mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-27 16:04:21 +00:00
chore: Add PyPI package support for uvx/pip installation (#1764)
* Initial plan * Add package structure and resource path utilities - Created langbot/ package with __init__.py and __main__.py entry point - Added paths utility to find frontend and resource files from package installation - Updated config loading to use resource paths - Updated frontend serving to use resource paths - Added MANIFEST.in for package data inclusion - Updated pyproject.toml with build system and entry points Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * Add PyPI publishing workflow and update license - Created GitHub Actions workflow to build frontend and publish to PyPI - Added license field to pyproject.toml to fix deprecation warning - Updated .gitignore to exclude build artifacts - Tested package building successfully Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * Add PyPI installation documentation - Created PYPI_INSTALLATION.md with detailed installation and usage instructions - Updated README.md to feature uvx/pip installation as recommended method - Updated README_EN.md with same changes for English documentation Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * Address code review feedback - Made package-data configuration more specific to langbot package only - Improved path detection with caching to avoid repeated file I/O - Removed sys.path searching which was incorrect for package data - Removed interactive input() call for non-interactive environment compatibility - Simplified error messages for version check Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * Fix code review issues - Use specific exception types instead of bare except - Fix misleading comments about directory levels - Remove redundant existence check before makedirs with exist_ok=True - Use context manager for file opening to ensure proper cleanup Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * Simplify package configuration and document behavioral differences - Removed redundant package-data configuration, relying on MANIFEST.in - Added documentation about behavioral differences between package and source installation - Clarified that include-package-data=true uses MANIFEST.in for data files Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> * chore: update pyproject.toml * chore: try pack templates in langbot/ * chore: update * chore: update * chore: update * chore: update * chore: update * chore: adjust dir structure * chore: fix imports * fix: read default-pipeline-config.json * fix: read default-pipeline-config.json * fix: tests * ci: publish pypi * chore: bump version 4.6.0-beta.1 for testing * chore: add templates/** * fix: send adapters and requesters icons * chore: bump version 4.6.0b2 for testing * chore: add platform field for docker-compose.yaml --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com> Co-authored-by: Junyan Qin <rockchinq@gmail.com>
This commit is contained in:
Copilot
@@ -0,0 +1,406 @@
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from __future__ import annotations
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import asyncio
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import typing
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import openai
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import openai.types.chat.chat_completion as chat_completion
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import httpx
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from .. import errors, requester
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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 OpenAIChatCompletions(requester.ProviderAPIRequester):
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"""OpenAI ChatCompletion API 请求器"""
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client: openai.AsyncClient
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default_config: dict[str, typing.Any] = {
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'base_url': 'https://api.openai.com/v1',
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'timeout': 120,
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}
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async def initialize(self):
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self.client = openai.AsyncClient(
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api_key='',
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base_url=self.requester_cfg['base_url'].replace(' ', ''),
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timeout=self.requester_cfg['timeout'],
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http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
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)
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async def _req(
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self,
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args: dict,
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extra_body: dict = {},
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) -> chat_completion.ChatCompletion:
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return await self.client.chat.completions.create(**args, extra_body=extra_body)
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async def _req_stream(
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self,
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args: dict,
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extra_body: dict = {},
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):
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async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
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yield chunk
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async def _make_msg(
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self,
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chat_completion: chat_completion.ChatCompletion,
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remove_think: bool = False,
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) -> provider_message.Message:
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chatcmpl_message = chat_completion.choices[0].message.model_dump()
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# 确保 role 字段存在且不为 None
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if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
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chatcmpl_message['role'] = 'assistant'
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# 处理思维链
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content = chatcmpl_message.get('content', '')
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reasoning_content = chatcmpl_message.get('reasoning_content', None)
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processed_content, _ = await self._process_thinking_content(
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content=content, reasoning_content=reasoning_content, remove_think=remove_think
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)
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chatcmpl_message['content'] = processed_content
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# 移除 reasoning_content 字段，避免传递给 Message
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if 'reasoning_content' in chatcmpl_message:
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del chatcmpl_message['reasoning_content']
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message = provider_message.Message(**chatcmpl_message)
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return message
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async def _process_thinking_content(
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self,
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content: str,
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reasoning_content: str = None,
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remove_think: bool = False,
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) -> tuple[str, str]:
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"""处理思维链内容
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Args:
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content: 原始内容
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reasoning_content: reasoning_content 字段内容
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remove_think: 是否移除思维链
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Returns:
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(处理后的内容, 提取的思维链内容)
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"""
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thinking_content = ''
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# 1. 从 reasoning_content 提取思维链
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if reasoning_content:
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thinking_content = reasoning_content
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# 2. 从 content 中提取 <think> 标签内容
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if content and '<think>' in content and '</think>' in content:
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import re
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think_pattern = r'<think>(.*?)</think>'
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think_matches = re.findall(think_pattern, content, re.DOTALL)
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if think_matches:
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# 如果已有 reasoning_content，则追加
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if thinking_content:
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thinking_content += '\n' + '\n'.join(think_matches)
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else:
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thinking_content = '\n'.join(think_matches)
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# 移除 content 中的 <think> 标签
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content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
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# 3. 根据 remove_think 参数决定是否保留思维链
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if remove_think:
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return content, ''
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else:
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# 如果有思维链内容，将其以 <think> 格式添加到 content 开头
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if thinking_content:
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content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
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return content, thinking_content
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async def _closure_stream(
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self,
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query: pipeline_query.Query,
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req_messages: list[dict],
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use_model: requester.RuntimeLLMModel,
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use_funcs: 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.MessageChunk:
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self.client.api_key = use_model.token_mgr.get_token()
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args = {}
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args['tools'] = tools
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# 设置此次请求中的messages
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messages = req_messages.copy()
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# 检查vision
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for msg in messages:
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if 'content' in msg and isinstance(msg['content'], list):
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for me in msg['content']:
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if me['type'] == 'image_base64':
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me['image_url'] = {'url': me['image_base64']}
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me['type'] = 'image_url'
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del me['image_base64']
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args['messages'] = messages
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args['stream'] = True
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# 流式处理状态
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# tool_calls_map: dict[str, provider_message.ToolCall] = {}
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chunk_idx = 0
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thinking_started = False
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thinking_ended = False
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role = 'assistant' # 默认角色
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tool_id = ''
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tool_name = ''
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# accumulated_reasoning = '' # 仅用于判断何时结束思维链
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async for chunk in self._req_stream(args, extra_body=extra_args):
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# 解析 chunk 数据
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if hasattr(chunk, 'choices') and chunk.choices:
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choice = chunk.choices[0]
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delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
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finish_reason = getattr(choice, 'finish_reason', None)
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else:
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delta = {}
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finish_reason = None
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# 从第一个 chunk 获取 role，后续使用这个 role
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if 'role' in delta and delta['role']:
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role = delta['role']
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# 获取增量内容
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delta_content = delta.get('content', '')
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reasoning_content = delta.get('reasoning_content', '')
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# 处理 reasoning_content
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if reasoning_content:
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# accumulated_reasoning += reasoning_content
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# 如果设置了 remove_think，跳过 reasoning_content
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if remove_think:
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chunk_idx += 1
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continue
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# 第一次出现 reasoning_content，添加 <think> 开始标签
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if not thinking_started:
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thinking_started = True
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delta_content = '<think>\n' + reasoning_content
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else:
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# 继续输出 reasoning_content
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delta_content = reasoning_content
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elif thinking_started and not thinking_ended and delta_content:
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# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
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thinking_ended = True
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delta_content = '\n</think>\n' + delta_content
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# 处理 content 中已有的 <think> 标签（如果需要移除）
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# if delta_content and remove_think and '<think>' in delta_content:
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# import re
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#
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# # 移除 <think> 标签及其内容
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# delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
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# 处理工具调用增量
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# delta_tool_calls = None
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if delta.get('tool_calls'):
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for tool_call in delta['tool_calls']:
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if tool_call['id'] and tool_call['function']['name']:
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tool_id = tool_call['id']
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tool_name = tool_call['function']['name']
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else:
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tool_call['id'] = tool_id
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tool_call['function']['name'] = tool_name
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if tool_call['type'] is None:
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tool_call['type'] = 'function'
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# 跳过空的第一个 chunk（只有 role 没有内容）
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if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
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chunk_idx += 1
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continue
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# 构建 MessageChunk - 只包含增量内容
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chunk_data = {
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'role': role,
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'content': delta_content if delta_content else None,
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'tool_calls': delta.get('tool_calls'),
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'is_final': bool(finish_reason),
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}
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# 移除 None 值
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chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
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yield provider_message.MessageChunk(**chunk_data)
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chunk_idx += 1
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async def _closure(
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self,
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query: pipeline_query.Query,
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req_messages: list[dict],
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use_model: requester.RuntimeLLMModel,
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use_funcs: 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 = use_model.token_mgr.get_token()
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args = {}
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args['tools'] = tools
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# 设置此次请求中的messages
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messages = req_messages.copy()
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# 检查vision
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for msg in messages:
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if 'content' in msg and isinstance(msg['content'], list):
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for me in msg['content']:
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if me['type'] == 'image_base64':
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me['image_url'] = {'url': me['image_base64']}
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me['type'] = 'image_url'
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del me['image_base64']
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args['messages'] = messages
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# 发送请求
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resp = await self._req(args, extra_body=extra_args)
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# 处理请求结果
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message = await self._make_msg(resp, remove_think)
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return message
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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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req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
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for m in messages:
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msg_dict = m.dict(exclude_none=True)
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content = msg_dict.get('content')
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if isinstance(content, list):
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# 检查 content 列表中是否每个部分都是文本
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if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
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# 将所有文本部分合并为一个字符串
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msg_dict['content'] = '\n'.join(part['text'] for part in content)
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req_messages.append(msg_dict)
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try:
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msg = await self._closure(
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query=query,
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req_messages=req_messages,
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use_model=model,
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use_funcs=funcs,
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extra_args=extra_args,
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remove_think=remove_think,
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)
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return msg
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
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except openai.BadRequestError as e:
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if 'context_length_exceeded' in e.message:
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raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
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else:
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raise errors.RequesterError(f'请求参数错误: {e.message}')
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except openai.AuthenticationError as e:
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raise errors.RequesterError(f'无效的 api-key: {e.message}')
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except openai.NotFoundError as e:
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raise errors.RequesterError(f'请求路径错误: {e.message}')
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except openai.RateLimitError as e:
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raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
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except openai.APIError as e:
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raise errors.RequesterError(f'请求错误: {e.message}')
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async def invoke_embedding(
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self,
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model: requester.RuntimeEmbeddingModel,
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input_text: list[str],
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extra_args: dict[str, typing.Any] = {},
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) -> list[list[float]]:
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"""调用 Embedding API"""
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self.client.api_key = model.token_mgr.get_token()
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args = {
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'model': model.model_entity.name,
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'input': input_text,
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}
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if model.model_entity.extra_args:
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args.update(model.model_entity.extra_args)
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args.update(extra_args)
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try:
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resp = await self.client.embeddings.create(**args)
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return [d.embedding for d in resp.data]
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
|
||||
except openai.BadRequestError as e:
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raise errors.RequesterError(f'请求参数错误: {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.MessageChunk:
|
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req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
|
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for m in messages:
|
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msg_dict = m.dict(exclude_none=True)
|
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content = msg_dict.get('content')
|
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if isinstance(content, list):
|
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# 检查 content 列表中是否每个部分都是文本
|
||||
if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
|
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# 将所有文本部分合并为一个字符串
|
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msg_dict['content'] = '\n'.join(part['text'] for part in content)
|
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req_messages.append(msg_dict)
|
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|
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try:
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async for item in self._closure_stream(
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query=query,
|
||||
req_messages=req_messages,
|
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use_model=model,
|
||||
use_funcs=funcs,
|
||||
extra_args=extra_args,
|
||||
remove_think=remove_think,
|
||||
):
|
||||
yield item
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
raise errors.RequesterError('请求超时')
|
||||
except openai.BadRequestError as e:
|
||||
if 'context_length_exceeded' in e.message:
|
||||
raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
|
||||
else:
|
||||
raise errors.RequesterError(f'请求参数错误: {e.message}')
|
||||
except openai.AuthenticationError as e:
|
||||
raise errors.RequesterError(f'无效的 api-key: {e.message}')
|
||||
except openai.NotFoundError as e:
|
||||
raise errors.RequesterError(f'请求路径错误: {e.message}')
|
||||
except openai.RateLimitError as e:
|
||||
raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
|
||||
except openai.APIError as e:
|
||||
raise errors.RequesterError(f'请求错误: {e.message}')
|
||||
Reference in New Issue
Block a user