fix(docs): update Go quickstart example to use NullableTimestamp for timestamp field

When a LabelGroup has tag=True, extracted key:value entities for that group
are automatically written to the memory unit's tags array. This lets entity
labels double as tags, enabling immediate filtering via the existing
tags/tags_match API params with no extra infrastructure.

- Add tag: bool = False to LabelGroup
- _inject_label_tags() helper called in both sync and batch extraction paths
- UI: add Tag checkbox per label group row
- Docs: document the new tag field
- Tests: 4 new unit tests covering all tag injection paths

docker/standalone/start-all.sh

@@ -97,7 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
     echo "🎛️  Starting Control Plane..."
     cd /app/control-plane
-    PORT=9999 node server.js &
+    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
     CP_PID=$!
     PIDS+=($CP_PID)
 else
@@ -110,7 +110,7 @@ echo "✅ Hindsight is running!"
 echo ""
 echo "📍 Access:"
 if [ "$ENABLE_CP" = "true" ]; then
-    echo "   Control Plane: http://localhost:9999"
+    echo "   Control Plane: http://localhost:${HINDSIGHT_CP_PORT:-9999}"
 fi
 if [ "$ENABLE_API" = "true" ]; then
     echo "   API:           http://localhost:8888"

hindsight-api/hindsight_api/alembic/versions/a2b3c4d5e6f7_add_text_signals_column.py

@@ -0,0 +1,88 @@
+"""Add text_signals column to memory_units for enriched BM25 indexing.
+
+text_signals stores a denormalized space-separated string of entity names
+(and future signals) to improve full-text search recall without polluting
+the stored fact text.
+
+- vchord: text_signals included in tokenize() at insert time
+- native: search_vector GENERATED column regenerated to include text_signals
+- pg_textsearch: no change (index only supports a single base column)
+
+Revision ID: a2b3c4d5e6f7
+Revises: z1u2v3w4x5y6
+Create Date: 2026-02-28
+"""
+
+import os
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "a2b3c4d5e6f7"
+down_revision: str | Sequence[str] | None = "aa2b3c4d5e6f"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def _detect_text_search_extension() -> str:
+    return os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    # Add text_signals column (nullable TEXT, populated at retain time)
+    op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS text_signals TEXT")
+
+    if text_search_ext == "native":
+        # Native PostgreSQL: drop and recreate the GENERATED tsvector column to include text_signals
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english',
+                    COALESCE(text, '') || ' ' ||
+                    COALESCE(context, '') || ' ' ||
+                    COALESCE(text_signals, '')
+                )
+            ) STORED
+        """)
+        # Recreate GIN index (was dropped with the column)
+        op.execute(f"""
+            CREATE INDEX IF NOT EXISTS idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    # vchord: tokenize() call in fact_storage.py is updated to include text_signals at insert time
+    # pg_textsearch: no change — index operates on the base `text` column only
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    if text_search_ext == "native":
+        op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_text_search")
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))
+            ) STORED
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS text_signals")

hindsight-api/hindsight_api/alembic/versions/b4c5d6e7f8a9_backfill_observation_scopes.py

@@ -0,0 +1,34 @@
+"""Backfill observation_scopes column if missing.
+
+This migration ensures observation_scopes exists even on databases that had
+revision z1u2v3w4x5y6 applied when it referred to the old text_signals migration
+(before it was renamed to a2b3c4d5e6f7). The ADD COLUMN IF NOT EXISTS makes this
+a no-op on databases that already have the column.
+
+Revision ID: b4c5d6e7f8a9
+Revises: a2b3c4d5e6f7
+Create Date: 2026-03-02
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "b4c5d6e7f8a9"
+down_revision: str | Sequence[str] | None = "a2b3c4d5e6f7"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS observation_scopes JSONB")
+
+
+def downgrade() -> None:
+    pass  # intentionally no-op — safe to leave the column in place

hindsight-api/hindsight_api/config.py

@@ -690,6 +690,13 @@ class HindsightConfig:
     consolidation_max_tokens: int
     observations_mission: str | None
 
+    # Entity labels (controlled vocabulary of key:value classification labels extracted at retain time)
+    # List of label group dicts: [{key, description, type, optional, values: [{value, description}]}]
+    entity_labels: list | None
+    # Whether to extract regular named entities alongside entity labels (default: True)
+    # When False: only label entities are extracted (or no entities at all if no labels configured)
+    entities_allow_free_form: bool
+
     # Reflect agent settings
     reflect_mission: str | None
 
@@ -770,6 +777,9 @@ class HindsightConfig:
         "retain_extraction_mode",
         "retain_mission",
         "retain_custom_instructions",
+        # Entity labels (controlled vocabulary for entity classification)
+        "entity_labels",
+        "entities_allow_free_form",
         # Consolidation settings
         "enable_observations",
         "observations_mission",
@@ -1118,6 +1128,8 @@ class HindsightConfig:
                 os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
             ),
             observations_mission=os.getenv(ENV_OBSERVATIONS_MISSION) or DEFAULT_OBSERVATIONS_MISSION,
+            entity_labels=None,
+            entities_allow_free_form=True,
             # Database migrations
             run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
             # Database connection pool

hindsight-api/hindsight_api/engine/entity_resolver.py

@@ -12,6 +12,7 @@ import asyncpg
 
 from .db_utils import acquire_with_retry
 from .memory_engine import fq_table
+from .retain.entity_labels import build_labels_lookup as _build_labels_lookup_from_config
 
 # Load spaCy model (singleton)
 _nlp = None
@@ -31,6 +32,11 @@ class EntityResolver:
         """
         self.pool = pool
 
+    @staticmethod
+    def _build_labels_lookup(entity_labels: list | None) -> set[str]:
+        """Build a set of valid 'key:value' entity label strings for fast lookup."""
+        return _build_labels_lookup_from_config(entity_labels)
+
     async def resolve_entities_batch(
         self,
         bank_id: str,
@@ -38,6 +44,7 @@ class EntityResolver:
         context: str,
         unit_event_date,
         conn=None,
+        entity_labels: list | None = None,
     ) -> list[str]:
         """
         Resolve multiple entities in batch (MUCH faster than sequential).
@@ -58,14 +65,25 @@ class EntityResolver:
         if not entities_data:
             return []
 
+        taxonomy_lookup = self._build_labels_lookup(entity_labels)
         if conn is None:
             async with acquire_with_retry(self.pool) as conn:
-                return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+                return await self._resolve_entities_batch_impl(
+                    conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+                )
         else:
-            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+            return await self._resolve_entities_batch_impl(
+                conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+            )
 
     async def _resolve_entities_batch_impl(
-        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
+        self,
+        conn,
+        bank_id: str,
+        entities_data: list[dict],
+        context: str,
+        unit_event_date,
+        taxonomy_lookup: set[str] | None = None,
     ) -> list[str]:
         # Query ALL candidates for this bank
         all_entities = await conn.fetch(
@@ -135,12 +153,19 @@ class EntityResolver:
         entities_to_update = []  # (entity_id, event_date)
         entities_to_create = []  # (idx, entity_data, event_date)
 
+        taxonomy_lookup = taxonomy_lookup or set()
+
         for idx, entity_data in enumerate(entities_data):
             entity_text = entity_data["text"]
             nearby_entities = entity_data.get("nearby_entities", [])
             # Use per-entity date if available, otherwise fall back to batch-level date
             entity_event_date = entity_data.get("event_date", unit_event_date)
 
+            # Taxonomy entities: skip fuzzy matching, use exact canonical name
+            if taxonomy_lookup and entity_text.lower() in taxonomy_lookup:
+                entities_to_create.append((idx, entity_data, entity_event_date))
+                continue
+
             candidates = all_candidates.get(entity_text, [])
 
             if not candidates:

hindsight-api/hindsight_api/engine/reflect/agent.py

@@ -431,7 +431,9 @@ async def run_reflect_agent(
 
         if is_last:
             # Force text response on last iteration - no tools
-            prompt = build_final_prompt(query, context_history, bank_profile, context, max_context_tokens=max_context_tokens)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -486,7 +488,9 @@ async def run_reflect_agent(
                 f"[REFLECT {reflect_id}] Context budget exceeded on iteration {iteration + 1}: "
                 f"~{estimated_tokens} tokens >= {max_context_tokens} limit. Forcing final synthesis."
             )
-            prompt = build_final_prompt(query, context_history, bank_profile, context, max_context_tokens=max_context_tokens)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -588,7 +592,9 @@ async def run_reflect_agent(
             # For other errors: retry if no evidence yet (but cap consecutive errors to avoid long hangs)
             elif not has_gathered_evidence and iteration < max_iterations - 1 and consecutive_errors < 2:
                 continue
-            prompt = build_final_prompt(query, context_history, bank_profile, context, max_context_tokens=max_context_tokens)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -659,7 +665,9 @@ async def run_reflect_agent(
                     directives_applied=directives_applied,
                 )
             # Empty response, force final
-            prompt = build_final_prompt(query, context_history, bank_profile, context, max_context_tokens=max_context_tokens)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[

hindsight-api/hindsight_api/engine/retain/embedding_processing.py

@@ -27,14 +27,21 @@ def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list
     """
     augmented_texts = []
     for fact in facts:
-        # Use occurred_start as the representative date
+        # Use occurred_start as the representative date, fall back to mentioned_at
         fact_date = fact.occurred_start or fact.mentioned_at
+        # Augment text with date and entity names for embedding (but store original text in DB)
+        # Entity names (including key:value labels) improve retrieval without polluting stored content
         if fact_date is not None:
             readable_date = format_date_fn(fact_date)
-            # Augment text with date for embedding (but store original text in DB)
-            augmented_text = f"{fact.fact_text} (happened in {readable_date})"
+            if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+                readable_end = format_date_fn(fact.occurred_end)
+                augmented_text = f"{fact.fact_text} (happened from {readable_date} to {readable_end})"
+            else:
+                augmented_text = f"{fact.fact_text} (happened in {readable_date})"
         else:
             augmented_text = fact.fact_text
+        if fact.entities:
+            augmented_text = f"{augmented_text} [{', '.join(fact.entities)}]"
         augmented_texts.append(augmented_text)
     return augmented_texts
 

hindsight-api/hindsight_api/engine/retain/entity_labels.py

@@ -0,0 +1,194 @@
+"""
+Entity labels models and helpers for retain pipeline.
+
+Defines a controlled vocabulary of key:value classification labels
+(e.g., 'pedagogy:scaffolding', 'interest:active') that are extracted
+at retain time and stored as entities.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field, create_model
+
+
+class LabelValue(BaseModel):
+    """A single allowed value for a label group."""
+
+    value: str
+    description: str = ""
+
+
+class LabelGroup(BaseModel):
+    """A label group (dimension) with its type and allowed values."""
+
+    key: str
+    description: str = ""
+    type: Literal["value", "multi-values", "text"] = "value"
+    optional: bool = True
+    tag: bool = False
+    values: list[LabelValue] = []
+
+
+class EntityLabelsConfig(BaseModel):
+    """Entity labels configuration for a bank (controlled vocabulary)."""
+
+    attributes: list[LabelGroup] = []
+
+
+def parse_entity_labels(raw: dict | list | None) -> EntityLabelsConfig | None:
+    """
+    Parse raw entity labels config into EntityLabelsConfig.
+
+    Accepts:
+    - None → returns None
+    - list → list of attribute dicts (each may use legacy free_values/multi_value or new type field)
+    - dict → {attributes: [...]}
+
+    Legacy migration (backward-compat):
+    - free_values=True          → type="text"
+    - multi_value=True          → type="multi-values"
+    - neither / free_values=False → type="value"
+
+    Args:
+        raw: Raw entity labels config from bank config
+
+    Returns:
+        EntityLabelsConfig or None if raw is None/empty
+    """
+    if raw is None:
+        return None
+
+    if isinstance(raw, list):
+        if not raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    if isinstance(raw, dict):
+        attrs_raw = raw.get("attributes", [])
+        if not attrs_raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in attrs_raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    return None
+
+
+def _migrate_label_group(raw: dict) -> dict:
+    """Migrate legacy free_values/multi_value fields to the new type field."""
+    if not isinstance(raw, dict) or "type" in raw:
+        return raw
+    patched = dict(raw)
+    if patched.get("free_values"):
+        patched["type"] = "text"
+    elif patched.get("multi_value"):
+        patched["type"] = "multi-values"
+    else:
+        patched["type"] = "value"
+    # Remove legacy keys so Pydantic doesn't error on unknown fields
+    patched.pop("free_values", None)
+    patched.pop("multi_value", None)
+    return patched
+
+
+def build_labels_model(labels_cfg: EntityLabelsConfig) -> type[BaseModel] | None:
+    """
+    Build a dynamic Pydantic model for structured label extraction.
+
+    Each LabelGroup becomes a typed field based on its type:
+    - type="text"                        → str | None  (always optional)
+    - type="value",   optional=True      → Literal["v1","v2"] | None
+    - type="value",   optional=False     → Literal["v1","v2"]  (required)
+    - type="multi-values"                → list[Literal["v1","v2"]]
+
+    Args:
+        labels_cfg: Parsed EntityLabelsConfig
+
+    Returns:
+        Dynamic Pydantic model class, or None if no groups defined
+    """
+    fields: dict = {}
+    for group in labels_cfg.attributes:
+        if not group.key:
+            continue
+        description = group.description or group.key
+
+        if group.type == "text":
+            # Free-form: any string value accepted, always optional
+            fields[group.key] = (str | None, Field(default=None, description=description))
+        else:
+            # Enum-constrained: must have defined values
+            if not group.values:
+                continue
+            values = tuple(v.value for v in group.values if v.value)
+            if not values:
+                continue
+            # Literal[("v1", "v2")] is equivalent to Literal["v1", "v2"] in Python 3.11+
+            literal_type = Literal[values]  # type: ignore[valid-type]
+            if group.type == "multi-values":
+                fields[group.key] = (
+                    list[literal_type],  # type: ignore[valid-type]
+                    Field(default_factory=list, description=description),
+                )
+            elif group.optional:
+                fields[group.key] = (
+                    literal_type | None,  # type: ignore[valid-type]
+                    Field(default=None, description=description),
+                )
+            else:
+                fields[group.key] = (
+                    literal_type,  # type: ignore[valid-type]
+                    Field(description=description),
+                )
+
+    if not fields:
+        return None
+
+    return create_model("Labels", **fields)
+
+
+def is_label_entity(text: str, labels_cfg: EntityLabelsConfig, labels_lookup: set[str]) -> bool:
+    """
+    Return True if entity text belongs to any configured label group.
+
+    For enum groups: checks the pre-built lookup set.
+    For text groups: checks that the text starts with a known key prefix.
+    """
+    if text.lower() in labels_lookup:
+        return True
+    for group in labels_cfg.attributes:
+        if group.type == "text" and group.key and text.lower().startswith(f"{group.key.lower()}:"):
+            return True
+    return False
+
+
+def build_labels_lookup(labels_cfg: EntityLabelsConfig | list | None) -> set[str]:
+    """
+    Build a set of valid 'key:value' label strings (lowercase) for fast lookup.
+
+    Accepts either EntityLabelsConfig or raw list/None for backwards compatibility.
+
+    Args:
+        labels_cfg: EntityLabelsConfig, raw list of attribute dicts, or None
+
+    Returns:
+        Set of lowercase 'key:value' strings
+    """
+    if labels_cfg is None:
+        return set()
+
+    # Accept raw list/dict for backwards compatibility
+    if not isinstance(labels_cfg, EntityLabelsConfig):
+        parsed = parse_entity_labels(labels_cfg)
+        if parsed is None:
+            return set()
+        labels_cfg = parsed
+
+    valid = set()
+    for group in labels_cfg.attributes:
+        if group.type == "text":
+            continue  # No fixed vocabulary — all values accepted in post-processing
+        for v in group.values:
+            if group.key and v.value:
+                valid.add(f"{group.key}:{v.value}".lower())
+    return valid

hindsight-api/hindsight_api/engine/retain/entity_processing.py

@@ -20,6 +20,7 @@ async def process_entities_batch(
     facts: list[ProcessedFact],
     log_buffer: list[str] = None,
     user_entities_per_content: dict[int, list[dict]] = None,
+    entity_labels: list | None = None,
 ) -> list[EntityLink]:
     """
     Process entities for all facts and create entity links.
@@ -90,6 +91,7 @@ async def process_entities_batch(
         fact_dates,
         entities_per_fact,
         log_buffer,  # Pass log_buffer for detailed logging
+        entity_labels=entity_labels,
     )
 
     return entity_links

hindsight-api/hindsight_api/engine/retain/fact_extraction.py

@@ -10,13 +10,20 @@ import json
 import logging
 import re
 from datetime import datetime, timedelta
-from typing import Literal
+from typing import Literal, cast
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
 
 from ...config import get_config
 from ..llm_wrapper import LLMConfig, OutputTooLongError
 from ..response_models import TokenUsage
+from .entity_labels import (
+    EntityLabelsConfig,
+    build_labels_lookup,
+    build_labels_model,
+    is_label_entity,
+    parse_entity_labels,
+)
 
 
 def _infer_temporal_date(fact_text: str, event_date: datetime | None) -> str | None:
@@ -692,10 +699,62 @@ Example: "Lost job → couldn't pay rent → moved apartment"
 - Fact 2: Moved apartment, causal_relations: [{target_index: 1, relation_type: "caused_by"}]"""
 
 
+def _build_labels_prompt_section(labels_cfg: EntityLabelsConfig | list | None, free_form_entities: bool = True) -> str:
+    """Build the entity labels classification section for the extraction prompt."""
+    if labels_cfg is None:
+        return ""
+
+    # Accept raw list for backwards compatibility
+    if isinstance(labels_cfg, list):
+        if not labels_cfg:
+            return ""
+        labels_cfg = parse_entity_labels(labels_cfg)
+        if labels_cfg is None:
+            return ""
+
+    if not labels_cfg.attributes:
+        return ""
+
+    if free_form_entities:
+        entities_instruction = "Classify each fact using the structured 'labels' field below. Continue extracting regular named entities in the 'entities' field."
+    else:
+        entities_instruction = "Classify each fact using the structured 'labels' field below. Do NOT add regular named entities — labels-only mode."
+
+    lines = [
+        "\n\n══════════════════════════════════════════════════════════════════════════",
+        "ENTITY LABELS - CLASSIFICATION ATTRIBUTES",
+        "══════════════════════════════════════════════════════════════════════════",
+        "",
+        entities_instruction,
+        "",
+        "For each fact, fill the 'labels' object. Each field is a label group:",
+        "",
+    ]
+
+    for attr in labels_cfg.attributes:
+        if attr.type == "text":
+            # Free-text: no predefined values — LLM writes any relevant string or null
+            lines.append(f"- {attr.key} (free text or null): {attr.description}")
+        else:
+            mode = "multi-value (list)" if attr.type == "multi-values" else "single value or null"
+            lines.append(f"- {attr.key} ({mode}): {attr.description}")
+            for v in attr.values:
+                desc = f" — {v.description}" if v.description else ""
+                lines.append(f'    • "{v.value}"{desc}')
+        lines.append("")
+
+    lines.append("Only assign labels when clearly applicable. Leave null/empty if the fact does not match.")
+    return "\n".join(lines)
+
+
 def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
     """
     Build extraction prompt and response schema based on config.
 
+    When a taxonomy is configured, dynamically builds a Pydantic model with a
+    typed `taxonomy_entities` field using an Enum built from valid taxonomy values.
+    This enables JSON schema enforcement for structured outputs.
+
     Returns:
         Tuple of (prompt, response_schema)
     """
@@ -738,9 +797,53 @@ def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
     # Add causal relationships section if enabled
     if extract_causal_links:
         prompt = prompt + CAUSAL_RELATIONSHIPS_SECTION
-        response_schema = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
+        base_fact_class = ExtractedFactVerbose if extraction_mode == "verbose" else ExtractedFact
+        base_response_class = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
     else:
-        response_schema = FactExtractionResponseNoCausal
+        base_fact_class = ExtractedFactNoCausal
+        base_response_class = FactExtractionResponseNoCausal
+
+    # Add entity labels section if configured and build dynamic schema
+    entity_labels_raw = getattr(config, "entity_labels", None)
+    labels_cfg = parse_entity_labels(entity_labels_raw)
+    free_form_entities = getattr(config, "entities_allow_free_form", True)
+    labels_section = _build_labels_prompt_section(labels_cfg, free_form_entities)
+    if labels_section:
+        prompt = prompt + labels_section
+
+    response_schema = base_response_class
+
+    if labels_cfg and labels_cfg.attributes:
+        LabelsModel = build_labels_model(labels_cfg)
+        if LabelsModel is not None:
+            dynamic_fields: dict = {
+                "labels": (
+                    LabelsModel,
+                    Field(
+                        description="Classification labels for this fact. Fill each applicable field; leave others null/empty."
+                    ),
+                )
+            }
+            if not free_form_entities:
+                dynamic_fields["entities"] = (
+                    list[Entity] | None,
+                    Field(default=None, description="Leave empty — labels-only mode"),
+                )
+            # Inherit parent's required fields and add 'labels' so it appears in the JSON schema
+            # required array (the base class json_schema_extra overrides required entirely)
+            base_extra = base_fact_class.model_config.get("json_schema_extra")
+            base_required = cast(dict, base_extra).get("required", []) if isinstance(base_extra, dict) else []
+            DynamicFact = create_model(
+                "LabelsFact",
+                __base__=base_fact_class,
+                __config__=ConfigDict(
+                    json_schema_mode="validation",
+                    json_schema_extra={"required": [*base_required, "labels"]},
+                ),
+                **dynamic_fields,
+            )
+            DynamicResponse = create_model("LabelsResponse", facts=(list[DynamicFact], ...))  # type: ignore[valid-type]
+            response_schema = DynamicResponse
 
     return prompt, response_schema
 
@@ -997,9 +1100,9 @@ async def _extract_facts_from_chunk(
                 # Add entities if present (validate as Entity objects)
                 # LLM sometimes returns strings instead of {"text": "..."} format
                 entities = get_value("entities")
+                validated_entities = []
                 if entities:
                     # Validate and normalize each entity
-                    validated_entities = []
                     for ent in entities:
                         if isinstance(ent, str):
                             # Normalize string to Entity object
@@ -1009,8 +1112,48 @@ async def _extract_facts_from_chunk(
                                 validated_entities.append(Entity.model_validate(ent))
                             except Exception as e:
                                 logger.warning(f"Invalid entity {ent}: {e}")
-                    if validated_entities:
-                        fact_data["entities"] = validated_entities
+
+                # Post-process label entities from structured labels object
+                entity_labels_raw = getattr(config, "entity_labels", None)
+                labels_cfg = parse_entity_labels(entity_labels_raw)
+                free_form_entities = getattr(config, "entities_allow_free_form", True)
+                if labels_cfg and labels_cfg.attributes:
+                    labels_lookup = build_labels_lookup(labels_cfg)
+                    labels_data = llm_fact.get("labels") or {}
+                    if isinstance(labels_data, dict):
+                        existing_texts_lower = {e.text.lower() for e in validated_entities}
+                        for group in labels_cfg.attributes:
+                            value = labels_data.get(group.key)
+                            if not value:
+                                continue
+                            values_list = value if isinstance(value, list) else [value]
+                            for v in values_list:
+                                if not isinstance(v, str) or not v.strip() or v.lower() in ("none", "null", "n/a"):
+                                    continue
+                                label_str = f"{group.key}:{v.strip()}"
+                                if group.type == "text":
+                                    if label_str.lower() not in existing_texts_lower:
+                                        validated_entities.append(Entity(text=label_str))
+                                        existing_texts_lower.add(label_str.lower())
+                                elif (
+                                    label_str.lower() in labels_lookup and label_str.lower() not in existing_texts_lower
+                                ):
+                                    validated_entities.append(Entity(text=label_str))
+                                    existing_texts_lower.add(label_str.lower())
+                                else:
+                                    logger.warning(f"Label '{label_str}' not in valid label values, skipping")
+
+                    # In labels-only mode, keep only label entities
+                    if not free_form_entities:
+                        validated_entities = [
+                            e for e in validated_entities if is_label_entity(e.text, labels_cfg, labels_lookup)
+                        ]
+                elif not free_form_entities:
+                    # No labels but free_form disabled: clear all entities
+                    validated_entities = []
+
+                if validated_entities:
+                    fact_data["entities"] = validated_entities
 
                 # Add per-fact causal relations (only if enabled in config)
                 if extract_causal_links:
@@ -1606,8 +1749,8 @@ async def extract_facts_from_contents_batch_api(
 
             # Entities
             entities = get_value("entities")
+            validated_entities = []
             if entities:
-                validated_entities = []
                 for ent in entities:
                     if isinstance(ent, str):
                         validated_entities.append(Entity(text=ent))
@@ -1616,8 +1759,45 @@ async def extract_facts_from_contents_batch_api(
                             validated_entities.append(Entity.model_validate(ent))
                         except Exception:
                             pass
-                if validated_entities:
-                    fact_data["entities"] = validated_entities
+
+            # Post-process label entities from structured labels object
+            entity_labels_raw = getattr(config, "entity_labels", None)
+            labels_cfg_batch = parse_entity_labels(entity_labels_raw)
+            free_form_entities_batch = getattr(config, "entities_allow_free_form", True)
+            if labels_cfg_batch and labels_cfg_batch.attributes:
+                labels_lookup_batch = build_labels_lookup(labels_cfg_batch)
+                labels_data = llm_fact.get("labels") or {}
+                if isinstance(labels_data, dict):
+                    existing_texts_lower = {e.text.lower() for e in validated_entities}
+                    for group in labels_cfg_batch.attributes:
+                        value = labels_data.get(group.key)
+                        if not value:
+                            continue
+                        values_list = value if isinstance(value, list) else [value]
+                        for v in values_list:
+                            if not isinstance(v, str) or not v.strip() or v.lower() in ("none", "null", "n/a"):
+                                continue
+                            label_str = f"{group.key}:{v.strip()}"
+                            if group.type == "text":
+                                if label_str.lower() not in existing_texts_lower:
+                                    validated_entities.append(Entity(text=label_str))
+                                    existing_texts_lower.add(label_str.lower())
+                            elif (
+                                label_str.lower() in labels_lookup_batch
+                                and label_str.lower() not in existing_texts_lower
+                            ):
+                                validated_entities.append(Entity(text=label_str))
+                                existing_texts_lower.add(label_str.lower())
+
+                if not free_form_entities_batch:
+                    validated_entities = [
+                        e for e in validated_entities if is_label_entity(e.text, labels_cfg_batch, labels_lookup_batch)
+                    ]
+            elif not free_form_entities_batch:
+                validated_entities = []
+
+            if validated_entities:
+                fact_data["entities"] = validated_entities
 
             # Causal relations
             if extract_causal_links:
@@ -1718,6 +1898,9 @@ async def extract_facts_from_contents_batch_api(
     # Step 7: Add temporal offsets
     _add_temporal_offsets(extracted_facts, contents)
 
+    # Step 8: Auto-tag facts from label groups with tag=True
+    _inject_label_tags(extracted_facts, config)
+
     logger.info(f"Batch API extracted {len(extracted_facts)} facts from {len(all_chunks_info)} chunks")
 
     return extracted_facts, chunks_metadata, total_usage
@@ -1850,6 +2033,9 @@ async def extract_facts_from_contents(
     # Step 4: Add time offsets to preserve ordering within each content
     _add_temporal_offsets(extracted_facts, contents)
 
+    # Step 5: Auto-tag facts from label groups with tag=True
+    _inject_label_tags(extracted_facts, config)
+
     return extracted_facts, chunks_metadata, total_usage
 
 
@@ -1905,3 +2091,24 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
             fact.occurred_end = parse_datetime_flexible(fact.occurred_end) + offset
         if fact.mentioned_at:
             fact.mentioned_at = parse_datetime_flexible(fact.mentioned_at) + offset
+
+
+def _inject_label_tags(facts: list[ExtractedFactType], config) -> None:
+    """
+    For label groups with tag=True, add extracted key:value label entities
+    to each fact's tags list. Modifies facts in place.
+
+    This lets entity labels double as tags, enabling filtering via the
+    existing tags API without any extra query infrastructure.
+    """
+    labels_cfg = parse_entity_labels(getattr(config, "entity_labels", None))
+    if not labels_cfg:
+        return
+    tag_group_keys = {g.key.lower() for g in labels_cfg.attributes if g.tag}
+    if not tag_group_keys:
+        return
+    for fact in facts:
+        label_tags = [e for e in fact.entities if ":" in e and e.split(":", 1)[0].lower() in tag_group_keys]
+        if label_tags:
+            existing = set(fact.tags)
+            fact.tags = fact.tags + [t for t in label_tags if t not in existing]

hindsight-api/hindsight_api/engine/retain/fact_storage.py

@@ -48,6 +48,7 @@ async def insert_facts_batch(
     document_ids = []
     tags_list = []
     observation_scopes_list = []
+    text_signals_list = []
 
     for fact in facts:
         fact_texts.append(_sanitize_text(fact.fact_text))
@@ -73,6 +74,15 @@ async def insert_facts_batch(
         observation_scopes_list.append(
             json.dumps(fact.observation_scopes) if fact.observation_scopes is not None else None
         )
+        # Build text_signals: entity names + date tokens for enriched BM25 indexing
+        signal_parts = []
+        if fact.entities:
+            signal_parts.extend(e.name for e in fact.entities)
+        if fact.occurred_start:
+            signal_parts.append(fact.occurred_start.strftime("%B %-d %Y"))
+        if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+            signal_parts.append(fact.occurred_end.strftime("%B %-d %Y"))
+        text_signals_list.append(" ".join(signal_parts) if signal_parts else None)
 
     # Batch insert all facts
     # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
@@ -80,18 +90,19 @@ async def insert_facts_batch(
     config = get_config()
     if config.text_search_extension == "vchord":
         # VectorChord: manually tokenize and insert search_vector
+        # text_signals (entity names etc.) are included in the tokenize input for enriched BM25
         query = f"""
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                        context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
-                       observation_scopes_json)
+                       observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                                      context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
-                                     observation_scopes, search_vector)
+                                     observation_scopes, text_signals, search_vector)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -101,25 +112,29 @@ async def insert_facts_batch(
                     '{{}}'::varchar[]
                 ),
                 observation_scopes_json,
-                tokenize(COALESCE(text, '') || ' ' || COALESCE(context, ''), 'llmlingua2')::bm25_catalog.bm25vector
+                text_signals,
+                tokenize(
+                    COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, ''),
+                    'llmlingua2'
+                )::bm25_catalog.bm25vector
             FROM input_data
             RETURNING id
         """
     else:  # native or pg_textsearch
-        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS (expression includes text_signals), don't include it
         # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
         query = f"""
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                        context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
-                       observation_scopes_json)
+                       observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                                      context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
-                                     observation_scopes)
+                                     observation_scopes, text_signals)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -128,7 +143,8 @@ async def insert_facts_batch(
                     (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                     '{{}}'::varchar[]
                 ),
-                observation_scopes_json
+                observation_scopes_json,
+                text_signals
             FROM input_data
             RETURNING id
         """
@@ -150,6 +166,7 @@ async def insert_facts_batch(
         document_ids,
         tags_list,
         observation_scopes_list,
+        text_signals_list,
     )
 
     unit_ids = [str(row["id"]) for row in results]

hindsight-api/hindsight_api/engine/retain/link_utils.py

@@ -150,6 +150,7 @@ async def extract_entities_batch_optimized(
     fact_dates: list,
     llm_entities: list[list[dict]],
     log_buffer: list[str] = None,
+    entity_labels: list | None = None,
 ) -> list[tuple]:
     """
     Process LLM-extracted entities for ALL facts in batch.
@@ -239,6 +240,7 @@ async def extract_entities_batch_optimized(
                 context=context,
                 unit_event_date=None,  # Not used when per-entity dates provided
                 conn=conn,  # Use main transaction connection
+                entity_labels=entity_labels,
             )
 
             _log(

hindsight-api/hindsight_api/engine/retain/orchestrator.py

@@ -472,6 +472,7 @@ async def retain_batch(
                 non_duplicate_facts,
                 log_buffer,
                 user_entities_per_content=user_entities_per_content,
+                entity_labels=getattr(config, "entity_labels", None),
             )
             log_buffer.append(f"[6] Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")
 

hindsight-api/hindsight_api/main.py

@@ -277,6 +277,8 @@ def main():
             consolidation_llm_batch_size=config.consolidation_llm_batch_size,
             consolidation_max_tokens=config.consolidation_max_tokens,
             observations_mission=config.observations_mission,
+            entity_labels=config.entity_labels,
+            entities_allow_free_form=config.entities_allow_free_form,
             skip_llm_verification=config.skip_llm_verification,
             lazy_reranker=config.lazy_reranker,
             run_migrations_on_startup=config.run_migrations_on_startup,

hindsight-api/tests/test_hierarchical_config.py

@@ -81,8 +81,12 @@ async def test_hierarchical_fields_categorization():
     assert "disposition_literalism" in configurable
     assert "disposition_empathy" in configurable
 
+    # Verify entity labels fields are included
+    assert "entities_allow_free_form" in configurable
+    assert "entity_labels" in configurable
+
     # Verify count is correct
-    assert len(configurable) == 11
+    assert len(configurable) == 13
 
     # Verify credential fields (NEVER exposed)
     assert "llm_api_key" in credentials

hindsight-clients/go/integration_test.go

@@ -67,7 +67,7 @@ func TestRetainWithContext(t *testing.T) {
 		Items: []MemoryItem{
 			{
 				Content:   "Bob went hiking in the mountains",
-				Timestamp: *NewNullableTime(PtrTime(timestamp)),
+				Timestamp: *NewNullableTimestamp(&Timestamp{TimeTime: &timestamp}),
 				Context:   *NewNullableString(PtrString("outdoor activities")),
 			},
 		},

hindsight-clients/python/hindsight_client/hindsight_client.py

@@ -180,16 +180,19 @@ class Hindsight:
             RetainResponse with success status and item count
         """
         from hindsight_client_api.models.entity_input import EntityInput
+        from hindsight_client_api.models.timestamp import Timestamp
 
         memory_items = []
         for item in items:
             entities = None
             if item.get("entities"):
                 entities = [EntityInput(text=e["text"], type=e.get("type")) for e in item["entities"]]
+            raw_ts = item.get("timestamp")
+            timestamp_val = Timestamp(actual_instance=raw_ts) if raw_ts is not None else None
             memory_items.append(
                 memory_item.MemoryItem(
                     content=item["content"],
-                    timestamp=item.get("timestamp"),
+                    timestamp=timestamp_val,
                     context=item.get("context"),
                     metadata=item.get("metadata"),
                     # Use item's document_id if provided, otherwise fall back to batch-level document_id
@@ -591,16 +594,19 @@ class Hindsight:
             RetainResponse with success status and item count
         """
         from hindsight_client_api.models.entity_input import EntityInput
+        from hindsight_client_api.models.timestamp import Timestamp
 
         memory_items = []
         for item in items:
             entities = None
             if item.get("entities"):
                 entities = [EntityInput(text=e["text"], type=e.get("type")) for e in item["entities"]]
+            raw_ts = item.get("timestamp")
+            timestamp_val = Timestamp(actual_instance=raw_ts) if raw_ts is not None else None
             memory_items.append(
                 memory_item.MemoryItem(
                     content=item["content"],
-                    timestamp=item.get("timestamp"),
+                    timestamp=timestamp_val,
                     context=item.get("context"),
                     metadata=item.get("metadata"),
                     # Use item's document_id if provided, otherwise fall back to batch-level document_id

hindsight-control-plane/package.json

@@ -11,7 +11,7 @@
     "public"
   ],
   "scripts": {
-    "dev": "next dev --turbopack -p 9999",
+    "dev": "next dev --turbopack -p ${PORT:-9999}",
     "build": "next build && npm run build:standalone",
     "build:standalone": "rm -rf standalone && SERVER_JS=$(find .next/standalone -path '*/node_modules' -prune -o -name 'server.js' -print | head -1) && test -n \"$SERVER_JS\" || (echo 'Error: server.js not found in .next/standalone - standalone build failed' && exit 1) && STANDALONE_ROOT=$(dirname \"$SERVER_JS\") && cp -r \"$STANDALONE_ROOT\" standalone && cp -r .next/standalone/node_modules standalone/node_modules && mkdir -p standalone/.next && cp -r .next/static standalone/.next/static && mkdir -p standalone/public && (cp -r public/* standalone/public/ 2>/dev/null || true)",
     "start": "next start",

hindsight-control-plane/src/components/bank-config-view.tsx

@@ -14,7 +14,10 @@ import {
   SelectValue,
 } from "@/components/ui/select";
 import { Alert, AlertDescription } from "@/components/ui/alert";
-import { Loader2, AlertCircle } from "lucide-react";
+import { Switch } from "@/components/ui/switch";
+import { Checkbox } from "@/components/ui/checkbox";
+import { Label } from "@/components/ui/label";
+import { Loader2, AlertCircle, Plus, Trash2, ChevronDown, ChevronRight } from "lucide-react";
 import { Card } from "@/components/ui/card";
 
 // ─── Types ────────────────────────────────────────────────────────────────────
@@ -38,6 +41,21 @@ type ObservationsEdits = {
   observations_mission: string | null;
 };
 
+type LabelValue = { value: string; description: string };
+type LabelGroup = {
+  key: string;
+  description: string;
+  type: "value" | "multi-values" | "text";
+  optional: boolean;
+  tag: boolean;
+  values: LabelValue[];
+};
+
+type EntityLabelsEdits = {
+  entity_labels: LabelGroup[] | null;
+  entities_allow_free_form: boolean;
+};
+
 type MCPEdits = {
   mcp_enabled_tools: string[] | null;
 };
@@ -96,6 +114,20 @@ function observationsSlice(config: Record<string, any>): ObservationsEdits {
   };
 }
 
+function entityLabelsSlice(config: Record<string, any>): EntityLabelsEdits {
+  const raw = config.entity_labels;
+  let attrs: LabelGroup[] | null = null;
+  if (Array.isArray(raw)) {
+    attrs = raw as LabelGroup[];
+  } else if (raw && typeof raw === "object" && Array.isArray(raw.attributes)) {
+    attrs = raw.attributes as LabelGroup[];
+  }
+  return {
+    entity_labels: attrs,
+    entities_allow_free_form: config.entities_allow_free_form ?? true,
+  };
+}
+
 function mcpSlice(config: Record<string, any>): MCPEdits {
   return {
     mcp_enabled_tools: config.mcp_enabled_tools ?? null,
@@ -124,16 +156,21 @@ export function BankConfigView() {
   const [observationsEdits, setObservationsEdits] = useState<ObservationsEdits>(
     observationsSlice({})
   );
+  const [entityLabelsEdits, setEntityLabelsEdits] = useState<EntityLabelsEdits>(
+    entityLabelsSlice({})
+  );
   const [reflectEdits, setReflectEdits] = useState<ProfileData>(DEFAULT_PROFILE);
   const [mcpEdits, setMcpEdits] = useState<MCPEdits>(mcpSlice({}));
 
   // Per-section saving/error state
   const [retainSaving, setRetainSaving] = useState(false);
   const [observationsSaving, setObservationsSaving] = useState(false);
+  const [entityLabelsSaving, setEntityLabelsSaving] = useState(false);
   const [reflectSaving, setReflectSaving] = useState(false);
   const [mcpSaving, setMcpSaving] = useState(false);
   const [retainError, setRetainError] = useState<string | null>(null);
   const [observationsError, setObservationsError] = useState<string | null>(null);
+  const [entityLabelsError, setEntityLabelsError] = useState<string | null>(null);
   const [reflectError, setReflectError] = useState<string | null>(null);
   const [mcpError, setMcpError] = useState<string | null>(null);
 
@@ -148,6 +185,10 @@ export function BankConfigView() {
     () => JSON.stringify(observationsEdits) !== JSON.stringify(observationsSlice(baseConfig)),
     [observationsEdits, baseConfig]
   );
+  const entityLabelsDirty = useMemo(
+    () => JSON.stringify(entityLabelsEdits) !== JSON.stringify(entityLabelsSlice(baseConfig)),
+    [entityLabelsEdits, baseConfig]
+  );
   const reflectDirty = useMemo(
     () => JSON.stringify(reflectEdits) !== JSON.stringify(baseProfile),
     [reflectEdits, baseProfile]
@@ -182,6 +223,7 @@ export function BankConfigView() {
       setBaseProfile(prof);
       setRetainEdits(retainSlice(cfg));
       setObservationsEdits(observationsSlice(cfg));
+      setEntityLabelsEdits(entityLabelsSlice(cfg));
       setReflectEdits(prof);
       setMcpEdits(mcpSlice(cfg));
     } catch (err) {
@@ -219,6 +261,24 @@ export function BankConfigView() {
     }
   };
 
+  const saveEntityLabels = async () => {
+    if (!bankId) return;
+    setEntityLabelsSaving(true);
+    setEntityLabelsError(null);
+    try {
+      const payload = {
+        entity_labels: entityLabelsEdits.entity_labels,
+        entities_allow_free_form: entityLabelsEdits.entities_allow_free_form,
+      };
+      await client.updateBankConfig(bankId, payload);
+      setBaseConfig((prev) => ({ ...prev, ...payload }));
+    } catch (err: any) {
+      setEntityLabelsError(err.message || "Failed to save entity labels settings");
+    } finally {
+      setEntityLabelsSaving(false);
+    }
+  };
+
   const saveReflect = async () => {
     if (!bankId) return;
     setReflectSaving(true);
@@ -340,6 +400,46 @@ export function BankConfigView() {
           )}
         </ConfigSection>
 
+        {/* Entity Labels Section */}
+        <ConfigSection
+          title="Entities"
+          description="Control entity extraction and define a controlled vocabulary of key:value classification labels (e.g. pedagogy:scaffolding, interest:active)"
+          error={entityLabelsError}
+          dirty={entityLabelsDirty}
+          saving={entityLabelsSaving}
+          onSave={saveEntityLabels}
+        >
+          <FieldRow
+            label="Free Form Entities"
+            description="Extract regular named entities (people, places, concepts) alongside label groups. Disable to restrict extraction to label groups only."
+          >
+            <div className="flex justify-end items-center gap-2">
+              <Label
+                htmlFor="entities-allow-free-form"
+                className="text-sm text-muted-foreground cursor-pointer select-none"
+              >
+                {entityLabelsEdits.entities_allow_free_form ? "Enabled" : "Disabled"}
+              </Label>
+              <Switch
+                id="entities-allow-free-form"
+                checked={entityLabelsEdits.entities_allow_free_form}
+                onCheckedChange={(v) =>
+                  setEntityLabelsEdits((prev) => ({ ...prev, entities_allow_free_form: v }))
+                }
+              />
+            </div>
+          </FieldRow>
+          <EntityLabelsEditor
+            value={entityLabelsEdits.entity_labels ?? []}
+            onChange={(attrs) =>
+              setEntityLabelsEdits((prev) => ({
+                ...prev,
+                entity_labels: attrs.length > 0 ? attrs : null,
+              }))
+            }
+          />
+        </ConfigSection>
+
         {/* Observations Section */}
         <ConfigSection
           title="Observations"
@@ -354,9 +454,9 @@ export function BankConfigView() {
             description="Enable automatic consolidation of facts into observations"
           >
             <div className="flex justify-end">
-              <Toggle
-                value={observationsEdits.enable_observations ?? false}
-                onChange={(v) =>
+              <Switch
+                checked={observationsEdits.enable_observations ?? false}
+                onCheckedChange={(v) =>
                   setObservationsEdits((prev) => ({ ...prev, enable_observations: v }))
                 }
               />
@@ -430,15 +530,18 @@ export function BankConfigView() {
             label="Restrict tools"
             description="When off, all tools are available. When on, only the selected tools can be invoked for this bank."
           >
-            <div className="flex justify-end">
-              <Toggle
-                value={mcpEdits.mcp_enabled_tools !== null}
-                onChange={(restricted) =>
+            <div className="flex items-center gap-2 justify-end">
+              <Switch
+                checked={mcpEdits.mcp_enabled_tools !== null}
+                onCheckedChange={(restricted) =>
                   setMcpEdits({
                     mcp_enabled_tools: restricted ? [...ALL_TOOLS] : null,
                   })
                 }
               />
+              <Label className="text-xs text-muted-foreground">
+                {mcpEdits.mcp_enabled_tools !== null ? "Enabled" : "Disabled"}
+              </Label>
             </div>
           </FieldRow>
           {mcpEdits.mcp_enabled_tools !== null && (
@@ -713,22 +816,216 @@ function TraitRow({
   );
 }
 
-// ─── Toggle ───────────────────────────────────────────────────────────────────
+// ─── EntityLabelsEditor ───────────────────────────────────────────────────────
+
+function emptyAttribute(): LabelGroup {
+  return {
+    key: "",
+    description: "",
+    type: "value",
+    optional: true,
+    tag: false,
+    values: [],
+  };
+}
+
+function emptyValue(): LabelValue {
+  return { value: "", description: "" };
+}
+
+function EntityLabelsEditor({
+  value,
+  onChange,
+}: {
+  value: LabelGroup[];
+  onChange: (attrs: LabelGroup[]) => void;
+}) {
+  const [expanded, setExpanded] = useState<Record<number, boolean>>({});
+
+  const updateAttr = (i: number, patch: Partial<LabelGroup>) => {
+    const next = value.map((a, idx) => (idx === i ? { ...a, ...patch } : a));
+    onChange(next);
+  };
+
+  const removeAttr = (i: number) => {
+    onChange(value.filter((_, idx) => idx !== i));
+    setExpanded((prev) => {
+      const next = { ...prev };
+      delete next[i];
+      return next;
+    });
+  };
+
+  const addAttr = () => {
+    const next = [...value, emptyAttribute()];
+    onChange(next);
+    setExpanded((prev) => ({ ...prev, [next.length - 1]: true }));
+  };
+
+  const updateVal = (attrIdx: number, valIdx: number, patch: Partial<LabelValue>) => {
+    const newValues = value[attrIdx].values.map((v, vi) =>
+      vi === valIdx ? { ...v, ...patch } : v
+    );
+    updateAttr(attrIdx, { values: newValues });
+  };
+
+  const removeVal = (attrIdx: number, valIdx: number) => {
+    updateAttr(attrIdx, { values: value[attrIdx].values.filter((_, vi) => vi !== valIdx) });
+  };
+
+  const addVal = (attrIdx: number) => {
+    updateAttr(attrIdx, { values: [...value[attrIdx].values, emptyValue()] });
+  };
 
-function Toggle({ value, onChange }: { value: boolean; onChange: (v: boolean) => void }) {
   return (
-    <button
-      type="button"
-      onClick={() => onChange(!value)}
-      className={`relative inline-flex h-6 w-11 items-center rounded-full transition-colors ${
-        value ? "bg-primary" : "bg-muted"
-      }`}
-    >
-      <span
-        className={`inline-block h-4 w-4 transform rounded-full bg-white transition-transform ${
-          value ? "translate-x-6" : "translate-x-1"
-        }`}
-      />
-    </button>
+    <div className="px-6 py-4 space-y-3">
+      <div className="flex items-center justify-between">
+        <div>
+          <p className="text-sm font-medium">Label Groups</p>
+          <p className="text-xs text-muted-foreground mt-0.5">
+            Classification labels extracted at retain time. Leave empty to disable.
+          </p>
+        </div>
+        {value.length > 0 && (
+          <span className="text-xs bg-primary/10 text-primary px-2 py-0.5 rounded-full shrink-0">
+            {value.length} group{value.length !== 1 ? "s" : ""}
+          </span>
+        )}
+      </div>
+
+      {value.length === 0 && (
+        <p className="text-xs text-muted-foreground italic">No label groups defined.</p>
+      )}
+
+      <div className="space-y-2">
+        {value.map((attr, i) => {
+          const isOpen = expanded[i] ?? false;
+          const isText = attr.type === "text";
+          const hasValues = !isText;
+          return (
+            <div key={i} className="border border-border/50 rounded-md bg-background">
+              {/* Attribute header */}
+              <div className="flex items-center gap-2 px-3 py-2">
+                <button
+                  type="button"
+                  onClick={() => setExpanded((prev) => ({ ...prev, [i]: !isOpen }))}
+                  className="text-muted-foreground hover:text-foreground shrink-0"
+                  disabled={isText}
+                >
+                  {isOpen && hasValues ? (
+                    <ChevronDown className="h-4 w-4" />
+                  ) : (
+                    <ChevronRight className={`h-4 w-4 ${isText ? "opacity-30" : ""}`} />
+                  )}
+                </button>
+                <Input
+                  placeholder="key (e.g. pedagogy)"
+                  value={attr.key}
+                  onChange={(e) => updateAttr(i, { key: e.target.value })}
+                  className="h-8 text-xs font-mono w-36 shrink-0"
+                />
+                <Input
+                  placeholder={isText ? "description / examples" : "description"}
+                  value={attr.description}
+                  onChange={(e) => updateAttr(i, { description: e.target.value })}
+                  className="h-8 text-xs flex-1 min-w-0"
+                />
+                {/* Type dropdown */}
+                <Select
+                  value={attr.type}
+                  onValueChange={(v: "value" | "multi-values" | "text") =>
+                    updateAttr(i, {
+                      type: v,
+                      // reset values when switching to free text
+                      ...(v === "text" ? { values: [] } : {}),
+                    })
+                  }
+                >
+                  <SelectTrigger className="h-8 text-xs w-32 shrink-0">
+                    <SelectValue />
+                  </SelectTrigger>
+                  <SelectContent>
+                    <SelectItem value="value" className="text-xs">
+                      Single value
+                    </SelectItem>
+                    <SelectItem value="multi-values" className="text-xs">
+                      Multi-values
+                    </SelectItem>
+                    <SelectItem value="text" className="text-xs">
+                      Free text
+                    </SelectItem>
+                  </SelectContent>
+                </Select>
+                {/* Tag checkbox — also write extracted labels as tags */}
+                <label className="flex items-center gap-1.5 text-xs text-muted-foreground shrink-0 cursor-pointer select-none">
+                  <Checkbox
+                    checked={attr.tag}
+                    onCheckedChange={(checked) => updateAttr(i, { tag: !!checked })}
+                    className="h-4 w-4"
+                  />
+                  tag
+                </label>
+                <button
+                  type="button"
+                  onClick={() => removeAttr(i)}
+                  className="text-muted-foreground hover:text-destructive shrink-0"
+                >
+                  <Trash2 className="h-3.5 w-3.5" />
+                </button>
+              </div>
+
+              {/* Values list — enum and multi-values only */}
+              {isOpen && hasValues && (
+                <div className="px-3 pb-3 space-y-1 border-t border-border/30 pt-2">
+                  {attr.values.length === 0 && (
+                    <p className="text-xs text-muted-foreground italic pl-5">No values yet.</p>
+                  )}
+                  {attr.values.map((v, vi) => (
+                    <div key={vi} className="flex items-center gap-2 pl-5">
+                      <Input
+                        placeholder="value"
+                        value={v.value}
+                        onChange={(e) => updateVal(i, vi, { value: e.target.value })}
+                        className="h-8 text-xs font-mono w-32 shrink-0"
+                      />
+                      <Input
+                        placeholder="description"
+                        value={v.description}
+                        onChange={(e) => updateVal(i, vi, { description: e.target.value })}
+                        className="h-8 text-xs flex-1 min-w-0"
+                      />
+                      <button
+                        type="button"
+                        onClick={() => removeVal(i, vi)}
+                        className="text-muted-foreground hover:text-destructive shrink-0"
+                      >
+                        <Trash2 className="h-3.5 w-3.5" />
+                      </button>
+                    </div>
+                  ))}
+                  <button
+                    type="button"
+                    onClick={() => addVal(i)}
+                    className="flex items-center gap-1 text-xs text-muted-foreground hover:text-foreground pl-5 mt-1"
+                  >
+                    <Plus className="h-3 w-3" />
+                    Add value
+                  </button>
+                </div>
+              )}
+            </div>
+          );
+        })}
+      </div>
+
+      <button
+        type="button"
+        onClick={addAttr}
+        className="flex items-center gap-1.5 text-xs text-muted-foreground hover:text-foreground"
+      >
+        <Plus className="h-3.5 w-3.5" />
+        Add attribute
+      </button>
+    </div>
   );
 }

hindsight-docs/docs/developer/configuration.md

@@ -553,6 +553,8 @@ Controls the retain (memory ingestion) pipeline.
 | `HINDSIGHT_API_RETAIN_BATCH_ENABLED` | Use LLM Batch API for fact extraction (50% cost savings, only with async operations) | `false` |
 | `HINDSIGHT_API_RETAIN_BATCH_POLL_INTERVAL_SECONDS` | Batch API polling interval in seconds | `60` |
 
+> **Entity labels** (`entity_labels`) and **free-form entity extraction** (`entities_allow_free_form`) are configured per bank via the [bank config API](/developer/api/memory-banks#retain-configuration), not as global environment variables — each bank can have its own controlled vocabulary. See [Entity Labels](/developer/retain#entity-labels) for details.
+
 #### Customizing retain: when to use what
 
 There are three levels of customization for the retain pipeline. Start with the simplest that covers your needs:

hindsight-docs/docs/developer/retain.md

@@ -199,6 +199,90 @@ Set `retain_mission` and `retain_extraction_mode` via the [bank config API](/dev
 
 ---
 
+## Entity Labels
+
+**Entity labels** let you define a controlled vocabulary of classification labels that are extracted at retain time and stored as entities alongside regular named entities. Each label takes the form `key:value` (e.g. `pedagogy:scaffolding`, `engagement:active`).
+
+Because labels become entities, they automatically:
+- Appear in the **knowledge graph** — two memories with `pedagogy:scaffolding` are linked
+- Improve **semantic and BM25 retrieval** — label strings are included in both the dense embedding and the sparse `text_signals` field
+- Support **labels-only mode** — optionally disable free-form entity extraction so only labels are stored
+
+Labels are configured per bank via `entity_labels` in the bank config.
+
+### Defining Label Groups
+
+Each label group defines one classification dimension:
+
+```json
+{
+  "entity_labels": [
+    {
+      "key": "engagement",
+      "description": "Student engagement level during the session",
+      "type": "value",
+      "optional": true,
+      "values": [
+        { "value": "active",  "description": "Student is actively participating" },
+        { "value": "passive", "description": "Student is listening but not participating" }
+      ]
+    },
+    {
+      "key": "pedagogy",
+      "description": "Teaching strategies used",
+      "type": "multi-values",
+      "values": [
+        { "value": "scaffolding",           "description": "Breaking complex tasks into smaller steps" },
+        { "value": "direct_instruction",    "description": "Explicit explanation by the teacher" },
+        { "value": "socratic_questioning",  "description": "Guiding through questions rather than answers" }
+      ]
+    }
+  ]
+}
+```
+
+| Field | Default | Description |
+|-------|---------|-------------|
+| `key` | — | Label group identifier. Becomes the prefix in `key:value` entities. |
+| `description` | `""` | Shown to the LLM to help it assign the right label. |
+| `type` | `"value"` | `"value"` → single enum value; `"multi-values"` → multiple enum values; `"text"` → free-form string. |
+| `values` | `[]` | Allowed values for `"value"` and `"multi-values"` types. Ignored for `"text"` type. |
+| `optional` | `true` | `true` → the LLM may skip this label if not applicable (default). `false` → LLM must always assign a value. Has no effect on `"multi-values"` groups (always optional). |
+| `tag` | `false` | `true` → also write extracted `key:value` entities as tags on the memory unit, enabling filtering via the standard `tags`/`tags_match` API parameters. |
+
+### Enum vs Free-text Labels
+
+**Enum groups** (`type: "value"` or `type: "multi-values"`): the LLM must pick from the predefined `values` list. Values not in the list are silently dropped. This is the most reliable option — the vocabulary is stable and graph clustering is tight. Use `"multi-values"` when a single fact can match multiple values.
+
+**Free-text groups** (`type: "text"`): the LLM can write any string value. The `values` field is ignored — use the `description` to provide examples and guidance instead.
+
+```json
+{
+  "key": "topic",
+  "description": "The specific subject being discussed. Examples: algebra, geometry, quadratic equations.",
+  "type": "text",
+  "optional": true,
+  "values": []
+}
+```
+
+The trade-off with free-text: the LLM may use different phrasings for the same concept across sessions (`topic:fractions` vs `topic:fraction arithmetic`), so graph linking is less reliable than with enum groups.
+
+### Labels-only Mode
+
+By default, entity labels are extracted **alongside** regular named entities (people, places, concepts). Set `entities_allow_free_form: false` to disable free-form extraction and store only label entities:
+
+```json
+{
+  "entity_labels": [...],
+  "entities_allow_free_form": false
+}
+```
+
+Configure both via the [bank config API](/developer/api/memory-banks#retain-configuration).
+
+---
+
 ## Observation Consolidation
 
 After `retain()` completes, Hindsight automatically triggers **observation consolidation** in the background. This process:

hindsight-docs/examples/api/quickstart.go

@@ -62,7 +62,7 @@ func main() {
 			{
 				Content:   "Alice got promoted",
 				Context:   *hindsight.NewNullableString(hindsight.PtrString("career update")),
-				Timestamp: *hindsight.NewNullableTime(hindsight.PtrTime(timestamp)),
+				Timestamp: *hindsight.NewNullableTimestamp(&hindsight.Timestamp{TimeTime: hindsight.PtrTime(timestamp)}),
 				Tags:      []string{"career"},
 			},
 		},

scripts/dev/start-control-plane.sh

@@ -28,6 +28,7 @@ fi
 
 # Map prefixed env vars to Next.js standard vars
 export HOSTNAME="${HINDSIGHT_CP_HOSTNAME:-0.0.0.0}"
+export PORT="${HINDSIGHT_CP_PORT:-9999}"
 
 # Run dev server
 npm run dev -w @vectorize-io/hindsight-control-plane

scripts/dev/start.sh

@@ -11,6 +11,7 @@ if [ -f "$ROOT_DIR/.env" ]; then
     set +a
 fi
 API_PORT="${HINDSIGHT_API_PORT:-8888}"
+CP_PORT="${HINDSIGHT_CP_PORT:-9999}"
 
 PIDS=()
 
@@ -70,7 +71,7 @@ echo ""
 echo "Hindsight is running!"
 echo ""
 echo "  API: http://localhost:${API_PORT}"
-echo "  Control Plane: http://localhost:9999"
+echo "  Control Plane: http://localhost:${CP_PORT}"
 echo ""
 echo "Press Ctrl+C to stop both services."
 echo ""