A campaign can import local .txt and .md files as Canon, Reference or Inspiration, and the class is load-bearing rather than a label: it decides the words a passage is framed with in the prompt, the weight it carries when passages are ranked, and which budget it competes in when the context is tight. This is a separate subsystem, which is the Phase 0B decision (IMPORTED-KNOWLEDGE-DESIGN.md §73). Story Cards do not carry classification, provenance, content identity, chunking, an index or a lifecycle, and they were not promoted into something that does. Nothing here reads or writes one. The subsystem, in backend/app/knowledge/: classes the three classes, their weights, and the prompt framing chunking deterministic, heading-aware, 60-800 tokens, no overlap fts SQLite FTS5 with porter stemming; scoped and bounded in SQL importer validate, hash, store, chunk, index — in one transaction embeddings local Ollama vectors through the shared provider retrieval query construction, hybrid merge, rerank inject the budgeted cut and the rendered prompt sections Relevance admission is a separate stage from ranking, and that separation is the milestone's most expensive lesson. An independent review found the first implementation deciding relevance with a floor expressed as a share of the best candidate — which the best clears by construction — so a passage was admitted on every turn regardless of the scene. A query about tide tables and container tonnage retrieved all five sources of a fantasy campaign, narrator-only hidden Canon among them. So the pipeline is now: candidate generation -> admission -> ranking -> class weighting -> budget Admission reads raw, candidate-set-independent signals: the cosine the model returned, and how many distinct meaningful query terms a passage contains. Ranking reads normalized ones, because bm25 has no fixed range and cosine's zero is not zero. Normalization decides order among things that matched; it can never decide whether anything matched. Authority is applied after admission, so a class orders what matched and never rescues what did not. Retrieval may therefore return nothing, and on a scene unrelated to the library it does. The other decisions that each replaced an obvious wrong one: - The class multiplies relevance rather than adding to it. An additive bonus satisfies "Canon outranks Reference" and makes "do not include irrelevant Canon" impossible, because a large enough constant wins on its own. - The semantic floor is measured, not guessed: 113 production-path pairs against nomic-embed-text put targeted matches at 0.55-0.85 and off-topic pairs at 0.36-0.56, and 0.58 sits between them. Because it is a property of that model and not of cosine similarity, it is keyed to the model rather than applied to whatever is configured: an embedding model with no measured calibration in this build does not borrow the number. Semantic admission is skipped, the campaign retrieves lexically, and the reason is stated in the knowledge status and in the turn's provenance. Degrading to lexical keeps the library usable; lending the threshold to an unmeasured model is how the admitted-everything defect would return. - One lexical term is not evidence. Two distinct meaningful terms, or one that is neither a standing campaign entity nor a negligible share of the query. The stop list grew from 42 words to 261, all function words — no subject matter, because a stop list that removes subject matter stops finding "The Silver Key". - Lexical retrieval is a production path, not a fallback. It finds the proper nouns and invented terms a setting bible is made of, and the library is fully usable with no embedding model configured. Safety is structural rather than filtered. Imported text reaches the prompt whole, inside a section that says what it is, under a rule stating the authority order in words and refusing every instruction inside it. No endpoint accepts a filesystem path, so H08 has no mechanism to escape from. Nothing renders imported content as HTML, so a script tag is five visible characters and a remote image is never fetched. Import, chunking, indexing, retrieval and a turn open no socket at all; only embeddings do, through the endpoint allowlist the memory bank already uses. Provenance is the rendered text, not a foreign key: deleting a source cannot turn a historical turn's evidence into dangling ids. Schema: knowledge_sources, knowledge_chunks, knowledge_embeddings, and an FTS5 virtual table attached to knowledge_chunks as a DDL hook so it is created and dropped with the table it indexes. Migration 92. A pre-M7 database opens unchanged and needs no sources to play. Bundle: the source content and the reader's judgements about it travel; the passages, index rows and vectors are rebuilt on import, so a restored campaign is searchable immediately without a reindex step. One runtime dependency: python-multipart, Starlette's multipart parser. It is what makes the upload surface possible, and the upload surface is why no pathname is ever accepted. The test doubles were the reason the defect shipped, so they were corrected too. The retrieval stub scored unrelated text at 0.06-0.20 where the real model scores it at 0.43-0.44, and its docstring said it had deliberately removed the constant component that "would put a similarity floor under every pair" — which is exactly the property real models have. The stub now has that floor, one test fails if it is ever removed, and another reproduces the superseded rule and asserts it is still fooled by the same fixture. Run against the pre-corrective implementation, the new suite fails 13 of 18. Tests: 939 passed, 14 skipped (836/7 at M6). 110 new across seven files, one of which mocks nothing between itself and Ollama and re-measures the similarity separation on every run. 43/43 checks in a real Firefox, reproduced. Docker build clean. Four other defects found by review or by the browser run were fixed here rather than carried: an unreachable relevance constant that appeared to enforce something and did not; acceptance tests using the wrong fixture files, so G07's trap was never exercised; a bidirectional override surviving into displayed filenames; and, from the implementation pass, the Insights panel showing M5's two state sections as raw keys and the source inspector refetching on every keystroke. M7 was independently reviewed, which returned PASS WITH CORRECTIVE WORK REQUIRED. Both blocking findings are closed, and closeout resolved the embedding-model calibration boundary the corrective pass had left as debt. planning/reports/M7-IMPLEMENTATION-REPORT.md carries the review, the corrective closeout and the closeout verification in sequence, none overwriting another. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017HdaXiFbscatQaLS7dJk6b
673 lines
22 KiB
Python
673 lines
22 KiB
Python
from datetime import datetime
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from typing import Annotated, Literal
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from pydantic import BaseModel, ConfigDict, Field, computed_field
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from . import images
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# Length caps (Phase 9). The VARCHAR caps are a correctness requirement rather
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# than only an abuse limit. Postgres enforces column lengths and SQLite never
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# did, so a longer value has to be a 422 here rather than a 500 at INSERT. The
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# text-column caps are generous abuse ceilings that a legitimate player does not
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# reach.
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NAME_MAX = 200 # Titles and names. VARCHAR(200).
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TAGS_MAX = 500 # VARCHAR(500).
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CARD_TYPE_MAX = 100 # VARCHAR(100).
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PROSE_MAX = 50_000 # Memory, author's note, prompts, entries, and notes.
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ACTION_MAX = 20_000 # One player action.
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MEMORY_TEXT_MAX = 5_000
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# A scenario cover image, stored inline as a base64 data URI. A 400x300 WebP at
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# the quality the editor encodes runs about 20 to 40 kB. A cap of 400 kB leaves
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# room for a client that downscales less aggressively, and it stops anyone from
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# storing a multi-megabyte PNG in a row that every list request reads.
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IMAGE_MAX = 400_000
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ICON_MAX = 16 # One emoji or glyph. VARCHAR(16).
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BRANCH_NAME_MAX = 80 # What a player called one line of the story. VARCHAR(80).
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PERSONA_NAME_MAX = 80 # The protagonist's name. VARCHAR(80).
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PERSONA_PRONOUNS_MAX = 40 # "they/them" and the like. VARCHAR(40).
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# M4: what a player called a Save Point. VARCHAR(120). Wider than a branch name
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# because these are sentences rather than labels — "Before entering the abbey"
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# is the example the specification uses throughout.
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CHECKPOINT_NAME_MAX = 120
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Name = Annotated[str, Field(max_length=NAME_MAX)]
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Tags = Annotated[str, Field(max_length=TAGS_MAX)]
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CardType = Annotated[str, Field(max_length=CARD_TYPE_MAX)]
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Prose = Annotated[str, Field(max_length=PROSE_MAX)]
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ActionText = Annotated[str, Field(max_length=ACTION_MAX)]
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Image = Annotated[str, Field(max_length=IMAGE_MAX)]
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Icon = Annotated[str, Field(max_length=ICON_MAX)]
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PersonaName = Annotated[str, Field(max_length=PERSONA_NAME_MAX)]
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PersonaPronouns = Annotated[str, Field(max_length=PERSONA_PRONOUNS_MAX)]
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CheckpointName = Annotated[str, Field(max_length=CHECKPOINT_NAME_MAX)]
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class ORMModel(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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# ---------- Story cards ----------
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class StoryCardBase(BaseModel):
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type: CardType = ""
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name: Name = ""
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keys: Prose = ""
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entry: Prose = ""
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notes: Prose = ""
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class StoryCardCreate(StoryCardBase):
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scenario_id: int | None = None
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adventure_id: int | None = None
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class StoryCardUpdate(BaseModel):
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type: CardType | None = None
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name: Name | None = None
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keys: Prose | None = None
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entry: Prose | None = None
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notes: Prose | None = None
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class StoryCardOut(ORMModel, StoryCardBase):
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id: int
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scenario_id: int | None
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adventure_id: int | None
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# ---------- Scenarios ----------
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class ScenarioBase(BaseModel):
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title: Name = "Untitled Scenario"
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description: Prose = ""
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prompt: Prose = ""
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memory: Prose = ""
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authors_note: Prose = ""
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ai_instructions: Prose = ""
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tags: Tags = ""
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# Cover art, either an https URL or a base64 data URI. See `app/images.py`.
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image: Image = ""
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# The emoji or glyph shown when `image` is empty.
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icon: Icon = ""
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# Phase 12: the RPG world-state template, holding stat definitions, bands,
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# rules, and milestones. `None` means the scenario has no RPG layer.
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stat_schema: dict | None = None
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class ScenarioCreate(ScenarioBase):
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pass
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class ScenarioUpdate(BaseModel):
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title: Name | None = None
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description: Prose | None = None
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prompt: Prose | None = None
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memory: Prose | None = None
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authors_note: Prose | None = None
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ai_instructions: Prose | None = None
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tags: Tags | None = None
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image: Image | None = None
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icon: Icon | None = None
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stat_schema: dict | None = None
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class ScenarioOut(ORMModel, ScenarioBase):
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id: int
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is_public: bool = False # Shared demo content, read-only for everyone.
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created_at: datetime
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updated_at: datetime
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story_cards: list[StoryCardOut] = []
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class ScenarioListItem(ORMModel):
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id: int
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title: str
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description: str
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tags: str
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is_public: bool = False
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updated_at: datetime
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# Read from the row so that `image_url` can be derived, and excluded from
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# the response, because a list of base64 data URIs would be megabytes of
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# JSON.
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image: str = Field("", exclude=True)
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icon: str = ""
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@computed_field
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@property
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def image_url(self) -> str:
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return images.public_url(self.id, self.image, self.updated_at)
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# ---------- Adventures ----------
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class AdventureCreate(BaseModel):
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scenario_id: int | None = None
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title: Name | None = None
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# The `${Placeholder}` values collected from the player at the start, which
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# is the AI Dungeon behavior.
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placeholders: dict[str, str] = {}
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# Phase 18: who the player is playing as, collected by the same modal. These
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# are independent of `placeholders`: a scenario that asks for `${Name}` is
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# asking its own question, and nothing here fills it in.
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persona_name: PersonaName = ""
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persona_pronouns: PersonaPronouns = ""
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persona_desc: Prose = ""
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class AdventureUpdate(BaseModel):
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title: Name | None = None
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memory: Prose | None = None
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authors_note: Prose | None = None
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ai_instructions: Prose | None = None
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story_summary: Prose | None = None
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auto_summarize: bool | None = None
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memory_bank_enabled: bool | None = None
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persona_name: PersonaName | None = None
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persona_pronouns: PersonaPronouns | None = None
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persona_desc: Prose | None = None
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class AdventureRefresh(BaseModel):
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"""The body for "Update from scenario".
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`placeholders` supplies answers the adventure has no stored value for. See
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`AdventureCreate.placeholders`. The answers are merged over the stored ones
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and saved.
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"""
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placeholders: dict[str, str] = {}
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class RefreshPlan(BaseModel):
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"""What a refresh would change. The confirm dialog is built from this."""
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scenario_id: int
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scenario_title: str
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has_changes: bool
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# Maps a field name to `{"old": ..., "new": ...}`, for differing fields
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# only.
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fields: dict[str, dict] = {}
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# Maps "added", "updated", or "removed" to a list of card names.
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cards: dict[str, list[str]] = {}
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# Maps "added" or "removed" to a list of stat paths. Live values are
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# otherwise kept.
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world_state: dict[str, list[str]] = {}
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# The `${Placeholder}` names the scenario asks for that the adventure has
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# no stored answer to. The client collects these and sends them back.
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placeholders_needed: list[str] = []
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class ActionOut(ORMModel):
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id: int
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adventure_id: int
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type: str
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text: str
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reasoning: str | None = None
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# Phase 12: the compact RPG state changes for this turn, read from the
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# model property. Legacy as of M5 and empty on new turns; kept so a pre-M5
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# campaign's chips still render.
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world_changes: list[dict] = []
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# M5: what this turn changed, as short lines for the chip under an AI
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# message. Read from `Action.state_summary`, which reads the small
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# bulk-loaded column rather than the deferred snapshot.
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state_summary: list[str] = []
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# SP9: the pager, such as `2/4`. It reports how many attempts this turn has
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# and which one is on screen. It is keyed on the parent, so it counts the
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# attempts of this turn rather than every node that shares a depth, and it
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# keeps counting them after one has been forked onto its own branch.
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#
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# A turn nobody has retaken reads 1/1, which is most turns, and the client
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# draws no pager for a count of one. The attempts themselves come from
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# `GET /actions/{id}/variants`, so this payload stays small.
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take_count: int = 1
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take_index: int = 0
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# Which line this node is on, so the pager can distinguish the two kinds of
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# step without asking the server. An attempt on this branch is a leaf with
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# nothing below it, so showing it is a local change. An attempt on another
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# branch has a story of its own, so moving to it is a branch switch.
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branch_id: int | None = None
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created_at: datetime
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class VariantOut(BaseModel):
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# Since SP4 every attempt is its own node, so each one has an id, and the
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# client needs that id. A fork is addressed by the attempt being promoted,
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# not by its position in a group that renumbers whenever an attempt is
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# added.
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id: int
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index: int
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text: str
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reasoning: str | None = None
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# See `ActionOut.branch_id`. It decides whether choosing this attempt is a
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# local step or a branch switch.
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branch_id: int | None = None
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created_at: str | None = None
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active: bool = False
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class VariantSelect(BaseModel):
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index: int = Field(ge=0)
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class BranchOut(ORMModel):
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"""One line through the story tree (Phase 14, SP5).
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This carries enough to draw the tree and nothing more. `fork_depth` is where
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this line leaves its parent, and `depth` is where it currently ends, so a
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fork is two numbers rather than a walk. `own_actions` counts the turns played
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on this branch itself. The rest of its story is borrowed from its ancestors,
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which is why the number is smaller than a reader expects.
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"""
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id: int
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parent_branch_id: int | None = None
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fork_depth: int | None = None
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depth: int
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own_actions: int = 0
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# M4: how many Save Points name a position on this line. Deleting the branch
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# deletes them with its story, so the panel warns with a number rather than
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# a vague caution. Zero for a line nobody has bookmarked, which is most.
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save_points: int = 0
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is_head: bool = False
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# NULL for a branch nobody has named. The client labels those from the fork
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# depth rather than the server inventing a name. See the column comment.
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name: str | None = None
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created_at: datetime
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class BranchRename(BaseModel):
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"""A name a player chose, or `null` to make the branch unnamed again."""
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name: Annotated[str, Field(max_length=BRANCH_NAME_MAX)] | None = None
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# ---------- Narrative state (M5) ----------
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class StateGroup(BaseModel):
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"""One labelled section of the state inspector.
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Rows carry the key as well as the label, because a manual correction has to
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name an entity and the user should not have to guess the identifier.
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"""
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title: str
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rows: list[dict] = []
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class NarrativeStateOut(BaseModel):
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"""The authoritative state at the active head.
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`groups` is the display form and `document` is the state itself. Both are
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returned because they answer different questions: the panel renders the
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first, and a correction form — or a test — needs the second to name a key.
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"""
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groups: list[StateGroup] = []
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empty: bool = True
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document: dict = {}
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class StateEventIn(BaseModel):
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"""One typed event, as a client proposes it.
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Deliberately loose about which fields are present: the event vocabulary is
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defined in `narrative/events.py` and enforced by `narrative/validate.py`,
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and duplicating those rules here would create a second, drifting copy of the
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allowlist. What this model does is bound the shapes — a type that is a
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string, values that are scalars, labels that are short strings — so a
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payload cannot smuggle a structure past Pydantic and reach the validator as
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something other than an event.
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"""
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model_config = ConfigDict(extra="allow")
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type: Annotated[str, Field(max_length=60)]
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class StateCorrection(BaseModel):
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"""A manual correction: the user overruling what the story established.
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`note` records why, in the user's words, and is kept on the proposal record
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so the audit says more than "the user changed this".
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"""
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events: Annotated[list[StateEventIn], Field(min_length=1, max_length=20)]
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note: Prose = ""
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class StateEventOut(ORMModel):
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"""One accepted change, for the audit view."""
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id: int
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action_id: int | None = None
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branch_id: int | None = None
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depth: int | None = None
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# The reader-facing position, matching the Save Point panel's vocabulary.
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turn: int | None = None
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sequence: int = 0
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event_type: str
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payload: dict = {}
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before: dict | None = None
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source: str = "accepted_story"
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created_at: datetime
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# ---------- Save Points (M4) ----------
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#
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# "Save Point" is the user-facing term and `checkpoint` is the internal one
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# (`BROWSER-UX-SPEC.md` §23). The wire format uses the internal name, as the
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# rest of this module does.
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class CheckpointOut(ORMModel):
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"""One Save Point: a name and the position it names.
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The position is reported three ways because the panel needs three different
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things from it. `turn` is what a reader counts — the same `depth + 1` the
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branch list shows. `depth` and `branch_id` are the coordinate itself.
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`on_path` says whether the position lies on the story being read, which is
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how the panel can tell a Save Point on this line from one naming a line the
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story has left; restoring either works, but they are not the same offer.
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`resolved` is false when the coordinate no longer names a live turn, which
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an action deleted out of the middle of a story can do. Restore refuses such
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a Save Point rather than moving the head somewhere approximate, so the list
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says so before the button is pressed.
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"""
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id: int
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adventure_id: int
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name: str
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note: str = ""
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branch_id: int
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depth: int
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turn: int = 0
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on_path: bool = True
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resolved: bool = True
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created_at: datetime
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updated_at: datetime
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class CheckpointCreate(BaseModel):
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"""A Save Point at wherever the story is being read.
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The position is not a field. A Save Point is made at the campaign's active
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head, which the server already knows, and accepting a coordinate from the
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client would be the second way to name a position — the thing this milestone
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exists not to build.
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"""
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name: CheckpointName
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note: Prose = ""
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class CheckpointRename(BaseModel):
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"""A new label, and nothing else.
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There is deliberately no coordinate here. `STORY-BRANCH-SEMANTICS.md` §24
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keeps a Save Point's meaning auditable by refusing to move one: rename it,
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or delete it and make another where you are.
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"""
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name: CheckpointName | None = None
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note: Prose | None = None
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|
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class ActionUpdate(BaseModel):
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text: ActionText
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|
|
|
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class ActionCreate(BaseModel):
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type: Literal["do", "say", "story", "continue"]
|
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text: ActionText = ""
|
|
# The node this action is played after (SP9). Omitting it means the tip,
|
|
# which is what every ordinary turn uses.
|
|
#
|
|
# Naming an attempt the story moved past is what creates a branch. Stepping
|
|
# between attempts costs nothing and creates nothing, and the fork happens
|
|
# on the first text written below one. That is the first moment the player
|
|
# states which line they mean. Before it, they were reading.
|
|
after_id: int | None = None
|
|
|
|
|
|
class TakeCreate(BaseModel):
|
|
"""Another attempt at a turn (SP9).
|
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|
|
`text` is what the player says instead, and it applies only when the turn was
|
|
the player's. An AI turn's other attempt is generated, so the field is
|
|
ignored there rather than rejected. The client makes the same request for
|
|
both, and the node type decides what happens.
|
|
"""
|
|
|
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text: ActionText = ""
|
|
|
|
|
|
class AdventureOut(ORMModel):
|
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id: int
|
|
scenario_id: int | None
|
|
title: str
|
|
memory: str
|
|
authors_note: str
|
|
ai_instructions: str
|
|
story_summary: str
|
|
auto_summarize: bool
|
|
memory_bank_enabled: bool
|
|
persona_name: str
|
|
persona_pronouns: str
|
|
persona_desc: str
|
|
created_at: datetime
|
|
updated_at: datetime
|
|
story_cards: list[StoryCardOut] = []
|
|
# The newest window of the story, not all of it. Older pages arrive from
|
|
# `GET /{id}/actions` as the reader scrolls up. `action_count` is the whole
|
|
# story's length, which is how the client knows more actions exist above.
|
|
actions: list[ActionOut] = []
|
|
action_count: int = 0
|
|
# M3. Whether the history controls have anywhere to go from where the story
|
|
# is. The client cannot work either out for itself: `can_undo` needs the
|
|
# campaign opening, which may be off the top of the loaded window, and
|
|
# `can_redo` needs the retained future, which the client is never sent.
|
|
can_undo: bool = False
|
|
can_redo: bool = False
|
|
|
|
|
|
class ActionPage(BaseModel):
|
|
"""A slice of the story, counted back from the newest action."""
|
|
|
|
actions: list[ActionOut] = []
|
|
total: int = 0
|
|
# Whether anything older than this slice exists. The server computes it, so
|
|
# the client never has to do arithmetic on positions to find the end.
|
|
has_more: bool = False
|
|
# The same two flags `AdventureOut` carries, so that the response to Undo,
|
|
# Redo or a turn updates the controls without a second request.
|
|
can_undo: bool = False
|
|
can_redo: bool = False
|
|
|
|
|
|
# ---------- Memory bank (Phase 6) ----------
|
|
|
|
class MemoryOut(ORMModel):
|
|
id: int
|
|
adventure_id: int
|
|
text: str
|
|
pinned: bool
|
|
forgotten: bool
|
|
embedded: bool # model property: embedding vector present
|
|
use_count: int
|
|
last_used_at: datetime | None
|
|
source_start: int | None
|
|
source_end: int | None
|
|
created_at: datetime
|
|
|
|
|
|
class MemoryCreate(BaseModel):
|
|
text: Annotated[str, Field(max_length=MEMORY_TEXT_MAX)]
|
|
|
|
|
|
class MemoryUpdate(BaseModel):
|
|
text: Annotated[str, Field(max_length=MEMORY_TEXT_MAX)] | None = None
|
|
pinned: bool | None = None
|
|
forgotten: bool | None = None
|
|
|
|
|
|
# ---------------------------------------------------------------- M7: knowledge
|
|
|
|
|
|
class KnowledgeSourceOut(BaseModel):
|
|
"""One imported source, as a list row.
|
|
|
|
Deliberately without `content`. A library of twenty files would otherwise
|
|
put every byte of every one of them on a screen that shows none of it;
|
|
`KnowledgeSourceDetail` is what serves the text when it is asked for.
|
|
"""
|
|
|
|
id: int
|
|
title: str
|
|
original_filename: str
|
|
classification: str
|
|
enabled: bool
|
|
visibility: str
|
|
always_include: bool
|
|
content_hash: str
|
|
byte_size: int
|
|
media_type: str
|
|
chunk_count: int
|
|
embedded_count: int
|
|
# The two halves of derived state, kept apart on purpose. Lexical retrieval
|
|
# is a supported production path, so "the vectors failed" and "the index
|
|
# failed" are different sentences with different consequences.
|
|
index_state: str
|
|
index_detail: str
|
|
embed_state: str
|
|
embed_detail: str
|
|
parser_version: int
|
|
chunking_version: int
|
|
imported_at: str | None = None
|
|
updated_at: str | None = None
|
|
|
|
|
|
class KnowledgeSourceDetail(KnowledgeSourceOut):
|
|
"""A source with its text, for the inspector.
|
|
|
|
`content` is the file as it was decoded, not the normalized form used for
|
|
hashing and search: the reader inspects what they imported
|
|
(`IMPORTED-KNOWLEDGE-DESIGN.md` §61).
|
|
"""
|
|
|
|
content: str
|
|
notes: str = ""
|
|
|
|
|
|
class KnowledgeChunkOut(BaseModel):
|
|
id: int
|
|
chunk_index: int
|
|
heading_path: str
|
|
text: str
|
|
token_count: int
|
|
content_hash: str
|
|
embedded: bool
|
|
embedding_model: str = ""
|
|
|
|
|
|
class KnowledgeSourceUpdate(BaseModel):
|
|
"""What a reader may change about a source without reimporting it.
|
|
|
|
Everything here is metadata or state. Nothing rewrites content, and nothing
|
|
is destructive: changing a classification re-frames and re-weights the same
|
|
passages, and disabling a source removes it from retrieval while leaving the
|
|
rows exactly where they are.
|
|
"""
|
|
|
|
title: str | None = None
|
|
classification: str | None = None
|
|
enabled: bool | None = None
|
|
visibility: str | None = None
|
|
always_include: bool | None = None
|
|
notes: str | None = None
|
|
|
|
|
|
class AdventureListItem(ORMModel):
|
|
id: int
|
|
scenario_id: int | None
|
|
scenario_title: str | None = None
|
|
title: str
|
|
updated_at: datetime
|
|
action_count: int = 0
|
|
# The end of the most recent narration, so a Continue card can show the
|
|
# story rather than only a turn count.
|
|
snippet: str = ""
|
|
# Cover art inherited from the parent scenario. See `app/images.py`.
|
|
image_url: str = ""
|
|
icon: str = ""
|
|
|
|
|
|
# ---------- Auth (Phase 8) ----------
|
|
|
|
class AuthCredentials(BaseModel):
|
|
email: Annotated[str, Field(max_length=320)] # VARCHAR(320).
|
|
# The upper bound keeps the scrypt cost constant. Without it, hashing a
|
|
# megabyte password would give an attacker free CPU time.
|
|
password: Annotated[str, Field(max_length=128)]
|
|
|
|
|
|
# ---------- Settings ----------
|
|
|
|
class SettingsOut(ORMModel):
|
|
endpoint_url: str
|
|
model: str
|
|
api_mode: str
|
|
temperature: float
|
|
max_output_tokens: int
|
|
context_token_budget: int
|
|
model_timeout_seconds: int
|
|
narrator_prompt: str
|
|
summary_model: str
|
|
embedding_model: str
|
|
memory_bank_capacity: int
|
|
memory_top_k: int
|
|
|
|
|
|
ScenarioOut.model_rebuild()
|
|
|
|
|
|
# ---------- AI Chat (power users) ----------
|
|
# A scratchpad for talking to a model directly, with no story framing. The
|
|
# server persists nothing, so these caps are per-request abuse limits only.
|
|
|
|
CHAT_MESSAGE_MAX = 100_000 # One message.
|
|
CHAT_TOTAL_MAX = 400_000 # The whole conversation sent per request.
|
|
CHAT_MESSAGES_MAX = 200 # Turns per request.
|
|
|
|
|
|
class ChatMessage(BaseModel):
|
|
role: Literal["system", "user", "assistant"]
|
|
content: Annotated[str, Field(max_length=CHAT_MESSAGE_MAX)]
|
|
|
|
|
|
class ChatRequest(BaseModel):
|
|
messages: Annotated[list[ChatMessage], Field(min_length=1, max_length=CHAT_MESSAGES_MAX)]
|
|
# If this field is empty or omitted, the user's configured model is used.
|
|
model: Name | None = None
|
|
temperature: Annotated[float, Field(ge=0, le=5)] | None = None
|
|
max_tokens: Annotated[int, Field(ge=1, le=100_000)] | None = None
|
|
|
|
|
|
class SettingsUpdate(BaseModel):
|
|
endpoint_url: Annotated[str, Field(max_length=500)] | None = None # VARCHAR(500).
|
|
model: Name | None = None
|
|
api_mode: Annotated[str, Field(max_length=20)] | None = None
|
|
temperature: Annotated[float, Field(ge=0, le=5)] | None = None
|
|
max_output_tokens: Annotated[int, Field(ge=1, le=100_000)] | None = None
|
|
context_token_budget: Annotated[int, Field(ge=256, le=200_000)] | None = None
|
|
# Seconds to wait for the model. The floor is high enough that a normal
|
|
# turn cannot trip it; the ceiling exists so that "wait longer" stays a
|
|
# number rather than becoming "wait forever".
|
|
model_timeout_seconds: Annotated[int, Field(ge=30, le=3600)] | None = None
|
|
narrator_prompt: Prose | None = None
|
|
summary_model: Name | None = None
|
|
embedding_model: Name | None = None
|
|
memory_bank_capacity: Annotated[int, Field(ge=1, le=1000)] | None = None
|
|
memory_top_k: Annotated[int, Field(ge=1, le=50)] | None = None
|