mirror of
https://github.com/cpacker/MemGPT.git
synced 2025-06-03 04:30:22 +00:00
6495180ee2
Co-authored-by: Matthew Zhou <mattzh1314@gmail.com> Co-authored-by: Charles Packer <packercharles@gmail.com>
587 lines
24 KiB
Python
587 lines
24 KiB
Python
import json
|
|
import re
|
|
from typing import Dict, List, Optional, Union
|
|
|
|
import anthropic
|
|
from anthropic import AsyncStream
|
|
from anthropic.types.beta import BetaMessage as AnthropicMessage
|
|
from anthropic.types.beta import BetaRawMessageStreamEvent
|
|
from anthropic.types.beta.message_create_params import MessageCreateParamsNonStreaming
|
|
from anthropic.types.beta.messages import BetaMessageBatch
|
|
from anthropic.types.beta.messages.batch_create_params import Request
|
|
|
|
from letta.errors import (
|
|
ContextWindowExceededError,
|
|
ErrorCode,
|
|
LLMAuthenticationError,
|
|
LLMBadRequestError,
|
|
LLMConnectionError,
|
|
LLMNotFoundError,
|
|
LLMPermissionDeniedError,
|
|
LLMRateLimitError,
|
|
LLMServerError,
|
|
LLMUnprocessableEntityError,
|
|
)
|
|
from letta.helpers.datetime_helpers import get_utc_time_int
|
|
from letta.llm_api.helpers import add_inner_thoughts_to_functions, unpack_all_inner_thoughts_from_kwargs
|
|
from letta.llm_api.llm_client_base import LLMClientBase
|
|
from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
|
|
from letta.log import get_logger
|
|
from letta.schemas.llm_config import LLMConfig
|
|
from letta.schemas.message import Message as PydanticMessage
|
|
from letta.schemas.openai.chat_completion_request import Tool
|
|
from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice, FunctionCall
|
|
from letta.schemas.openai.chat_completion_response import Message as ChoiceMessage
|
|
from letta.schemas.openai.chat_completion_response import ToolCall, UsageStatistics
|
|
from letta.services.provider_manager import ProviderManager
|
|
from letta.tracing import trace_method
|
|
|
|
DUMMY_FIRST_USER_MESSAGE = "User initializing bootup sequence."
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
class AnthropicClient(LLMClientBase):
|
|
|
|
def request(self, request_data: dict) -> dict:
|
|
client = self._get_anthropic_client(async_client=False)
|
|
response = client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
|
|
return response.model_dump()
|
|
|
|
async def request_async(self, request_data: dict) -> dict:
|
|
client = self._get_anthropic_client(async_client=True)
|
|
response = await client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
|
|
return response.model_dump()
|
|
|
|
@trace_method
|
|
async def stream_async(self, request_data: dict) -> AsyncStream[BetaRawMessageStreamEvent]:
|
|
client = self._get_anthropic_client(async_client=True)
|
|
request_data["stream"] = True
|
|
return await client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
|
|
|
|
@trace_method
|
|
async def send_llm_batch_request_async(
|
|
self,
|
|
agent_messages_mapping: Dict[str, List[PydanticMessage]],
|
|
agent_tools_mapping: Dict[str, List[dict]],
|
|
agent_llm_config_mapping: Dict[str, LLMConfig],
|
|
) -> BetaMessageBatch:
|
|
"""
|
|
Sends a batch request to the Anthropic API using the provided agent messages and tools mappings.
|
|
|
|
Args:
|
|
agent_messages_mapping: A dict mapping agent_id to their list of PydanticMessages.
|
|
agent_tools_mapping: A dict mapping agent_id to their list of tool dicts.
|
|
agent_llm_config_mapping: A dict mapping agent_id to their LLM config
|
|
|
|
Returns:
|
|
BetaMessageBatch: The batch response from the Anthropic API.
|
|
|
|
Raises:
|
|
ValueError: If the sets of agent_ids in the two mappings do not match.
|
|
Exception: Transformed errors from the underlying API call.
|
|
"""
|
|
# Validate that both mappings use the same set of agent_ids.
|
|
if set(agent_messages_mapping.keys()) != set(agent_tools_mapping.keys()):
|
|
raise ValueError("Agent mappings for messages and tools must use the same agent_ids.")
|
|
|
|
try:
|
|
requests = {
|
|
agent_id: self.build_request_data(
|
|
messages=agent_messages_mapping[agent_id],
|
|
llm_config=agent_llm_config_mapping[agent_id],
|
|
tools=agent_tools_mapping[agent_id],
|
|
)
|
|
for agent_id in agent_messages_mapping
|
|
}
|
|
|
|
client = self._get_anthropic_client(async_client=True)
|
|
|
|
anthropic_requests = [
|
|
Request(custom_id=agent_id, params=MessageCreateParamsNonStreaming(**params)) for agent_id, params in requests.items()
|
|
]
|
|
|
|
batch_response = await client.beta.messages.batches.create(requests=anthropic_requests)
|
|
|
|
return batch_response
|
|
|
|
except Exception as e:
|
|
# Enhance logging here if additional context is needed
|
|
logger.error("Error during send_llm_batch_request_async.", exc_info=True)
|
|
raise self.handle_llm_error(e)
|
|
|
|
@trace_method
|
|
def _get_anthropic_client(self, async_client: bool = False) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic]:
|
|
override_key = ProviderManager().get_anthropic_override_key()
|
|
if async_client:
|
|
return anthropic.AsyncAnthropic(api_key=override_key) if override_key else anthropic.AsyncAnthropic()
|
|
return anthropic.Anthropic(api_key=override_key) if override_key else anthropic.Anthropic()
|
|
|
|
@trace_method
|
|
def build_request_data(
|
|
self,
|
|
messages: List[PydanticMessage],
|
|
llm_config: LLMConfig,
|
|
tools: Optional[List[dict]] = None,
|
|
force_tool_call: Optional[str] = None,
|
|
) -> dict:
|
|
# TODO: This needs to get cleaned up. The logic here is pretty confusing.
|
|
# TODO: I really want to get rid of prefixing, it's a recipe for disaster code maintenance wise
|
|
prefix_fill = True
|
|
if not self.use_tool_naming:
|
|
raise NotImplementedError("Only tool calling supported on Anthropic API requests")
|
|
|
|
if not llm_config.max_tokens:
|
|
raise ValueError("Max tokens must be set for anthropic")
|
|
|
|
data = {
|
|
"model": llm_config.model,
|
|
"max_tokens": llm_config.max_tokens,
|
|
"temperature": llm_config.temperature,
|
|
}
|
|
|
|
# Extended Thinking
|
|
if llm_config.enable_reasoner:
|
|
data["thinking"] = {
|
|
"type": "enabled",
|
|
"budget_tokens": llm_config.max_reasoning_tokens,
|
|
}
|
|
# `temperature` may only be set to 1 when thinking is enabled. Please consult our documentation at https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#important-considerations-when-using-extended-thinking'
|
|
data["temperature"] = 1.0
|
|
|
|
# Silently disable prefix_fill for now
|
|
prefix_fill = False
|
|
|
|
# Tools
|
|
# For an overview on tool choice:
|
|
# https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
|
|
if not tools:
|
|
# Special case for summarization path
|
|
tools_for_request = None
|
|
tool_choice = None
|
|
elif llm_config.enable_reasoner:
|
|
# NOTE: reasoning models currently do not allow for `any`
|
|
tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
|
|
tools_for_request = [Tool(function=f) for f in tools]
|
|
elif force_tool_call is not None:
|
|
tool_choice = {"type": "tool", "name": force_tool_call}
|
|
tools_for_request = [Tool(function=f) for f in tools if f["name"] == force_tool_call]
|
|
|
|
# need to have this setting to be able to put inner thoughts in kwargs
|
|
if not llm_config.put_inner_thoughts_in_kwargs:
|
|
logger.warning(
|
|
f"Force setting put_inner_thoughts_in_kwargs to True for Claude because there is a forced tool call: {force_tool_call}"
|
|
)
|
|
llm_config.put_inner_thoughts_in_kwargs = True
|
|
else:
|
|
if llm_config.put_inner_thoughts_in_kwargs:
|
|
# tool_choice_type other than "auto" only plays nice if thinking goes inside the tool calls
|
|
tool_choice = {"type": "any", "disable_parallel_tool_use": True}
|
|
else:
|
|
tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
|
|
tools_for_request = [Tool(function=f) for f in tools] if tools is not None else None
|
|
|
|
# Add tool choice
|
|
if tool_choice:
|
|
data["tool_choice"] = tool_choice
|
|
|
|
# Add inner thoughts kwarg
|
|
# TODO: Can probably make this more efficient
|
|
if tools_for_request and len(tools_for_request) > 0 and llm_config.put_inner_thoughts_in_kwargs:
|
|
tools_with_inner_thoughts = add_inner_thoughts_to_functions(
|
|
functions=[t.function.model_dump() for t in tools_for_request],
|
|
inner_thoughts_key=INNER_THOUGHTS_KWARG,
|
|
inner_thoughts_description=INNER_THOUGHTS_KWARG_DESCRIPTION,
|
|
)
|
|
tools_for_request = [Tool(function=f) for f in tools_with_inner_thoughts]
|
|
|
|
if tools_for_request and len(tools_for_request) > 0:
|
|
# TODO eventually enable parallel tool use
|
|
data["tools"] = convert_tools_to_anthropic_format(tools_for_request)
|
|
|
|
# Messages
|
|
inner_thoughts_xml_tag = "thinking"
|
|
|
|
# Move 'system' to the top level
|
|
if messages[0].role != "system":
|
|
raise RuntimeError(f"First message is not a system message, instead has role {messages[0].role}")
|
|
data["system"] = messages[0].content if isinstance(messages[0].content, str) else messages[0].content[0].text
|
|
data["messages"] = [
|
|
m.to_anthropic_dict(
|
|
inner_thoughts_xml_tag=inner_thoughts_xml_tag,
|
|
put_inner_thoughts_in_kwargs=bool(llm_config.put_inner_thoughts_in_kwargs),
|
|
)
|
|
for m in messages[1:]
|
|
]
|
|
|
|
# Ensure first message is user
|
|
if data["messages"][0]["role"] != "user":
|
|
data["messages"] = [{"role": "user", "content": DUMMY_FIRST_USER_MESSAGE}] + data["messages"]
|
|
|
|
# Handle alternating messages
|
|
data["messages"] = merge_tool_results_into_user_messages(data["messages"])
|
|
|
|
# Prefix fill
|
|
# https://docs.anthropic.com/en/api/messages#body-messages
|
|
# NOTE: cannot prefill with tools for opus:
|
|
# Your API request included an `assistant` message in the final position, which would pre-fill the `assistant` response. When using tools with "claude-3-opus-20240229"
|
|
if prefix_fill and not llm_config.put_inner_thoughts_in_kwargs and "opus" not in data["model"]:
|
|
data["messages"].append(
|
|
# Start the thinking process for the assistant
|
|
{"role": "assistant", "content": f"<{inner_thoughts_xml_tag}>"},
|
|
)
|
|
|
|
return data
|
|
|
|
def handle_llm_error(self, e: Exception) -> Exception:
|
|
if isinstance(e, anthropic.APIConnectionError):
|
|
logger.warning(f"[Anthropic] API connection error: {e.__cause__}")
|
|
return LLMConnectionError(
|
|
message=f"Failed to connect to Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
details={"cause": str(e.__cause__) if e.__cause__ else None},
|
|
)
|
|
|
|
if isinstance(e, anthropic.RateLimitError):
|
|
logger.warning("[Anthropic] Rate limited (429). Consider backoff.")
|
|
return LLMRateLimitError(
|
|
message=f"Rate limited by Anthropic: {str(e)}",
|
|
code=ErrorCode.RATE_LIMIT_EXCEEDED,
|
|
)
|
|
|
|
if isinstance(e, anthropic.BadRequestError):
|
|
logger.warning(f"[Anthropic] Bad request: {str(e)}")
|
|
if "prompt is too long" in str(e).lower():
|
|
# If the context window is too large, we expect to receive:
|
|
# 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'prompt is too long: 200758 tokens > 200000 maximum'}}
|
|
return ContextWindowExceededError(
|
|
message=f"Bad request to Anthropic (context window exceeded): {str(e)}",
|
|
)
|
|
else:
|
|
return LLMBadRequestError(
|
|
message=f"Bad request to Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.AuthenticationError):
|
|
logger.warning(f"[Anthropic] Authentication error: {str(e)}")
|
|
return LLMAuthenticationError(
|
|
message=f"Authentication failed with Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.PermissionDeniedError):
|
|
logger.warning(f"[Anthropic] Permission denied: {str(e)}")
|
|
return LLMPermissionDeniedError(
|
|
message=f"Permission denied by Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.NotFoundError):
|
|
logger.warning(f"[Anthropic] Resource not found: {str(e)}")
|
|
return LLMNotFoundError(
|
|
message=f"Resource not found in Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.UnprocessableEntityError):
|
|
logger.warning(f"[Anthropic] Unprocessable entity: {str(e)}")
|
|
return LLMUnprocessableEntityError(
|
|
message=f"Invalid request content for Anthropic: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
if isinstance(e, anthropic.APIStatusError):
|
|
logger.warning(f"[Anthropic] API status error: {str(e)}")
|
|
return LLMServerError(
|
|
message=f"Anthropic API error: {str(e)}",
|
|
code=ErrorCode.INTERNAL_SERVER_ERROR,
|
|
details={
|
|
"status_code": e.status_code if hasattr(e, "status_code") else None,
|
|
"response": str(e.response) if hasattr(e, "response") else None,
|
|
},
|
|
)
|
|
|
|
return super().handle_llm_error(e)
|
|
|
|
# TODO: Input messages doesn't get used here
|
|
# TODO: Clean up this interface
|
|
def convert_response_to_chat_completion(
|
|
self,
|
|
response_data: dict,
|
|
input_messages: List[PydanticMessage],
|
|
) -> ChatCompletionResponse:
|
|
"""
|
|
Example response from Claude 3:
|
|
response.json = {
|
|
'id': 'msg_01W1xg9hdRzbeN2CfZM7zD2w',
|
|
'type': 'message',
|
|
'role': 'assistant',
|
|
'content': [
|
|
{
|
|
'type': 'text',
|
|
'text': "<thinking>Analyzing user login event. This is Chad's first
|
|
interaction with me. I will adjust my personality and rapport accordingly.</thinking>"
|
|
},
|
|
{
|
|
'type':
|
|
'tool_use',
|
|
'id': 'toolu_01Ka4AuCmfvxiidnBZuNfP1u',
|
|
'name': 'core_memory_append',
|
|
'input': {
|
|
'name': 'human',
|
|
'content': 'Chad is logging in for the first time. I will aim to build a warm
|
|
and welcoming rapport.',
|
|
'request_heartbeat': True
|
|
}
|
|
}
|
|
],
|
|
'model': 'claude-3-haiku-20240307',
|
|
'stop_reason': 'tool_use',
|
|
'stop_sequence': None,
|
|
'usage': {
|
|
'input_tokens': 3305,
|
|
'output_tokens': 141
|
|
}
|
|
}
|
|
"""
|
|
response = AnthropicMessage(**response_data)
|
|
prompt_tokens = response.usage.input_tokens
|
|
completion_tokens = response.usage.output_tokens
|
|
finish_reason = remap_finish_reason(str(response.stop_reason))
|
|
|
|
content = None
|
|
reasoning_content = None
|
|
reasoning_content_signature = None
|
|
redacted_reasoning_content = None
|
|
tool_calls = None
|
|
|
|
if len(response.content) > 0:
|
|
for content_part in response.content:
|
|
if content_part.type == "text":
|
|
content = strip_xml_tags(string=content_part.text, tag="thinking")
|
|
if content_part.type == "tool_use":
|
|
# hack for tool rules
|
|
input = json.loads(json.dumps(content_part.input))
|
|
if "id" in input and input["id"].startswith("toolu_") and "function" in input:
|
|
arguments = str(input["function"]["arguments"])
|
|
else:
|
|
arguments = json.dumps(content_part.input, indent=2)
|
|
tool_calls = [
|
|
ToolCall(
|
|
id=content_part.id,
|
|
type="function",
|
|
function=FunctionCall(
|
|
name=content_part.name,
|
|
arguments=arguments,
|
|
),
|
|
)
|
|
]
|
|
if content_part.type == "thinking":
|
|
reasoning_content = content_part.thinking
|
|
reasoning_content_signature = content_part.signature
|
|
if content_part.type == "redacted_thinking":
|
|
redacted_reasoning_content = content_part.data
|
|
|
|
else:
|
|
raise RuntimeError("Unexpected empty content in response")
|
|
|
|
assert response.role == "assistant"
|
|
choice = Choice(
|
|
index=0,
|
|
finish_reason=finish_reason,
|
|
message=ChoiceMessage(
|
|
role=response.role,
|
|
content=content,
|
|
reasoning_content=reasoning_content,
|
|
reasoning_content_signature=reasoning_content_signature,
|
|
redacted_reasoning_content=redacted_reasoning_content,
|
|
tool_calls=tool_calls,
|
|
),
|
|
)
|
|
|
|
chat_completion_response = ChatCompletionResponse(
|
|
id=response.id,
|
|
choices=[choice],
|
|
created=get_utc_time_int(),
|
|
model=response.model,
|
|
usage=UsageStatistics(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=prompt_tokens + completion_tokens,
|
|
),
|
|
)
|
|
if self.llm_config.put_inner_thoughts_in_kwargs:
|
|
chat_completion_response = unpack_all_inner_thoughts_from_kwargs(
|
|
response=chat_completion_response, inner_thoughts_key=INNER_THOUGHTS_KWARG
|
|
)
|
|
|
|
return chat_completion_response
|
|
|
|
|
|
def convert_tools_to_anthropic_format(tools: List[Tool]) -> List[dict]:
|
|
"""See: https://docs.anthropic.com/claude/docs/tool-use
|
|
|
|
OpenAI style:
|
|
"tools": [{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "find_movies",
|
|
"description": "find ....",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
PARAM: {
|
|
"type": PARAM_TYPE, # eg "string"
|
|
"description": PARAM_DESCRIPTION,
|
|
},
|
|
...
|
|
},
|
|
"required": List[str],
|
|
}
|
|
}
|
|
}
|
|
]
|
|
|
|
Anthropic style:
|
|
"tools": [{
|
|
"name": "find_movies",
|
|
"description": "find ....",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
PARAM: {
|
|
"type": PARAM_TYPE, # eg "string"
|
|
"description": PARAM_DESCRIPTION,
|
|
},
|
|
...
|
|
},
|
|
"required": List[str],
|
|
}
|
|
}
|
|
]
|
|
|
|
Two small differences:
|
|
- 1 level less of nesting
|
|
- "parameters" -> "input_schema"
|
|
"""
|
|
formatted_tools = []
|
|
for tool in tools:
|
|
formatted_tool = {
|
|
"name": tool.function.name,
|
|
"description": tool.function.description,
|
|
"input_schema": tool.function.parameters or {"type": "object", "properties": {}, "required": []},
|
|
}
|
|
formatted_tools.append(formatted_tool)
|
|
|
|
return formatted_tools
|
|
|
|
|
|
def merge_tool_results_into_user_messages(messages: List[dict]):
|
|
"""Anthropic API doesn't allow role 'tool'->'user' sequences
|
|
|
|
Example HTTP error:
|
|
messages: roles must alternate between "user" and "assistant", but found multiple "user" roles in a row
|
|
|
|
From: https://docs.anthropic.com/claude/docs/tool-use
|
|
You may be familiar with other APIs that return tool use as separate from the model's primary output,
|
|
or which use a special-purpose tool or function message role.
|
|
In contrast, Anthropic's models and API are built around alternating user and assistant messages,
|
|
where each message is an array of rich content blocks: text, image, tool_use, and tool_result.
|
|
"""
|
|
|
|
# TODO walk through the messages list
|
|
# When a dict (dict_A) with 'role' == 'user' is followed by a dict with 'role' == 'user' (dict B), do the following
|
|
# dict_A["content"] = dict_A["content"] + dict_B["content"]
|
|
|
|
# The result should be a new merged_messages list that doesn't have any back-to-back dicts with 'role' == 'user'
|
|
merged_messages = []
|
|
if not messages:
|
|
return merged_messages
|
|
|
|
# Start with the first message in the list
|
|
current_message = messages[0]
|
|
|
|
for next_message in messages[1:]:
|
|
if current_message["role"] == "user" and next_message["role"] == "user":
|
|
# Merge contents of the next user message into current one
|
|
current_content = (
|
|
current_message["content"]
|
|
if isinstance(current_message["content"], list)
|
|
else [{"type": "text", "text": current_message["content"]}]
|
|
)
|
|
next_content = (
|
|
next_message["content"]
|
|
if isinstance(next_message["content"], list)
|
|
else [{"type": "text", "text": next_message["content"]}]
|
|
)
|
|
merged_content = current_content + next_content
|
|
current_message["content"] = merged_content
|
|
else:
|
|
# Append the current message to result as it's complete
|
|
merged_messages.append(current_message)
|
|
# Move on to the next message
|
|
current_message = next_message
|
|
|
|
# Append the last processed message to the result
|
|
merged_messages.append(current_message)
|
|
|
|
return merged_messages
|
|
|
|
|
|
def remap_finish_reason(stop_reason: str) -> str:
|
|
"""Remap Anthropic's 'stop_reason' to OpenAI 'finish_reason'
|
|
|
|
OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
|
|
see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
|
|
|
|
From: https://docs.anthropic.com/claude/reference/migrating-from-text-completions-to-messages#stop-reason
|
|
|
|
Messages have a stop_reason of one of the following values:
|
|
"end_turn": The conversational turn ended naturally.
|
|
"stop_sequence": One of your specified custom stop sequences was generated.
|
|
"max_tokens": (unchanged)
|
|
|
|
"""
|
|
if stop_reason == "end_turn":
|
|
return "stop"
|
|
elif stop_reason == "stop_sequence":
|
|
return "stop"
|
|
elif stop_reason == "max_tokens":
|
|
return "length"
|
|
elif stop_reason == "tool_use":
|
|
return "function_call"
|
|
else:
|
|
raise ValueError(f"Unexpected stop_reason: {stop_reason}")
|
|
|
|
|
|
def strip_xml_tags(string: str, tag: Optional[str]) -> str:
|
|
if tag is None:
|
|
return string
|
|
# Construct the regular expression pattern to find the start and end tags
|
|
tag_pattern = f"<{tag}.*?>|</{tag}>"
|
|
# Use the regular expression to replace the tags with an empty string
|
|
return re.sub(tag_pattern, "", string)
|
|
|
|
|
|
def strip_xml_tags_streaming(string: str, tag: Optional[str]) -> str:
|
|
if tag is None:
|
|
return string
|
|
|
|
# Handle common partial tag cases
|
|
parts_to_remove = [
|
|
"<", # Leftover start bracket
|
|
f"<{tag}", # Opening tag start
|
|
f"</{tag}", # Closing tag start
|
|
f"/{tag}>", # Closing tag end
|
|
f"{tag}>", # Opening tag end
|
|
f"/{tag}", # Partial closing tag without >
|
|
">", # Leftover end bracket
|
|
]
|
|
|
|
result = string
|
|
for part in parts_to_remove:
|
|
result = result.replace(part, "")
|
|
|
|
return result
|