adai/MemoryBear
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(implicit memory): upgrade pydantic v2 compatibility and confidence level handling

Browse Source 
- Replace deprecated `.dict()` with `.model_dump(mode='json')` for pydantic v2 compatibility
- Convert confidence level from enum-based strings to numerical values (0-100 scale)
- Add confidence level mapping in controller (high: 85, medium: 50, low: 20)
- Update dimension analyzer to handle both string and numeric confidence inputs
- Refactor habit analyzer confidence level validation logic
- Remove ConfidenceLevel enum import and replace with integer-based approach
- Update memory config validators for numerical confidence level support
- Ensure all implicit memory schemas use model_dump for serialization
- Improve type consistency across memory analytics modules
This commit is contained in:
Ke Sun
2026-01-08 17:50:01 +08:00
parent e05f33b286
commit 7167c2002f
7 changed files with 195 additions and 105 deletions
Download Patch File Download Diff File Expand all files Collapse all files

56
api/app/core/validators/memory_config_validators.py
View File

@@ -64,6 +64,11 @@ def validate_model_exists_and_active(
) -> tuple[str, bool]:
"""Validate that a model exists and is active.
This function performs tenant-aware model validation with detailed error messages:
- If model doesn't exist at all: "Model not found"
- If model exists but belongs to different tenant: "Model belongs to different tenant" with details
- If model exists and accessible but inactive: "Model is inactive"
Args:
model_id: Model UUID to validate
model_type: Type of model ("llm", "embedding", "rerank")
@@ -76,7 +81,7 @@ def validate_model_exists_and_active(
Tuple of (model_name, is_active)
Raises:
ModelNotFoundError: If model does not exist
ModelNotFoundError: If model does not exist or belongs to different tenant
ModelInactiveError: If model exists but is inactive
"""
from app.repositories.model_repository import ModelConfigRepository
@@ -84,21 +89,48 @@ def validate_model_exists_and_active(
start_time = time.time()
try:
# First check if model exists at all (without tenant filtering)
model_without_tenant = ModelConfigRepository.get_by_id(db, model_id, tenant_id=None)
# Then check with tenant filtering
model = ModelConfigRepository.get_by_id(db, model_id, tenant_id)
elapsed_ms = (time.time() - start_time) * 1000
if not model:
logger.warning(
"Model not found",
extra={"model_id": str(model_id), "model_type": model_type, "elapsed_ms": elapsed_ms}
)
raise ModelNotFoundError(
model_id=model_id,
model_type=model_type,
config_id=config_id,
workspace_id=workspace_id,
message=f"{model_type.title()} model {model_id} not found"
)
if model_without_tenant:
# Model exists but belongs to different tenant
logger.warning(
"Model belongs to different tenant",
extra={
"model_id": str(model_id),
"model_type": model_type,
"model_name": model_without_tenant.name,
"model_tenant_id": str(model_without_tenant.tenant_id),
"requested_tenant_id": str(tenant_id),
"is_public": model_without_tenant.is_public,
"elapsed_ms": elapsed_ms
}
)
raise ModelNotFoundError(
model_id=model_id,
model_type=model_type,
config_id=config_id,
workspace_id=workspace_id,
message=f"{model_type.title()} model {model_id} ({model_without_tenant.name}) belongs to a different tenant (model tenant: {model_without_tenant.tenant_id}, workspace tenant: {tenant_id}). The model is not public and cannot be accessed from this workspace."
)
else:
# Model doesn't exist at all
logger.warning(
"Model not found",
extra={"model_id": str(model_id), "model_type": model_type, "elapsed_ms": elapsed_ms}
)
raise ModelNotFoundError(
model_id=model_id,
model_type=model_type,
config_id=config_id,
workspace_id=workspace_id,
message=f"{model_type.title()} model {model_id} not found"
)
if not model.is_active:
logger.warning(