Richie
b995e9b6cb
Introduce four ORM models and their Alembic migration to support phrase-based query matching in the ebook RAG engine: - EbookCandidatePhrase: high-recall phrase candidates extracted per book, with source flags (ngram/yake/spacy/capitalized/metadata), scoring, and LLM judge results. - EbookProtectedPhrase: phrases accepted by the LLM judge, with canonical id, importance, and nesting controls. - EbookPhraseAlias: normalized aliases mapping to protected phrases. - EbookChunkPhraseMention: precomputed phrase occurrences within chunks. Export the new models from python.orm.richie and add a JSON_DOCUMENT helper (JSON with JSONB postgres variant) for storing sample contexts.
53 lines
1.2 KiB
Python
53 lines
1.2 KiB
Python
"""Richie database ORM exports."""
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from __future__ import annotations
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from python.orm.richie.audiobook import Audiobook, AudiobookAuthor, AudiobookSeries
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from python.orm.richie.base import RichieBase, TableBase, TableBaseBig, TableBaseSmall
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from python.orm.richie.contact import (
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Contact,
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ContactNeed,
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ContactRelationship,
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Need,
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RelationshipType,
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)
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from python.orm.richie.ebook import (
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EbookCandidatePhrase,
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EbookChapter,
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EbookChunk,
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EbookChunkEmbedding1024,
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EbookChunkEmbedding2560,
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EbookChunkEmbedding4096,
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EbookChunkPhraseMention,
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EbookEmbeddingModel,
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EbookPhraseAlias,
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EbookProtectedPhrase,
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EbookSource,
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)
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__all__ = [
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"Audiobook",
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"AudiobookAuthor",
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"AudiobookSeries",
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"Contact",
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"ContactNeed",
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"ContactRelationship",
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"EbookCandidatePhrase",
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"EbookChapter",
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"EbookChunk",
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"EbookChunkEmbedding1024",
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"EbookChunkEmbedding2560",
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"EbookChunkEmbedding4096",
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"EbookChunkPhraseMention",
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"EbookEmbeddingModel",
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"EbookPhraseAlias",
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"EbookProtectedPhrase",
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"EbookSource",
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"Need",
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"RelationshipType",
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"RichieBase",
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"TableBase",
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"TableBaseBig",
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"TableBaseSmall",
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]
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