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[Bedrock] Fix Anthropic file_id support - async path + document URL→base64 + beta header filtering (#25047) (#25050)

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Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
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ishaan-berri 2026-04-02 21:13:56 -07:00 • committed by GitHub
parent ee3e848ded
commit 1e5b79d887
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3 changed files with 291 additions and 25 deletions
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115
litellm/llms/bedrock/chat/invoke_handler.py
View file

@ -855,6 +855,32 @@ class BedrockLLM(BaseAWSLLM):
endpoint_url = f"{endpoint_url}/model/{modelId}/invoke"
proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/invoke"
if acompletion and provider == "anthropic" and self.is_claude_messages_api_model(
model
):
if isinstance(client, HTTPHandler):
client = None
return self._async_anthropic_messages_completion(
model=model,
messages=messages,
endpoint_url=endpoint_url,
proxy_endpoint_url=proxy_endpoint_url,
credentials=credentials,
aws_region_name=aws_region_name,
model_response=model_response,
print_verbose=print_verbose,
encoding=encoding,
logging_obj=logging_obj,
optional_params=optional_params,
stream=stream,
litellm_params=litellm_params,
logger_fn=logger_fn,
extra_headers=extra_headers,
timeout=timeout,
client=client,
stream_chunk_size=stream_chunk_size,
) # type: ignore[return-value]
prompt, chat_history = self.convert_messages_to_prompt(
model, messages, provider, custom_prompt_dict
)
@ -1148,6 +1174,95 @@ class BedrockLLM(BaseAWSLLM):
encoding=encoding,
)
async def _async_anthropic_messages_completion(
self,
model: str,
messages: list,
endpoint_url: str,
proxy_endpoint_url: str,
credentials,
aws_region_name: str,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
logging_obj: Logging,
optional_params: dict,
stream,
litellm_params=None,
logger_fn=None,
extra_headers: Optional[dict] = None,
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[AsyncHTTPHandler] = None,
stream_chunk_size: int = 1024,
) -> Union[ModelResponse, CustomStreamWrapper]:
transformed_request = await litellm.AmazonAnthropicClaudeConfig().async_transform_request(
model=model,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params or {},
headers=extra_headers or {},
)
data = json.dumps(transformed_request)
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
prepped = self.get_request_headers(
credentials=credentials,
aws_region_name=aws_region_name,
extra_headers=extra_headers,
endpoint_url=endpoint_url,
data=data,
headers=headers,
)
logging_obj.pre_call(
input=messages,
api_key="",
additional_args={
"complete_input_dict": data,
"api_base": proxy_endpoint_url,
"headers": prepped.headers,
},
)
if stream is True:
return await self.async_streaming(
model=model,
messages=messages,
data=data,
api_base=proxy_endpoint_url,
model_response=model_response,
print_verbose=print_verbose,
encoding=encoding,
logging_obj=logging_obj,
optional_params=optional_params,
stream=True,
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=prepped.headers,
timeout=timeout,
client=client,
stream_chunk_size=stream_chunk_size,
)
return await self.async_completion(
model=model,
messages=messages,
data=data,
api_base=proxy_endpoint_url,
model_response=model_response,
print_verbose=print_verbose,
encoding=encoding,
logging_obj=logging_obj,
optional_params=optional_params,
stream=stream, # type: ignore
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=prepped.headers,
timeout=timeout,
client=client,
)
async def async_completion(
self,
model: str,

188
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
View file

@ -2,6 +2,14 @@ from typing import TYPE_CHECKING, Any, List, Optional
import httpx
from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
from litellm.litellm_core_utils.prompt_templates.factory import (
convert_to_anthropic_image_obj,
)
from litellm.litellm_core_utils.prompt_templates.image_handling import (
async_convert_url_to_base64,
convert_url_to_base64,
)
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
AmazonInvokeConfig,
@ -85,8 +93,62 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
litellm_params: dict,
headers: dict,
) -> dict:
# Filter out AWS authentication parameters before passing to Anthropic transformation
# AWS params should only be used for signing requests, not included in request body
_anthropic_request = self._build_bedrock_anthropic_request_base(
model=model,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
headers=headers,
)
self._convert_document_url_sources_to_base64(_anthropic_request)
beta_list = self._compute_bedrock_invoke_beta_headers(
model=model,
messages=messages,
optional_params=optional_params,
headers=headers,
)
if beta_list:
_anthropic_request["anthropic_beta"] = beta_list
return _anthropic_request
async def async_transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
_anthropic_request = self._build_bedrock_anthropic_request_base(
model=model,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
headers=headers,
)
await self._async_convert_document_url_sources_to_base64(_anthropic_request)
beta_list = self._compute_bedrock_invoke_beta_headers(
model=model,
messages=messages,
optional_params=optional_params,
headers=headers,
)
if beta_list:
_anthropic_request["anthropic_beta"] = beta_list
return _anthropic_request
def _build_bedrock_anthropic_request_base(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
filtered_params = {
k: v
for k, v in optional_params.items()
@ -94,7 +156,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
}
filtered_params = self._normalize_bedrock_tool_search_tools(filtered_params)
_anthropic_request = AnthropicConfig.transform_request(
anthropic_request = AnthropicConfig.transform_request(
self,
model=model,
messages=messages,
@ -103,28 +165,31 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
headers=headers,
)
_anthropic_request.pop("model", None)
_anthropic_request.pop("stream", None)
# Bedrock Invoke doesn't support output_format parameter
_anthropic_request.pop("output_format", None)
# Bedrock Invoke doesn't support output_config parameter
# Fixes: https://github.com/BerriAI/litellm/issues/22797
_anthropic_request.pop("output_config", None)
if "anthropic_version" not in _anthropic_request:
_anthropic_request["anthropic_version"] = self.anthropic_version
anthropic_request.pop("model", None)
anthropic_request.pop("stream", None)
anthropic_request.pop("output_format", None)
anthropic_request.pop("output_config", None)
if "anthropic_version" not in anthropic_request:
anthropic_request["anthropic_version"] = self.anthropic_version
# Remove `custom` field from tools (Bedrock doesn't support it)
# Claude Code sends `custom: {defer_loading: true}` on tool definitions,
# which causes Bedrock to reject the request with "Extra inputs are not permitted"
# Ref: https://github.com/BerriAI/litellm/issues/22847
remove_custom_field_from_tools(_anthropic_request)
remove_custom_field_from_tools(anthropic_request)
return anthropic_request
def _compute_bedrock_invoke_beta_headers(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
headers: dict,
) -> List[str]:
tools = optional_params.get("tools")
tool_search_used = self.is_tool_search_used(tools)
programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools)
input_examples_used = self.is_input_examples_used(tools)
beta_set = set(get_anthropic_beta_from_headers(headers))
user_beta_set = set(get_anthropic_beta_from_headers(headers))
beta_set = set(user_beta_set)
auto_betas = self.get_anthropic_beta_list(
model=model,
optional_params=optional_params,
@ -142,12 +207,91 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
if "opus-4" in model.lower() or "opus_4" in model.lower():
beta_set.add("tool-search-tool-2025-10-19")
# Filter out beta headers that Bedrock Invoke doesn't support
# Uses centralized configuration from anthropic_beta_headers_config.json
beta_list = list(beta_set)
_anthropic_request["anthropic_beta"] = beta_list
auto_beta_list = filter_and_transform_beta_headers(
beta_headers=list(beta_set - user_beta_set),
provider="bedrock",
)
return sorted(user_beta_set.union(set(auto_beta_list)))
return _anthropic_request
def _convert_document_url_sources_to_base64(self, anthropic_request: dict) -> None:
"""
Bedrock Invoke does not accept document URL sources. Convert to base64 payloads.
"""
messages = anthropic_request.get("messages")
if not isinstance(messages, list):
return
for message in messages:
if not isinstance(message, dict):
continue
content = message.get("content")
if not isinstance(content, list):
continue
for block in content:
if not isinstance(block, dict) or block.get("type") != "document":
continue
source = block.get("source")
if not isinstance(source, dict) or source.get("type") != "url":
continue
source_url = source.get("url")
if not isinstance(source_url, str):
continue
inferred_format: Optional[str] = None
if source_url.lower().endswith(".pdf"):
inferred_format = "application/pdf"
base64_url = convert_url_to_base64(url=source_url)
image_chunk = convert_to_anthropic_image_obj(
openai_image_url=base64_url,
format=inferred_format,
)
block["source"] = {
"type": "base64",
"media_type": image_chunk["media_type"],
"data": image_chunk["data"],
}
async def _async_convert_document_url_sources_to_base64(
self, anthropic_request: dict
) -> None:
"""
Async version of document URL conversion for async completion paths.
"""
messages = anthropic_request.get("messages")
if not isinstance(messages, list):
return
for message in messages:
if not isinstance(message, dict):
continue
content = message.get("content")
if not isinstance(content, list):
continue
for block in content:
if not isinstance(block, dict) or block.get("type") != "document":
continue
source = block.get("source")
if not isinstance(source, dict) or source.get("type") != "url":
continue
source_url = source.get("url")
if not isinstance(source_url, str):
continue
inferred_format: Optional[str] = None
if source_url.lower().endswith(".pdf"):
inferred_format = "application/pdf"
base64_url = await async_convert_url_to_base64(url=source_url)
image_chunk = convert_to_anthropic_image_obj(
openai_image_url=base64_url,
format=inferred_format,
)
block["source"] = {
"type": "base64",
"media_type": image_chunk["media_type"],
"data": image_chunk["data"],
}
def _normalize_bedrock_tool_search_tools(self, optional_params: dict) -> dict:
"""

13
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
View file

@ -12,6 +12,7 @@ from typing import (
import httpx
from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
AnthropicMessagesConfig,
@ -436,7 +437,8 @@ class AmazonAnthropicClaudeMessagesConfig(
)
input_examples_used = anthropic_model_info.is_input_examples_used(tools)
beta_set = set(get_anthropic_beta_from_headers(headers))
user_beta_set = set(get_anthropic_beta_from_headers(headers))
beta_set = set(user_beta_set)
auto_betas = anthropic_model_info.get_anthropic_beta_list(
model=model,
optional_params=anthropic_messages_optional_request_params,
@ -460,8 +462,13 @@ class AmazonAnthropicClaudeMessagesConfig(
if "tool-search-tool-2025-10-19" in beta_set:
beta_set.add("tool-examples-2025-10-29")
if beta_set:
anthropic_messages_request["anthropic_beta"] = list(beta_set)
filtered_auto_betas = filter_and_transform_beta_headers(
beta_headers=list(beta_set - user_beta_set),
provider="bedrock",
)
filtered_betas = sorted(user_beta_set.union(set(filtered_auto_betas)))
if filtered_betas:
anthropic_messages_request["anthropic_beta"] = filtered_betas
return anthropic_messages_request