feat(data-layer): DL-7 - Character Memory CRUD with Vector Embeddings

packages/data-layer/src/monitor_data/db/qdrant.py

@@ -130,6 +130,41 @@ def get_client(self) -> QdrantClientLib:
             raise RuntimeError("Qdrant client not connected. Call connect() first.")
         return self._client
 
+    def embed_text(self, text: str) -> list[float]:
+        """
+        Generate vector embedding for text.
+
+        WARNING: This is a PLACEHOLDER implementation that returns zero vectors!
+        In production, this MUST use a real embedding model (OpenAI, Anthropic, local).
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Vector embedding (list of floats)
+
+        Note:
+            Currently returns a zero vector of DEFAULT_VECTOR_SIZE.
+            This produces meaningless similarity scores and should NOT be deployed to production.
+            Set QDRANT_EMBEDDING_DISABLED=true in environment to suppress warning.
+        """
+        import os
+        import logging
+
+        # Production safety check: warn if embeddings are disabled without acknowledgment
+        if os.getenv("QDRANT_EMBEDDING_DISABLED", "").lower() != "true":
+            logger = logging.getLogger(__name__)
+            logger.warning(
+                "PLACEHOLDER EMBEDDING IN USE! "
+                "Set QDRANT_EMBEDDING_DISABLED=true to acknowledge zero-vector embeddings, "
+                "or implement real embedding generation. "
+                "This will produce meaningless similarity scores!"
+            )
+
+        # Placeholder: return zero vector
+        # In production, call embedding API (OpenAI, Anthropic, etc.)
+        return [0.0] * DEFAULT_VECTOR_SIZE
+
     def ensure_collection(self, collection_name: str) -> None:
         """
         Ensure collection exists with correct configuration.

packages/data-layer/src/monitor_data/middleware/auth.py

@@ -117,13 +117,7 @@
     "mongodb_get_proposed_change": ["*"],
     "mongodb_list_proposed_changes": ["*"],
     "mongodb_update_proposed_change": ["CanonKeeper"],
-    # =========================================================================
-    # MONGODB OPERATIONS - Memories
-    # =========================================================================
-    "mongodb_create_memory": ["MemoryManager"],
-    "mongodb_get_memories": ["*"],
-    "mongodb_update_memory": ["MemoryManager"],
-    "mongodb_search_memories": ["*"],
+    # (Memory operations moved to DL-7 section below)
     # =========================================================================
     # MONGODB OPERATIONS - Character Sheets
     # =========================================================================
@@ -192,6 +186,19 @@
     "mongodb_update_resolution": ["Orchestrator", "CanonKeeper"],
     "mongodb_delete_resolution": ["CanonKeeper"],
     # =========================================================================
+    # MONGODB OPERATIONS - Character Memories (DL-7)
+    # =========================================================================
+    "mongodb_create_memory": ["*"],
+    "mongodb_get_memory": ["*"],
+    "mongodb_list_memories": ["*"],
+    "mongodb_update_memory": ["*"],
+    "mongodb_delete_memory": ["*"],
+    # =========================================================================
+    # QDRANT OPERATIONS - Memory Embeddings (DL-7)
+    # =========================================================================
+    "qdrant_embed_memory": ["*"],
+    "qdrant_search_memories": ["*"],
+    # =========================================================================
     # COMPOSITE OPERATIONS
     # =========================================================================
     "composite_get_entity_full": ["*"],

packages/data-layer/src/monitor_data/schemas/__init__.py

@@ -98,7 +98,19 @@
 # from monitor_data.schemas.entities import *
 # from monitor_data.schemas.facts import *
 # from monitor_data.schemas.scenes import *
-# from monitor_data.schemas.memories import *
+from monitor_data.schemas.memories import (
+    MemoryCreate,
+    MemoryUpdate,
+    MemoryFilter,
+    MemoryResponse,
+    MemoryListResponse,
+    MemoryEmbedRequest,
+    MemoryEmbedResponse,
+    MemorySearchRequest,
+    MemorySearchResult,
+    MemorySearchResponse,
+)
+
 # from monitor_data.schemas.sources import *
 # from monitor_data.schemas.queries import *
 # from monitor_data.schemas.composite import *
@@ -172,4 +184,15 @@
     "CollectionInfo",
     "CollectionInfoRequest",
     "CollectionInfoResponse",
+    # Memory schemas
+    "MemoryCreate",
+    "MemoryUpdate",
+    "MemoryFilter",
+    "MemoryResponse",
+    "MemoryListResponse",
+    "MemoryEmbedRequest",
+    "MemoryEmbedResponse",
+    "MemorySearchRequest",
+    "MemorySearchResult",
+    "MemorySearchResponse",
 ]

packages/data-layer/src/monitor_data/schemas/memories.py

@@ -0,0 +1,176 @@
+"""
+Pydantic schemas for CharacterMemory operations.
+
+LAYER: 1 (data-layer)
+IMPORTS FROM: External libraries (pydantic, uuid, datetime) and base schemas
+CALLED BY: mongodb_tools.py, qdrant_tools.py
+
+These schemas define the data contracts for memory CRUD and vector operations.
+Memories are subjective records belonging to specific entities (characters).
+"""
+
+from datetime import datetime
+from typing import Optional, Dict, Any
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+
+# =============================================================================
+# MEMORY SCHEMAS
+# =============================================================================
+
+
+class MemoryCreate(BaseModel):
+    """Request to create a CharacterMemory document."""
+
+    entity_id: UUID = Field(description="Entity (character) who owns this memory")
+    text: str = Field(min_length=1, max_length=5000, description="Memory content")
+    scene_id: Optional[UUID] = Field(None, description="Scene where memory originated")
+    linked_fact_id: Optional[UUID] = Field(
+        None, description="Optional anchor to canonical Fact"
+    )
+    emotional_valence: float = Field(
+        default=0.0,
+        ge=-1.0,
+        le=1.0,
+        description="Emotional charge: -1.0 (negative) to 1.0 (positive)",
+    )
+    importance: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Importance for recall: 0.0 (trivial) to 1.0 (critical)",
+    )
+    certainty: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="Certainty of memory: 0.0 (false) to 1.0 (certain)",
+    )
+    metadata: Dict[str, Any] = Field(
+        default_factory=dict, description="Additional memory metadata"
+    )
+
+
+class MemoryUpdate(BaseModel):
+    """Request to update a CharacterMemory document."""
+
+    importance: Optional[float] = Field(
+        None,
+        ge=0.0,
+        le=1.0,
+        description="Update importance (affects recall priority)",
+    )
+    certainty: Optional[float] = Field(
+        None, ge=0.0, le=1.0, description="Update certainty"
+    )
+    emotional_valence: Optional[float] = Field(
+        None, ge=-1.0, le=1.0, description="Update emotional charge"
+    )
+    metadata: Optional[Dict[str, Any]] = Field(None, description="Update metadata")
+
+
+class MemoryFilter(BaseModel):
+    """Filter for listing/searching memories."""
+
+    entity_id: Optional[UUID] = Field(None, description="Filter by entity")
+    scene_id: Optional[UUID] = Field(None, description="Filter by scene")
+    min_importance: Optional[float] = Field(
+        None, ge=0.0, le=1.0, description="Minimum importance threshold"
+    )
+    max_importance: Optional[float] = Field(
+        None, ge=0.0, le=1.0, description="Maximum importance threshold"
+    )
+    min_emotional_valence: Optional[float] = Field(
+        None, ge=-1.0, le=1.0, description="Minimum emotional valence"
+    )
+    max_emotional_valence: Optional[float] = Field(
+        None, ge=-1.0, le=1.0, description="Maximum emotional valence"
+    )
+    limit: int = Field(default=100, ge=1, le=1000)
+    offset: int = Field(default=0, ge=0)
+
+
+class MemoryResponse(BaseModel):
+    """Response with CharacterMemory data."""
+
+    memory_id: UUID
+    entity_id: UUID
+    text: str
+    scene_id: Optional[UUID]
+    linked_fact_id: Optional[UUID]
+    emotional_valence: float
+    importance: float
+    certainty: float
+    metadata: Dict[str, Any]
+    created_at: datetime
+    last_accessed: datetime
+    access_count: int
+
+
+class MemoryListResponse(BaseModel):
+    """Response with list of memories."""
+
+    memories: list[MemoryResponse]
+    total: int
+    limit: int
+    offset: int
+
+
+# =============================================================================
+# MEMORY VECTOR SCHEMAS (QDRANT)
+# =============================================================================
+
+
+class MemoryEmbedRequest(BaseModel):
+    """Request to embed a memory in Qdrant."""
+
+    memory_id: UUID = Field(description="Memory UUID")
+    text: str = Field(min_length=1, max_length=5000, description="Memory text to embed")
+    entity_id: UUID = Field(description="Entity who owns this memory")
+    scene_id: Optional[UUID] = Field(None, description="Scene where memory originated")
+    importance: float = Field(default=0.5, ge=0.0, le=1.0)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class MemoryEmbedResponse(BaseModel):
+    """Response after embedding a memory."""
+
+    memory_id: UUID
+    point_id: str  # Qdrant point ID (typically str(memory_id))
+    collection: str = "memories"
+    success: bool
+
+
+class MemorySearchRequest(BaseModel):
+    """Request to search memories semantically."""
+
+    query_text: str = Field(min_length=1, max_length=5000, description="Search query")
+    entity_id: Optional[UUID] = Field(None, description="Filter by entity")
+    scene_id: Optional[UUID] = Field(None, description="Filter by scene")
+    min_importance: Optional[float] = Field(None, ge=0.0, le=1.0)
+    top_k: int = Field(default=10, ge=1, le=100, description="Number of results")
+
+
+class MemorySearchResult(BaseModel):
+    """Single memory search result with score."""
+
+    memory_id: UUID
+    entity_id: UUID
+    text: Optional[str] = Field(
+        None,
+        description="Memory text (not stored in Qdrant, requires MongoDB fetch)",
+    )
+    scene_id: Optional[UUID]
+    importance: float
+    score: float = Field(description="Similarity score (higher = more relevant)")
+    metadata: Dict[str, Any]
+
+
+class MemorySearchResponse(BaseModel):
+    """Response with semantic search results."""
+
+    results: list[MemorySearchResult]
+    query: str
+    top_k: int