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Merged
Ashish-dwi99 merged 1 commit into from
Apr 1, 2026
Merged

V2.2.5 #20

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100 changes: 96 additions & 4 deletions dhee/adapters/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,6 +90,9 @@ def __init__(
from dhee.core.session_tracker import SessionTracker
self._tracker = SessionTracker()

# Hook registry for harness integration
self._hooks: Dict[str, List[Callable]] = self._init_hooks()

# Session tracking (kept for backward compat with session_start/session_end)
self._session_id: Optional[str] = None
self._session_start_time: Optional[float] = None
Expand All @@ -110,6 +113,46 @@ def provider(self) -> str:
def buddhi(self):
return self._buddhi

@property
def kernel(self):
"""Access the CognitionKernel for direct state manipulation."""
return self._kernel

# ------------------------------------------------------------------
# Hook registry
# ------------------------------------------------------------------

_HOOK_EVENTS = frozenset([
"pre_remember", "post_remember",
"pre_recall", "post_recall",
"pre_context", "post_context",
"pre_checkpoint", "post_checkpoint",
])

def _init_hooks(self) -> Dict[str, List[Callable]]:
"""Create a fresh hook registry."""
return {event: [] for event in self._HOOK_EVENTS}

def register_hook(self, event: str, callback: Callable) -> None:
"""Register a callback for a lifecycle event.

Events: pre_remember, post_remember, pre_recall, post_recall,
pre_context, post_context, pre_checkpoint, post_checkpoint.

Pre-hooks receive the arguments dict. Post-hooks receive the result dict.
"""
if event not in self._hooks:
raise ValueError(f"Unknown hook event: {event}. Valid: {list(self._hooks.keys())}")
self._hooks[event].append(callback)

def _fire_hooks(self, event: str, data: Any) -> None:
"""Fire all registered hooks for an event."""
for callback in self._hooks.get(event, []):
try:
callback(data)
except Exception:
logger.debug("Hook %s failed", event)

# ------------------------------------------------------------------
# Tool 1: remember
# ------------------------------------------------------------------
Expand All @@ -126,6 +169,7 @@ def remember(
checks for "remember to X when Y" patterns.
"""
uid = user_id or self._user_id
self._fire_hooks("pre_remember", {"content": content, "user_id": uid, "metadata": metadata})

# Auto-tier memory content
from dhee.core.session_tracker import classify_tier
Expand Down Expand Up @@ -157,6 +201,7 @@ def remember(
if intention:
response["detected_intention"] = intention.to_dict()

self._fire_hooks("post_remember", response)
return response

# ------------------------------------------------------------------
Expand All @@ -171,6 +216,7 @@ def recall(
) -> List[Dict[str, Any]]:
"""Search memory for relevant facts. 0 LLM calls. 1 embedding."""
uid = user_id or self._user_id
self._fire_hooks("pre_recall", {"query": query, "user_id": uid, "limit": limit})
results = self._engram.search(query, user_id=uid, limit=limit)
formatted = [
{
Expand All @@ -185,6 +231,7 @@ def recall(
signals = self._tracker.on_recall(query, formatted)
self._handle_tracker_signals(signals, uid)

self._fire_hooks("post_recall", formatted)
return formatted

# ------------------------------------------------------------------
Expand All @@ -203,15 +250,21 @@ def context(
operational: If True, return compact actionable-only format.
"""
uid = user_id or self._user_id
self._fire_hooks("pre_context", {
"task_description": task_description, "user_id": uid, "operational": operational,
})
self._tracker.on_context(task_description)
hyper_ctx = self._buddhi.get_hyper_context(
user_id=uid,
task_description=task_description,
memory=self._engram._memory,
)
if operational:
return hyper_ctx.to_operational_dict()
return hyper_ctx.to_dict()
result = hyper_ctx.to_operational_dict()
else:
result = hyper_ctx.to_dict()
self._fire_hooks("post_context", result)
return result

# ------------------------------------------------------------------
# Tool 4: checkpoint
Expand Down Expand Up @@ -253,6 +306,10 @@ def checkpoint(
8. Selective forgetting → utility-based cleanup
"""
uid = user_id or self._user_id
self._fire_hooks("pre_checkpoint", {
"summary": summary, "user_id": uid, "task_type": task_type,
"outcome_score": outcome_score, "status": status,
})
self._tracker.on_checkpoint()

# Auto-fill task_type if not provided
Expand All @@ -269,6 +326,7 @@ def checkpoint(
what_worked = outcome.get("what_worked")

result: Dict[str, Any] = {}
score = max(0.0, min(1.0, float(outcome_score))) if outcome_score is not None else None

# 1. Session digest
try:
Expand Down Expand Up @@ -298,8 +356,7 @@ def checkpoint(
pass

# 3. Outcome recording
if task_type and outcome_score is not None:
score = max(0.0, min(1.0, float(outcome_score)))
if task_type and score is not None:
insight = self._buddhi.record_outcome(
user_id=uid, task_type=task_type, score=score,
)
Expand Down Expand Up @@ -351,6 +408,7 @@ def checkpoint(
forget_result = self._kernel.selective_forget(uid)
result.update(forget_result)

self._fire_hooks("post_checkpoint", result)
return result

# ------------------------------------------------------------------
Expand Down Expand Up @@ -433,6 +491,40 @@ def _handle_tracker_signals(self, signals: Dict[str, Any], user_id: str) -> None
except Exception:
pass

# ------------------------------------------------------------------
# Cognition health (harness monitoring)
# ------------------------------------------------------------------

def cognition_health(self, user_id: Optional[str] = None) -> Dict[str, Any]:
"""Health status of all cognitive subsystems.

Returns counts, utility stats, and degradation warnings.
Useful for harness dashboards and monitoring. Zero LLM calls.
"""
uid = user_id or self._user_id
health: Dict[str, Any] = {}

health["kernel"] = self._kernel.get_stats()
health["buddhi"] = self._buddhi.get_stats()

warnings: List[str] = []
try:
policies = self._kernel.policies.get_user_policies(uid)
low_util = [p for p in policies if p.utility < -0.2 and p.apply_count >= 3]
if low_util:
warnings.append(f"{len(low_util)} policies with negative utility")
active_intentions = self._kernel.intentions.get_active(uid)
if len(active_intentions) > 20:
warnings.append(f"{len(active_intentions)} active intentions (consider cleanup)")
contradictions = self._kernel.beliefs.get_contradictions(uid)
if len(contradictions) > 5:
warnings.append(f"{len(contradictions)} unresolved belief contradictions")
except Exception:
pass

health["warnings"] = warnings
return health

# ------------------------------------------------------------------
# Phase 3: Belief management
# ------------------------------------------------------------------
Expand Down
151 changes: 29 additions & 122 deletions dhee/core/buddhi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1024,63 +1024,22 @@ def reflect(
except Exception:
pass

# Delegate cross-structure learning to kernel
# Kernel handles: policy outcomes, step extraction, belief-policy decay,
# intention feedback, episode connections
success = what_worked is not None
if what_worked:
try:
matched = self._kernel.policies.match_policies(
user_id, task_type, f"{task_type} task",
)
for policy in matched:
self._kernel.policies.record_outcome(
policy.id,
success=True,
baseline_score=baseline_score,
actual_score=outcome_score,
)

completed = self._kernel.tasks.get_tasks_by_type(
user_id, task_type, limit=10,
)
if len(completed) >= 3:
task_dicts = [t.to_dict() for t in completed]
self._kernel.policies.extract_from_tasks(
user_id, task_dicts, task_type,
)
# Step-level policy extraction from failure patterns
self._kernel.policies.extract_step_policies(
user_id, task_dicts, task_type,
)
except Exception:
pass

self._kernel.record_learning_outcomes(
user_id, task_type, success=True,
baseline_score=baseline_score, actual_score=outcome_score,
)
if what_failed:
try:
matched = self._kernel.policies.match_policies(
user_id, task_type, f"{task_type} task",
)
for policy in matched:
self._kernel.policies.record_outcome(
policy.id,
success=False,
baseline_score=baseline_score,
actual_score=outcome_score,
)
except Exception:
pass

# Extract step policies from failure patterns
try:
completed = self._kernel.tasks.get_tasks_by_type(
user_id, task_type, limit=10,
)
if len(completed) >= 3:
task_dicts = [t.to_dict() for t in completed]
self._kernel.policies.extract_step_policies(
user_id, task_dicts, task_type,
)
except Exception:
pass
self._kernel.record_learning_outcomes(
user_id, task_type, success=False,
baseline_score=baseline_score, actual_score=outcome_score,
)

# Update beliefs based on outcomes (via kernel)
# Update beliefs based on outcomes (buddhi-owned: text-based matching)
if what_worked:
try:
relevant = self._kernel.beliefs.get_relevant_beliefs(
Expand All @@ -1105,70 +1064,38 @@ def reflect(
except Exception:
pass

# Cross-structure: when beliefs are challenged, check dependent policies
try:
for belief in relevant:
if belief.confidence < 0.3:
claim_words = set(belief.claim.lower().split()[:5])
for policy in self._kernel.policies._policies.values():
if policy.user_id != user_id:
continue
approach_words = set(policy.action.approach.lower().split())
if len(claim_words & approach_words) >= 2:
policy.utility *= 0.8
policy.updated_at = time.time()
except Exception:
pass

# Record outcomes on matched insights (utility tracking)
if what_worked:
try:
matched_insights = self._get_relevant_insights(
user_id, f"{task_type} task",
)
for insight in matched_insights[:5]:
insight.record_outcome(
success=True,
baseline_score=baseline_score,
actual_score=outcome_score,
)
self._save_insights()
except Exception:
pass

if what_failed:
try:
matched_insights = self._get_relevant_insights(
user_id, f"{task_type} task",
# Buddhi-owned: insight utility tracking (buddhi owns insights)
try:
matched_insights = self._get_relevant_insights(
user_id, f"{task_type} task",
)
for insight in matched_insights[:5]:
insight.record_outcome(
success=success,
baseline_score=baseline_score,
actual_score=outcome_score,
)
for insight in matched_insights[:5]:
insight.record_outcome(
success=False,
baseline_score=baseline_score,
actual_score=outcome_score,
)
self._save_insights()
except Exception:
pass
self._save_insights()
except Exception:
pass

# Record outcomes on matched contrastive pairs
# Buddhi-owned: contrastive pair utility (buddhi owns contrastive store)
try:
store = self._get_contrastive()
matched_pairs = store.retrieve_contrasts(
f"{task_type} task", user_id=user_id, limit=5,
)
task_succeeded = what_worked is not None
for pair in matched_pairs:
pair.record_outcome(
success=task_succeeded,
success=success,
baseline_score=baseline_score,
actual_score=outcome_score,
)
store._save_all()
except Exception:
pass

# Cross-structure: positive policy delta -> reinforce related heuristics
# Buddhi-owned: heuristic reinforcement from positive policy deltas
if what_worked:
try:
matched_policies = self._kernel.policies.match_policies(
Expand All @@ -1190,26 +1117,6 @@ def reflect(
except Exception:
pass

# Record outcomes on recently triggered intentions
try:
triggered = [
i for i in self._kernel.intentions._intentions.values()
if i.user_id == user_id
and i.status == "triggered"
and i.was_useful is None
]
task_succeeded = what_worked is not None and (
outcome_score is None or outcome_score >= 0.5
)
for intention in triggered:
self._kernel.intentions.record_outcome(
intention.id,
useful=task_succeeded,
outcome_score=outcome_score,
)
except Exception:
pass

return new_insights

def _validate_used_heuristics(
Expand Down
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