mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
8eaa96390a
* refactor(file_chunker): replace file parser with file chunker component - Rename file_parser module to file_chunker across codebase - Update BaseFileParser to BaseFileChunker with corresponding component type - Rename LinkedFileParser to MarkdownFileChunker for markdown-specific chunking - Rename ChunkedFileParser to DefaultFileChunker for default byte-based chunking - Update documentation references from file_parser to file_chunker - Modify dependency injection in BaseStep to use file_chunker instead of file_parser - Update configuration and component registration to use new chunker naming - Rename all related test files and update test assertions accordingly - Add recursive option to scan_store_changes_step in default configuration * feat(database): enhance Neo4j connection with environment variable support - Add support for NEO4J_PASSWORD environment variable as fallback - Make password parameter optional in constructor with validation - Update chromadb dependency from 1.3.5 to 1.5.7 - Configure CORS credentials based on origin settings - Import os module for environment variable access * feat(config): add timezone support and remove unused dialog directory - Added timezone field to application config with IANA timezone support - Removed unused dialog_dir configuration and related directory creation - Replaced date.today() with timezone-aware now() function across daily operations - Created evolve module with timezone-aware datetime functionality - Updated daily_create, daily_list, and daily_reindex steps to use timezone-aware dates * refactor(steps): update file chunker implementation - Replace ChunkedFileParser with DefaultFileChunker in background steps - Add module docstring to evolve steps package - Update return type annotation to reflect new chunker class usage * refactor(components): rename embedding and llm components to as_embedding and as_llm - Rename reme4/components/embedding to reme4/components/as_embedding - Rename reme4/components/llm to reme4/components/as_llm - Update all imports and references from embedding to as_embedding - Update all imports and references from llm to as_llm - Change BaseEmbedding to BaseAsEmbedding and update inheritance - Change BaseLLM to BaseAsLLM and update inheritance - Update component types from LLM/EMBEDDING to AS_LLM/AS_EMBEDDING - Update configuration keys from embedding/llm to as_embedding/as_llm - Update all property references from llm to as_llm in step classes - Update test assertions to use new component enum values * refactor(embedding_store): rename embedding parameter to as_embedding - Updated configuration key from 'embedding' to 'as_embedding' - Renamed class attribute from 'embedding' to 'as_embedding' - Updated method calls to use 'as_embedding' instead of 'embedding' - Changed parameter name in constructor from 'embedding' to 'as_embedding' - Updated documentation to reflect new parameter name - Modified health check to use 'as_embedding' property * feat(agent_wrapper): add unified agent wrapper component with multiple backends - Introduce BaseAgentWrapper abstract base class for agent implementations - Add AsAgentWrapper implementation using AgentScope framework - Add CcAgentWrapper implementation using Claude Code SDK - Register agent_wrapper component type in ComponentEnum - Configure default agent_wrapper settings in default.yaml - Implement tool integration for both AgentScope and Claude Code backends - Support fluent configuration via set_system_prompt() and add_tools() methods * feat(agent-wrapper): add structured output support for agent wrappers - Import SystemMsg in AsAgentWrapper for structured output handling - Add output_schema parameter support in AsAgentWrapper with generate_structured_output - Implement set_output_schema method in BaseAgentWrapper for chaining configuration - Add output schema support in CcAgentWrapper with JSON schema format option - Return structured output when available in CcAgentWrapper response - Refactor kwargs handling to use default values consistently across wrapper classes
152 lines
3.6 KiB
TOML
152 lines
3.6 KiB
TOML
[build-system]
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requires = ["setuptools", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "reme_ai"
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dynamic = ["version"]
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description = "Remember Me, Refine Me."
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authors = [
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{ name = "jinli.yl", email = "jinli.yl@alibaba-inc.com" },
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{ name = "dengjiaji.djj", email = "dengjiaji.djj@alibaba-inc.com" },
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{ name = "caozouying.czy", email = "caozouying.czy@alibaba-inc.com" },
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{ name = "weikangzhou.zwk", email = "weikangzhou.zwk@alibaba-inc.com" },
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]
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license = { file = "LICENSE" }
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readme = "README.md"
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requires-python = ">=3.10"
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classifiers = [
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"Development Status :: 4 - Beta",
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"Intended Audience :: Developers",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.10",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Software Development :: Libraries :: Python Modules",
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"Topic :: Software Development :: Libraries :: Application Frameworks",
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"Typing :: Typed",
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]
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keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http", "reme", "personal"]
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dependencies = [
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"sqlite-vec>=0.1.6",
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"prompt_toolkit>=3.0.52",
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"rich>=14.2.0",
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"asyncpg>=0.31.0",
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"chromadb>=1.5.7",
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"dashscope>=1.25.1",
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"elasticsearch>=9.2.0",
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"fastapi>=0.121.3",
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"fastmcp>=2.14.1",
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"httpx>=0.28.1",
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"loguru>=0.7.3",
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"mcp>=1.25.0",
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"numpy>=2.2.6",
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"openai>=2.8.1",
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"pandas>=2.3.3",
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"pydantic>=2.12.4",
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"qdrant-client>=1.16.0",
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"tavily-python>=0.7.13",
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"tiktoken>=0.12.0",
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"tqdm>=4.67.1",
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"transformers>=4.57.3",
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"uvicorn>=0.40.0",
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"watchfiles>=1.1.1",
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"pyyaml>=6.0.3",
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]
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[project.optional-dependencies]
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obvec = [
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"pyobvector>=0.1.20",
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"sqlalchemy>=2.0",
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# pyobvector imports Expression from sqlglot; removed from sqlglot 30+ top-level API
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"sqlglot>=25,<30",
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]
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seekdb = [
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# pyseekdb 1.2+ requires Python >=3.11 (no wheels on 3.10)
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"pyseekdb>=1.2.0; python_version >= '3.11'",
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]
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zvec = [
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"zvec",
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]
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ray = [
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"ray",
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]
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dev = [
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"jupyter-book",
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"ghp-import",
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"myst-nb",
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"sphinxcontrib-bibtex",
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"furo",
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"sphinxcontrib-mermaid",
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"pre-commit",
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"pytest>=8.0",
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"pytest-asyncio>=0.23",
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]
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full = [
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"reme_ai[dev,ray,light]",
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]
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litellm = [
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"litellm==1.80.0",
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]
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light = [
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"agentscope==1.0.20",
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"flowllm[reme]>=0.2.0.10",
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]
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core = [
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"agentscope==1.0.20",
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]
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["reme_ai*", "reme*"]
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exclude = ["test*", "cookbook*", "doc*", "library*", "dist*"]
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[tool.setuptools.package-data]
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reme_ai = [
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"**/*.yaml",
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"**/*.py",
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"**/*.json",
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]
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reme = [
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"**/*.yaml",
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"**/*.py",
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"**/*.json",
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]
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[tool.setuptools.dynamic]
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version = { attr = "reme.__version__" }
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[project.urls]
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Homepage = "https://github.com/agentscope-ai/ReMe"
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Documentation = "https://reme.agentscope.io/"
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Repository = "https://github.com/agentscope-ai/ReMe"
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[project.scripts]
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reme = "reme_ai.main:main"
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reme2 = "reme.reme:main"
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remecli = "reme.reme_cli:main"
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[tool.pytest.ini_options]
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asyncio_default_fixture_loop_scope = "function"
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# Script-style tests that need to be run with `python test_*.py`
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testpaths = ["tests"]
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python_files = ["test_*.py"]
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python_functions = ["test_*"]
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# Exclude script-style tests that require manual execution
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addopts = "--ignore=tests/test_embedding.py --ignore=tests/test_embedding_cache.py --ignore=tests/test_embedding_sync.py --ignore=tests/test_file_store.py"
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# python -m build && twine upload dist/*
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