940 lines
34 KiB
Python
940 lines
34 KiB
Python
"""
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Calling + translation logic for anthropic's `/v1/messages` endpoint
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"""
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import copy
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import json
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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List,
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Optional,
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Tuple,
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Union,
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cast,
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)
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import httpx # type: ignore
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import litellm
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import litellm.litellm_core_utils
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import litellm.types
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import litellm.types.utils
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from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.types.llms.anthropic import (
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ContentBlockDelta,
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ContentBlockStart,
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ContentBlockStop,
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MessageBlockDelta,
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MessageStartBlock,
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UsageDelta,
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)
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from litellm.types.llms.openai import (
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ChatCompletionRedactedThinkingBlock,
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ChatCompletionThinkingBlock,
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ChatCompletionToolCallChunk,
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)
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from litellm.types.utils import (
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Delta,
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GenericStreamingChunk,
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LlmProviders,
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ModelResponse,
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ModelResponseStream,
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StreamingChoices,
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Usage,
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)
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from ...base import BaseLLM
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from ..common_utils import AnthropicError, process_anthropic_headers
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from .transformation import AnthropicConfig
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
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from litellm.llms.base_llm.chat.transformation import BaseConfig
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async def make_call(
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client: Optional[AsyncHTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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timeout: Optional[Union[float, httpx.Timeout]],
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json_mode: bool,
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) -> Tuple[Any, httpx.Headers]:
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if client is None:
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client = litellm.module_level_aclient
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try:
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response = await client.post(
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api_base, headers=headers, data=data, stream=True, timeout=timeout
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)
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except httpx.HTTPStatusError as e:
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error_headers = getattr(e, "headers", None)
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise AnthropicError(
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status_code=e.response.status_code,
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message=await e.response.aread(),
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headers=error_headers,
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)
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except Exception as e:
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for exception in litellm.LITELLM_EXCEPTION_TYPES:
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if isinstance(e, exception):
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raise e
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raise AnthropicError(status_code=500, message=str(e))
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completion_stream = ModelResponseIterator(
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streaming_response=response.aiter_lines(),
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sync_stream=False,
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json_mode=json_mode,
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=completion_stream, # Pass the completion stream for logging
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additional_args={"complete_input_dict": data},
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)
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return completion_stream, response.headers
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def make_sync_call(
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client: Optional[HTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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timeout: Optional[Union[float, httpx.Timeout]],
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json_mode: bool,
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) -> Tuple[Any, httpx.Headers]:
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if client is None:
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client = litellm.module_level_client # re-use a module level client
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try:
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response = client.post(
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api_base, headers=headers, data=data, stream=True, timeout=timeout
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)
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except httpx.HTTPStatusError as e:
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error_headers = getattr(e, "headers", None)
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise AnthropicError(
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status_code=e.response.status_code,
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message=e.response.read(),
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headers=error_headers,
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)
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except Exception as e:
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for exception in litellm.LITELLM_EXCEPTION_TYPES:
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if isinstance(e, exception):
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raise e
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raise AnthropicError(status_code=500, message=str(e))
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if response.status_code != 200:
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response_headers = getattr(response, "headers", None)
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raise AnthropicError(
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status_code=response.status_code,
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message=response.read(),
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headers=response_headers,
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)
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completion_stream = ModelResponseIterator(
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streaming_response=response.iter_lines(), sync_stream=True, json_mode=json_mode
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response="first stream response received",
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additional_args={"complete_input_dict": data},
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)
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return completion_stream, response.headers
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class AnthropicChatCompletion(BaseLLM):
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def __init__(self) -> None:
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super().__init__()
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async def acompletion_stream_function(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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timeout: Union[float, httpx.Timeout],
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client: Optional[AsyncHTTPHandler],
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encoding,
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api_key,
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logging_obj,
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stream,
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_is_function_call,
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data: dict,
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json_mode: bool,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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headers={},
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):
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from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
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data["stream"] = True
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completion_stream, headers = await make_call(
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client=client,
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api_base=api_base,
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headers=headers,
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data=json.dumps(data),
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model=model,
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messages=messages,
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logging_obj=logging_obj,
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timeout=timeout,
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json_mode=json_mode,
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)
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streamwrapper = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="anthropic",
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logging_obj=logging_obj,
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_response_headers=process_anthropic_headers(headers),
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)
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return streamwrapper
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async def acompletion_function(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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timeout: Union[float, httpx.Timeout],
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encoding,
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api_key,
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logging_obj,
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stream,
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_is_function_call,
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data: dict,
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optional_params: dict,
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json_mode: bool,
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litellm_params: dict,
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provider_config: "BaseConfig",
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logger_fn=None,
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headers={},
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client: Optional[AsyncHTTPHandler] = None,
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) -> Union[ModelResponse, "CustomStreamWrapper"]:
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async_handler = client or get_async_httpx_client(
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llm_provider=litellm.LlmProviders.ANTHROPIC
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)
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try:
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response = await async_handler.post(
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api_base, headers=headers, json=data, timeout=timeout
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)
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except Exception as e:
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## LOGGING
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logging_obj.post_call(
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input=messages,
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api_key=api_key,
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original_response=str(e),
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additional_args={"complete_input_dict": data},
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)
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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if error_response and hasattr(error_response, "text"):
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error_text = getattr(error_response, "text", error_text)
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raise AnthropicError(
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message=error_text,
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status_code=status_code,
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headers=error_headers,
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)
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return provider_config.transform_response(
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model=model,
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raw_response=response,
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model_response=model_response,
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logging_obj=logging_obj,
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api_key=api_key,
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request_data=data,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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encoding=encoding,
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json_mode=json_mode,
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)
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def completion(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_llm_provider: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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optional_params: dict,
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timeout: Union[float, httpx.Timeout],
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litellm_params: dict,
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acompletion=None,
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logger_fn=None,
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headers={},
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client=None,
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):
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from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
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from litellm.utils import ProviderConfigManager
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optional_params = copy.deepcopy(optional_params)
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stream = optional_params.pop("stream", None)
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json_mode: bool = optional_params.pop("json_mode", False)
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is_vertex_request: bool = optional_params.pop("is_vertex_request", False)
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_is_function_call = False
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messages = copy.deepcopy(messages)
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headers = AnthropicConfig().validate_environment(
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api_key=api_key,
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headers=headers,
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model=model,
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messages=messages,
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optional_params={**optional_params, "is_vertex_request": is_vertex_request},
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litellm_params=litellm_params,
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)
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config = ProviderConfigManager.get_provider_chat_config(
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model=model,
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provider=LlmProviders(custom_llm_provider),
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)
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if config is None:
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raise ValueError(
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f"Provider config not found for model: {model} and provider: {custom_llm_provider}"
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)
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data = config.transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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## LOGGING
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logging_obj.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"api_base": api_base,
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"headers": headers,
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},
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)
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print_verbose(f"_is_function_call: {_is_function_call}")
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if acompletion is True:
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if (
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stream is True
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): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
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print_verbose("makes async anthropic streaming POST request")
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data["stream"] = stream
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return self.acompletion_stream_function(
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model=model,
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messages=messages,
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data=data,
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api_base=api_base,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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logging_obj=logging_obj,
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optional_params=optional_params,
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stream=stream,
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_is_function_call=_is_function_call,
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json_mode=json_mode,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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timeout=timeout,
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client=(
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client
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if client is not None and isinstance(client, AsyncHTTPHandler)
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else None
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),
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)
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else:
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return self.acompletion_function(
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model=model,
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messages=messages,
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data=data,
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api_base=api_base,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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provider_config=config,
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logging_obj=logging_obj,
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optional_params=optional_params,
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stream=stream,
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_is_function_call=_is_function_call,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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client=client,
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json_mode=json_mode,
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timeout=timeout,
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)
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else:
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## COMPLETION CALL
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if (
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stream is True
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): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
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data["stream"] = stream
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completion_stream, headers = make_sync_call(
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client=client,
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api_base=api_base,
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headers=headers, # type: ignore
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data=json.dumps(data),
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model=model,
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messages=messages,
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logging_obj=logging_obj,
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timeout=timeout,
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json_mode=json_mode,
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)
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return CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="anthropic",
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logging_obj=logging_obj,
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_response_headers=process_anthropic_headers(headers),
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)
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else:
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if client is None or not isinstance(client, HTTPHandler):
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client = _get_httpx_client(
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params={"timeout": timeout}
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)
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else:
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client = client
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try:
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response = client.post(
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api_base,
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headers=headers,
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data=json.dumps(data),
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timeout=timeout,
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)
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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if error_response and hasattr(error_response, "text"):
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error_text = getattr(error_response, "text", error_text)
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raise AnthropicError(
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message=error_text,
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status_code=status_code,
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headers=error_headers,
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)
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return config.transform_response(
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model=model,
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raw_response=response,
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model_response=model_response,
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logging_obj=logging_obj,
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api_key=api_key,
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request_data=data,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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encoding=encoding,
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json_mode=json_mode,
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)
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def embedding(self):
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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|
|
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class ModelResponseIterator:
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def __init__(
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self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
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):
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self.streaming_response = streaming_response
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self.response_iterator = self.streaming_response
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self.content_blocks: List[ContentBlockDelta] = []
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self.tool_index = -1
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self.json_mode = json_mode
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|
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# Track if we're currently streaming a response_format tool
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self.is_response_format_tool: bool = False
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# Track if we've converted any response_format tools (affects finish_reason)
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self.converted_response_format_tool: bool = False
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|
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def check_empty_tool_call_args(self) -> bool:
|
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"""
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Check if the tool call block so far has been an empty string
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"""
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args = ""
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# if text content block -> skip
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if len(self.content_blocks) == 0:
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return False
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if (
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self.content_blocks[0]["delta"]["type"] == "text_delta"
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or self.content_blocks[0]["delta"]["type"] == "thinking_delta"
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):
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return False
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for block in self.content_blocks:
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if block["delta"]["type"] == "input_json_delta":
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args += block["delta"].get("partial_json", "") # type: ignore
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if len(args) == 0:
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return True
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return False
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def _handle_usage(self, anthropic_usage_chunk: Union[dict, UsageDelta]) -> Usage:
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return AnthropicConfig().calculate_usage(
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usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=None
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)
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def _content_block_delta_helper(
|
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self, chunk: dict
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) -> Tuple[
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str,
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Optional[ChatCompletionToolCallChunk],
|
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List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]],
|
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Dict[str, Any],
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]:
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"""
|
|
Helper function to handle the content block delta
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"""
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text = ""
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tool_use: Optional[ChatCompletionToolCallChunk] = None
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provider_specific_fields = {}
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content_block = ContentBlockDelta(**chunk) # type: ignore
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thinking_blocks: List[
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Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
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] = []
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self.content_blocks.append(content_block)
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if "text" in content_block["delta"]:
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text = content_block["delta"]["text"]
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elif "partial_json" in content_block["delta"]:
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tool_use = {
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"id": None,
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"type": "function",
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"function": {
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"name": None,
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"arguments": content_block["delta"]["partial_json"],
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},
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"index": self.tool_index,
|
|
}
|
|
elif "citation" in content_block["delta"]:
|
|
provider_specific_fields["citation"] = content_block["delta"]["citation"]
|
|
elif (
|
|
"thinking" in content_block["delta"]
|
|
or "signature" in content_block["delta"]
|
|
):
|
|
thinking_blocks = [
|
|
ChatCompletionThinkingBlock(
|
|
type="thinking",
|
|
thinking=content_block["delta"].get("thinking") or "",
|
|
signature=content_block["delta"].get("signature"),
|
|
)
|
|
]
|
|
provider_specific_fields["thinking_blocks"] = thinking_blocks
|
|
|
|
return text, tool_use, thinking_blocks, provider_specific_fields
|
|
|
|
def _handle_reasoning_content(
|
|
self,
|
|
thinking_blocks: List[
|
|
Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
|
|
],
|
|
) -> Optional[str]:
|
|
"""
|
|
Handle the reasoning content
|
|
"""
|
|
reasoning_content = None
|
|
for block in thinking_blocks:
|
|
thinking_content = cast(Optional[str], block.get("thinking"))
|
|
if reasoning_content is None:
|
|
reasoning_content = ""
|
|
if thinking_content is not None:
|
|
reasoning_content += thinking_content
|
|
return reasoning_content
|
|
|
|
def _handle_redacted_thinking_content(
|
|
self,
|
|
content_block_start: ContentBlockStart,
|
|
provider_specific_fields: Dict[str, Any],
|
|
) -> Tuple[List[ChatCompletionRedactedThinkingBlock], Dict[str, Any]]:
|
|
"""
|
|
Handle the redacted thinking content
|
|
"""
|
|
thinking_blocks = [
|
|
ChatCompletionRedactedThinkingBlock(
|
|
type="redacted_thinking",
|
|
data=content_block_start["content_block"]["data"], # type: ignore
|
|
)
|
|
]
|
|
provider_specific_fields["thinking_blocks"] = thinking_blocks
|
|
|
|
return thinking_blocks, provider_specific_fields
|
|
|
|
def get_content_block_start(self, chunk: dict) -> ContentBlockStart:
|
|
from litellm.types.llms.anthropic import (
|
|
ContentBlockStartText,
|
|
ContentBlockStartToolUse,
|
|
)
|
|
|
|
if chunk.get("content_block", {}).get("type") == "tool_use":
|
|
content_block_start = ContentBlockStartToolUse(**chunk) # type: ignore
|
|
else:
|
|
content_block_start = ContentBlockStartText(**chunk) # type: ignore
|
|
|
|
return content_block_start
|
|
|
|
def chunk_parser(self, chunk: dict) -> ModelResponseStream:
|
|
try:
|
|
type_chunk = chunk.get("type", "") or ""
|
|
|
|
text = ""
|
|
tool_use: Optional[ChatCompletionToolCallChunk] = None
|
|
finish_reason = ""
|
|
usage: Optional[Usage] = None
|
|
provider_specific_fields: Dict[str, Any] = {}
|
|
reasoning_content: Optional[str] = None
|
|
thinking_blocks: Optional[
|
|
List[
|
|
Union[
|
|
ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
|
|
]
|
|
]
|
|
] = None
|
|
|
|
# Always use index=0 for OpenAI choice format (fixes multi-choice errors)
|
|
index = 0
|
|
if type_chunk == "content_block_delta":
|
|
"""
|
|
Anthropic content chunk
|
|
chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}
|
|
"""
|
|
(
|
|
text,
|
|
tool_use,
|
|
thinking_blocks,
|
|
provider_specific_fields,
|
|
) = self._content_block_delta_helper(chunk=chunk)
|
|
if thinking_blocks:
|
|
reasoning_content = self._handle_reasoning_content(
|
|
thinking_blocks=thinking_blocks
|
|
)
|
|
elif type_chunk == "content_block_start":
|
|
"""
|
|
event: content_block_start
|
|
data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}
|
|
"""
|
|
|
|
content_block_start = self.get_content_block_start(chunk=chunk)
|
|
self.content_blocks = [] # reset content blocks when new block starts
|
|
if content_block_start["content_block"]["type"] == "text":
|
|
text = content_block_start["content_block"]["text"]
|
|
elif content_block_start["content_block"]["type"] == "tool_use":
|
|
self.tool_index += 1
|
|
tool_use = {
|
|
"id": content_block_start["content_block"]["id"],
|
|
"type": "function",
|
|
"function": {
|
|
"name": content_block_start["content_block"]["name"],
|
|
"arguments": "",
|
|
},
|
|
"index": self.tool_index,
|
|
}
|
|
elif (
|
|
content_block_start["content_block"]["type"] == "redacted_thinking"
|
|
):
|
|
(
|
|
thinking_blocks,
|
|
provider_specific_fields,
|
|
) = self._handle_redacted_thinking_content( # type: ignore
|
|
content_block_start=content_block_start,
|
|
provider_specific_fields=provider_specific_fields,
|
|
)
|
|
elif type_chunk == "content_block_stop":
|
|
ContentBlockStop(**chunk) # type: ignore
|
|
# check if tool call content block
|
|
is_empty = self.check_empty_tool_call_args()
|
|
if is_empty:
|
|
tool_use = {
|
|
"id": None,
|
|
"type": "function",
|
|
"function": {
|
|
"name": None,
|
|
"arguments": "{}",
|
|
},
|
|
"index": self.tool_index,
|
|
}
|
|
# Reset response_format tool tracking when block stops
|
|
self.is_response_format_tool = False
|
|
elif type_chunk == "message_delta":
|
|
finish_reason, usage = self._handle_message_delta(chunk)
|
|
elif type_chunk == "message_start":
|
|
"""
|
|
Anthropic
|
|
chunk = {
|
|
"type": "message_start",
|
|
"message": {
|
|
"id": "msg_vrtx_011PqREFEMzd3REdCoUFAmdG",
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"model": "claude-3-sonnet-20240229",
|
|
"content": [],
|
|
"stop_reason": null,
|
|
"stop_sequence": null,
|
|
"usage": {
|
|
"input_tokens": 270,
|
|
"output_tokens": 1
|
|
}
|
|
}
|
|
}
|
|
"""
|
|
message_start_block = MessageStartBlock(**chunk) # type: ignore
|
|
if "usage" in message_start_block["message"]:
|
|
usage = self._handle_usage(
|
|
anthropic_usage_chunk=message_start_block["message"]["usage"]
|
|
)
|
|
elif type_chunk == "error":
|
|
"""
|
|
{"type":"error","error":{"details":null,"type":"api_error","message":"Internal server error"} }
|
|
"""
|
|
_error_dict = chunk.get("error", {}) or {}
|
|
message = _error_dict.get("message", None) or str(chunk)
|
|
raise AnthropicError(
|
|
message=message,
|
|
status_code=500, # it looks like Anthropic API does not return a status code in the chunk error - default to 500
|
|
)
|
|
|
|
text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use)
|
|
|
|
returned_chunk = ModelResponseStream(
|
|
choices=[
|
|
StreamingChoices(
|
|
index=index,
|
|
delta=Delta(
|
|
content=text,
|
|
tool_calls=[tool_use] if tool_use is not None else None,
|
|
provider_specific_fields=(
|
|
provider_specific_fields
|
|
if provider_specific_fields
|
|
else None
|
|
),
|
|
thinking_blocks=(
|
|
thinking_blocks if thinking_blocks else None
|
|
),
|
|
reasoning_content=reasoning_content,
|
|
),
|
|
finish_reason=finish_reason,
|
|
)
|
|
],
|
|
usage=usage,
|
|
)
|
|
|
|
return returned_chunk
|
|
|
|
except json.JSONDecodeError:
|
|
raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
|
|
|
|
def _handle_json_mode_chunk(
|
|
self, text: str, tool_use: Optional[ChatCompletionToolCallChunk]
|
|
) -> Tuple[str, Optional[ChatCompletionToolCallChunk]]:
|
|
"""
|
|
If JSON mode is enabled, convert the tool call to a message.
|
|
|
|
Anthropic returns the JSON schema as part of the tool call
|
|
OpenAI returns the JSON schema as part of the content, this handles placing it in the content
|
|
|
|
Tool streaming follows Anthropic's fine-grained streaming pattern:
|
|
- content_block_start: Contains complete tool info (id, name, empty arguments)
|
|
- content_block_delta: Contains argument deltas (partial_json)
|
|
- content_block_stop: Signals end of tool
|
|
|
|
Reference: https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/fine-grained-tool-streaming
|
|
|
|
Args:
|
|
text: str
|
|
tool_use: Optional[ChatCompletionToolCallChunk]
|
|
Returns:
|
|
Tuple[str, Optional[ChatCompletionToolCallChunk]]
|
|
|
|
text: The text to use in the content
|
|
tool_use: The ChatCompletionToolCallChunk to use in the chunk response
|
|
"""
|
|
if not self.json_mode or tool_use is None:
|
|
return text, tool_use
|
|
|
|
# Check if this is a new tool call (has id)
|
|
if tool_use.get("id") is not None:
|
|
# New tool call from content_block_start - tool name is always complete here
|
|
# (per Anthropic's fine-grained streaming pattern)
|
|
tool_name = tool_use.get("function", {}).get("name", "")
|
|
self.is_response_format_tool = tool_name == RESPONSE_FORMAT_TOOL_NAME
|
|
|
|
# Convert tool to content if we're tracking a response_format tool
|
|
if self.is_response_format_tool:
|
|
message = AnthropicConfig._convert_tool_response_to_message(
|
|
tool_calls=[tool_use]
|
|
)
|
|
if message is not None:
|
|
text = message.content or ""
|
|
tool_use = None
|
|
# Track that we converted a response_format tool
|
|
self.converted_response_format_tool = True
|
|
|
|
return text, tool_use
|
|
|
|
def _handle_message_delta(self, chunk: dict) -> Tuple[str, Optional[Usage]]:
|
|
"""
|
|
Handle message_delta event for finish_reason and usage.
|
|
|
|
Args:
|
|
chunk: The message_delta chunk
|
|
|
|
Returns:
|
|
Tuple of (finish_reason, usage)
|
|
"""
|
|
message_delta = MessageBlockDelta(**chunk) # type: ignore
|
|
finish_reason = map_finish_reason(
|
|
finish_reason=message_delta["delta"].get("stop_reason", "stop") or "stop"
|
|
)
|
|
# Override finish_reason to "stop" if we converted response_format tools
|
|
# (matches OpenAI behavior and non-streaming Anthropic implementation)
|
|
if self.converted_response_format_tool:
|
|
finish_reason = "stop"
|
|
usage = self._handle_usage(anthropic_usage_chunk=message_delta["usage"])
|
|
return finish_reason, usage
|
|
|
|
# Sync iterator
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
try:
|
|
chunk = self.response_iterator.__next__()
|
|
except StopIteration:
|
|
raise StopIteration
|
|
except ValueError as e:
|
|
raise RuntimeError(f"Error receiving chunk from stream: {e}")
|
|
|
|
try:
|
|
str_line = chunk
|
|
if isinstance(chunk, bytes): # Handle binary data
|
|
str_line = chunk.decode("utf-8") # Convert bytes to string
|
|
index = str_line.find("data:")
|
|
if index != -1:
|
|
str_line = str_line[index:]
|
|
|
|
if str_line.startswith("data:"):
|
|
data_json = json.loads(str_line[5:])
|
|
return self.chunk_parser(chunk=data_json)
|
|
else:
|
|
return GenericStreamingChunk(
|
|
text="",
|
|
is_finished=False,
|
|
finish_reason="",
|
|
usage=None,
|
|
index=0,
|
|
tool_use=None,
|
|
)
|
|
except StopIteration:
|
|
raise StopIteration
|
|
except ValueError as e:
|
|
raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
|
|
|
|
# Async iterator
|
|
def __aiter__(self):
|
|
self.async_response_iterator = self.streaming_response.__aiter__()
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
chunk = await self.async_response_iterator.__anext__()
|
|
except StopAsyncIteration:
|
|
raise StopAsyncIteration
|
|
except ValueError as e:
|
|
raise RuntimeError(f"Error receiving chunk from stream: {e}")
|
|
|
|
try:
|
|
str_line = chunk
|
|
if isinstance(chunk, bytes): # Handle binary data
|
|
str_line = chunk.decode("utf-8") # Convert bytes to string
|
|
index = str_line.find("data:")
|
|
if index != -1:
|
|
str_line = str_line[index:]
|
|
|
|
if str_line.startswith("data:"):
|
|
data_json = json.loads(str_line[5:])
|
|
return self.chunk_parser(chunk=data_json)
|
|
else:
|
|
return GenericStreamingChunk(
|
|
text="",
|
|
is_finished=False,
|
|
finish_reason="",
|
|
usage=None,
|
|
index=0,
|
|
tool_use=None,
|
|
)
|
|
except StopAsyncIteration:
|
|
raise StopAsyncIteration
|
|
except ValueError as e:
|
|
raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
|
|
|
|
def convert_str_chunk_to_generic_chunk(self, chunk: str) -> ModelResponseStream:
|
|
"""
|
|
Convert a string chunk to a GenericStreamingChunk
|
|
|
|
Note: This is used for Anthropic pass through streaming logging
|
|
|
|
We can move __anext__, and __next__ to use this function since it's common logic.
|
|
Did not migrate them to minmize changes made in 1 PR.
|
|
"""
|
|
str_line = chunk
|
|
if isinstance(chunk, bytes): # Handle binary data
|
|
str_line = chunk.decode("utf-8") # Convert bytes to string
|
|
index = str_line.find("data:")
|
|
if index != -1:
|
|
str_line = str_line[index:]
|
|
|
|
if str_line.startswith("data:"):
|
|
data_json = json.loads(str_line[5:])
|
|
return self.chunk_parser(chunk=data_json)
|
|
else:
|
|
return ModelResponseStream()
|