712fc3f1cd
Add Neo4j as an optional graph-native storage layer (ingestion profile). Introduces infra/neo4j with a singleton async driver wrapper and an idempotent bootstrap of constraints + indexes, wired into the FastAPI lifespan. Integration tests skip when no live Neo4j is reachable. Refs #186
120 lines
6.3 KiB
Python
120 lines
6.3 KiB
Python
"""Centralized application settings — loaded from environment variables."""
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from __future__ import annotations
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import os
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from dataclasses import dataclass, field
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@dataclass(frozen=True)
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class Settings:
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app_version: str = "dev"
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conversion_engine: str = "local" # "local" or "remote"
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deployment_mode: str = "self-hosted" # "self-hosted" or "huggingface"
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docling_serve_url: str = "http://localhost:5001"
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docling_serve_api_key: str | None = None
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conversion_timeout: int = 900
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document_timeout: float = 120.0 # Docling-level per-document timeout (seconds)
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lock_timeout: int = 300 # converter lock acquisition timeout (seconds)
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max_concurrent_analyses: int = 3
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default_table_mode: str = "accurate" # "accurate" or "fast"
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max_page_count: int = 0 # 0 = unlimited (upload validation)
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max_file_size: int = 0 # 0 = unlimited (Docling-level, bytes)
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max_file_size_mb: int = 50 # upload limit in MB (0 = unlimited)
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rate_limit_rpm: int = 100 # requests per minute per IP (0 = disabled)
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batch_page_size: int = 0 # 0 = disabled, > 0 = pages per batch
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opensearch_url: str = "" # empty = disabled
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embedding_url: str = "" # empty = disabled (e.g. http://localhost:8001)
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neo4j_uri: str = "" # empty = disabled (e.g. bolt://neo4j:7687)
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neo4j_user: str = "neo4j"
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neo4j_password: str = "changeme"
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opensearch_default_limit: int = 1000 # max chunks returned by get_chunks
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embedding_dimension: int = 384 # Granite Embedding 30M / all-MiniLM-L6-v2
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upload_dir: str = "./uploads"
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db_path: str = "./data/docling_studio.db"
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cors_origins: list[str] = field(
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default_factory=lambda: ["http://localhost:3000", "http://localhost:5173"]
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)
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def __post_init__(self) -> None:
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errors: list[str] = []
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if self.document_timeout <= 0:
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errors.append(f"document_timeout must be > 0 (got {self.document_timeout})")
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if self.conversion_timeout <= 0:
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errors.append(f"conversion_timeout must be > 0 (got {self.conversion_timeout})")
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if self.lock_timeout <= 0:
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errors.append(f"lock_timeout must be > 0 (got {self.lock_timeout})")
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if self.max_concurrent_analyses < 1:
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errors.append(
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f"max_concurrent_analyses must be >= 1 (got {self.max_concurrent_analyses})"
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)
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if self.max_page_count < 0:
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errors.append(f"max_page_count must be >= 0 (got {self.max_page_count})")
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if self.max_file_size < 0:
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errors.append(f"max_file_size must be >= 0 (got {self.max_file_size})")
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if self.max_file_size_mb < 0:
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errors.append(f"max_file_size_mb must be >= 0 (got {self.max_file_size_mb})")
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if self.rate_limit_rpm < 0:
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errors.append(f"rate_limit_rpm must be >= 0 (got {self.rate_limit_rpm})")
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if self.batch_page_size < 0:
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errors.append(f"batch_page_size must be >= 0 (got {self.batch_page_size})")
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if self.opensearch_default_limit < 1:
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errors.append(
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f"opensearch_default_limit must be >= 1 (got {self.opensearch_default_limit})"
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)
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if self.embedding_dimension < 1:
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errors.append(f"embedding_dimension must be >= 1 (got {self.embedding_dimension})")
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if self.default_table_mode not in ("accurate", "fast"):
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errors.append(
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f"default_table_mode must be 'accurate' or 'fast' (got '{self.default_table_mode}')"
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)
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# Timeout cascade: document_timeout < lock_timeout < conversion_timeout
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if self.document_timeout > 0 and self.lock_timeout > 0 and self.conversion_timeout > 0:
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if self.document_timeout >= self.lock_timeout:
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errors.append(
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f"document_timeout ({self.document_timeout}s) must be "
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f"< lock_timeout ({self.lock_timeout}s)"
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)
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if self.lock_timeout >= self.conversion_timeout:
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errors.append(
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f"lock_timeout ({self.lock_timeout}s) must be "
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f"< conversion_timeout ({self.conversion_timeout}s)"
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)
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if errors:
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raise ValueError("Invalid settings:\n " + "\n ".join(errors))
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@classmethod
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def from_env(cls) -> Settings:
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"""Build a Settings instance from environment variables."""
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cors_raw = os.environ.get("CORS_ORIGINS", "http://localhost:3000,http://localhost:5173")
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return cls(
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app_version=os.environ.get("APP_VERSION", "dev"),
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conversion_engine=os.environ.get("CONVERSION_ENGINE", "local"),
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deployment_mode=os.environ.get("DEPLOYMENT_MODE", "self-hosted"),
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docling_serve_url=os.environ.get("DOCLING_SERVE_URL", "http://localhost:5001"),
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docling_serve_api_key=os.environ.get("DOCLING_SERVE_API_KEY"),
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conversion_timeout=int(os.environ.get("CONVERSION_TIMEOUT", "900")),
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document_timeout=float(os.environ.get("DOCUMENT_TIMEOUT", "120.0")),
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lock_timeout=int(os.environ.get("LOCK_TIMEOUT", "300")),
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max_concurrent_analyses=int(os.environ.get("MAX_CONCURRENT_ANALYSES", "3")),
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default_table_mode=os.environ.get("DEFAULT_TABLE_MODE", "accurate"),
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max_page_count=int(os.environ.get("MAX_PAGE_COUNT", "0")),
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max_file_size=int(os.environ.get("MAX_FILE_SIZE", "0")),
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max_file_size_mb=int(os.environ.get("MAX_FILE_SIZE_MB", "50")),
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rate_limit_rpm=int(os.environ.get("RATE_LIMIT_RPM", "100")),
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batch_page_size=int(os.environ.get("BATCH_PAGE_SIZE", "10")),
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opensearch_url=os.environ.get("OPENSEARCH_URL", ""),
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embedding_url=os.environ.get("EMBEDDING_URL", ""),
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neo4j_uri=os.environ.get("NEO4J_URI", ""),
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neo4j_user=os.environ.get("NEO4J_USER", "neo4j"),
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neo4j_password=os.environ.get("NEO4J_PASSWORD", "changeme"),
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opensearch_default_limit=int(os.environ.get("OPENSEARCH_DEFAULT_LIMIT", "1000")),
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embedding_dimension=int(os.environ.get("EMBEDDING_DIMENSION", "384")),
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upload_dir=os.environ.get("UPLOAD_DIR", "./uploads"),
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db_path=os.environ.get("DB_PATH", "./data/docling_studio.db"),
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cors_origins=[o.strip() for o in cors_raw.split(",")],
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)
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# Module-level singleton — import this from other modules.
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settings = Settings.from_env()
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