Feature1

[rexdivakar]

PR Type

Enhancement, Tests

---

Description

• Comprehensive memory management system: Added advanced memory lifecycle management with tiering (WARM, COLD, ARCHIVED, HIBERNATED), conflict resolution with automatic detection and multiple resolution strategies, and quality monitoring with health scoring and duplicate detection

• Observability and monitoring framework: Implemented memory observability module with retrieval explanation, similarity visualization, performance profiling, and comprehensive health monitoring system with status levels and topic coverage analysis

• Memory operations and optimization: Added memory cloning, template-based creation, batch updates, maintenance scheduling, archival policies, and garbage collection with configurable strategies

• SaaS automation controller: Created unified automation management for memory optimization tasks with policy-based triggers (threshold, schedule, continuous, manual) and lazy-loaded feature modules

• Advanced compression engine: Implemented RCC-style compression, token pruning with semantic preservation, and episodic-to-semantic memory conversion with quality metrics

• Conversation-aware memory system: Added turn-by-turn parsing, speaker attribution, topic segmentation, sentiment analysis, and multi-level summarization capabilities

• Adaptive learning engine: Implemented access pattern tracking, intelligent refresh recommendations, and adaptive compression level suggestions based on memory behavior

• Enhanced API infrastructure: Added authentication, rate limiting, Prometheus metrics middleware, 20+ observability endpoints, and improved error handling with conflict resolution support

• Comprehensive test coverage: Added end-to-end QA test suite with 10 major test categories and dedicated conflict resolution test suite with multiple resolution strategy validation

• Minor improvements: Type annotation enhancements for code clarity

---

Diagram Walkthrough

flowchart LR
  A["Memory Service"] -- "conflict detection" --> B["Conflict Resolution"]
  A -- "lifecycle management" --> C["Memory Lifecycle"]
  A -- "quality monitoring" --> D["Memory Health"]
  E["API Layer"] -- "metrics collection" --> F["Prometheus Metrics"]
  E -- "authentication" --> G["Auth Service"]
  H["Automation Controller"] -- "manages" --> I["Optimization Policies"]
  I -- "triggers" --> J["Compression Engine"]
  I -- "triggers" --> K["Summarization"]
  I -- "triggers" --> L["Consolidation"]
  M["Observability Monitor"] -- "explains" --> N["Retrieval Results"]
  M -- "profiles" --> O["Query Performance"]
  P["Conversation Memory"] -- "extracts" --> Q["Memories from Conversations"]
  R["Adaptive Learning"] -- "analyzes" --> S["Access Patterns"]
  S -- "recommends" --> T["Optimizations"]

Loading

File Walkthrough

	Relevant files
Tests	2 files test_e2e_saas_qa.py End-to-End QA Test Suite for HippocampAI SaaS Stack            tests/test_e2e_saas_qa.py Comprehensive end-to-end QA test suite with 10 major test categories covering initialization, multi-tenant isolation, memory operations, retrieval quality, entity extraction, summarization, temporal reasoning, cross-interface consistency, CRUD operations, and error handling Includes custom QAReport class for tracking test results, pass/fail rates, feature coverage, and generating formatted reports with color-coded output Tests cover SaaS API backend, Python SDK functionality, and consistency between interfaces using real HTTP requests and SDK client operations Provides detailed test descriptions, expected vs actual output comparisons, and actionable suggestions for improvement +1406/-0 test_conflict_resolution.py Memory conflict resolution test suite                                        tests/test_conflict_resolution.py Comprehensive test suite for memory conflict detection and resolution functionality Tests direct contradictions, dietary preferences, allergies, and temporal conflicts Validates multiple resolution strategies (temporal, confidence, importance, user review) Includes similarity calculation tests and conflict metadata tracking verification +483/-0
Enhancement	10 files async_app.py Enhanced API with Auth, Metrics, Observability, and Conflict Resolution src/hippocampai/api/async_app.py Added authentication and rate limiting initialization in lifespan context manager with PostgreSQL connection pool and AuthService setup Integrated Prometheus metrics middleware for monitoring with /metrics endpoint and conditional availability handling Added AuthMiddleware registration and admin routes inclusion with error handling for optional dependencies Enhanced error handling for memory creation with ConflictResolutionError and MemoryNotFoundError exceptions, supporting conflict resolution strategies Added 20+ new observability endpoints for debugging, temporal features, conflict resolution, health monitoring, and memory lifecycle management Improved type hints throughout with explicit return types and cast() for type safety Added batch memory update model BatchMemoryUpdate with memory_id field separate from MemoryUpdate +1202/-53 memory_observability.py Memory Observability and Debugging Framework                          src/hippocampai/pipeline/memory_observability.py New module providing memory debugging and observability features including retrieval explanation, similarity visualization, and performance profiling Implements MemoryObservabilityMonitor class with methods to explain retrieval results, visualize scores, generate access heatmaps, and profile query performance Includes data models for RetrievalExplanation, SimilarityVisualization, MemoryAccessHeatmap, and QueryPerformanceProfile with detailed metrics Provides performance snapshot generation, slow query identification, and comprehensive performance reporting with bottleneck analysis Includes PerformanceTimer context manager for operation timing and @profile_operation decorator for function profiling Generates human-readable explanations of retrieval rankings based on score breakdowns, memory properties, and contributing factors +607/-0  memory_service.py Advanced memory management with conflict resolution and lifecycle management src/hippocampai/services/memory_service.py Added comprehensive conflict resolution system with automatic detection and resolution of contradictory memories Implemented memory lifecycle management with tiering (WARM, COLD, ARCHIVED, HIBERNATED) and temperature-based access tracking Integrated quality monitoring with health scoring, duplicate detection, stale memory identification, and topic coverage analysis Added conversation-aware memory methods for tracking conversation turns, summarization, and extracting memories from conversations Implemented memory merge engine with merge suggestions, preview capabilities, and multiple merge strategies Added Prometheus metrics tracking for memory operations and search requests Enhanced error handling with proper exception types and memory access locking to prevent concurrent modifications +1292/-23 memory_health.py Memory quality and health monitoring system implementation src/hippocampai/monitoring/memory_health.py Created comprehensive memory health monitoring system with health scoring (0-100) and status levels (EXCELLENT to CRITICAL) Implemented duplicate cluster detection with multiple clustering types (EXACT, SOFT, PARAPHRASE, VARIANT) Added stale memory detection with staleness scoring and archival recommendations Implemented topic coverage analysis with coverage levels (COMPREHENSIVE to MISSING) and gap identification Added quality report generation combining health scores, duplicate clusters, stale memories, and topic coverage Implemented helper methods for calculating freshness, diversity, consistency, and coverage scores +841/-0  memory_operations.py Advanced memory operations for cloning, batch updates, and maintenance src/hippocampai/pipeline/memory_operations.py Implemented memory cloning with configurable options for ID, timestamps, metadata, tags, and user/session assignment Added template-based memory creation with variable substitution and instantiation Implemented batch update operations with flexible filtering and modification capabilities Added maintenance scheduling system for automatic memory maintenance tasks Implemented archival policies with multiple trigger conditions (stale, low importance, low confidence, access count) Added garbage collection with configurable policies (AGGRESSIVE, MODERATE, CONSERVATIVE) and space reclamation tracking +849/-0  automation.py SaaS automation controller for memory optimization policies src/hippocampai/saas/automation.py Created automation controller for unified management of memory optimization tasks Implemented policy-based automation with support for threshold, schedule, continuous, and manual triggers Added lazy-loaded feature modules for summarization, consolidation, compression, decay, health monitoring, and conflict resolution Implemented policy creation, retrieval, and deletion with user-specific configurations Added methods to check if optimizations should run and execute them with force override capability Implemented comprehensive statistics gathering with automation feature status +440/-0  advanced_compression.py Advanced memory compression engine with RCC and semantic conversion src/hippocampai/pipeline/advanced_compression.py Implements advanced memory compression techniques including RCC-style algorithm, token pruning with semantic preservation, and episodic-to-semantic memory conversion Provides compression quality metrics and evaluation methods with multiple compression strategies Includes entity/fact extraction, deduplication, and heuristic/LLM-based compression methods Offers semantic memory extraction and abstraction level determination for knowledge consolidation +805/-0  conversation_memory.py Conversation-aware memory tracking and analysis system      src/hippocampai/pipeline/conversation_memory.py Implements conversation-aware memory system with turn-by-turn parsing and speaker attribution Provides topic segmentation, sentiment analysis, and dialogue flow tracking capabilities Extracts action items, decisions, and unresolved questions from conversations Generates multi-level conversation summaries with LLM support for advanced analysis +847/-0  memory_health.py Memory health monitoring and quality assessment system      src/hippocampai/pipeline/memory_health.py Monitors memory store health with comprehensive scoring across quality, diversity, freshness, and coverage dimensions Detects stale memories, duplicate clusters, and near-duplicates with merge suggestions Analyzes topic coverage gaps and provides actionable recommendations for memory optimization Calculates semantic similarity and evaluates memory metadata completeness +726/-0  adaptive_learning.py Adaptive learning engine for access patterns and optimization src/hippocampai/pipeline/adaptive_learning.py Tracks and analyzes memory access patterns including frequency, periodicity, and co-occurrence relationships Provides intelligent refresh recommendations based on staleness, importance, and access frequency Recommends adaptive compression levels based on access patterns and memory age Detects trend directions (increasing/stable/decreasing) and identifies peak access hours +653/-0
Formatting	2 files provider_groq.py Type annotation improvement for Groq provider                        src/hippocampai/adapters/provider_groq.py Adds explicit type annotation for result variable in generate method Improves code clarity by casting return value to str type +2/-1      temporal_analytics.py Type annotation for temporal analytics initialization        src/hippocampai/pipeline/temporal_analytics.py Adds return type annotation -> None to __init__ method +1/-1
Additional files	101 files .env.example +4/-0      ci.yml +1/-1      CHAT_DEMO_SUMMARY.md +0/-402  Dockerfile +4/-6      MONITORING_STATUS.md +0/-339  QUICKSTART.md +0/-117  README.md +54/-39  index.html +861/-0  chat_advanced.py +542/-0  deployment_readiness_check.py +415/-0  docker-compose.yml +55/-0    ADVANCED_COMPRESSION_GUIDE.md +721/-0  ARCHITECTURE.md +263/-1  AUTO_SUMMARIZATION_GUIDE.md +911/-0  CELERY_GUIDE.md +850/-0  CELERY_OPTIMIZATION_AND_TRACING.md +0/-1258 CELERY_USAGE_GUIDE.md +0/-524  CHANGELOG.md +160/-0  DEPLOYMENT_READINESS_REPORT.md +461/-0  GETTING_STARTED.md +18/-10  IMPLEMENTATION_COMPLETE.md +374/-0  LIBRARY_COMPLETE_REFERENCE.md +1/-1      MEMORY_CONFLICT_RESOLUTION_GUIDE.md +458/-0  MEMORY_MANAGEMENT.md +1001/-0 MONITORING_INTEGRATION_GUIDE.md +553/-0  QUICK_START_SIMPLE.md +370/-0  README.md +217/-80 REORGANIZATION_SUMMARY.md +0/-157  SAAS_API_COMPLETE_REFERENCE.md +0/-1435 SAAS_GUIDE.md +939/-0  UNIFIED_GUIDE.md +329/-0  WHY_WE_BUILT_HIPPOCAMPAI.md +645/-0  conflict_and_provenance_demo.py +434/-0  example_saas_control.py +263/-0  memory_health_and_observability_demo.py +478/-0  simple_api_mem0_style.py +63/-0    simple_api_session_style.py +77/-0    mypy.ini +17/-0    pyproject.toml +34/-5    requirements.txt +0/-34    init_postgres.sh +116/-0  verify_postgres.sh +125/-0  verify_postgres_docker.sh +124/-0  __init__.py +78/-0    provider_anthropic.py +2/-1      provider_ollama.py +3/-3      provider_openai.py +2/-1      admin_routes.py +506/-0  app.py +31/-12  celery_routes.py +14/-12  intelligence_routes.py +14/-12  middleware.py +302/-0  add_metadata_columns.sql +33/-0    auth_service.py +426/-0  models.py +172/-0  rate_limiter.py +188/-0  schema.sql +207/-0  local.py +8/-4      remote.py +46/-15  celery_app.pyi +31/-0    main.py +4/-4      client.py +3318/-75 client_extensions.py +24/-12  config.py +80/-0    embedder.py +5/-3      enhanced_client.py +30/-17  knowledge_graph.py +22/-8    memory_graph.py +10/-9    scheduler.py +5/-5      memory.py +1/-1      provenance.py +233/-0  search.py +1/-1      session.py +11/-7    __init__.py +58/-0    memory_tracker.py +394/-0  metrics.py +632/-0  prometheus_metrics.py +358/-0  storage.py +585/-0  manager.py +2/-2      optimized_client.py +73/-41  __init__.py +67/-0    auto_consolidation.py +447/-0  auto_summarization.py +727/-0  conflict_resolution.py +560/-0  entity_recognition.py +2/-2      errors.py +24/-0    fact_extraction.py +1/-1      importance_decay.py +525/-0  insights.py +1/-1      memory_lifecycle.py +375/-0  memory_merge.py +693/-0  memory_quality.py +605/-0  provenance_tracker.py +555/-0  relationship_mapping.py +2/-2      smart_updater.py +2/-2      summarization.py +3/-3      temporal.py +5/-5      temporal_enhancement.py +700/-0  bm25.py +5/-2      __init__.py +31/-0    Additional files not shown

---

[sourcery-ai]

We failed to fetch the diff for pull request #24

You can try again by commenting this pull request with @sourcery-ai review, or contact us for help.

[qodo-code-review]

PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
🔴	Cross-tenant data exposure Description: The GET /v1/monitoring/access-patterns/{user_id} endpoint allows fetching all access patterns for any user_id parameter without authentication or authorization, enabling cross-tenant data exposure; require authenticated user and verify ownership/tenant. async_app.py [1719-1736] Referred Code @app.get("/v1/monitoring/access-patterns/{user_id}") async def get_all_access_patterns(user_id: str) -> dict[str, Any]: """Get all access patterns for a user. Returns list of all memories with their access statistics. """ try: from hippocampai.monitoring.memory_tracker import get_tracker tracker = get_tracker() patterns = tracker.get_all_access_patterns(user_id=user_id) return { "user_id": user_id, "total_memories": len(patterns), "patterns": [p.model_dump() for p in patterns], } except Exception as e:
⚪	Insecure dependency on private client Description: The rate limiter is initialized with the internal Redis client via app.state.rate_limiter = RateLimiter(_redis_store.store._client), which exposes and depends on a private attribute (_client) and may bypass configured security wrappers or connection options, risking misuse of raw connections and potential SSRF/credential leakage if later reused elsewhere; provide a safe accessor or wrapper instead of directly using a private client reference. async_app.py [161-173] Referred Code db_pool = await asyncpg.create_pool( host=config.postgres_host, port=config.postgres_port, database=config.postgres_db, user=config.postgres_user, password=config.postgres_password, min_size=2, max_size=10, ) app.state.db_pool = db_pool # Store pool for direct access app.state.auth_service = AuthService(db_pool) app.state.rate_limiter = RateLimiter(_redis_store.store._client) # Use existing Redis app.state.user_auth_enabled = user_auth_enabled
	Unauthenticated metrics exposure Description: The /metrics endpoint exposes Prometheus metrics without authentication or IP restrictions when prometheus is available, potentially leaking sensitive operational data (endpoints, latencies, resource usage) to unauthenticated users; restrict access via auth, network policy, or separate port. async_app.py [385-393] Referred Code @app.get("/metrics") async def metrics() -> Response: """Prometheus metrics endpoint.""" if not PROMETHEUS_AVAILABLE: raise HTTPException( status_code=501, detail="Prometheus metrics not available - install prometheus-client" ) return Response(content=get_metrics(), media_type="text/plain")
	Silent auth middleware failure Description: Authentication middleware registration is attempted but failures are silently downgraded to a warning (logger.warning) and the app proceeds without auth, which can leave all new admin and observability endpoints unprotected in production; fail-fast or enforce explicit configuration to avoid running without authentication. async_app.py [218-226] Referred Code # Add authentication middleware (will lazy-load from app.state) try: from hippocampai.api.middleware import AuthMiddleware app.add_middleware(AuthMiddleware) logger.info("AuthMiddleware registered (will lazy-load services from app.state)") except Exception as e: logger.warning(f"Could not register AuthMiddleware: {e}")
	Unprotected admin endpoints Description: Admin routes are included without any visible authentication/authorization guard in this diff, creating a risk of exposing privileged operations publicly; ensure admin routes are protected by robust authz and not registered when auth is unavailable. async_app.py [247-253] Referred Code try: from hippocampai.api.admin_routes import router as admin_router app.include_router(admin_router) logger.info("Admin routes registered successfully") except ImportError as e: logger.warning(f"Could not load admin routes: {e}")
	Sensitive observability data exposure Description: Observability endpoints (/v1/observability/*) return detailed scoring breakdowns and internal metrics without any authentication or redaction, which can reveal model behavior, memory contents, and scoring internals to unauthorized users; require auth and consider redacting sensitive fields. async_app.py [932-1014] Referred Code @app.post("/v1/observability/explain") async def explain_retrieval_results( request: ExplainRetrievalRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Explain why specific memories were retrieved. Returns detailed explanations including: - Score breakdowns (vector, BM25, recency, importance) - Contributing factors - Human-readable explanations """ try: # First, recall memories results = await service.recall_memories( user_id=request.user_id, query=request.query, k=request.k, ) ... (clipped 62 lines)
	Unauthenticated profiling/heatmap Description: Heatmap and profiling endpoints compute and return aggregate access counts and precise timings without authentication, potentially leaking user behavior patterns and performance characteristics; enforce auth and rate limit to mitigate reconnaissance and privacy risks. async_app.py [1017-1188] Referred Code @app.post("/v1/observability/heatmap") async def generate_heatmap( request: AccessHeatmapRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Generate memory access heatmap. Returns access patterns including: - Access by hour/day - Hot and cold memories - Peak usage times """ try: # Get user memories with their access counts memories = await service.get_memories( user_id=request.user_id, limit=1000, # Reasonable limit for analysis ) # Calculate access patterns heatmap_data = { ... (clipped 151 lines)
	PII/content exposure via context window Description: The temporal context window endpoint returns raw memory texts and timestamps directly, which could leak sensitive content to unauthorized callers if auth fails or is disabled; ensure authorization checks per user and redact or minimize returned fields. async_app.py [1239-1270] Referred Code } except Exception as e: logger.error(f"Error forecasting patterns: {e}") raise HTTPException(status_code=500, detail=str(e)) @app.post("/v1/temporal/context-window") async def get_context_window( request: ContextWindowRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Get adaptive temporal context window. Auto-adjusts time range based on query and context type. """ try: # Get user memories sorted by relevance or recency memories = await service.get_memories(user_id=request.user_id, limit=1000) if request.context_type == "recent": relevant_memories = sorted(memories, key=lambda m: m.updated_at, reverse=True)[:10] ... (clipped 11 lines)
	Unauthenticated conflict enumeration Description: Conflict detection endpoint uses recall_memories on each memory text and returns similarity details without authentication, enabling content enumeration and model probing; protect with auth, rate limiting, and possibly cap response detail. async_app.py [1321-1365] Referred Code @app.post("/v1/conflicts/detect") async def detect_conflicts( request: ConflictDetectionRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Detect memory conflicts (contradictions, duplicates). Returns list of detected conflicts with details. """ try: # Get memories to check for conflicts if request.memory_id: memory = await service.get_memory(request.memory_id) if not memory: raise HTTPException(status_code=404, detail="Memory not found") memories_to_check = [memory] else: memories_to_check = await service.get_memories(user_id=request.user_id, limit=1000) conflicts = [] # Check for temporal conflicts ... (clipped 24 lines)
	Inadequate user authorization Description: Health score endpoint computes and returns duplicate ratios, retrieval success rates, and counts for a given user_id without verifying the caller is that user or authorized, enabling user data inference; add authorization checks and avoid accepting arbitrary user_id. async_app.py [1466-1515] Referred Code - Quality indicators - Issues detected - Recommendations """ try: # Calculate overall memory health score memories = await service.get_memories(user_id=request.user_id) if not memories: return { "user_id": request.user_id, "health_score": 1.0, # Perfect score for empty memory "details": { "memory_count": 0, "avg_age_days": 0, "duplicate_ratio": 0, "retrieval_success_rate": 1.0, }, } # Calculate metrics current_time = datetime.now(timezone.utc) ... (clipped 29 lines)
	Activity metadata leakage Description: Freshness calculation uses memory.created_at and updated_at directly and returns access_count and precise timing deltas which can reveal user activity; without auth and RBAC, this leaks sensitive behavioral metadata. async_app.py [1141-1160] Referred Code # Get the memory memory = await service.get_memory(memory_id=request.memory_id) if not memory: raise HTTPException(status_code=404, detail="Memory not found") if not memory: raise HTTPException(status_code=404, detail="Memory not found") # Calculate freshness reference = request.reference_date or datetime.now(timezone.utc) age = (reference - memory.created_at).total_seconds() last_access = (reference - memory.updated_at).total_seconds() return { "memory_id": memory.id, "age_days": age / (24 * 3600), "last_access_days": last_access / (24 * 3600), "access_count": memory.access_count, "freshness_score": 1.0 / (1.0 + age / (30 * 24 * 3600)), # 30-day half-life } except HTTPException:
	Unauthorized tier migration Description: Tier migration accepts arbitrary target_tier from client and performs migration with no explicit authorization checks shown, risking unauthorized data movement or DoS via repeated tiering; enforce admin-only access and validate requester permissions. async_app.py [1826-1845] Referred Code try: # Validate tier try: target_tier = MemoryTier(request.target_tier.lower()) except ValueError: raise HTTPException( status_code=400, detail=f"Invalid tier. Must be one of: {[t.value for t in MemoryTier]}", ) # Perform migration success = await service.migrate_memory_tier(request.memory_id, target_tier) if not success: raise HTTPException(status_code=404, detail="Memory not found") return { "memory_id": request.memory_id, "target_tier": target_tier.value, "migrated": True, }
	Partial security initialization Description: Database connection pool credentials are taken from configuration and established during app startup, and failures downgrade to warnings leaving app running without auth/rate limiting; this can lead to a partial-security startup state—fail fast when security dependencies (AuthService/RateLimiter) cannot be initialized in production. async_app.py [144-170] Referred Code try: import os import asyncpg from hippocampai.auth.auth_service import AuthService from hippocampai.auth.rate_limiter import RateLimiter # Check if user auth is enabled user_auth_enabled = os.getenv("USER_AUTH_ENABLED", "false").lower() == "true" # Ensure Redis is connected if _redis_store is not None: await _redis_store.connect() if _redis_store.store._client is None: raise RuntimeError("Redis client not initialized") db_pool = await asyncpg.create_pool( host=config.postgres_host, port=config.postgres_port, database=config.postgres_db, ... (clipped 6 lines)
	Computational side-channel risk Description: Diversity scoring uses embeddings and pairwise similarity for up to 100 memories; depending on embedder, this could allow timing/cost-based side-channels when invoked via API without rate limiting; add input caps and rate limiting around any endpoint exposing this computation. memory_health.py [530-563] Referred Code def _calculate_similarity_matrix(self, embeddings: np.ndarray) -> np.ndarray: """Calculate pairwise cosine similarity matrix.""" # Normalize embeddings norms = np.linalg.norm(embeddings, axis=1, keepdims=True) normalized = embeddings / np.maximum(norms, 1e-10) # Calculate cosine similarity similarity_matrix = np.dot(normalized, normalized.T) return cast(np.ndarray[Any, Any], similarity_matrix) def _calculate_freshness_score(self, memories: list[Memory], now: datetime) -> float: """Calculate freshness score (0-100) based on recency and access.""" if not memories: return 0.0 freshness_scores = [] for memory in memories: days_old = (now - memory.created_at).days days_since_access = (now - memory.updated_at).days if memory.updated_at else days_old ... (clipped 13 lines)
	Sensitive retrieval explanations Description: The explain retrieval endpoint returns raw memory text, per-score breakdowns, and IDs for top results which can expose sensitive content and internal scoring weights; ensure strong authentication and consider masking PII and internal metrics. async_app.py [905-967] Referred Code query: str user_id: str k: int = 5 class VisualizeScoresRequest(BaseModel): """Request to visualize similarity scores.""" query: str user_id: str top_k: int = 10 class AccessHeatmapRequest(BaseModel): """Request for memory access heatmap.""" user_id: str time_period_days: int = 30 ... (clipped 42 lines)
	Auth disabled fallback behavior Description: On exception initializing AuthService/RateLimiter, app.state.user_auth_enabled is forced to False and the app continues, enabling unauthenticated operation unexpectedly; enforce configuration that prevents startup without auth in secured environments. async_app.py [171-180] Referred Code app.state.auth_service = AuthService(db_pool) app.state.rate_limiter = RateLimiter(_redis_store.store._client) # Use existing Redis app.state.user_auth_enabled = user_auth_enabled logger.info(f"AuthService initialized (user_auth_enabled={user_auth_enabled})") logger.info("RateLimiter initialized successfully") except Exception as e: logger.warning(f"Could not initialize AuthService/RateLimiter: {e}") app.state.auth_service = None
Ticket Compliance
⚪	🎫 No ticket provided Create ticket/issue
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🟢	Generic: Meaningful Naming and Self-Documenting Code Objective: Ensure all identifiers clearly express their purpose and intent, making code self-documenting Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
🔴	Generic: Secure Error Handling Objective: To prevent the leakage of sensitive system information through error messages while providing sufficient detail for internal debugging. Status: Internal details exposed: Exceptions are surfaced to clients via HTTPException(detail=str(e)) and detailed dicts, potentially leaking internal info like conflict IDs, timestamps, or system messages. Referred Code error_detail = { "error": "Memory conflict detected", "message": str(e), "conflict_id": getattr(e, "conflict_id", None), "resolution_options": [s.value for s in ConflictResolutionStrategy], "help": ( "Set metadata.strategy to one of the resolution_options " "and metadata.auto_resolve=true to automatically resolve conflicts" ), } raise HTTPException(status_code=status_code, detail=error_detail) except MemoryNotFoundError as e: # Handle missing memory errors (e.g. duplicate not found) error_detail = { "error": "Memory not found", "message": str(e), "help": "The memory was detected as a duplicate but could not be found", } raise HTTPException(status_code=404, detail=error_detail) except HTTPException: ... (clipped 1378 lines) Learn more about managing compliance generic rules or creating your own custom rules
⚪	Generic: Comprehensive Audit Trails Objective: To create a detailed and reliable record of critical system actions for security analysis and compliance. Status: Missing audit logging: New critical endpoints (auth init, admin routes, conflict resolution, provenance, lifecycle changes) do not emit structured audit logs with user ID, action, and outcome, making reconstruction of actions uncertain. Referred Code try: import os import asyncpg from hippocampai.auth.auth_service import AuthService from hippocampai.auth.rate_limiter import RateLimiter # Check if user auth is enabled user_auth_enabled = os.getenv("USER_AUTH_ENABLED", "false").lower() == "true" # Ensure Redis is connected if _redis_store is not None: await _redis_store.connect() if _redis_store.store._client is None: raise RuntimeError("Redis client not initialized") db_pool = await asyncpg.create_pool( host=config.postgres_host, port=config.postgres_port, database=config.postgres_db, ... (clipped 1696 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Robust Error Handling and Edge Case Management Objective: Ensure comprehensive error handling that provides meaningful context and graceful degradation Status: Generic 500 errors: Multiple endpoints catch broad Exception and return HTTP 500 with str(e) without input validation or more specific handling for edge cases like missing inputs or None results. Referred Code try: # First, recall memories results = await service.recall_memories( user_id=request.user_id, query=request.query, k=request.k, ) # Return retrieval explanations return { "query": request.query, "results_count": len(results), "explanations": [ { "memory_id": result.memory.id, "text": result.memory.text, "score": result.score, "scores": result.breakdown, } for result in results ], ... (clipped 803 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Logging Practices Objective: To ensure logs are useful for debugging and auditing without exposing sensitive information like PII, PHI, or cardholder data. Status: Logging of errors: Error logs use logger.error with exc_info and f-strings including exception messages which may include sensitive data from requests or backend systems without demonstrated redaction. Referred Code except Exception as e: logger.error(f"Create memory failed: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) ... (clipped 1352 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Security-First Input Validation and Data Handling Objective: Ensure all data inputs are validated, sanitized, and handled securely to prevent vulnerabilities Status: Missing validation: New endpoints accept user-controlled fields (e.g., strategy, target_tier, query strings) and environment-based auth toggles without explicit sanitization or authorization checks shown in the diff. Referred Code conflict_strategy = request.metadata.get("strategy") if request.metadata else None if conflict_strategy: try: # Validate strategy is a valid enum value strategy = ConflictResolutionStrategy(conflict_strategy) service.conflict_resolution_strategy = strategy except ValueError: raise HTTPException( status_code=400, detail=( f"Invalid conflict resolution strategy: {conflict_strategy}. " f"Must be one of: {[s.value for s in ConflictResolutionStrategy]}" ), ) # Set auto-resolve based on request auto_resolve = request.metadata.get("auto_resolve", True) if request.metadata else True try: memory = await service.create_memory( text=request.text, ... (clipped 1424 lines) Learn more about managing compliance generic rules or creating your own custom rules
Update

Compliance status legend

🟢 - Fully Compliant
🟡 - Partial Compliant
🔴 - Not Compliant
⚪ - Requires Further Human Verification
🏷️ - Compliance label

[qodo-code-review]

PR Code Suggestions ✨

Latest suggestions up to 88a362b

Category	Suggestion	Impact
Security	✅ Prevent ambiguous unauthenticated headers Suggestion Impact: The commit implemented the suggested logic: it added a type annotation for headers, returned early with X-User-Auth: false when user_auth is false, and raised a RuntimeError if user_auth is true but api_key is missing, otherwise setting both headers. code diff: + headers: dict[str, str] = {} + + if not self.user_auth: headers["X-User-Auth"] = "false" - + return headers + + # Auth required + if not self.api_key: + # Fail fast to avoid sending misleading auth headers + raise RuntimeError( + "Authentication enabled but no API key provided. " + "Set api_key or HIPPOCAMPAI_API_KEY." + ) + + headers["X-User-Auth"] = "true" + headers["Authorization"] = f"Bearer {self.api_key}" return headers In _get_auth_headers, raise an error if user_auth is true but api_key is missing, instead of sending a misleading X-User-Auth: true header without an Authorization token. src/hippocampai/client.py [311-326] def _get_auth_headers(self) -> dict[str, str]: """Get authentication headers for API requests. Returns: Dict with authentication headers """ - headers = {} + headers: dict[str, str] = {} - if self.user_auth: - headers["X-User-Auth"] = "true" - if self.api_key: - headers["Authorization"] = f"Bearer {self.api_key}" - else: + if not self.user_auth: headers["X-User-Auth"] = "false" + return headers + # Auth required + if not self.api_key: + # Fail fast to avoid sending misleading auth headers + raise RuntimeError( + "Authentication enabled but no API key provided. " + "Set api_key or HIPPOCAMPAI_API_KEY." + ) + + headers["X-User-Auth"] = "true" + headers["Authorization"] = f"Bearer {self.api_key}" return headers [Suggestion processed] Suggestion importance[1-10]: 9 __ Why: This is a critical security suggestion that prevents sending an ambiguous authentication header (X-User-Auth: true) without a corresponding Authorization token, which could lead to incorrect security handling by downstream services.	High
	Restrict public metrics exposure Protect the /metrics endpoint with an authentication check for admin users to prevent public exposure of potentially sensitive operational data. src/hippocampai/api/async_app.py [385-392] @app.get("/metrics") -async def metrics() -> Response: +async def metrics(request: Request) -> Response: """Prometheus metrics endpoint.""" if not PROMETHEUS_AVAILABLE: raise HTTPException( status_code=501, detail="Prometheus metrics not available - install prometheus-client" ) + # Basic protection: require auth when user auth is enabled + user_auth_enabled = getattr(app.state, "user_auth_enabled", False) + if user_auth_enabled: + # Expect AuthMiddleware to set request.state.principal or similar + principal = getattr(request.state, "principal", None) + if not principal or not getattr(principal, "is_admin", False): + raise HTTPException(status_code=403, detail="Forbidden") return Response(content=get_metrics(), media_type="text/plain") [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a security risk in the newly added /metrics endpoint and proposes a valid authentication check to mitigate potential information disclosure.	Medium
	Enforce bounds to prevent DoS Add server-side validation for time_period_days in the /v1/observability/heatmap endpoint to prevent potential denial-of-service attacks from large analysis windows. src/hippocampai/api/async_app.py [1017-1051] @app.post("/v1/observability/heatmap") async def generate_heatmap( request: AccessHeatmapRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: - """Generate memory access heatmap. + """Generate memory access heatmap.""" + try: + # Enforce sane bounds + if request.time_period_days < 1 or request.time_period_days > 365: + raise HTTPException(status_code=400, detail="time_period_days must be between 1 and 365") - Returns access patterns including: - - Access by hour/day - - Hot and cold memories - - Peak usage times - """ - try: - # Get user memories with their access counts + # Bound the retrieval size to avoid heavy scans + MAX_ANALYSIS_LIMIT = 1000 memories = await service.get_memories( user_id=request.user_id, - limit=1000, # Reasonable limit for analysis + limit=MAX_ANALYSIS_LIMIT, ) - # Calculate access patterns heatmap_data = { "user_id": request.user_id, "time_period_days": request.time_period_days, "total_memories": len(memories), - "total_accesses": sum(m.access_count for m in memories), + "total_accesses": sum(getattr(m, "access_count", 0) for m in memories), "memory_access_counts": [ - {"id": m.id, "access_count": m.access_count} - for m in sorted(memories, key=lambda x: x.access_count, reverse=True) + {"id": m.id, "access_count": getattr(m, "access_count", 0)} + for m in sorted(memories, key=lambda x: getattr(x, "access_count", 0), reverse=True) ], } return heatmap_data + except HTTPException: + raise except Exception as e: logger.error(f"Error generating heatmap: {e}") raise HTTPException(status_code=500, detail=str(e)) [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: The suggestion correctly points out a potential DoS vector by not validating the time_period_days input, and the proposed fix is accurate and improves the endpoint's robustness.	Medium
Possible issue	✅ Make merge+delete operation fail-safe Suggestion Impact: The commit modified the merge branch to first compute payload/vector, upsert the merged memory, and only then delete the originals inside a try/except, logging errors and preserving originals on failure. code diff: + elif resolution.action == "merge": + # Delete originals, store merged atomically as best as possible + if resolution.updated_memory: + try: + # Prepare merged payload/vector first to reduce failure window + merged_collection = resolution.updated_memory.collection_name( + self.config.collection_facts, self.config.collection_prefs + ) + merged_vector = self.embedder.encode_single(resolution.updated_memory.text) + merged_payload = resolution.updated_memory.model_dump(mode="json") + + # Upsert merged memory first + self.qdrant.upsert( + collection_name=merged_collection, + id=resolution.updated_memory.id, + vector=merged_vector, + payload=merged_payload, + ) + + # Only delete originals after successful upsert + for mem_id in resolution.deleted_memory_ids: + self.delete_memory(mem_id, user_id) + + memory = resolution.updated_memory + logger.info( + f"Auto-resolve: Merged into memory {memory.id}, " + f"deleted {len(resolution.deleted_memory_ids)} memories" + ) + + # Track provenance for merged memory + self.provenance_tracker.track_merge( + memory, + [conflict.memory_1, conflict.memory_2], + merge_strategy=resolution_strategy, + ) + except Exception as merge_err: + logger.error(f"Auto-resolve merge failed, originals preserved: {merge_err}") + # Do not delete originals if upsert failed + # Optionally add conflict flags for manual review In the merge action of conflict resolution, upsert the new merged memory before deleting the original memories to prevent data loss if the upsert operation fails. src/hippocampai/client.py [638-656] elif resolution.action == "merge": - # Delete originals, store merged + # Delete originals, store merged atomically as best as possible if resolution.updated_memory: - for mem_id in resolution.deleted_memory_ids: - self.delete_memory(mem_id, user_id) + try: + # Prepare merged payload/vector first to reduce failure window + merged_collection = resolution.updated_memory.collection_name( + self.config.collection_facts, self.config.collection_prefs + ) + merged_vector = self.embedder.encode_single(resolution.updated_memory.text) + merged_payload = resolution.updated_memory.model_dump(mode="json") - # Store the merged memory - merged_collection = resolution.updated_memory.collection_name( - self.config.collection_facts, self.config.collection_prefs - ) - merged_vector = self.embedder.encode_single( - resolution.updated_memory.text - ) - self.qdrant.upsert( - collection_name=merged_collection, - id=resolution.updated_memory.id, - vector=merged_vector, - payload=resolution.updated_memory.model_dump(mode="json"), - ) + # Upsert merged memory first + self.qdrant.upsert( + collection_name=merged_collection, + id=resolution.updated_memory.id, + vector=merged_vector, + payload=merged_payload, + ) + # Only delete originals after successful upsert + for mem_id in resolution.deleted_memory_ids: + self.delete_memory(mem_id, user_id) + + memory = resolution.updated_memory + logger.info( + f"Auto-resolve: Merged into memory {memory.id}, " + f"deleted {len(resolution.deleted_memory_ids)} memories" + ) + + # Track provenance for merged memory + self.provenance_tracker.track_merge( + memory, + [conflict.memory_1, conflict.memory_2], + merge_strategy=resolution_strategy, + ) + except Exception as merge_err: + logger.error(f"Auto-resolve merge failed, originals preserved: {merge_err}") + # Do not delete originals if upsert failed + # Optionally add conflict flags for manual review + [Suggestion processed] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a data integrity risk where deleting memories before upserting the merged one could lead to data loss on failure. Reversing the order to upsert first makes the operation more robust.	Medium
	Harden Qdrant get response handling In get_memory, add more robust validation for the result from qdrant.get to ensure it is a dictionary containing a non-empty payload key before attempting to access it. src/hippocampai/client.py [1218-1253] def get_memory(self, memory_id: str) -> Optional[Memory]: """ Get a single memory by ID. - ... """ try: # Try facts collection first result = self.qdrant.get( collection_name=self.config.collection_facts, id=memory_id, ) - if result: + if result is not None and isinstance(result, dict) and "payload" in result and result["payload"]: return Memory(**result["payload"]) # Try prefs collection result = self.qdrant.get( collection_name=self.config.collection_prefs, id=memory_id, ) - if result: + if result is not None and isinstance(result, dict) and "payload" in result and result["payload"]: return Memory(**result["payload"]) return None except Exception as e: logger.error(f"Error retrieving memory {memory_id}: {e}") return None [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: This suggestion improves the robustness of the get_memory method by adding more explicit checks on the response from qdrant.get, preventing potential KeyError or TypeError if the API returns an unexpected structure.	Medium
	✅ Prevent side effects during reads Suggestion Impact: The commit changed access metric updates to run asynchronously with asyncio.create_task using a deep copy, avoiding mutation of the returned object, and updated Redis caching to use model_dump(mode="json"), matching the suggestion’s intent. code diff: + # Increment access count asynchronously without mutating the returned object + memory_copy = memory.model_copy(deep=True) + memory_copy.access_count += 1 + asyncio.create_task(self._update_memory_access(memory_copy)) return memory # Fall back to vector store @@ -427,12 +428,13 @@ memory = Memory(**payload) if track_access and self.enable_lifecycle_management: - # Update access count - memory.access_count += 1 - await self._update_memory_access(memory) - - # Cache in Redis - await self.redis.set_memory(memory_id, memory.model_dump()) + # Update access metrics asynchronously using a copy + memory_copy = memory.model_copy(deep=True) + memory_copy.access_count += 1 + asyncio.create_task(self._update_memory_access(memory_copy)) + + # Cache in Redis without in-place mutations + await self.redis.set_memory(memory_id, memory.model_dump(mode="json")) Decouple read and write operations in get_memory by updating access metrics asynchronously. Use asyncio.create_task to prevent blocking the read path and avoid potential race conditions. src/hippocampai/services/memory_service.py [410-438] # Try Redis cache first cached = await self.redis.get_memory(memory_id) if cached: memory = Memory(**cached) if track_access and self.enable_lifecycle_management: - # Update access count and track in background - memory.access_count += 1 - await self._update_memory_access(memory) + # Increment access count asynchronously without mutating the returned object + asyncio.create_task(self._update_memory_access(memory.copy(deep=True))) return memory # Fall back to vector store # Try both collections for collection in [self.qdrant.collection_facts, self.qdrant.collection_prefs]: try: point = self.qdrant.get(collection_name=collection, id=memory_id) if point: payload = point["payload"] memory = Memory(**payload) if track_access and self.enable_lifecycle_management: - # Update access count - memory.access_count += 1 - await self._update_memory_access(memory) + # Update access metrics asynchronously using a copy + asyncio.create_task(self._update_memory_access(memory.copy(deep=True))) - # Cache in Redis - await self.redis.set_memory(memory_id, memory.model_dump()) + # Cache in Redis without in-place mutations + await self.redis.set_memory(memory_id, memory.model_dump(mode="json")) return memory except Exception as e: logger.debug(f"Memory {memory_id} not found in {collection}: {e}") [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: This is a good suggestion that improves performance and concurrent correctness by moving write operations out of the read path using asyncio.create_task, which is a valid and beneficial architectural change.	Medium
General	Avoid per-call metric imports Move Prometheus metric imports to the module level to avoid repeated import overhead on every function call. Use a global flag to conditionally execute metric tracking logic, improving performance. src/hippocampai/services/memory_service.py [380-394] -# Track Prometheus metrics -try: - from hippocampai.monitoring.prometheus_metrics import ( - memories_created_total, - memory_operations_total, - memory_size_bytes, - ) +# at module top (once): +# try: +# from hippocampai.monitoring.prometheus_metrics import ( +# memories_created_total, +# memory_operations_total, +# memory_size_bytes, +# search_requests_total, +# search_results_count, +# ) +# _METRICS_ENABLED = True +# except Exception: +# _METRICS_ENABLED = False - memory_operations_total.labels(operation="create", status="success").inc() - memories_created_total.labels(memory_type=memory.type.value).inc() - memory_size_bytes.labels(memory_type=memory.type.value).observe( - len(text.encode("utf-8")) - ) -except Exception as metrics_err: - logger.warning(f"Failed to track Prometheus metrics: {metrics_err}") +# ... inside create_memory after successful store/cache: +if '_METRICS_ENABLED' in globals() and _METRICS_ENABLED: + try: + memory_operations_total.labels(operation="create", status="success").inc() + memories_created_total.labels(memory_type=memory.type.value).inc() + memory_size_bytes.labels(memory_type=memory.type.value).observe( + len(text.encode("utf-8")) + ) + except Exception as metrics_err: + logger.debug(f"Metrics emit failed: {metrics_err}") [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies that local imports in a frequently called function are a performance anti-pattern and proposes a standard solution of moving the import to the module level, which improves performance and code structure.	Low
Update

---

Previous suggestions

✅ Suggestions up to commit e92d133

Category	Suggestion	Impact
High-level	PR introduces an entire ecosystem monolithically The PR is excessively large and introduces a monolithic, custom framework for features like observability and scheduling. It should instead integrate mature, standard tools like OpenTelemetry and Celery to reduce complexity and improve maintainability. Examples: src/hippocampai/client.py [227-292] from hippocampai.pipeline.conflict_resolution import ( ConflictResolutionStrategy, MemoryConflictResolver, ) from hippocampai.pipeline.provenance_tracker import ProvenanceTracker self.conflict_resolver = MemoryConflictResolver( embedder=self.embedder, llm=self.llm, default_strategy=ConflictResolutionStrategy.TEMPORAL, ... (clipped 56 lines) src/hippocampai/services/memory_service.py [135-164] Solution Walkthrough: Before: class MemoryClient: def __init__(self, enable_scheduler=True, enable_telemetry=True): # Instantiates many custom, in-process components directly self.conflict_resolver = MemoryConflictResolver(...) self.lifecycle_manager = MemoryLifecycleManager(...) self.health_monitor = MemoryHealthMonitor(...) self.observability = MemoryObservabilityMonitor(...) self.scheduler = CustomScheduler() if enable_scheduler else None # ... and many more def remember(self, text, auto_resolve_conflicts=True, ...): # ... # Tightly coupled logic for conflict resolution if auto_resolve_conflicts: conflicts = self.conflict_resolver.detect_conflicts(...) # ... complex resolution logic embedded in the method def start_scheduler(self): # Starts a custom, in-process scheduler if self.scheduler: self.scheduler.start() After: # In a separate worker/service for background tasks (e.g., using Celery) from celery import Celery app = Celery('tasks', broker='redis://localhost') @app.task def resolve_conflicts_task(user_id): # ... logic for conflict resolution @app.on_after_configure.connect def setup_periodic_tasks(sender, **kwargs): # Use Celery Beat for scheduling sender.add_periodic_task(3600.0, maintenance_task.s('all_users')) # In the main application class MemoryClient: def __init__(self): # Use standard observability tools self.tracer = OpenTelemetry.get_tracer(__name__) # ... other initializations are simplified def remember(self, text, ...): with self.tracer.start_as_current_span("remember"): # ... core logic to store memory # Offload complex tasks to a background worker resolve_conflicts_task.delay(user_id) Suggestion importance[1-10]: 9 __ Why: This is a critical architectural suggestion that correctly identifies the immense scope and risk of introducing a large, custom, and tightly-coupled framework, proposing a more maintainable and scalable approach using standard external tools.	High
Possible issue	✅ Prevent dangerous recursive conflict resolution Suggestion Impact: The commit replaces self.remember with self.add and passes auto_resolve_conflicts=False when storing the merged memory, directly addressing the recursion risk. code diff: @@ -3652,11 +3652,13 @@ elif resolution.action == "merge": # Create merged memory, delete originals if resolution.updated_memory: - self.remember( + # Use `add` with auto_resolve_conflicts=False to prevent recursion + self.add( text=resolution.updated_memory.text, user_id=user_id, type=resolution.updated_memory.type.value, importance=resolution.updated_memory.importance, + auto_resolve_conflicts=False, ) for mem_id in resolution.deleted_memory_ids: self.delete_memory(mem_id) Prevent potential infinite recursion in auto_resolve_conflicts by calling self.add with auto_resolve_conflicts=False when storing a merged memory. src/hippocampai/client.py [3652-3665] elif resolution.action == "merge": # Create merged memory, delete originals if resolution.updated_memory: - self.remember( + # Use `add` with auto_resolve_conflicts=False to prevent recursion + self.add( text=resolution.updated_memory.text, user_id=user_id, type=resolution.updated_memory.type.value, importance=resolution.updated_memory.importance, + auto_resolve_conflicts=False, ) for mem_id in resolution.deleted_memory_ids: self.delete_memory(mem_id) deleted_ids.append(mem_id) merged_count += 1 resolved_count += 1 [Suggestion processed] Suggestion importance[1-10]: 9 __ Why: This suggestion correctly identifies a potential infinite recursion bug in the newly added auto_resolve_conflicts method and provides a correct fix to prevent it.	High
	✅ Fix race condition and memory leak Suggestion Impact: The commit replaced manual lock creation with MEMORY_LOCKS.setdefault, wrapped the critical section in a try/finally, and popped the lock in finally. It also adjusted logging/messages and passed track_access=False as suggested. code diff: + # Atomically get or create a lock for this memory ID + lock = MEMORY_LOCKS.setdefault(memory_id, asyncio.Lock()) + + try: + async with lock: + # Get memory to check ownership and determine collection + memory = await self.get_memory(memory_id, track_access=False) + if not memory: + return False + + # Check authorization + if user_id and memory.user_id != user_id: + logger.warning( + f"Unauthorized delete attempt for memory {memory_id} by user {user_id}" + ) + return False + + # Delete from vector store + collection = memory.collection_name( + self.qdrant.collection_facts, self.qdrant.collection_prefs + ) + self.qdrant.delete(collection_name=collection, ids=[memory_id]) + + # Delete from Redis cache + await self.redis.delete_memory(memory_id) + + logger.info(f"Deleted memory {memory_id}") + return True + finally: + # Clean up the lock to prevent memory leak + MEMORY_LOCKS.pop(memory_id, None) Fix a race condition and memory leak in the MEMORY_LOCKS implementation by using dict.setdefault() for atomic lock creation and a try...finally block to ensure locks are removed after use. src/hippocampai/services/memory_service.py [517-520] # Memory locks to prevent concurrent modifications MEMORY_LOCKS = {} class MemoryManagementService: ... async def delete_memory(self, memory_id: str, user_id: Optional[str] = None) -> bool: """ ... """ - # Get or create lock for this memory ID - if memory_id not in MEMORY_LOCKS: - MEMORY_LOCKS[memory_id] = asyncio.Lock() + # Atomically get or create a lock for this memory ID + lock = MEMORY_LOCKS.setdefault(memory_id, asyncio.Lock()) - async with MEMORY_LOCKS[memory_id]: - # Get memory to check ownership and determine collection - memory = await self.get_memory(memory_id) - if not memory: - return False -... + try: + async with lock: + # Get memory to check ownership and determine collection + memory = await self.get_memory(memory_id, track_access=False) + if not memory: + return False + # Check authorization + if user_id and memory.user_id != user_id: + logger.warning( + f"Unauthorized delete attempt for memory {memory_id} by user {user_id}" + ) + return False + + # Delete from vector store + collection = memory.collection_name( + self.qdrant.collection_facts, self.qdrant.collection_prefs + ) + self.qdrant.delete(collection_name=collection, ids=[memory_id]) + + # Delete from Redis cache + await self.redis.delete_memory(memory_id) + + logger.info(f"Deleted memory {memory_id}") + return True + finally: + # Clean up the lock to prevent memory leak + MEMORY_LOCKS.pop(memory_id, None) + Suggestion importance[1-10]: 9 __ Why: The suggestion correctly identifies and fixes a critical race condition and a memory leak in the locking mechanism, which are significant issues for a long-running, concurrent service.	High
	✅ Improve conflict detection performance and accuracy Suggestion Impact: The commit replaced fetching all memories via get_memories with a semantic recall call and adjusted the construction of other_memories accordingly. code diff: - # Refetch memories to include the newly stored one - all_memories = self.get_memories(user_id, limit=100) - # Remove the newly stored memory from the list to compare against others - other_memories = [m for m in all_memories if m.id != memory.id] + # Find semantically similar memories to check for conflicts + # This is more efficient and accurate than fetching all memories. + recalled_results = self.recall(query=memory.text, user_id=user_id, k=10) + other_memories = [res.memory for res in recalled_results if res.memory.id != memory.id] To improve conflict detection, replace the inefficient get_memories call with recall to fetch a smaller, more relevant set of semantically similar memories. src/hippocampai/client.py [588-601] # AUTO-RESOLVE CONFLICTS (Mem0-style) if auto_resolve_conflicts: self.telemetry.add_event(trace_id, "auto_conflict_resolution", status="in_progress") - # Refetch memories to include the newly stored one - all_memories = self.get_memories(user_id, limit=100) - # Remove the newly stored memory from the list to compare against others - other_memories = [m for m in all_memories if m.id != memory.id] + # Find semantically similar memories to check for conflicts + # This is more efficient and accurate than fetching all memories. + recalled_results = self.recall(query=memory.text, user_id=user_id, k=10) + other_memories = [res.memory for res in recalled_results if res.memory.id != memory.id] # Detect conflicts with the newly stored memory # Use LLM if available for better conflict detection conflicts = self.conflict_resolver.detect_conflicts( memory, other_memories, check_llm=(self.llm is not None) ) [Suggestion processed] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a significant performance and accuracy issue in the new conflict resolution logic and proposes a much more efficient and effective approach using semantic search.	Medium
	✅ Fix missing argument in function Suggestion Impact: The commit updated resolve_conflict to pass user_id to service.delete_memory in the "merge" strategy, and similarly added user_id for the "keep_latest" path as well. code diff: @@ -1409,7 +1451,7 @@ "conflict_type": conflict["conflict_type"], }, ) - await service.delete_memory(memory2_details["id"]) + await service.delete_memory(memory2_details["id"], user_id=request.user_id) elif request.strategy == "keep_latest": # Keep the more recent memory @@ -1420,10 +1462,14 @@ mem1_time = datetime.fromisoformat(memory1_details["created_at"]) mem2_time = datetime.fromisoformat(memory2_details["created_at"]) - to_delete = memory1_details["id"] if mem2_time > mem1_time else memory2_details["id"] - to_keep = memory2_details["id"] if mem2_time > mem1_time else memory1_details["id"] - - await service.delete_memory(to_delete) + if mem2_time > mem1_time: + to_delete = memory1_details["id"] + to_keep = memory2_details["id"] + else: # Keep memory1 if newer or timestamps are equal + to_delete = memory2_details["id"] + to_keep = memory1_details["id"] + + await service.delete_memory(to_delete, user_id=request.user_id) # Update metadata on kept memory await service.update_memory( Fix a bug in resolve_conflict by passing the user_id from the request to the service.delete_memory call within the "merge" strategy to prevent a TypeError. src/hippocampai/api/async_app.py [1370-1413] @app.post("/v1/conflicts/resolve") async def resolve_conflict( request: ConflictResolutionRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Resolve a detected memory conflict. Applies specified resolution strategy. """ try: # Get the conflict details conflicts = await service.detect_memory_conflicts(user_id=request.user_id) if not conflicts: raise HTTPException(status_code=404, detail="No conflicts found") # Find the specific conflict conflict = None for c in conflicts: if c["conflict_id"] == request.conflict_id: conflict = c break if not conflict: raise HTTPException(status_code=404, detail="Conflict not found") # Apply resolution based on strategy if request.strategy == "merge": # Get the conflicting memories memory1_details = conflict["memory_1"] memory2_details = conflict["memory_2"] # Merge their text merged_text = f"{memory1_details['text']}\n---\n{memory2_details['text']}" await service.update_memory( memory_id=memory1_details["id"], text=merged_text, metadata={ "merged_from": memory2_details["id"], "merge_strategy": request.strategy, "conflict_type": conflict["conflict_type"], }, ) - await service.delete_memory(memory2_details["id"]) + await service.delete_memory(memory2_details["id"], user_id=request.user_id) elif request.strategy == "keep_latest": ... Suggestion importance[1-10]: 8 __ Why: This suggestion correctly identifies a bug that would cause a TypeError at runtime due to a missing user_id argument in the service.delete_memory call, which is critical for the function's correctness.	Medium
	✅ Fix missing argument and edge-case Suggestion Impact: The commit updated the keep_latest branch to include user_id when calling service.delete_memory and replaced the ternary with explicit if/else logic that keeps memory1 when timestamps are equal. code diff: @@ -1409,7 +1451,7 @@ "conflict_type": conflict["conflict_type"], }, ) - await service.delete_memory(memory2_details["id"]) + await service.delete_memory(memory2_details["id"], user_id=request.user_id) elif request.strategy == "keep_latest": # Keep the more recent memory @@ -1420,10 +1462,14 @@ mem1_time = datetime.fromisoformat(memory1_details["created_at"]) mem2_time = datetime.fromisoformat(memory2_details["created_at"]) - to_delete = memory1_details["id"] if mem2_time > mem1_time else memory2_details["id"] - to_keep = memory2_details["id"] if mem2_time > mem1_time else memory1_details["id"] - - await service.delete_memory(to_delete) + if mem2_time > mem1_time: + to_delete = memory1_details["id"] + to_keep = memory2_details["id"] + else: # Keep memory1 if newer or timestamps are equal + to_delete = memory2_details["id"] + to_keep = memory1_details["id"] + + await service.delete_memory(to_delete, user_id=request.user_id) # Update metadata on kept memory await service.update_memory( memory_id=to_keep, In resolve_conflict under the "keep_latest" strategy, pass the user_id to service.delete_memory to prevent a TypeError and add logic to handle cases where memory timestamps are identical. src/hippocampai/api/async_app.py [1414-1435] @app.post("/v1/conflicts/resolve") async def resolve_conflict( ... elif request.strategy == "keep_latest": # Keep the more recent memory memory1_details = conflict["memory_1"] memory2_details = conflict["memory_2"] # Parse timestamps mem1_time = datetime.fromisoformat(memory1_details["created_at"]) mem2_time = datetime.fromisoformat(memory2_details["created_at"]) - to_delete = memory1_details["id"] if mem2_time > mem1_time else memory2_details["id"] - to_keep = memory2_details["id"] if mem2_time > mem1_time else memory1_details["id"] + if mem2_time > mem1_time: + to_delete = memory1_details["id"] + to_keep = memory2_details["id"] + else: # Keep memory1 if newer or timestamps are equal + to_delete = memory2_details["id"] + to_keep = memory1_details["id"] - await service.delete_memory(to_delete) + await service.delete_memory(to_delete, user_id=request.user_id) # Update metadata on kept memory await service.update_memory( memory_id=to_keep, metadata={ "conflict_resolved": True, "resolution_strategy": request.strategy, "deleted_memory_id": to_delete, }, ) ... Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a critical bug where a missing user_id in the service.delete_memory call would cause a TypeError. It also correctly points out an unhandled edge case for equal timestamps, improving the logic's robustness.	Medium
	✅ Fix freshness calculation and redundancy Suggestion Impact: The commit removed the redundant second "if not memory" check and changed the freshness calculations to use abs() for age and last_access. Additional security and formatting changes were also made. code diff: # Get the memory - memory = await service.get_memory(memory_id=request.memory_id) + memory = await service.get_memory(memory_id=request.memory_id, track_access=False) if not memory: raise HTTPException(status_code=404, detail="Memory not found") - if not memory: - raise HTTPException(status_code=404, detail="Memory not found") + + # Authorization: Verify requester owns the memory + if memory.user_id != request.user_id: + logger.warning( + f"Unauthorized freshness access attempt: user {request.user_id} " + f"tried to access memory {request.memory_id} owned by {memory.user_id}" + ) + raise HTTPException( + status_code=403, + detail="Forbidden: You don't have permission to access this memory", + ) # Calculate freshness reference = request.reference_date or datetime.now(timezone.utc) - age = (reference - memory.created_at).total_seconds() - last_access = (reference - memory.updated_at).total_seconds() + age = abs((reference - memory.created_at).total_seconds()) + last_access = abs((reference - memory.updated_at).total_seconds()) + + # Sanitize access_count to prevent precise behavioral tracking + # Round to ranges instead of exact counts + sanitized_access_count = "low" if memory.access_count < 10 else ( + "medium" if memory.access_count < 50 else "high" + ) return { "memory_id": memory.id, - "age_days": age / (24 * 3600), - "last_access_days": last_access / (24 * 3600), - "access_count": memory.access_count, - "freshness_score": 1.0 / (1.0 + age / (30 * 24 * 3600)), # 30-day half-life + "age_days": round(age / (24 * 3600), 1), # Round to 1 decimal + "last_access_days": round(last_access / (24 * 3600), 1), # Round to 1 decimal + "access_frequency": sanitized_access_count, # Categorical instead of exact + "freshness_score": round(1.0 / (1.0 + age / (30 * 24 * 3600)), 3), } In calculate_freshness, remove the redundant if not memory check and use abs() when calculating age and last_access to prevent negative time differences from a past reference_date. src/hippocampai/api/async_app.py [1127-1165] @app.post("/v1/temporal/freshness") async def calculate_freshness( request: FreshnessScoreRequest, service: MemoryManagementService = Depends(get_service), ) -> dict[str, Any]: """Calculate memory freshness score. ... """ try: # Get the memory memory = await service.get_memory(memory_id=request.memory_id) if not memory: raise HTTPException(status_code=404, detail="Memory not found") - if not memory: - raise HTTPException(status_code=404, detail="Memory not found") # Calculate freshness reference = request.reference_date or datetime.now(timezone.utc) - age = (reference - memory.created_at).total_seconds() - last_access = (reference - memory.updated_at).total_seconds() + age = abs((reference - memory.created_at).total_seconds()) + last_access = abs((reference - memory.updated_at).total_seconds()) return { "memory_id": memory.id, "age_days": age / (24 * 3600), "last_access_days": last_access / (24 * 3600), "access_count": memory.access_count, "freshness_score": 1.0 / (1.0 + age / (30 * 24 * 3600)), # 30-day half-life } except HTTPException: raise except Exception as e: logger.error(f"Error calculating freshness: {e}") raise HTTPException(status_code=500, detail=str(e)) Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies a redundant code block and a potential bug in the calculate_freshness function where a past reference_date could lead to negative time values and incorrect scores. Using abs() is a valid fix for this bug.	Low
General	Improve performance of batch memory deletion Improve the performance of deleting conflicting memories by executing the deletion tasks concurrently using asyncio.gather instead of sequentially in a for loop. src/hippocampai/services/memory_service.py [351-356] # Delete conflicting memories in a single batch -for mem_id in memories_to_delete: +if memories_to_delete: try: - await self.delete_memory(mem_id) - logger.info(f"Deleted conflicting memory: {mem_id}") + # Assuming a batch delete method exists or can be added. + # This is more efficient than deleting one by one. + # For Qdrant, we can batch deletions. + # We'll need to know which collection each memory belongs to. + # For simplicity here, we'll assume they are all facts, but a real implementation + # would need to group by collection. + + # This is a simplified batch delete. A full implementation would group IDs by collection. + # For now, we'll stick to the original loop but with a note on improvement. + # A better approach would be a new `batch_delete` method. + # As we can't add a new method, we'll keep the loop but it's a known issue. + + # Let's assume we can at least group them. + memories_to_delete_list = list(memories_to_delete) + + # This requires fetching memories again to know their collection, which is inefficient. + # The best fix is to pass memory objects, not just IDs. + # Given the constraints, we'll stick to the original loop but acknowledge its inefficiency. + # The original code is kept as a full refactor is complex. + # However, a key improvement is to avoid re-fetching. + + # The conflict resolution logic should provide memory objects to delete, not just IDs. + # Assuming `conflicts` provides full memory objects: + + # This suggestion cannot be fully implemented without changing other parts of the code. + # The most direct improvement to the existing snippet is to use asyncio.gather. + + delete_tasks = [self.delete_memory(mem_id) for mem_id in memories_to_delete] + results = await asyncio.gather(*delete_tasks, return_exceptions=True) + + for mem_id, result in zip(memories_to_delete, results): + if isinstance(result, Exception): + logger.warning(f"Failed to delete memory {mem_id}: {result}") + else: + logger.info(f"Deleted conflicting memory: {mem_id}") + except Exception as e: - logger.warning(f"Failed to delete memory {mem_id}: {e}") + logger.error(f"Batch deletion of conflicting memories failed: {e}") Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies a performance issue with deleting memories one by one in a loop and proposes a valid improvement using asyncio.gather to run deletions concurrently, reducing total execution time.	Low