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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:
142
src/langbot/pkg/provider/modelmgr/requester.py
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142
src/langbot/pkg/provider/modelmgr/requester.py
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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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from ...entity.persistence import model as persistence_model
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import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
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from . import token
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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 RuntimeLLMModel:
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"""运行时模型"""
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model_entity: persistence_model.LLMModel
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"""模型数据"""
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token_mgr: token.TokenManager
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"""api key管理器"""
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requester: ProviderAPIRequester
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"""请求器实例"""
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def __init__(
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self,
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model_entity: persistence_model.LLMModel,
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token_mgr: token.TokenManager,
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requester: ProviderAPIRequester,
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):
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self.model_entity = model_entity
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self.token_mgr = token_mgr
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self.requester = requester
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class RuntimeEmbeddingModel:
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"""运行时 Embedding 模型"""
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model_entity: persistence_model.EmbeddingModel
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"""模型数据"""
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token_mgr: token.TokenManager
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"""api key管理器"""
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requester: ProviderAPIRequester
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"""请求器实例"""
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def __init__(
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self,
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model_entity: persistence_model.EmbeddingModel,
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token_mgr: token.TokenManager,
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requester: ProviderAPIRequester,
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):
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self.model_entity = model_entity
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self.token_mgr = token_mgr
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self.requester = requester
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class ProviderAPIRequester(metaclass=abc.ABCMeta):
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"""Provider API请求器"""
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name: str = None
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ap: app.Application
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default_config: dict[str, typing.Any] = {}
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requester_cfg: dict[str, typing.Any] = {}
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def __init__(self, ap: app.Application, config: dict[str, typing.Any]):
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self.ap = ap
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self.requester_cfg = {**self.default_config}
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self.requester_cfg.update(config)
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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 invoke_llm(
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self,
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query: pipeline_query.Query,
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model: 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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"""调用API
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Args:
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model (RuntimeLLMModel): 使用的模型信息
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messages (typing.List[llm_entities.Message]): 消息对象列表
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funcs (typing.List[tools_entities.LLMFunction], optional): 使用的工具函数列表. Defaults to None.
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extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
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remove_think (bool, optional): 是否移思考中的消息. Defaults to False.
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Returns:
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llm_entities.Message: 返回消息对象
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"""
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pass
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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: 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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"""调用API
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Args:
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model (RuntimeLLMModel): 使用的模型信息
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messages (typing.List[provider_message.Message]): 消息对象列表
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funcs (typing.List[resource_tool.LLMTool], optional): 使用的工具函数列表. Defaults to None.
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extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
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remove_think (bool, optional): 是否移除思考中的消息. Defaults to False.
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Returns:
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typing.AsyncGenerator[provider_message.MessageChunk]: 返回消息对象
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"""
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pass
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async def invoke_embedding(
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self,
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model: RuntimeEmbeddingModel,
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input_text: typing.List[str],
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extra_args: dict[str, typing.Any] = {},
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) -> typing.List[typing.List[float]]:
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"""调用 Embedding API
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Args:
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model (RuntimeEmbeddingModel): 使用的模型信息
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input_text (typing.List[str]): 输入文本
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extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
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Returns:
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typing.List[typing.List[float]]: 返回的 embedding 向量
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"""
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pass
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