[build-system]
requires = ["hatchling", "python-semantic-release", "build"]
build-backend = "hatchling.build"

[project]
name = "mcp-memory-service"
version = "12.4.0"
description = "Semantic memory layer for AI applications. REST API + MCP transport + knowledge graph + autonomous consolidation. Works with 14+ AI clients. Self-host, zero cloud cost."
readme = "README.md"
requires-python = ">=3.10"
keywords = [
    "semantic-memory", "agent-memory", "ai-memory", "long-term-memory",
    "multi-agent", "langgraph", "crewai", "autogen", "agentic-ai",
    "knowledge-graph", "vector-database", "ai-agents", "rag",
    "semantic-search", "memory-consolidation", "rest-api", "fastapi",
    "mcp", "model-context-protocol", "sqlite-vec", "cloudflare",
    "open-source", "privacy-first", "self-hosted", "ai-assistant"
]
classifiers = [
    "Development Status :: 5 - Production/Stable",
    "Intended Audience :: Developers",
    "Topic :: Software Development :: Libraries :: Python Modules",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Database :: Database Engines/Servers",
    "License :: OSI Approved :: Apache Software License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11", 
    "Programming Language :: Python :: 3.12",
    "Operating System :: OS Independent",
    "Environment :: Console",
    "Framework :: FastAPI"
]
authors = [
    { name = "Heinrich Krupp", email = "heinrich.krupp@gmail.com" }
]
license = { text = "Apache-2.0" }
dependencies = [
    "aiofiles>=23.2.1",
    "aiohttp>=3.13.3",
    "aiosqlite>=0.20.0",
    "apscheduler>=3.11.0",
    "authlib>=1.6.9",
    "build>=0.10.0",
    "certifi>=2026.10.19",
    "chardet>=5.0.0",
    "click>=8.0.0",
    "cryptography>=46.0.6",
    "fastapi>=0.115.5",
    "httpx>=0.24.0",
    "mcp>=1.19.0,<2.0.0",
    "numpy>=1.24.0",
    "psutil>=5.9.0",
    "PyJWT[crypto]>=2.12.0",
    "pypdf>=6.9.1",
    "PyYAML>=6.0",
    "python-dotenv>=1.0.0",
    "python-multipart>=0.0.22",
    "requests>=2.32.4",
    "sqlite-vec>=0.1.6",
    "sqlite-vec>=0.1.6",
    "sse-starlette>=2.1.0",
    "typing-extensions>=4.0.0; python_version < '3.11'",
    "uvicorn>=0.30.0",
    "zeroconf>=0.130.0",
]

[project.optional-dependencies]
# Development dependencies for testing and code quality
dev = [
    "pytest>=8.0.0",
    "pytest-asyncio>=0.23.0",
    "pytest-cov>=4.0.0",
    "pytest-benchmark>=4.0.0",
    "pytest-timeout>=2.0.0",
    "pytest-xdist>=3.6.0",
    "pytest-subtests>=0.10.0",
    "black>=26.3.1",
    "ruff>=0.1.0",
]
# Machine learning dependencies for semantic search and embeddings
ml = [
    "sentence-transformers>=2.2.2",
    "tokenizers>=0.22.2",
    "torch>=2.0.0",
    "transformers>=5.5.0,<6.0.0",
    # torch.onnx.export routes through the dynamo exporter, which imports
    # onnxscript. Without it the quality ranker cannot export a model at all --
    # `ONNXRankerModel('nvidia-quality-classifier-deberta')` dies with
    # ModuleNotFoundError partway through the export. It was declared only in
    # tools/docker/Dockerfile.quality-cpu, the image that pre-exports models, so
    # a plain `pip install mcp-memory-service[ml]` could never do the export
    # itself. Same lower bound as that Dockerfile.
    "onnxscript>=0.1.0",
    # Clustering for consolidation; see the [clustering] extra.
    "scikit-learn>=1.3.0",
    "wandb>=0.18.0"  # Fix for Issue #311: Ensure wandb is compatible with protobuf 5.x
]
# Consolidation clustering. DBSCAN and hierarchical clustering come from
# scikit-learn; without it MCP_CLUSTERING_ALGORITHM can only be 'simple', and
# 'auto' resolves there. Kept out of [sqlite] so the lightweight ONNX install
# stays lightweight -- scikit-learn pulls scipy with it.
clustering = [
    "scikit-learn>=1.3.0",
]
# NLI contradiction detection (Phase 3, RFC #732)
nli = [
    "transformers>=5.5.0,<6.0.0",
    "onnxruntime>=1.17.0",
]
# SQLite-vec with lightweight ONNX embeddings (recommended for most users)
# The ONNX embedding path needs both onnxruntime and tokenizers; shipping only
# onnxruntime leaves the backend unable to initialize and silently falls back to
# hash pseudo-vectors (see issue #162).
sqlite = [
    "onnxruntime>=1.14.1",
    "tokenizers>=0.22.2",
    # Needed to fetch pre-exported ONNX models (onnx-community/<model>-ONNX)
    # when a non-default MCP_EMBEDDING_MODEL is requested under USE_ONNX.
    "huggingface_hub>=0.20"
]
# SQLite-vec with full ML capabilities (for advanced features)
sqlite-ml = [
    "sentence-transformers>=2.2.2",
    "mcp-memory-service[sqlite,ml]"
]
# Milvus backend — Milvus Lite (single file), self-hosted Milvus, or Zilliz Cloud.
# Shipped with the ML extras so sentence-transformers embeddings are available.
milvus = [
    # The milvus-tests job in .github/workflows/ci.yml gates the 2.x and 3.x
    # lines, and the upper bound ships exactly what CI tests. Widen it to the
    # next major only together with a gating CI leg for that major (#1112).
    # .github/dependabot.yml ignores major updates for pymilvus, because a group
    # bump rewrites this line rather than respecting it (#976, #1086).
    "pymilvus>=2.5.0,<4.0.0",
    "milvus-lite>=2.4.10",
    # The default embedding path has been ONNX-first since v11, and [ml] below
    # only brings torch/sentence-transformers. Without onnxruntime a Milvus-only
    # install has no ONNX runtime to fall back on — same gap the sqlite extra
    # documents above (issue #162, reported for milvus in #213).
    "onnxruntime>=1.14.1",
    # milvus-lite 2.5.x imports `pkg_resources` unguarded at module top
    # (milvus_lite/__init__.py) but never declares setuptools itself. That API
    # was removed in setuptools 82.0.0, so anything from 82 up breaks the import
    # with ModuleNotFoundError -- and so does having no setuptools at all, which
    # is the default in a venv since Python 3.12 dropped it from ensurepip.
    # Declaring the bound unconditionally covers both: it guarantees setuptools
    # is present and keeps it on a version that still ships pkg_resources.
    # .github/dependabot.yml carries a matching ignore entry -- lift both
    # together, never just one.
    "setuptools<82",
    "mcp-memory-service[ml]",
]
# Full installation including all optional dependencies
full = [
    "mcp-memory-service[sqlite,ml,dev]"
]

[project.scripts]
memory = "mcp_memory_service.cli.main:main"
memory-server = "mcp_memory_service.cli.main:memory_server_main"
mcp-memory-server = "mcp_memory_service.mcp_server:main"

# Keep in sync with pyproject-lite.toml so both distributions link to the same place.
[project.urls]
Homepage = "https://github.com/doobidoo/mcp-memory-service"
Documentation = "https://github.com/doobidoo/mcp-memory-service#readme"
Repository = "https://github.com/doobidoo/mcp-memory-service"
Issues = "https://github.com/doobidoo/mcp-memory-service/issues"

[tool.hatch.build.targets.wheel]
packages = ["src/mcp_memory_service"]
artifacts = ["**/*.yaml"]

[tool.hatch.version]
path = "src/mcp_memory_service/__init__.py"

[tool.semantic_release]
version_variable = [
    "src/mcp_memory_service/_version.py:__version__",
    "pyproject.toml:version",
    "pyproject-lite.toml:version"
]
branch = "main"
changelog_file = "CHANGELOG.md"
build_command = "pip install build && python -m build"
build_command_env = []
dist_path = "dist/"
upload_to_pypi = true
upload_to_release = true
commit_message = "chore(release): bump version to {version}"

[tool.semantic_release.commit_parser_options]
allowed_tags = [
    "build",
    "chore",
    "ci",
    "docs",
    "feat",
    "fix",
    "perf",
    "style",
    "refactor",
    "test"
]
minor_tags = ["feat"]
patch_tags = ["fix", "perf"]

[tool.semantic_release.changelog]
template_dir = "templates"
changelog_sections = [
    ["feat", "Features"],
    ["fix", "Bug Fixes"],
    ["perf", "Performance"],
    ["refactor", "Code Refactoring"],
    ["test", "Tests"]
]

[dependency-groups]
dev = [
    "pytest-asyncio>=1.4.0",
]