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fix(backend): use typed Prisma input classes for strict type checking

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Replace dict literals with properly typed Prisma input classes to fix
pyright type errors. This ensures type safety when calling Prisma create,
upsert, and update operations.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Swifty
2025-12-09 16:42:38 +01:00
parent 0b25c643a4
commit 9e67f0bf45
17 changed files with 324 additions and 292 deletions
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24
autogpt_platform/backend/backend/data/api_key.py
View File

@@ -6,7 +6,7 @@ from typing import Optional
from autogpt_libs.api_key.keysmith import APIKeySmith
from prisma.enums import APIKeyPermission, APIKeyStatus
from prisma.models import APIKey as PrismaAPIKey
from prisma.types import APIKeyWhereUniqueInput
from prisma.types import APIKeyCreateInput, APIKeyWhereUniqueInput
from pydantic import BaseModel, Field
from backend.data.includes import MAX_USER_API_KEYS_FETCH
@@ -83,17 +83,17 @@ async def create_api_key(
generated_key = keysmith.generate_key()
saved_key_obj = await PrismaAPIKey.prisma().create(
data={
"id": str(uuid.uuid4()),
"name": name,
"head": generated_key.head,
"tail": generated_key.tail,
"hash": generated_key.hash,
"salt": generated_key.salt,
"permissions": [p for p in permissions],
"description": description,
"userId": user_id,
}
data=APIKeyCreateInput(
id=str(uuid.uuid4()),
name=name,
head=generated_key.head,
tail=generated_key.tail,
hash=generated_key.hash,
salt=generated_key.salt,
permissions=permissions,
description=description,
userId=user_id,
)
)
return APIKeyInfo.from_db(saved_key_obj), generated_key.key

16
autogpt_platform/backend/backend/data/credit_ceiling_test.py
View File

@@ -11,6 +11,7 @@ import pytest
from prisma.enums import CreditTransactionType
from prisma.errors import UniqueViolationError
from prisma.models import CreditTransaction, User, UserBalance
from prisma.types import UserBalanceCreateInput, UserBalanceUpsertInput, UserCreateInput
from backend.data.credit import UserCredit
from backend.util.json import SafeJson
@@ -21,11 +22,11 @@ async def create_test_user(user_id: str) -> None:
"""Create a test user for ceiling tests."""
try:
await User.prisma().create(
data={
"id": user_id,
"email": f"test-{user_id}@example.com",
"name": f"Test User {user_id[:8]}",
}
data=UserCreateInput(
id=user_id,
email=f"test-{user_id}@example.com",
name=f"Test User {user_id[:8]}",
)
)
except UniqueViolationError:
# User already exists, continue
@@ -33,7 +34,10 @@ async def create_test_user(user_id: str) -> None:
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=0),
update={"balance": 0},
),
)

24
autogpt_platform/backend/backend/data/credit_concurrency_test.py
View File

@@ -14,6 +14,7 @@ import pytest
from prisma.enums import CreditTransactionType
from prisma.errors import UniqueViolationError
from prisma.models import CreditTransaction, User, UserBalance
from prisma.types import UserBalanceCreateInput, UserBalanceUpsertInput, UserCreateInput
from backend.data.credit import POSTGRES_INT_MAX, UsageTransactionMetadata, UserCredit
from backend.util.exceptions import InsufficientBalanceError
@@ -28,11 +29,11 @@ async def create_test_user(user_id: str) -> None:
"""Create a test user with initial balance."""
try:
await User.prisma().create(
data={
"id": user_id,
"email": f"test-{user_id}@example.com",
"name": f"Test User {user_id[:8]}",
}
data=UserCreateInput(
id=user_id,
email=f"test-{user_id}@example.com",
name=f"Test User {user_id[:8]}",
)
)
except UniqueViolationError:
# User already exists, continue
@@ -41,7 +42,10 @@ async def create_test_user(user_id: str) -> None:
# Ensure UserBalance record exists
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=0),
update={"balance": 0},
),
)
@@ -342,10 +346,10 @@ async def test_integer_overflow_protection(server: SpinTestServer):
# First, set balance near max
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": max_int - 100},
"update": {"balance": max_int - 100},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=max_int - 100),
update={"balance": max_int - 100},
),
)
# Try to add more than possible - should clamp to POSTGRES_INT_MAX

13
autogpt_platform/backend/backend/data/credit_integration_test.py
View File

@@ -8,6 +8,7 @@ which would have caught the CreditTransactionType enum casting bug.
import pytest
from prisma.enums import CreditTransactionType
from prisma.models import CreditTransaction, User, UserBalance
from prisma.types import UserCreateInput
from backend.data.credit import (
AutoTopUpConfig,
@@ -29,12 +30,12 @@ async def cleanup_test_user():
# Create the user first
try:
await User.prisma().create(
data={
"id": user_id,
"email": f"test-{user_id}@example.com",
"topUpConfig": SafeJson({}),
"timezone": "UTC",
}
data=UserCreateInput(
id=user_id,
email=f"test-{user_id}@example.com",
topUpConfig=SafeJson({}),
timezone="UTC",
)
)
except Exception:
# User might already exist, that's fine

66
autogpt_platform/backend/backend/data/credit_refund_test.py
View File

@@ -12,6 +12,12 @@ import pytest
import stripe
from prisma.enums import CreditTransactionType
from prisma.models import CreditRefundRequest, CreditTransaction, User, UserBalance
from prisma.types import (
CreditRefundRequestCreateInput,
CreditTransactionCreateInput,
UserBalanceCreateInput,
UserCreateInput,
)
from backend.data.credit import UserCredit
from backend.util.json import SafeJson
@@ -35,32 +41,32 @@ async def setup_test_user_with_topup():
# Create user
await User.prisma().create(
data={
"id": REFUND_TEST_USER_ID,
"email": f"{REFUND_TEST_USER_ID}@example.com",
"name": "Refund Test User",
}
data=UserCreateInput(
id=REFUND_TEST_USER_ID,
email=f"{REFUND_TEST_USER_ID}@example.com",
name="Refund Test User",
)
)
# Create user balance
await UserBalance.prisma().create(
data={
"userId": REFUND_TEST_USER_ID,
"balance": 1000, # $10
}
data=UserBalanceCreateInput(
userId=REFUND_TEST_USER_ID,
balance=1000, # $10
)
)
# Create a top-up transaction that can be refunded
topup_tx = await CreditTransaction.prisma().create(
data={
"userId": REFUND_TEST_USER_ID,
"amount": 1000,
"type": CreditTransactionType.TOP_UP,
"transactionKey": "pi_test_12345",
"runningBalance": 1000,
"isActive": True,
"metadata": SafeJson({"stripe_payment_intent": "pi_test_12345"}),
}
data=CreditTransactionCreateInput(
userId=REFUND_TEST_USER_ID,
amount=1000,
type=CreditTransactionType.TOP_UP,
transactionKey="pi_test_12345",
runningBalance=1000,
isActive=True,
metadata=SafeJson({"stripe_payment_intent": "pi_test_12345"}),
)
)
return topup_tx
@@ -93,12 +99,12 @@ async def test_deduct_credits_atomic(server: SpinTestServer):
# Create refund request record (simulating webhook flow)
await CreditRefundRequest.prisma().create(
data={
"userId": REFUND_TEST_USER_ID,
"amount": 500,
"transactionKey": topup_tx.transactionKey, # Should match the original transaction
"reason": "Test refund",
}
data=CreditRefundRequestCreateInput(
userId=REFUND_TEST_USER_ID,
amount=500,
transactionKey=topup_tx.transactionKey, # Should match the original transaction
reason="Test refund",
)
)
# Call deduct_credits
@@ -286,12 +292,12 @@ async def test_concurrent_refunds(server: SpinTestServer):
refund_requests = []
for i in range(5):
req = await CreditRefundRequest.prisma().create(
data={
"userId": REFUND_TEST_USER_ID,
"amount": 100, # $1 each
"transactionKey": topup_tx.transactionKey,
"reason": f"Test refund {i}",
}
data=CreditRefundRequestCreateInput(
userId=REFUND_TEST_USER_ID,
amount=100, # $1 each
transactionKey=topup_tx.transactionKey,
reason=f"Test refund {i}",
)
)
refund_requests.append(req)

53
autogpt_platform/backend/backend/data/credit_test.py
View File

@@ -3,6 +3,11 @@ from datetime import datetime, timedelta, timezone
import pytest
from prisma.enums import CreditTransactionType
from prisma.models import CreditTransaction, UserBalance
from prisma.types import (
CreditTransactionCreateInput,
UserBalanceCreateInput,
UserBalanceUpsertInput,
)
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.block import get_block
@@ -23,10 +28,10 @@ async def disable_test_user_transactions():
old_date = datetime.now(timezone.utc) - timedelta(days=35) # More than a month ago
await UserBalance.prisma().upsert(
where={"userId": DEFAULT_USER_ID},
data={
"create": {"userId": DEFAULT_USER_ID, "balance": 0},
"update": {"balance": 0, "updatedAt": old_date},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=DEFAULT_USER_ID, balance=0),
update={"balance": 0, "updatedAt": old_date},
),
)
@@ -140,23 +145,23 @@ async def test_block_credit_reset(server: SpinTestServer):
# Manually create a transaction with month 1 timestamp to establish history
await CreditTransaction.prisma().create(
data={
"userId": DEFAULT_USER_ID,
"amount": 100,
"type": CreditTransactionType.TOP_UP,
"runningBalance": 1100,
"isActive": True,
"createdAt": month1, # Set specific timestamp
}
data=CreditTransactionCreateInput(
userId=DEFAULT_USER_ID,
amount=100,
type=CreditTransactionType.TOP_UP,
runningBalance=1100,
isActive=True,
createdAt=month1, # Set specific timestamp
)
)
# Update user balance to match
await UserBalance.prisma().upsert(
where={"userId": DEFAULT_USER_ID},
data={
"create": {"userId": DEFAULT_USER_ID, "balance": 1100},
"update": {"balance": 1100},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=DEFAULT_USER_ID, balance=1100),
update={"balance": 1100},
),
)
# Now test month 2 behavior
@@ -175,14 +180,14 @@ async def test_block_credit_reset(server: SpinTestServer):
# Create a month 2 transaction to update the last transaction time
await CreditTransaction.prisma().create(
data={
"userId": DEFAULT_USER_ID,
"amount": -700, # Spent 700 to get to 400
"type": CreditTransactionType.USAGE,
"runningBalance": 400,
"isActive": True,
"createdAt": month2,
}
data=CreditTransactionCreateInput(
userId=DEFAULT_USER_ID,
amount=-700, # Spent 700 to get to 400
type=CreditTransactionType.USAGE,
runningBalance=400,
isActive=True,
createdAt=month2,
)
)
# Move to month 3

66
autogpt_platform/backend/backend/data/credit_underflow_test.py
View File

@@ -12,6 +12,7 @@ import pytest
from prisma.enums import CreditTransactionType
from prisma.errors import UniqueViolationError
from prisma.models import CreditTransaction, User, UserBalance
from prisma.types import UserBalanceCreateInput, UserBalanceUpsertInput, UserCreateInput
from backend.data.credit import POSTGRES_INT_MIN, UserCredit
from backend.util.test import SpinTestServer
@@ -21,11 +22,11 @@ async def create_test_user(user_id: str) -> None:
"""Create a test user for underflow tests."""
try:
await User.prisma().create(
data={
"id": user_id,
"email": f"test-{user_id}@example.com",
"name": f"Test User {user_id[:8]}",
}
data=UserCreateInput(
id=user_id,
email=f"test-{user_id}@example.com",
name=f"Test User {user_id[:8]}",
)
)
except UniqueViolationError:
# User already exists, continue
@@ -33,7 +34,10 @@ async def create_test_user(user_id: str) -> None:
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=0),
update={"balance": 0},
),
)
@@ -66,14 +70,14 @@ async def test_debug_underflow_step_by_step(server: SpinTestServer):
initial_balance_target = POSTGRES_INT_MIN + 100
# Use direct database update to set the balance close to underflow
from prisma.models import UserBalance
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": initial_balance_target},
"update": {"balance": initial_balance_target},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(
userId=user_id, balance=initial_balance_target
),
update={"balance": initial_balance_target},
),
)
current_balance = await credit_system.get_credits(user_id)
@@ -110,10 +114,10 @@ async def test_debug_underflow_step_by_step(server: SpinTestServer):
# Set balance to exactly POSTGRES_INT_MIN
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": POSTGRES_INT_MIN},
"update": {"balance": POSTGRES_INT_MIN},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=POSTGRES_INT_MIN),
update={"balance": POSTGRES_INT_MIN},
),
)
edge_balance = await credit_system.get_credits(user_id)
@@ -147,15 +151,13 @@ async def test_underflow_protection_large_refunds(server: SpinTestServer):
# Set up balance close to underflow threshold to test the protection
# Set balance to POSTGRES_INT_MIN + 1000, then try to subtract 2000
# This should trigger underflow protection
from prisma.models import UserBalance
test_balance = POSTGRES_INT_MIN + 1000
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": test_balance},
"update": {"balance": test_balance},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=test_balance),
update={"balance": test_balance},
),
)
current_balance = await credit_system.get_credits(user_id)
@@ -212,15 +214,13 @@ async def test_multiple_large_refunds_cumulative_underflow(server: SpinTestServe
try:
# Set up balance close to underflow threshold
from prisma.models import UserBalance
initial_balance = POSTGRES_INT_MIN + 500 # Close to minimum but with some room
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": initial_balance},
"update": {"balance": initial_balance},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=initial_balance),
update={"balance": initial_balance},
),
)
# Apply multiple refunds that would cumulatively underflow
@@ -290,15 +290,13 @@ async def test_concurrent_large_refunds_no_underflow(server: SpinTestServer):
try:
# Set up balance close to underflow threshold
from prisma.models import UserBalance
initial_balance = POSTGRES_INT_MIN + 1000 # Close to minimum
await UserBalance.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, "balance": initial_balance},
"update": {"balance": initial_balance},
},
data=UserBalanceUpsertInput(
create=UserBalanceCreateInput(userId=user_id, balance=initial_balance),
update={"balance": initial_balance},
),
)
async def large_refund(amount: int, label: str):

17
autogpt_platform/backend/backend/data/credit_user_balance_migration_test.py
View File

@@ -14,6 +14,7 @@ import pytest
from prisma.enums import CreditTransactionType
from prisma.errors import UniqueViolationError
from prisma.models import CreditTransaction, User, UserBalance
from prisma.types import UserBalanceCreateInput, UserCreateInput
from backend.data.credit import UsageTransactionMetadata, UserCredit
from backend.util.json import SafeJson
@@ -24,11 +25,11 @@ async def create_test_user(user_id: str) -> None:
"""Create a test user for migration tests."""
try:
await User.prisma().create(
data={
"id": user_id,
"email": f"test-{user_id}@example.com",
"name": f"Test User {user_id[:8]}",
}
data=UserCreateInput(
id=user_id,
email=f"test-{user_id}@example.com",
name=f"Test User {user_id[:8]}",
)
)
except UniqueViolationError:
# User already exists, continue
@@ -121,7 +122,7 @@ async def test_detect_stale_user_balance_queries(server: SpinTestServer):
try:
# Create UserBalance with specific value
await UserBalance.prisma().create(
data={"userId": user_id, "balance": 5000} # $50
data=UserBalanceCreateInput(userId=user_id, balance=5000) # $50
)
# Verify that get_credits returns UserBalance value (5000), not any stale User.balance value
@@ -160,7 +161,9 @@ async def test_concurrent_operations_use_userbalance_only(server: SpinTestServer
try:
# Set initial balance in UserBalance
await UserBalance.prisma().create(data={"userId": user_id, "balance": 1000})
await UserBalance.prisma().create(
data=UserBalanceCreateInput(userId=user_id, balance=1000)
)
# Run concurrent operations to ensure they all use UserBalance atomic operations
async def concurrent_spend(amount: int, label: str):

37
autogpt_platform/backend/backend/data/execution.py
View File

@@ -27,6 +27,7 @@ from prisma.models import (
AgentNodeExecutionKeyValueData,
)
from prisma.types import (
AgentGraphExecutionCreateInput,
AgentGraphExecutionUpdateManyMutationInput,
AgentGraphExecutionWhereInput,
AgentNodeExecutionCreateInput,
@@ -34,7 +35,7 @@ from prisma.types import (
AgentNodeExecutionKeyValueDataCreateInput,
AgentNodeExecutionUpdateInput,
AgentNodeExecutionWhereInput,
AgentNodeExecutionWhereUniqueInput,
_AgentNodeExecutionWhereUnique_id_Input,
)
from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
from pydantic.fields import Field
@@ -714,18 +715,18 @@ async def create_graph_execution(
The id of the AgentGraphExecution and the list of ExecutionResult for each node.
"""
result = await AgentGraphExecution.prisma().create(
data={
"agentGraphId": graph_id,
"agentGraphVersion": graph_version,
"executionStatus": ExecutionStatus.INCOMPLETE,
"inputs": SafeJson(inputs),
"credentialInputs": (
data=AgentGraphExecutionCreateInput(
agentGraphId=graph_id,
agentGraphVersion=graph_version,
executionStatus=ExecutionStatus.INCOMPLETE,
inputs=SafeJson(inputs),
credentialInputs=(
SafeJson(credential_inputs) if credential_inputs else Json({})
),
"nodesInputMasks": (
nodesInputMasks=(
SafeJson(nodes_input_masks) if nodes_input_masks else Json({})
),
"NodeExecutions": {
NodeExecutions={
"create": [
AgentNodeExecutionCreateInput(
agentNodeId=node_id,
@@ -741,10 +742,10 @@ async def create_graph_execution(
for node_id, node_input in starting_nodes_input
]
},
"userId": user_id,
"agentPresetId": preset_id,
"parentGraphExecutionId": parent_graph_exec_id,
},
userId=user_id,
agentPresetId=preset_id,
parentGraphExecutionId=parent_graph_exec_id,
),
include=GRAPH_EXECUTION_INCLUDE_WITH_NODES,
)
@@ -836,10 +837,10 @@ async def upsert_execution_output(
"""
Insert AgentNodeExecutionInputOutput record for as one of AgentNodeExecution.Output.
"""
data: AgentNodeExecutionInputOutputCreateInput = {
"name": output_name,
"referencedByOutputExecId": node_exec_id,
}
data = AgentNodeExecutionInputOutputCreateInput(
name=output_name,
referencedByOutputExecId=node_exec_id,
)
if output_data is not None:
data["data"] = SafeJson(output_data)
await AgentNodeExecutionInputOutput.prisma().create(data=data)
@@ -957,7 +958,7 @@ async def update_node_execution_status(
if res := await AgentNodeExecution.prisma().update(
where=cast(
AgentNodeExecutionWhereUniqueInput,
_AgentNodeExecutionWhereUnique_id_Input,
{
"id": node_exec_id,
"executionStatus": {"in": [s.value for s in allowed_from]},

34
autogpt_platform/backend/backend/data/human_review.py
View File

@@ -10,7 +10,11 @@ from typing import Optional
from prisma.enums import ReviewStatus
from prisma.models import PendingHumanReview
from prisma.types import PendingHumanReviewUpdateInput
from prisma.types import (
PendingHumanReviewCreateInput,
PendingHumanReviewUpdateInput,
PendingHumanReviewUpsertInput,
)
from pydantic import BaseModel
from backend.server.v2.executions.review.model import (
@@ -66,20 +70,20 @@ async def get_or_create_human_review(
# Upsert - get existing or create new review
review = await PendingHumanReview.prisma().upsert(
where={"nodeExecId": node_exec_id},
data={
"create": {
"userId": user_id,
"nodeExecId": node_exec_id,
"graphExecId": graph_exec_id,
"graphId": graph_id,
"graphVersion": graph_version,
"payload": SafeJson(input_data),
"instructions": message,
"editable": editable,
"status": ReviewStatus.WAITING,
},
"update": {}, # Do nothing on update - keep existing review as is
},
data=PendingHumanReviewUpsertInput(
create=PendingHumanReviewCreateInput(
userId=user_id,
nodeExecId=node_exec_id,
graphExecId=graph_exec_id,
graphId=graph_id,
graphVersion=graph_version,
payload=SafeJson(input_data),
instructions=message,
editable=editable,
status=ReviewStatus.WAITING,
),
update={}, # Do nothing on update - keep existing review as is
),
)
logger.info(

14
autogpt_platform/backend/backend/data/onboarding.py
View File

@@ -7,7 +7,11 @@ import prisma
import pydantic
from prisma.enums import OnboardingStep
from prisma.models import UserOnboarding
from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
from prisma.types import (
UserOnboardingCreateInput,
UserOnboardingUpdateInput,
UserOnboardingUpsertInput,
)
from backend.data import execution as execution_db
from backend.data.credit import get_user_credit_model
@@ -112,10 +116,10 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
return await UserOnboarding.prisma().upsert(
where={"userId": user_id},
data={
"create": {"userId": user_id, **update},
"update": update,
},
data=UserOnboardingUpsertInput(
create=UserOnboardingCreateInput(userId=user_id, **update),
update=update,
),
)

43
autogpt_platform/backend/backend/server/v2/chat/tools/_test_data.py
View File

@@ -3,6 +3,7 @@ from datetime import UTC, datetime
from os import getenv
import pytest
from prisma.types import ProfileCreateInput
from pydantic import SecretStr
from backend.blocks.firecrawl.scrape import FirecrawlScrapeBlock
@@ -49,13 +50,13 @@ async def setup_test_data():
# 1b. Create a profile with username for the user (required for store agent lookup)
username = user.email.split("@")[0]
await prisma.profile.create(
data={
"userId": user.id,
"username": username,
"name": f"Test User {username}",
"description": "Test user profile",
"links": [], # Required field - empty array for test profiles
}
data=ProfileCreateInput(
userId=user.id,
username=username,
name=f"Test User {username}",
description="Test user profile",
links=[], # Required field - empty array for test profiles
)
)
# 2. Create a test graph with agent input -> agent output
@@ -172,13 +173,13 @@ async def setup_llm_test_data():
# 1b. Create a profile with username for the user (required for store agent lookup)
username = user.email.split("@")[0]
await prisma.profile.create(
data={
"userId": user.id,
"username": username,
"name": f"Test User {username}",
"description": "Test user profile for LLM tests",
"links": [], # Required field - empty array for test profiles
}
data=ProfileCreateInput(
userId=user.id,
username=username,
name=f"Test User {username}",
description="Test user profile for LLM tests",
links=[], # Required field - empty array for test profiles
)
)
# 2. Create test OpenAI credentials for the user
@@ -332,13 +333,13 @@ async def setup_firecrawl_test_data():
# 1b. Create a profile with username for the user (required for store agent lookup)
username = user.email.split("@")[0]
await prisma.profile.create(
data={
"userId": user.id,
"username": username,
"name": f"Test User {username}",
"description": "Test user profile for Firecrawl tests",
"links": [], # Required field - empty array for test profiles
}
data=ProfileCreateInput(
userId=user.id,
username=username,
name=f"Test User {username}",
description="Test user profile for Firecrawl tests",
links=[], # Required field - empty array for test profiles
)
)
# NOTE: We deliberately do NOT create Firecrawl credentials for this user

14
autogpt_platform/backend/backend/server/v2/library/db.py
View File

@@ -802,18 +802,16 @@ async def add_store_agent_to_library(
# Create LibraryAgent entry
added_agent = await prisma.models.LibraryAgent.prisma().create(
data={
"User": {"connect": {"id": user_id}},
"AgentGraph": {
data=prisma.types.LibraryAgentCreateInput(
User={"connect": {"id": user_id}},
AgentGraph={
"connect": {
"graphVersionId": {"id": graph.id, "version": graph.version}
}
},
"isCreatedByUser": False,
"settings": SafeJson(
_initialize_graph_settings(graph_model).model_dump()
),
},
isCreatedByUser=False,
settings=SafeJson(_initialize_graph_settings(graph_model).model_dump()),
),
include=library_agent_include(
user_id, include_nodes=False, include_executions=False
),

15
autogpt_platform/backend/backend/server/v2/store/db.py
View File

@@ -248,7 +248,9 @@ async def log_search_term(search_query: str):
date = datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0)
try:
await prisma.models.SearchTerms.prisma().create(
data={"searchTerm": search_query, "createdDate": date}
data=prisma.types.SearchTermsCreateInput(
searchTerm=search_query, createdDate=date
)
)
except Exception as e:
# Fail silently here so that logging search terms doesn't break the app
@@ -1430,13 +1432,10 @@ async def _approve_sub_agent(
# Create new version if no matching version found
next_version = max((v.version for v in listing.Versions or []), default=0) + 1
await prisma.models.StoreListingVersion.prisma(tx).create(
data={
**_create_sub_agent_version_data(sub_graph, heading, main_agent_name),
"version": next_version,
"storeListingId": listing.id,
}
)
sub_agent_data = _create_sub_agent_version_data(sub_graph, heading, main_agent_name)
sub_agent_data["version"] = next_version
sub_agent_data["storeListingId"] = listing.id
await prisma.models.StoreListingVersion.prisma(tx).create(data=sub_agent_data)
await prisma.models.StoreListing.prisma(tx).update(
where={"id": listing.id}, data={"hasApprovedVersion": True}
)

13
autogpt_platform/backend/test/e2e_test_data.py
View File

@@ -22,6 +22,7 @@ import random
from typing import Any, Dict, List
from faker import Faker
from prisma.types import AgentBlockCreateInput
from backend.data.api_key import create_api_key
from backend.data.credit import get_user_credit_model
@@ -177,12 +178,12 @@ class TestDataCreator:
for block in blocks_to_create:
try:
await prisma.agentblock.create(
data={
"id": block.id,
"name": block.name,
"inputSchema": "{}",
"outputSchema": "{}",
}
data=AgentBlockCreateInput(
id=block.id,
name=block.name,
inputSchema="{}",
outputSchema="{}",
)
)
except Exception as e:
print(f"Error creating block {block.name}: {e}")

142
autogpt_platform/backend/test/test_data_creator.py
View File

@@ -30,13 +30,19 @@ from prisma.types import (
AgentGraphCreateInput,
AgentNodeCreateInput,
AgentNodeLinkCreateInput,
AgentPresetCreateInput,
AnalyticsDetailsCreateInput,
AnalyticsMetricsCreateInput,
APIKeyCreateInput,
CreditTransactionCreateInput,
IntegrationWebhookCreateInput,
LibraryAgentCreateInput,
ProfileCreateInput,
StoreListingCreateInput,
StoreListingReviewCreateInput,
StoreListingVersionCreateInput,
UserCreateInput,
UserOnboardingCreateInput,
)
faker = Faker()
@@ -172,14 +178,14 @@ async def main():
for _ in range(num_presets): # Create 1 AgentPreset per user
graph = random.choice(agent_graphs)
preset = await db.agentpreset.create(
data={
"name": faker.sentence(nb_words=3),
"description": faker.text(max_nb_chars=200),
"userId": user.id,
"agentGraphId": graph.id,
"agentGraphVersion": graph.version,
"isActive": True,
}
data=AgentPresetCreateInput(
name=faker.sentence(nb_words=3),
description=faker.text(max_nb_chars=200),
userId=user.id,
agentGraphId=graph.id,
agentGraphVersion=graph.version,
isActive=True,
)
)
agent_presets.append(preset)
@@ -220,18 +226,18 @@ async def main():
)
library_agent = await db.libraryagent.create(
data={
"userId": user.id,
"agentGraphId": graph.id,
"agentGraphVersion": graph.version,
"creatorId": creator_profile.id if creator_profile else None,
"imageUrl": get_image() if random.random() < 0.5 else None,
"useGraphIsActiveVersion": random.choice([True, False]),
"isFavorite": random.choice([True, False]),
"isCreatedByUser": random.choice([True, False]),
"isArchived": random.choice([True, False]),
"isDeleted": random.choice([True, False]),
}
data=LibraryAgentCreateInput(
userId=user.id,
agentGraphId=graph.id,
agentGraphVersion=graph.version,
creatorId=creator_profile.id if creator_profile else None,
imageUrl=get_image() if random.random() < 0.5 else None,
useGraphIsActiveVersion=random.choice([True, False]),
isFavorite=random.choice([True, False]),
isCreatedByUser=random.choice([True, False]),
isArchived=random.choice([True, False]),
isDeleted=random.choice([True, False]),
)
)
library_agents.append(library_agent)
@@ -392,13 +398,13 @@ async def main():
user = random.choice(users)
slug = faker.slug()
listing = await db.storelisting.create(
data={
"agentGraphId": graph.id,
"agentGraphVersion": graph.version,
"owningUserId": user.id,
"hasApprovedVersion": random.choice([True, False]),
"slug": slug,
}
data=StoreListingCreateInput(
agentGraphId=graph.id,
agentGraphVersion=graph.version,
owningUserId=user.id,
hasApprovedVersion=random.choice([True, False]),
slug=slug,
)
)
store_listings.append(listing)
@@ -408,26 +414,26 @@ async def main():
for listing in store_listings:
graph = [g for g in agent_graphs if g.id == listing.agentGraphId][0]
version = await db.storelistingversion.create(
data={
"agentGraphId": graph.id,
"agentGraphVersion": graph.version,
"name": graph.name or faker.sentence(nb_words=3),
"subHeading": faker.sentence(),
"videoUrl": get_video_url() if random.random() < 0.3 else None,
"imageUrls": [get_image() for _ in range(3)],
"description": faker.text(),
"categories": [faker.word() for _ in range(3)],
"isFeatured": random.choice([True, False]),
"isAvailable": True,
"storeListingId": listing.id,
"submissionStatus": random.choice(
data=StoreListingVersionCreateInput(
agentGraphId=graph.id,
agentGraphVersion=graph.version,
name=graph.name or faker.sentence(nb_words=3),
subHeading=faker.sentence(),
videoUrl=get_video_url() if random.random() < 0.3 else None,
imageUrls=[get_image() for _ in range(3)],
description=faker.text(),
categories=[faker.word() for _ in range(3)],
isFeatured=random.choice([True, False]),
isAvailable=True,
storeListingId=listing.id,
submissionStatus=random.choice(
[
prisma.enums.SubmissionStatus.PENDING,
prisma.enums.SubmissionStatus.APPROVED,
prisma.enums.SubmissionStatus.REJECTED,
]
),
}
)
)
store_listing_versions.append(version)
@@ -469,51 +475,47 @@ async def main():
try:
await db.useronboarding.create(
data={
"userId": user.id,
"completedSteps": completed_steps,
"walletShown": random.choice([True, False]),
"notified": (
data=UserOnboardingCreateInput(
userId=user.id,
completedSteps=completed_steps,
walletShown=random.choice([True, False]),
notified=(
random.sample(completed_steps, k=min(3, len(completed_steps)))
if completed_steps
else []
),
"rewardedFor": (
rewardedFor=(
random.sample(completed_steps, k=min(2, len(completed_steps)))
if completed_steps
else []
),
"usageReason": (
usageReason=(
random.choice(["personal", "business", "research", "learning"])
if random.random() < 0.7
else None
),
"integrations": random.sample(
integrations=random.sample(
["github", "google", "discord", "slack"], k=random.randint(0, 2)
),
"otherIntegrations": (
faker.word() if random.random() < 0.2 else None
),
"selectedStoreListingVersionId": (
otherIntegrations=(faker.word() if random.random() < 0.2 else None),
selectedStoreListingVersionId=(
random.choice(store_listing_versions).id
if store_listing_versions and random.random() < 0.5
else None
),
"onboardingAgentExecutionId": (
onboardingAgentExecutionId=(
random.choice(agent_graph_executions).id
if agent_graph_executions and random.random() < 0.3
else None
),
"agentRuns": random.randint(0, 10),
}
agentRuns=random.randint(0, 10),
)
)
except Exception as e:
print(f"Error creating onboarding for user {user.id}: {e}")
# Try simpler version
await db.useronboarding.create(
data={
"userId": user.id,
}
data=UserOnboardingCreateInput(userId=user.id)
)
# Insert IntegrationWebhooks for some users
@@ -544,20 +546,20 @@ async def main():
for user in users:
api_key = APIKeySmith().generate_key()
await db.apikey.create(
data={
"name": faker.word(),
"head": api_key.head,
"tail": api_key.tail,
"hash": api_key.hash,
"salt": api_key.salt,
"status": prisma.enums.APIKeyStatus.ACTIVE,
"permissions": [
data=APIKeyCreateInput(
name=faker.word(),
head=api_key.head,
tail=api_key.tail,
hash=api_key.hash,
salt=api_key.salt,
status=prisma.enums.APIKeyStatus.ACTIVE,
permissions=[
prisma.enums.APIKeyPermission.EXECUTE_GRAPH,
prisma.enums.APIKeyPermission.READ_GRAPH,
],
"description": faker.text(),
"userId": user.id,
}
description=faker.text(),
userId=user.id,
)
)
# Refresh materialized views

25
autogpt_platform/backend/test/test_data_updater.py
View File

@@ -16,6 +16,7 @@ from datetime import datetime, timedelta
import prisma.enums
from faker import Faker
from prisma import Json, Prisma
from prisma.types import CreditTransactionCreateInput, StoreListingReviewCreateInput
faker = Faker()
@@ -166,16 +167,16 @@ async def main():
score = random.choices([1, 2, 3, 4, 5], weights=[5, 10, 20, 40, 25])[0]
await db.storelistingreview.create(
data={
"storeListingVersionId": version.id,
"reviewByUserId": reviewer.id,
"score": score,
"comments": (
data=StoreListingReviewCreateInput(
storeListingVersionId=version.id,
reviewByUserId=reviewer.id,
score=score,
comments=(
faker.text(max_nb_chars=200)
if random.random() < 0.7
else None
),
}
)
)
new_reviews_count += 1
@@ -244,17 +245,17 @@ async def main():
)
await db.credittransaction.create(
data={
"userId": user.id,
"amount": amount,
"type": transaction_type,
"metadata": Json(
data=CreditTransactionCreateInput(
userId=user.id,
amount=amount,
type=transaction_type,
metadata=Json(
{
"source": "test_updater",
"timestamp": datetime.now().isoformat(),
}
),
}
)
)
transaction_count += 1