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Core Data Structure - MemoryEntry (Atomic Entry)
Paper Reference: Section 3.1 - Atomic Entries {m_k}
Each MemoryEntry represents a self-contained, disambiguated fact extracted
from dialogue via the De-linearization transformation F_θ
"""
from typing import List, Optional
from pydantic import BaseModel, Field
import uuid
class MemoryEntry(BaseModel):
"""
Atomic Entry - Self-contained memory unit indexed across three orthogonal layers
Paper Reference: Section 3.1 - Eq. (3), (4)
Generated by De-linearization: m_k = F_θ(W_t) = Φ_time ∘ Φ_coref ∘ Φ_extract(W_t)
Indexed via: M(m_k) = {v_k (semantic), h_k (lexical), R_k (symbolic)}
"""
entry_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
# [Semantic Layer] - Dense embedding base (v_k = E_dense(S_k))
lossless_restatement: str = Field(
...,
description="Self-contained fact with Φ_coref (no pronouns) and Φ_time (absolute timestamps)"
)
# [Lexical Layer] - Sparse keyword vectors (h_k = Sparse(S_k))
keywords: List[str] = Field(
default_factory=list,
description="Core keywords for BM25-style exact matching"
)
# [Symbolic Layer] - Metadata constraints (R_k = {(key, val)})
timestamp: Optional[str] = Field(
None,
description="Standardized time in ISO 8601 format (YYYY-MM-DDTHH:MM:SS)"
)
location: Optional[str] = Field(
None,
description="Natural language location description"
)
persons: List[str] = Field(
default_factory=list,
description="List of extracted persons"
)
entities: List[str] = Field(
default_factory=list,
description="List of extracted entities (companies, products, etc.)"
)
topic: Optional[str] = Field(
None,
description="Topic phrase summarized by LLM"
)
class Config:
json_schema_extra = {
"example": {
"entry_id": "550e8400-e29b-41d4-a716-446655440000",
"lossless_restatement": "Alice discussed the marketing strategy for new product XYZ with Bob at Starbucks in Shanghai on November 15, 2025 at 14:30.",
"keywords": ["Alice", "Bob", "product XYZ", "marketing strategy", "discussion"],
"timestamp": "2025-11-15T14:30:00",
"location": "Starbucks, Shanghai",
"persons": ["Alice", "Bob"],
"entities": ["product XYZ"],
"topic": "Product marketing strategy discussion"
}
}
class Dialogue(BaseModel):
"""
Original dialogue entry
"""
dialogue_id: int
speaker: str
content: str
timestamp: Optional[str] = None # ISO 8601 format
def __str__(self) -> str:
time_str = f"[{self.timestamp}] " if self.timestamp else ""
return f"{time_str}{self.speaker}: {self.content}"
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