refactor: remove dead code and clarify observations vs mental models (#512)

* refactor: remove dead code and clarify observations vs mental models

- Delete engine/mental_models/ module (stale Pydantic models with wrong
  schema, describing an old design where mental models were directives;
  had no importers outside itself)
- Remove unused imports in api/http.py (acquire_with_retry, Observation)
- Remove unused Pydantic models in api/http.py (BanksResponse,
  ObservationEvidenceResponse)
- Add clarifying NOTE to consolidation/consolidator.py distinguishing
  observations (auto-generated bottom-up) from mental models (user-defined
  pinned reflections refreshed via reflect)

* chore: run generate scripts after dead code removal

hindsight-api/hindsight_api/api/http.py

@@ -71,9 +71,7 @@ def FieldWithDefault(default_factory: Callable, **kwargs) -> Any:
 
 
 from hindsight_api.config import get_config
-from hindsight_api.engine.db_utils import acquire_with_retry
 from hindsight_api.engine.memory_engine import Budget, _current_schema, _get_tiktoken_encoding, fq_table
-from hindsight_api.engine.reflect.observations import Observation
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, MemoryFact, TokenUsage
 from hindsight_api.engine.search.tags import TagsMatch
 from hindsight_api.extensions import HttpExtension, OperationValidationError, load_extension
@@ -764,14 +762,6 @@ class ReflectResponse(BaseModel):
     )
 
 
-class BanksResponse(BaseModel):
-    """Response model for banks list endpoint."""
-
-    model_config = ConfigDict(json_schema_extra={"example": {"banks": ["user123", "bank_alice", "bank_bob"]}})
-
-    banks: list[str]
-
-
 class DispositionTraits(BaseModel):
     """Disposition traits that influence how memories are formed and interpreted."""
 
@@ -1310,15 +1300,6 @@ class BankStatsResponse(BaseModel):
 # Mental Model models
 
 
-class ObservationEvidenceResponse(BaseModel):
-    """A single piece of evidence supporting an observation."""
-
-    memory_id: str = Field(description="ID of the memory unit this evidence comes from")
-    quote: str = Field(description="Exact quote from the memory supporting the observation")
-    relevance: str = Field(description="Brief explanation of how this quote supports the observation")
-    timestamp: str = Field(description="When the source memory was created (ISO format)")
-
-
 # =========================================================================
 # Directive Models
 # =========================================================================

hindsight-api/hindsight_api/engine/consolidation/consolidator.py

@@ -9,6 +9,10 @@ Observations are stored in memory_units with fact_type='observation' and include
 - proof_count: Number of supporting memories
 - source_memory_ids: Array of memory UUIDs that contribute to this observation
 - history: JSONB tracking changes over time
+
+NOTE: Observations are distinct from mental models (pinned reflections).
+- Observations: auto-generated bottom-up by this engine from raw facts (memory_units table, fact_type='observation')
+- Mental models: user-defined queries stored in the mental_models table, refreshed on demand via reflect
 """
 
 import json

hindsight-api/hindsight_api/engine/mental_models/__init__.py

@@ -1,14 +0,0 @@
-"""
-Mental models module for Hindsight.
-
-Mental models contain directives - hard rules that are injected into reflect prompts.
-Directives are user-defined and their observations are user-provided (not LLM-generated).
-
-Other types of consolidated knowledge are handled by:
-- Learnings: Automatic bottom-up consolidation from facts
-- Pinned Reflections: User-curated living documents
-"""
-
-from .models import MentalModel, MentalModelSubtype
-
-__all__ = ["MentalModel", "MentalModelSubtype"]

hindsight-api/hindsight_api/engine/mental_models/models.py

@@ -1,53 +0,0 @@
-"""
-Pydantic models for mental models.
-"""
-
-from datetime import datetime, timezone
-from enum import Enum
-
-from pydantic import BaseModel, Field
-
-
-class MentalModelSubtype(str, Enum):
-    """Subtype of mental model.
-
-    Currently only DIRECTIVE is supported. Other types of consolidated knowledge
-    are handled by:
-    - Learnings: Automatic bottom-up consolidation from facts
-    - Pinned Reflections: User-curated living documents
-    """
-
-    DIRECTIVE = "directive"  # User-defined hard rules, observations user-provided
-
-
-class MentalModel(BaseModel):
-    """
-    A mental model representing synthesized understanding.
-
-    Mental models are the agent's consolidated knowledge. Unlike raw facts,
-    mental models provide:
-    - A one-liner description for quick scanning/retrieval
-    - A full summary for deep understanding
-    - Links to related mental models
-    """
-
-    id: str = Field(description="Unique identifier within the bank")
-    bank_id: str = Field(description="Bank this mental model belongs to")
-    subtype: MentalModelSubtype = Field(description="How this model was created")
-    name: str = Field(description="Human-readable name")
-    description: str = Field(description="One-liner for quick scanning and retrieval matching")
-    summary: str | None = Field(default=None, description="Full synthesized understanding")
-
-    # References
-    entity_id: str | None = Field(default=None, description="Reference to entities table when type=entity")
-    source_facts: list[str] = Field(default_factory=list, description="Fact IDs used to generate summary")
-    links: list[str] = Field(default_factory=list, description="Related mental model IDs")
-
-    # Tags for scoped visibility (similar to document tags)
-    tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility filtering")
-
-    # Timestamps
-    last_updated: datetime | None = Field(default=None, description="When summary was last regenerated")
-    created_at: datetime = Field(
-        default_factory=lambda: datetime.now(timezone.utc), description="When this model was created"
-    )