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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>
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Copilot
206
src/langbot/pkg/api/http/service/model.py
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206
src/langbot/pkg/api/http/service/model.py
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from __future__ import annotations
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import uuid
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import sqlalchemy
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from langbot_plugin.api.entities.builtin.provider import message as provider_message
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from ....core import app
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from ....entity.persistence import model as persistence_model
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from ....entity.persistence import pipeline as persistence_pipeline
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from ....provider.modelmgr import requester as model_requester
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class LLMModelsService:
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ap: app.Application
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def __init__(self, ap: app.Application) -> None:
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self.ap = ap
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async def get_llm_models(self, include_secret: bool = True) -> list[dict]:
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result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
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models = result.all()
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masked_columns = []
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if not include_secret:
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masked_columns = ['api_keys']
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return [
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self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model, masked_columns)
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for model in models
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]
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async def create_llm_model(self, model_data: dict) -> str:
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model_data['uuid'] = str(uuid.uuid4())
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await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_model.LLMModel).values(**model_data))
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llm_model = await self.get_llm_model(model_data['uuid'])
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await self.ap.model_mgr.load_llm_model(llm_model)
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# check if default pipeline has no model bound
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result = await self.ap.persistence_mgr.execute_async(
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sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
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persistence_pipeline.LegacyPipeline.is_default == True
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)
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)
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pipeline = result.first()
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if pipeline is not None and pipeline.config['ai']['local-agent']['model'] == '':
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pipeline_config = pipeline.config
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pipeline_config['ai']['local-agent']['model'] = model_data['uuid']
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pipeline_data = {'config': pipeline_config}
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await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
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return model_data['uuid']
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async def get_llm_model(self, model_uuid: str) -> dict | None:
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result = await self.ap.persistence_mgr.execute_async(
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sqlalchemy.select(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
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)
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model = result.first()
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if model is None:
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return None
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return self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model)
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async def update_llm_model(self, model_uuid: str, model_data: dict) -> None:
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if 'uuid' in model_data:
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del model_data['uuid']
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.update(persistence_model.LLMModel)
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.where(persistence_model.LLMModel.uuid == model_uuid)
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.values(**model_data)
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)
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await self.ap.model_mgr.remove_llm_model(model_uuid)
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llm_model = await self.get_llm_model(model_uuid)
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await self.ap.model_mgr.load_llm_model(llm_model)
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async def delete_llm_model(self, model_uuid: str) -> None:
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.delete(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
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)
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await self.ap.model_mgr.remove_llm_model(model_uuid)
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async def test_llm_model(self, model_uuid: str, model_data: dict) -> None:
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runtime_llm_model: model_requester.RuntimeLLMModel | None = None
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if model_uuid != '_':
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for model in self.ap.model_mgr.llm_models:
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if model.model_entity.uuid == model_uuid:
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runtime_llm_model = model
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break
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if runtime_llm_model is None:
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raise Exception('model not found')
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else:
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runtime_llm_model = await self.ap.model_mgr.init_runtime_llm_model(model_data)
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# Mon Nov 10 2025: Commented for some providers may not support thinking parameter
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# # 有些模型厂商默认开启了思考功能，测试容易延迟
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# extra_args = model_data.get('extra_args', {})
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# if not extra_args or 'thinking' not in extra_args:
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# extra_args['thinking'] = {'type': 'disabled'}
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await runtime_llm_model.requester.invoke_llm(
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query=None,
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model=runtime_llm_model,
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messages=[provider_message.Message(role='user', content='Hello, world! Please just reply a "Hello".')],
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funcs=[],
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# extra_args=extra_args,
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)
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class EmbeddingModelsService:
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ap: app.Application
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def __init__(self, ap: app.Application) -> None:
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self.ap = ap
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async def get_embedding_models(self) -> list[dict]:
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result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
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models = result.all()
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return [self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model) for model in models]
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async def create_embedding_model(self, model_data: dict) -> str:
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model_data['uuid'] = str(uuid.uuid4())
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
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)
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embedding_model = await self.get_embedding_model(model_data['uuid'])
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await self.ap.model_mgr.load_embedding_model(embedding_model)
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return model_data['uuid']
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async def get_embedding_model(self, model_uuid: str) -> dict | None:
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result = await self.ap.persistence_mgr.execute_async(
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sqlalchemy.select(persistence_model.EmbeddingModel).where(
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persistence_model.EmbeddingModel.uuid == model_uuid
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)
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)
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model = result.first()
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if model is None:
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return None
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return self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
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async def update_embedding_model(self, model_uuid: str, model_data: dict) -> None:
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if 'uuid' in model_data:
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del model_data['uuid']
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.update(persistence_model.EmbeddingModel)
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.where(persistence_model.EmbeddingModel.uuid == model_uuid)
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.values(**model_data)
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)
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await self.ap.model_mgr.remove_embedding_model(model_uuid)
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embedding_model = await self.get_embedding_model(model_uuid)
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await self.ap.model_mgr.load_embedding_model(embedding_model)
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async def delete_embedding_model(self, model_uuid: str) -> None:
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.delete(persistence_model.EmbeddingModel).where(
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persistence_model.EmbeddingModel.uuid == model_uuid
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)
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)
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await self.ap.model_mgr.remove_embedding_model(model_uuid)
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async def test_embedding_model(self, model_uuid: str, model_data: dict) -> None:
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runtime_embedding_model: model_requester.RuntimeEmbeddingModel | None = None
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if model_uuid != '_':
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for model in self.ap.model_mgr.embedding_models:
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if model.model_entity.uuid == model_uuid:
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runtime_embedding_model = model
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break
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if runtime_embedding_model is None:
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raise Exception('model not found')
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else:
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runtime_embedding_model = await self.ap.model_mgr.init_runtime_embedding_model(model_data)
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await runtime_embedding_model.requester.invoke_embedding(
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model=runtime_embedding_model,
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input_text=['Hello, world!'],
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extra_args={},
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)
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