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2e818da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | from __future__ import annotations
from typing import Literal, Optional, List, Dict, Any
from pydantic import BaseModel, Field
NodeStatus = Literal["ongoing", "completed"]
class NodeData(BaseModel):
id: str
label: str
description: str = ""
status: NodeStatus = "ongoing"
depth: int = 1
complexity: int = Field(3, ge=1, le=5, description="Conceptual density: 1=simple, 5=very complex")
parent_id: Optional[str] = None
children_ids: List[str] = Field(default_factory=list)
# Which uploaded paper(s) this node draws from -> scopes its lessons/RAG. Empty = all.
document_ids: List[str] = Field(default_factory=list)
# Canonical page/bbox-grounded evidence behind this node. These IDs let
# lessons, chat, graphs, and citations transfer the same source anchors.
evidence_ids: List[str] = Field(default_factory=list)
project_memory_ids: List[str] = Field(default_factory=list)
# True when this node synthesizes overlapping treatments from 2+ source documents
# (see BrainAgent.cleanup_curriculum) -> tutoring agents use this to avoid
# conflating the papers' individual treatments when explaining the concept.
is_merged: bool = Field(False)
merge_summary: str = Field("", description="What's shared vs. distinct across source documents, when is_merged is True")
# "exploration" = spawned by the GraphCuratorAgent from off-curriculum student
# activity (see COMMIT_PROJECT) -> rendered as a distinct color from planned
# curriculum nodes so the student can see what they discovered vs. what was planned.
origin: Literal["curriculum", "exploration"] = "curriculum"
# How the student's cross-session Cognee memory reshaped this node when the
# curriculum was generated -> this is what makes memory LOAD-BEARING on the
# graph's structure (not just soft prompt context). "new" = genuinely new
# material; "review" = a light pass because prior sessions show it's largely
# mastered; "scaffold" = inserted to shore up a gap memory surfaced. Brain Agent
# sets it during generation from query_prior_knowledge (see BrainAgent).
memory_tag: Literal["new", "review", "scaffold"] = "new"
class KnowledgeEdge(BaseModel):
id: str
source: str
target: str
edge_type: Literal["prerequisite", "related", "contains"] = "prerequisite"
class NodePatch(BaseModel):
node_id: str
status: Optional[NodeStatus] = None
updated_label: Optional[str] = None
updated_description: Optional[str] = None
new_children: Optional[List[str]] = Field(None, description="IDs of newly generated sub-topics")
class FormulaStep(BaseModel):
latex: str
explanation: str
class FormulaContent(BaseModel):
main_latex: str
steps: List[FormulaStep] = Field(default_factory=list)
class TextRefContent(BaseModel):
body_markdown: str
source_label: str = ""
source_url: str = ""
class HTML5VisualPayload(BaseModel):
html_code: str = "" # iframe path: graph / 3d / 2d_anim / decline
animation_type: Literal["formula", "graph", "2d_text", "3d", "2d_anim"]
explanation: str = Field("", description="A 2-3 sentence explanation describing exactly what the visualization demonstrates, how to use the interactive elements/sliders, and how it connects to the study material.")
formula: Optional[FormulaContent] = None
text_ref: Optional[TextRefContent] = None
# Honest transparency signal for the frontend: "paper" only when the engine
# actually verified specific content against the uploaded source (a verbatim
# anchor match, or -- for D3 charts, which have no anchor concept -- at least
# one generated label/value found verbatim in the source chunks). "web" is
# reserved for when a cited external source was used instead (TextRefEngine
# already accepts web_results; not yet wired to a live caller). Anything else
# is illustrative/from the model's own knowledge and must say so.
source: Literal["paper", "web", "model_knowledge"] = "model_knowledge"
# App-owned registered templates are deterministic HTML and must never enter
# the generated-code sandbox repair loop.
trusted_template: bool = False
template_id: str = ""
class ExternalAction(BaseModel):
action_type: Literal["YOUTUBE_FETCH", "GENERATE_FLASHCARDS"]
parameters: Dict[str, Any]
class OrchestratorAction(BaseModel):
intent: Literal["UPDATE_GRAPH", "GENERATE_VISUAL", "STREAM_CHAT", "TOOL_CALL"]
chat_stream_response: str = ""
graph_patches: Optional[List[NodePatch]] = None
visual_payload: Optional[HTML5VisualPayload] = None
tool_execution: Optional[ExternalAction] = None
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