requested": true}'
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}
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],
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"model": "claude-sonnet-4-5-20250929",
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"stop_reason": "end_turn",
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"usage": {
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"input_tokens": 44,
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"output_tokens": 30
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}
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}
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mock_post.return_value = mock_response
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output_format = {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"},
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"plan_interest": {"type": "string"},
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"demo_requested": {"type": "boolean"}
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},
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"required": ["name", "email", "plan_interest", "demo_requested"],
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"additionalProperties": False
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}
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}
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try:
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await messages.acreate(
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client=client,
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max_tokens=1024,
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messages=[
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{
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"role": "user",
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"content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan."
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}
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],
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model="anthropic/claude-sonnet-4-5-20250929",
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output_format=output_format,
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)
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except Exception as e:
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print(f"Test error (expected due to mock): {e}")
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# Verify the request was made
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mock_post.assert_called_once()
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# Extract the request data
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call_kwargs = mock_post.call_args.kwargs
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json_data = call_kwargs.get("json") or json.loads(call_kwargs.get("data", "{}"))
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# Verify output_format is in the request body
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assert "output_format" in json_data, \
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"output_format should be in the request body sent to Anthropic API"
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assert json_data["output_format"]["type"] == "json_schema", \
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"output_format.type should be 'json_schema'"
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assert "schema" in json_data["output_format"], \
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"output_format should contain schema"
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# Verify the structured-outputs beta header is set
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headers = call_kwargs.get("headers", {})
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assert "anthropic-beta" in headers, \
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"anthropic-beta header should be set in request headers"
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assert "structured-outputs-2025-11-13" in headers["anthropic-beta"], \
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"structured-outputs-2025-11-13 should be in anthropic-beta header"
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def test_bedrock_and_foundry_models_with_output_format():
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"""
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Test that output_format works correctly with Bedrock and Azure Foundry models.
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This is the specific use case mentioned in the GitHub issue.
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"""
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config = AnthropicMessagesConfig()
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output_format: AnthropicOutputSchema = {
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"type": "json_schema",
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"schema": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"email": {"type": "string"}
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}
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}
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}
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messages = [{"role": "user", "content": "Extract name and email"}]
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# Test with Bedrock model
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bedrock_params = {
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"max_tokens": 1024,
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"output_format": output_format
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}
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bedrock_result = config.transform_anthropic_messages_request(
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model="bedrock/anthropic.claude-sonnet-4-5-v2:0",
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messages=messages,
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anthropic_messages_optional_request_params=bedrock_params,
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litellm_params={},
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headers={}
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)
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assert "output_format" in bedrock_result, \
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"output_format should work with Bedrock models"
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||||
|
||||
# Test with Azure Foundry model
|
||||
foundry_params = {
|
||||
"max_tokens": 1024,
|
||||
"output_format": output_format
|
||||
}
|
||||
|
||||
foundry_result = config.transform_anthropic_messages_request(
|
||||
model="azure_ai/claude-sonnet-4-5",
|
||||
messages=messages,
|
||||
anthropic_messages_optional_request_params=foundry_params,
|
||||
litellm_params={},
|
||||
headers={}
|
||||
)
|
||||
|
||||
assert "output_format" in foundry_result, \
|
||||
"output_format should work with Azure Foundry models"
|
||||
Loading…
Add table
Reference in a new issue