136 lines
5.6 KiB
Python
136 lines
5.6 KiB
Python
"""Immutable records emitted by the Evidence preprocessing pipeline."""
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import re
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from datetime import UTC, datetime
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from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator, model_validator
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from tht.ports.evidence import (
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normalize_aware_datetime,
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validate_canonical_uri,
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validate_namespaced_value,
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validate_safe_metadata,
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)
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_NAMESPACED_ID = re.compile(r"^[a-z][a-z0-9_-]*:[A-Za-z0-9._:-]+$")
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_SHA256 = re.compile(r"^sha256:[0-9a-f]{64}$")
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def _validate_namespaced_id(value: str) -> str:
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if not _NAMESPACED_ID.fullmatch(value):
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raise ValueError("identifier must be namespaced as '<kind>:<stable-value>'")
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return value
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def _validate_hash(value: str) -> str:
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if not _SHA256.fullmatch(value):
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raise ValueError("content hash must be 'sha256:' followed by 64 lowercase hex digits")
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return value
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class _CanonicalValue(BaseModel):
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model_config = ConfigDict(
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frozen=True, extra="forbid", validate_default=True, revalidate_instances="always"
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)
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class _WithMetadata(_CanonicalValue):
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metadata: dict[str, JsonValue] = Field(default_factory=dict)
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_frozen_metadata = field_validator("metadata")(validate_safe_metadata)
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class CanonicalDocument(_WithMetadata):
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document_id: str
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source_id: str
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source_uri: str
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source_fingerprint: str = Field(min_length=1)
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content_hash: str
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title: str = ""
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content: str
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media_type: str = "text/plain"
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modified_at: datetime | None = None
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pipeline_version: str = Field(min_length=1)
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_document_id = field_validator("document_id")(_validate_namespaced_id)
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_source_id = field_validator("source_id")(_validate_namespaced_id)
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_source_uri = field_validator("source_uri")(validate_canonical_uri)
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_source_fingerprint = field_validator("source_fingerprint")(validate_namespaced_value)
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_content_hash = field_validator("content_hash")(_validate_hash)
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_modified_at = field_validator("modified_at")(normalize_aware_datetime)
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class CanonicalChunk(_WithMetadata):
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chunk_id: str
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document_id: str
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ordinal: int = Field(ge=0)
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content: str
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content_hash: str
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source_uri: str
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pipeline_version: str = Field(min_length=1)
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_chunk_id = field_validator("chunk_id")(_validate_namespaced_id)
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_document_id = field_validator("document_id")(_validate_namespaced_id)
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_content_hash = field_validator("content_hash")(_validate_hash)
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_source_uri = field_validator("source_uri")(validate_canonical_uri)
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class CorpusManifest(_WithMetadata):
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"""Description of one internally consistent publishable generation."""
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schema_version: int = Field(default=1, ge=1)
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manifest_id: str | None = None
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created_at: datetime = Field(default_factory=lambda: datetime.now(UTC))
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pipeline_version: str = Field(default="evidence-v1", min_length=1)
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embedding_model: str | None = None
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embedding_dimensions: int | None = Field(default=None, gt=0)
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vector_generation: str | None = None
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documents: tuple[CanonicalDocument, ...] = Field(default_factory=tuple)
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chunks: tuple[CanonicalChunk, ...] = Field(default_factory=tuple)
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_manifest_id = field_validator("manifest_id")(
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lambda value: _validate_namespaced_id(value) if value is not None else None
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)
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_vector_generation = field_validator("vector_generation")(
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lambda value: _validate_namespaced_id(value) if value is not None else None
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)
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_created_at = field_validator("created_at")(normalize_aware_datetime)
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@model_validator(mode="after")
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def validate_generation(self) -> "CorpusManifest":
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if (self.embedding_model is None) != (self.embedding_dimensions is None):
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raise ValueError("embedding_model and embedding_dimensions must be set together")
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if self.vector_generation is not None and self.embedding_model is None:
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raise ValueError("vector_generation requires embedding model and dimension compatibility")
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document_ids = [document.document_id for document in self.documents]
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source_ids = [document.source_id for document in self.documents]
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chunk_ids = [chunk.chunk_id for chunk in self.chunks]
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self._require_unique("document_id", document_ids)
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self._require_unique("source_id", source_ids)
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self._require_unique("chunk_id", chunk_ids)
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documents = {document.document_id: document for document in self.documents}
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ordinals: dict[str, list[int]] = {}
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for document in self.documents:
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if document.pipeline_version != self.pipeline_version:
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raise ValueError("document pipeline_version must match manifest pipeline_version")
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for chunk in self.chunks:
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document = documents.get(chunk.document_id)
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if document is None:
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raise ValueError(f"chunk references unknown document: {chunk.document_id}")
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if chunk.pipeline_version != self.pipeline_version:
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raise ValueError("chunk pipeline_version must match manifest pipeline_version")
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if chunk.source_uri != document.source_uri:
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raise ValueError("chunk source_uri must match its document provenance")
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ordinals.setdefault(chunk.document_id, []).append(chunk.ordinal)
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for document_id, values in ordinals.items():
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if sorted(values) != list(range(len(values))):
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raise ValueError(f"chunk ordinals must be unique and contiguous for {document_id}")
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return self
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@staticmethod
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def _require_unique(field: str, values: list[str]) -> None:
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if len(values) != len(set(values)):
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raise ValueError(f"{field} values must be unique")
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