structure saas with tools
This commit is contained in:
Davidson Gomes
@@ -0,0 +1,846 @@
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"""
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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 Any, Callable, Dict, List, Optional, Tuple, Union, cast
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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 import LlmProviders
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.llms.base_llm.chat.transformation import BaseConfig
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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_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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ModelResponseStream,
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StreamingChoices,
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Usage,
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)
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from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager
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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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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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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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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 = HTTPHandler(timeout=timeout) # type: ignore
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else:
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client = client
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|
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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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except Exception as e:
|
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status_code = getattr(e, "status_code", 500)
|
||||
error_headers = getattr(e, "headers", None)
|
||||
error_text = getattr(e, "text", str(e))
|
||||
error_response = getattr(e, "response", None)
|
||||
if error_headers is None and error_response:
|
||||
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,
|
||||
logging_obj=logging_obj,
|
||||
api_key=api_key,
|
||||
request_data=data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
encoding=encoding,
|
||||
json_mode=json_mode,
|
||||
)
|
||||
|
||||
def embedding(self):
|
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# logic for parsing in - calling - parsing out model embedding calls
|
||||
pass
|
||||
|
||||
|
||||
class ModelResponseIterator:
|
||||
def __init__(
|
||||
self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
|
||||
):
|
||||
self.streaming_response = streaming_response
|
||||
self.response_iterator = self.streaming_response
|
||||
self.content_blocks: List[ContentBlockDelta] = []
|
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self.tool_index = -1
|
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self.json_mode = json_mode
|
||||
|
||||
def check_empty_tool_call_args(self) -> bool:
|
||||
"""
|
||||
Check if the tool call block so far has been an empty string
|
||||
"""
|
||||
args = ""
|
||||
# if text content block -> skip
|
||||
if len(self.content_blocks) == 0:
|
||||
return False
|
||||
|
||||
if (
|
||||
self.content_blocks[0]["delta"]["type"] == "text_delta"
|
||||
or self.content_blocks[0]["delta"]["type"] == "thinking_delta"
|
||||
):
|
||||
return False
|
||||
|
||||
for block in self.content_blocks:
|
||||
if block["delta"]["type"] == "input_json_delta":
|
||||
args += block["delta"].get("partial_json", "") # type: ignore
|
||||
|
||||
if len(args) == 0:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _handle_usage(self, anthropic_usage_chunk: Union[dict, UsageDelta]) -> Usage:
|
||||
return AnthropicConfig().calculate_usage(
|
||||
usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=None
|
||||
)
|
||||
|
||||
def _content_block_delta_helper(
|
||||
self, chunk: dict
|
||||
) -> Tuple[
|
||||
str,
|
||||
Optional[ChatCompletionToolCallChunk],
|
||||
List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]],
|
||||
Dict[str, Any],
|
||||
]:
|
||||
"""
|
||||
Helper function to handle the content block delta
|
||||
"""
|
||||
text = ""
|
||||
tool_use: Optional[ChatCompletionToolCallChunk] = None
|
||||
provider_specific_fields = {}
|
||||
content_block = ContentBlockDelta(**chunk) # type: ignore
|
||||
thinking_blocks: List[
|
||||
Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
|
||||
] = []
|
||||
|
||||
self.content_blocks.append(content_block)
|
||||
if "text" in content_block["delta"]:
|
||||
text = content_block["delta"]["text"]
|
||||
elif "partial_json" in content_block["delta"]:
|
||||
tool_use = {
|
||||
"id": None,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": None,
|
||||
"arguments": content_block["delta"]["partial_json"],
|
||||
},
|
||||
"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 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
|
||||
|
||||
index = int(chunk.get("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 = ContentBlockStart(**chunk) # type: ignore
|
||||
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 = [
|
||||
ChatCompletionRedactedThinkingBlock(
|
||||
type="redacted_thinking",
|
||||
data=content_block_start["content_block"]["data"],
|
||||
)
|
||||
]
|
||||
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,
|
||||
}
|
||||
elif type_chunk == "message_delta":
|
||||
"""
|
||||
Anthropic
|
||||
chunk = {'type': 'message_delta', 'delta': {'stop_reason': 'max_tokens', 'stop_sequence': None}, 'usage': {'output_tokens': 10}}
|
||||
"""
|
||||
# TODO - get usage from this chunk, set in response
|
||||
message_delta = MessageBlockDelta(**chunk) # type: ignore
|
||||
finish_reason = map_finish_reason(
|
||||
finish_reason=message_delta["delta"].get("stop_reason", "stop")
|
||||
or "stop"
|
||||
)
|
||||
usage = self._handle_usage(anthropic_usage_chunk=message_delta["usage"])
|
||||
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
|
||||
|
||||
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 self.json_mode is True and tool_use is not None:
|
||||
message = AnthropicConfig._convert_tool_response_to_message(
|
||||
tool_calls=[tool_use]
|
||||
)
|
||||
if message is not None:
|
||||
text = message.content or ""
|
||||
tool_use = None
|
||||
|
||||
return text, tool_use
|
||||
|
||||
# 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()
|
||||
Reference in New Issue
Block a user