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