from __future__ import annotations
import typing
import json
import platform
import socket
import anthropic
import httpx
from .. import errors, requester
from ....utils import image
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.provider.message as provider_message
class AnthropicMessages(requester.ProviderAPIRequester):
"""Anthropic Messages API 请求器"""
client: anthropic.AsyncAnthropic
default_config: dict[str, typing.Any] = {
'base_url': 'https://api.anthropic.com',
'timeout': 120,
}
async def initialize(self):
# 兼容 Windows 缺失 TCP_KEEPINTVL 和 TCP_KEEPCNT 的问题
if platform.system() == 'Windows':
if not hasattr(socket, 'TCP_KEEPINTVL'):
socket.TCP_KEEPINTVL = 0
if not hasattr(socket, 'TCP_KEEPCNT'):
socket.TCP_KEEPCNT = 0
httpx_client = anthropic._base_client.AsyncHttpxClientWrapper(
base_url=self.requester_cfg['base_url'],
# cast to a valid type because mypy doesn't understand our type narrowing
timeout=typing.cast(httpx.Timeout, self.requester_cfg['timeout']),
limits=anthropic._constants.DEFAULT_CONNECTION_LIMITS,
follow_redirects=True,
trust_env=True,
)
self.client = anthropic.AsyncAnthropic(
api_key='',
http_client=httpx_client,
base_url=self.requester_cfg['base_url'],
)
async def invoke_llm(
self,
query: pipeline_query.Query,
model: requester.RuntimeLLMModel,
messages: typing.List[provider_message.Message],
funcs: typing.List[resource_tool.LLMTool] = None,
extra_args: dict[str, typing.Any] = {},
remove_think: bool = False,
) -> provider_message.Message:
self.client.api_key = model.token_mgr.get_token()
args = extra_args.copy()
args['model'] = model.model_entity.name
# 处理消息
# system
system_role_message = None
for i, m in enumerate(messages):
if m.role == 'system':
system_role_message = m
break
if system_role_message:
messages.pop(i)
if isinstance(system_role_message, provider_message.Message) and isinstance(system_role_message.content, str):
args['system'] = system_role_message.content
req_messages = []
for m in messages:
if m.role == 'tool':
tool_call_id = m.tool_call_id
req_messages.append(
{
'role': 'user',
'content': [
{
'type': 'tool_result',
'tool_use_id': tool_call_id,
'is_error': False,
'content': [{'type': 'text', 'text': m.content}],
}
],
}
)
continue
msg_dict = m.dict(exclude_none=True)
if isinstance(m.content, str) and m.content.strip() != '':
msg_dict['content'] = [{'type': 'text', 'text': m.content}]
elif isinstance(m.content, list):
for i, ce in enumerate(m.content):
if ce.type == 'image_base64':
image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
alter_image_ele = {
'type': 'image',
'source': {
'type': 'base64',
'media_type': f'image/{image_format}',
'data': image_b64,
},
}
msg_dict['content'][i] = alter_image_ele
if m.tool_calls:
for tool_call in m.tool_calls:
msg_dict['content'].append(
{
'type': 'tool_use',
'id': tool_call.id,
'name': tool_call.function.name,
'input': json.loads(tool_call.function.arguments),
}
)
del msg_dict['tool_calls']
req_messages.append(msg_dict)
args['messages'] = req_messages
if 'thinking' in args:
args['thinking'] = {'type': 'enabled', 'budget_tokens': 10000}
if funcs:
tools = await self.ap.tool_mgr.generate_tools_for_anthropic(funcs)
if tools:
args['tools'] = tools
try:
resp = await self.client.messages.create(**args)
args = {
'content': '',
'role': resp.role,
}
assert type(resp) is anthropic.types.message.Message
for block in resp.content:
if not remove_think and block.type == 'thinking':
args['content'] = '\n' + block.thinking + '\n\n' + args['content']
elif block.type == 'text':
args['content'] += block.text
elif block.type == 'tool_use':
assert type(block) is anthropic.types.tool_use_block.ToolUseBlock
tool_call = provider_message.ToolCall(
id=block.id,
type='function',
function=provider_message.FunctionCall(name=block.name, arguments=json.dumps(block.input)),
)
if 'tool_calls' not in args:
args['tool_calls'] = []
args['tool_calls'].append(tool_call)
return provider_message.Message(**args)
except anthropic.AuthenticationError as e:
raise errors.RequesterError(f'api-key 无效: {e.message}')
except anthropic.BadRequestError as e:
raise errors.RequesterError(str(e.message))
except anthropic.NotFoundError as e:
if 'model: ' in str(e):
raise errors.RequesterError(f'模型无效: {e.message}')
else:
raise errors.RequesterError(f'请求地址无效: {e.message}')
async def invoke_llm_stream(
self,
query: pipeline_query.Query,
model: requester.RuntimeLLMModel,
messages: typing.List[provider_message.Message],
funcs: typing.List[resource_tool.LLMTool] = None,
extra_args: dict[str, typing.Any] = {},
remove_think: bool = False,
) -> provider_message.Message:
self.client.api_key = model.token_mgr.get_token()
args = extra_args.copy()
args['model'] = model.model_entity.name
args['stream'] = True
# 处理消息
# system
system_role_message = None
for i, m in enumerate(messages):
if m.role == 'system':
system_role_message = m
break
if system_role_message:
messages.pop(i)
if isinstance(system_role_message, provider_message.Message) and isinstance(system_role_message.content, str):
args['system'] = system_role_message.content
req_messages = []
for m in messages:
if m.role == 'tool':
tool_call_id = m.tool_call_id
req_messages.append(
{
'role': 'user',
'content': [
{
'type': 'tool_result',
'tool_use_id': tool_call_id,
'is_error': False, # 暂时直接写false
'content': [
{'type': 'text', 'text': m.content}
], # 这里要是list包裹，应该是多个返回的情况？type类型好像也可以填其他的，暂时只写text
}
],
}
)
continue
msg_dict = m.dict(exclude_none=True)
if isinstance(m.content, str) and m.content.strip() != '':
msg_dict['content'] = [{'type': 'text', 'text': m.content}]
elif isinstance(m.content, list):
for i, ce in enumerate(m.content):
if ce.type == 'image_base64':
image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
alter_image_ele = {
'type': 'image',
'source': {
'type': 'base64',
'media_type': f'image/{image_format}',
'data': image_b64,
},
}
msg_dict['content'][i] = alter_image_ele
if isinstance(msg_dict['content'], str) and msg_dict['content'] == '':
msg_dict['content'] = [] # 这里不知道为什么会莫名有个空导致content为字符
if m.tool_calls:
for tool_call in m.tool_calls:
msg_dict['content'].append(
{
'type': 'tool_use',
'id': tool_call.id,
'name': tool_call.function.name,
'input': json.loads(tool_call.function.arguments),
}
)
del msg_dict['tool_calls']
req_messages.append(msg_dict)
if 'thinking' in args:
args['thinking'] = {'type': 'enabled', 'budget_tokens': 10000}
args['messages'] = req_messages
if funcs:
tools = await self.ap.tool_mgr.generate_tools_for_anthropic(funcs)
if tools:
args['tools'] = tools
try:
role = 'assistant' # 默认角色
# chunk_idx = 0
think_started = False
think_ended = False
finish_reason = False
content = ''
tool_name = ''
tool_id = ''
async for chunk in await self.client.messages.create(**args):
tool_call = {'id': None, 'function': {'name': None, 'arguments': None}, 'type': 'function'}
if isinstance(
chunk, anthropic.types.raw_content_block_start_event.RawContentBlockStartEvent
): # 记录开始
if chunk.content_block.type == 'tool_use':
if chunk.content_block.name is not None:
tool_name = chunk.content_block.name
if chunk.content_block.id is not None:
tool_id = chunk.content_block.id
tool_call['function']['name'] = tool_name
tool_call['function']['arguments'] = ''
tool_call['id'] = tool_id
if not remove_think:
if chunk.content_block.type == 'thinking' and not remove_think:
think_started = True
elif chunk.content_block.type == 'text' and chunk.index != 0 and not remove_think:
think_ended = True
continue
elif isinstance(chunk, anthropic.types.raw_content_block_delta_event.RawContentBlockDeltaEvent):
if chunk.delta.type == 'thinking_delta':
if think_started:
think_started = False
content = '\n' + chunk.delta.thinking
elif remove_think:
continue
else:
content = chunk.delta.thinking
elif chunk.delta.type == 'text_delta':
if think_ended:
think_ended = False
content = '\n\n' + chunk.delta.text
else:
content = chunk.delta.text
elif chunk.delta.type == 'input_json_delta':
tool_call['function']['arguments'] = chunk.delta.partial_json
tool_call['function']['name'] = tool_name
tool_call['id'] = tool_id
elif isinstance(chunk, anthropic.types.raw_content_block_stop_event.RawContentBlockStopEvent):
continue # 记录raw_content_block结束的
elif isinstance(chunk, anthropic.types.raw_message_delta_event.RawMessageDeltaEvent):
if chunk.delta.stop_reason == 'end_turn':
finish_reason = True
elif isinstance(chunk, anthropic.types.raw_message_stop_event.RawMessageStopEvent):
continue # 这个好像是完全结束
else:
# print(chunk)
self.ap.logger.debug(f'anthropic chunk: {chunk}')
continue
args = {
'content': content,
'role': role,
'is_final': finish_reason,
'tool_calls': None if tool_call['id'] is None else [tool_call],
}
# if chunk_idx == 0:
# chunk_idx += 1
# continue
# assert type(chunk) is anthropic.types.message.Chunk
yield provider_message.MessageChunk(**args)
# return llm_entities.Message(**args)
except anthropic.AuthenticationError as e:
raise errors.RequesterError(f'api-key 无效: {e.message}')
except anthropic.BadRequestError as e:
raise errors.RequesterError(str(e.message))
except anthropic.NotFoundError as e:
if 'model: ' in str(e):
raise errors.RequesterError(f'模型无效: {e.message}')
else:
raise errors.RequesterError(f'请求地址无效: {e.message}')