mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-21 13:04:20 +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
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from __future__ import annotations
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import os
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import traceback
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from . import strategy
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from .. import stage, entities
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import langbot_plugin.api.entities.builtin.platform.message as platform_message
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from ...utils import importutil
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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from . import strategies
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importutil.import_modules_in_pkg(strategies)
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@stage.stage_class('LongTextProcessStage')
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class LongTextProcessStage(stage.PipelineStage):
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"""Long message processing stage
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Rewrite:
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- resp_message_chain
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"""
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strategy_impl: strategy.LongTextStrategy | None
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async def initialize(self, pipeline_config: dict):
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config = pipeline_config['output']['long-text-processing']
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if config['strategy'] == 'none':
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self.strategy_impl = None
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return
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if config['strategy'] == 'image':
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use_font = config['font-path']
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try:
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# 检查是否存在
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if not os.path.exists(use_font):
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# 若是windows系统，使用微软雅黑
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if os.name == 'nt':
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use_font = 'C:/Windows/Fonts/msyh.ttc'
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if not os.path.exists(use_font):
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self.ap.logger.warn(
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'Font file not found, and Windows system font cannot be used, switch to forward message component to send long messages, you can adjust the related settings in the configuration file.'
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)
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config['blob_message_strategy'] = 'forward'
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else:
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self.ap.logger.info('Using Windows system font: ' + use_font)
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config['font-path'] = use_font
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else:
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self.ap.logger.warn(
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'Font file not found, and system font cannot be used, switch to forward message component to send long messages, you can adjust the related settings in the configuration file.'
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)
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pipeline_config['output']['long-text-processing']['strategy'] = 'forward'
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except Exception:
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traceback.print_exc()
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self.ap.logger.error(
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'Failed to load font file ({}), switch to forward message component to send long messages, you can adjust the related settings in the configuration file.'.format(
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use_font
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)
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)
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pipeline_config['output']['long-text-processing']['strategy'] = 'forward'
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for strategy_cls in strategy.preregistered_strategies:
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if strategy_cls.name == config['strategy']:
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self.strategy_impl = strategy_cls(self.ap)
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break
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else:
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raise ValueError(f'Long message processing strategy not found: {config["strategy"]}')
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await self.strategy_impl.initialize()
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async def process(self, query: pipeline_query.Query, stage_inst_name: str) -> entities.StageProcessResult:
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if self.strategy_impl is None:
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self.ap.logger.debug('Long message processing strategy is not set, skip long message processing.')
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return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
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# 检查是否包含非 Plain 组件
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contains_non_plain = False
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for msg in query.resp_message_chain[-1]:
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if not isinstance(msg, platform_message.Plain):
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contains_non_plain = True
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break
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if contains_non_plain:
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self.ap.logger.debug('Message contains non-Plain components, skip long message processing.')
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elif (
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len(str(query.resp_message_chain[-1]))
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> query.pipeline_config['output']['long-text-processing']['threshold']
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):
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query.resp_message_chain[-1] = platform_message.MessageChain(
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await self.strategy_impl.process(str(query.resp_message_chain[-1]), query)
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)
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return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
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@@ -0,0 +1,35 @@
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# 转发消息组件
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from __future__ import annotations
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from .. import strategy as strategy_model
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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.platform.message as platform_message
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ForwardMessageDiaplay = platform_message.ForwardMessageDiaplay
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Forward = platform_message.Forward
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@strategy_model.strategy_class('forward')
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class ForwardComponentStrategy(strategy_model.LongTextStrategy):
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async def process(self, message: str, query: pipeline_query.Query) -> list[platform_message.MessageComponent]:
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display = ForwardMessageDiaplay(
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title='Group chat history',
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brief='[Chat history]',
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source='Chat history',
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preview=['User: ' + message],
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summary='View 1 forwarded message',
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)
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node_list = [
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platform_message.ForwardMessageNode(
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sender_id=query.adapter.bot_account_id,
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sender_name='User',
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message_chain=platform_message.MessageChain([platform_message.Plain(text=message)]),
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)
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]
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forward = Forward(display=display, node_list=node_list)
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return [forward]
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@@ -0,0 +1,211 @@
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from __future__ import annotations
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import os
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import base64
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import time
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import re
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from PIL import Image, ImageDraw, ImageFont
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import functools
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from .. import strategy as strategy_model
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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.platform.message as platform_message
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@strategy_model.strategy_class('image')
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class Text2ImageStrategy(strategy_model.LongTextStrategy):
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async def initialize(self):
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pass
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@functools.lru_cache(maxsize=16)
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def get_font(self, font_path: str):
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return ImageFont.truetype(
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font_path,
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32,
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encoding='utf-8',
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)
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async def process(self, message: str, query: pipeline_query.Query) -> list[platform_message.MessageComponent]:
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img_path = self.text_to_image(
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text_str=message,
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save_as='temp/{}.png'.format(int(time.time())),
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query=query,
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)
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compressed_path, size = self.compress_image(img_path, outfile='temp/{}_compressed.png'.format(int(time.time())))
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with open(compressed_path, 'rb') as f:
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img = f.read()
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b64 = base64.b64encode(img)
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# 删除图片
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os.remove(img_path)
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if os.path.exists(compressed_path):
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os.remove(compressed_path)
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return [
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platform_message.Image(
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base64=b64.decode('utf-8'),
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)
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]
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def indexNumber(self, path=''):
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"""
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查找字符串中数字所在串中的位置
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:param path:目标字符串
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:return:<class 'list'>: <class 'list'>: [['1', 16], ['2', 35], ['1', 51]]
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"""
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kv = []
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nums = []
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beforeDatas = re.findall('[\\d]+', path)
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for num in beforeDatas:
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indexV = []
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times = path.count(num)
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if times > 1:
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if num not in nums:
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indexs = re.finditer(num, path)
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for index in indexs:
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iV = []
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i = index.span()[0]
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iV.append(num)
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iV.append(i)
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kv.append(iV)
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nums.append(num)
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else:
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index = path.find(num)
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indexV.append(num)
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indexV.append(index)
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kv.append(indexV)
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# 根据数字位置排序
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indexSort = []
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resultIndex = []
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for vi in kv:
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indexSort.append(vi[1])
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indexSort.sort()
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for i in indexSort:
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for v in kv:
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if i == v[1]:
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resultIndex.append(v)
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return resultIndex
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def get_size(self, file):
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# 获取文件大小:KB
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size = os.path.getsize(file)
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return size / 1024
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def get_outfile(self, infile, outfile):
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if outfile:
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return outfile
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dir, suffix = os.path.splitext(infile)
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outfile = '{}-out{}'.format(dir, suffix)
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return outfile
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def compress_image(self, infile, outfile='', kb=100, step=20, quality=90):
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"""不改变图片尺寸压缩到指定大小
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:param infile: 压缩源文件
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:param outfile: 压缩文件保存地址
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:param mb: 压缩目标,KB
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:param step: 每次调整的压缩比率
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:param quality: 初始压缩比率
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:return: 压缩文件地址，压缩文件大小
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"""
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o_size = self.get_size(infile)
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if o_size <= kb:
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return infile, o_size
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outfile = self.get_outfile(infile, outfile)
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while o_size > kb:
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im = Image.open(infile)
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im.save(outfile, quality=quality)
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if quality - step < 0:
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break
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quality -= step
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o_size = self.get_size(outfile)
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return outfile, self.get_size(outfile)
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def text_to_image(
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self,
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text_str: str,
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save_as='temp.png',
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width=800,
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query: pipeline_query.Query = None,
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):
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text_str = text_str.replace('\t', ' ')
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# 分行
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lines = text_str.split('\n')
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# 计算并分割
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final_lines = []
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text_width = width - 80
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self.ap.logger.debug('lines: {}, text_width: {}'.format(lines, text_width))
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for line in lines:
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# 如果长了就分割
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line_width = self.get_font(query.pipeline_config['output']['long-text-processing']['font-path']).getlength(
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line
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)
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self.ap.logger.debug('line_width: {}'.format(line_width))
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if line_width < text_width:
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final_lines.append(line)
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continue
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else:
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rest_text = line
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while True:
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# 分割最前面的一行
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point = int(len(rest_text) * (text_width / line_width))
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# 检查断点是否在数字中间
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numbers = self.indexNumber(rest_text)
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for number in numbers:
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if number[1] < point < number[1] + len(number[0]) and number[1] != 0:
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point = number[1]
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break
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final_lines.append(rest_text[:point])
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rest_text = rest_text[point:]
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line_width = self.get_font(
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query.pipeline_config['output']['long-text-processing']['font-path']
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).getlength(rest_text)
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if line_width < text_width:
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final_lines.append(rest_text)
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break
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else:
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continue
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# 准备画布
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img = Image.new('RGBA', (width, max(280, len(final_lines) * 35 + 65)), (255, 255, 255, 255))
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draw = ImageDraw.Draw(img, mode='RGBA')
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self.ap.logger.debug('正在绘制图片...')
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# 绘制正文
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line_number = 0
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offset_x = 20
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offset_y = 30
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for final_line in final_lines:
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draw.text(
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(offset_x, offset_y + 35 * line_number),
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final_line,
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fill=(0, 0, 0),
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font=self.get_font(query.pipeline_config['output']['long-text-processing']['font-path']),
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)
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# 遍历此行,检查是否有emoji
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idx_in_line = 0
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for ch in final_line:
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# 检查字符占位宽
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char_code = ord(ch)
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if char_code >= 127:
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idx_in_line += 1
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else:
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idx_in_line += 0.5
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line_number += 1
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self.ap.logger.debug('正在保存图片...')
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img.save(save_as)
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return save_as
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@@ -0,0 +1,65 @@
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from __future__ import annotations
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import abc
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import typing
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from ...core import app
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import langbot_plugin.api.entities.builtin.platform.message as platform_message
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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preregistered_strategies: list[typing.Type[LongTextStrategy]] = []
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def strategy_class(
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name: str,
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) -> typing.Callable[[typing.Type[LongTextStrategy]], typing.Type[LongTextStrategy]]:
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"""Long text processing strategy class decorator
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Args:
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name (str): Strategy name
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Returns:
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typing.Callable[[typing.Type[LongTextStrategy]], typing.Type[LongTextStrategy]]: Decorator
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"""
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def decorator(cls: typing.Type[LongTextStrategy]) -> typing.Type[LongTextStrategy]:
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assert issubclass(cls, LongTextStrategy)
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cls.name = name
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preregistered_strategies.append(cls)
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return cls
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return decorator
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class LongTextStrategy(metaclass=abc.ABCMeta):
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"""Long text processing strategy abstract class"""
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name: str
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ap: app.Application
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def __init__(self, ap: app.Application):
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self.ap = ap
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async def initialize(self):
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pass
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@abc.abstractmethod
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async def process(self, message: str, query: pipeline_query.Query) -> list[platform_message.MessageComponent]:
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"""处理长文本
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If the text length exceeds the threshold, this method will be called.
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Args:
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message (str): Message
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query (core_entities.Query): Query object
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Returns:
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list[platform_message.MessageComponent]: Converted platform message components
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"""
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return []
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