Merge branch 'dev' into aarushikansal/add-new-health-endpoint

autogpt_platform/backend/backend/app.py

@@ -24,11 +24,12 @@ def main(**kwargs):
     Run all the processes required for the AutoGPT-server (REST and WebSocket APIs).
     """
 
-    from backend.executor import ExecutionManager, ExecutionScheduler
+    from backend.executor import DatabaseManager, ExecutionManager, ExecutionScheduler
     from backend.server.rest_api import AgentServer
     from backend.server.ws_api import WebsocketServer
 
     run_processes(
+        DatabaseManager(),
         ExecutionManager(),
         ExecutionScheduler(),
         WebsocketServer(),

autogpt_platform/backend/backend/data/credit.py

@@ -18,7 +18,7 @@
     LlmModel,
 )
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
-from backend.data.block import Block, BlockInput
+from backend.data.block import Block, BlockInput, get_block
 from backend.util.settings import Config
 
 
@@ -96,7 +96,7 @@ async def spend_credits(
         self,
         user_id: str,
         user_credit: int,
-        block: Block,
+        block_id: str,
         input_data: BlockInput,
         data_size: float,
         run_time: float,
@@ -107,7 +107,7 @@ async def spend_credits(
         Args:
             user_id (str): The user ID.
             user_credit (int): The current credit for the user.
-            block (Block): The block that is being used.
+            block_id (str): The block ID.
             input_data (BlockInput): The input data for the block.
             data_size (float): The size of the data being processed.
             run_time (float): The time taken to run the block.
@@ -208,12 +208,16 @@ async def spend_credits(
         self,
         user_id: str,
         user_credit: int,
-        block: Block,
+        block_id: str,
         input_data: BlockInput,
         data_size: float,
         run_time: float,
         validate_balance: bool = True,
     ) -> int:
+        block = get_block(block_id)
+        if not block:
+            raise ValueError(f"Block not found: {block_id}")
+
         cost, matching_filter = self._block_usage_cost(
             block=block, input_data=input_data, data_size=data_size, run_time=run_time
         )

autogpt_platform/backend/backend/data/graph.py

@@ -7,7 +7,7 @@
 import prisma.types
 from prisma.models import AgentGraph, AgentGraphExecution, AgentNode, AgentNodeLink
 from prisma.types import AgentGraphInclude
-from pydantic import BaseModel, PrivateAttr
+from pydantic import BaseModel
 from pydantic_core import PydanticUndefinedType
 
 from backend.blocks.basic import AgentInputBlock, AgentOutputBlock
@@ -51,17 +51,8 @@ class Node(BaseDbModel):
     block_id: str
     input_default: BlockInput = {}  # dict[input_name, default_value]
     metadata: dict[str, Any] = {}
-
-    _input_links: list[Link] = PrivateAttr(default=[])
-    _output_links: list[Link] = PrivateAttr(default=[])
-
-    @property
-    def input_links(self) -> list[Link]:
-        return self._input_links
-
-    @property
-    def output_links(self) -> list[Link]:
-        return self._output_links
+    input_links: list[Link] = []
+    output_links: list[Link] = []
 
     @staticmethod
     def from_db(node: AgentNode):
@@ -73,8 +64,8 @@ def from_db(node: AgentNode):
             input_default=json.loads(node.constantInput),
             metadata=json.loads(node.metadata),
         )
-        obj._input_links = [Link.from_db(link) for link in node.Input or []]
-        obj._output_links = [Link.from_db(link) for link in node.Output or []]
+        obj.input_links = [Link.from_db(link) for link in node.Input or []]
+        obj.output_links = [Link.from_db(link) for link in node.Output or []]
         return obj
 
 

autogpt_platform/backend/backend/data/queue.py

@@ -17,14 +17,6 @@ def default(self, o):
 
 
 class AbstractEventQueue(ABC):
-    @abstractmethod
-    def connect(self):
-        pass
-
-    @abstractmethod
-    def close(self):
-        pass
-
     @abstractmethod
     def put(self, execution_result: ExecutionResult):
         pass
@@ -36,26 +28,23 @@ def get(self) -> ExecutionResult | None:
 
 class RedisEventQueue(AbstractEventQueue):
     def __init__(self):
-        self.connection = None
         self.queue_name = redis.QUEUE_NAME
 
-    def connect(self):
-        self.connection = redis.connect()
+    @property
+    def connection(self):
+        return redis.get_redis()
 
     def put(self, execution_result: ExecutionResult):
-        if self.connection:
-            message = json.dumps(execution_result.model_dump(), cls=DateTimeEncoder)
-            logger.info(f"Putting execution result to Redis {message}")
-            self.connection.lpush(self.queue_name, message)
+        message = json.dumps(execution_result.model_dump(), cls=DateTimeEncoder)
+        logger.info(f"Putting execution result to Redis {message}")
+        self.connection.lpush(self.queue_name, message)
 
     def get(self) -> ExecutionResult | None:
-        if self.connection:
-            message = self.connection.rpop(self.queue_name)
-            if message is not None and isinstance(message, (str, bytes, bytearray)):
-                data = json.loads(message)
-                logger.info(f"Getting execution result from Redis {data}")
-                return ExecutionResult(**data)
+        message = self.connection.rpop(self.queue_name)
+        if message is not None and isinstance(message, (str, bytes, bytearray)):
+            data = json.loads(message)
+            logger.info(f"Getting execution result from Redis {data}")
+            return ExecutionResult(**data)
+        elif message is not None:
+            logger.error(f"Failed to get execution result from Redis {message}")
         return None
-
-    def close(self):
-        redis.disconnect()

autogpt_platform/backend/backend/exec.py

@@ -1,12 +1,13 @@
 from backend.app import run_processes
-from backend.executor import ExecutionManager
+from backend.executor import DatabaseManager, ExecutionManager
 
 
 def main():
     """
     Run all the processes required for the AutoGPT-server REST API.
     """
     run_processes(
+        DatabaseManager(),
         ExecutionManager(),
     )
 

autogpt_platform/backend/backend/executor/__init__.py

@@ -1,7 +1,9 @@
+from .database import DatabaseManager
 from .manager import ExecutionManager
 from .scheduler import ExecutionScheduler
 
 __all__ = [
+    "DatabaseManager",
     "ExecutionManager",
     "ExecutionScheduler",
 ]

autogpt_platform/backend/backend/executor/database.py

@@ -0,0 +1,75 @@
+from functools import wraps
+from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
+
+from backend.data.credit import get_user_credit_model
+from backend.data.execution import (
+    ExecutionResult,
+    create_graph_execution,
+    get_execution_results,
+    get_incomplete_executions,
+    get_latest_execution,
+    update_execution_status,
+    update_graph_execution_stats,
+    update_node_execution_stats,
+    upsert_execution_input,
+    upsert_execution_output,
+)
+from backend.data.graph import get_graph, get_node
+from backend.data.queue import RedisEventQueue
+from backend.util.service import AppService, expose
+from backend.util.settings import Config
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+class DatabaseManager(AppService):
+
+    def __init__(self):
+        super().__init__(port=Config().database_api_port)
+        self.use_db = True
+        self.use_redis = True
+        self.event_queue = RedisEventQueue()
+
+    @expose
+    def send_execution_update(self, execution_result_dict: dict[Any, Any]):
+        self.event_queue.put(ExecutionResult(**execution_result_dict))
+
+    @staticmethod
+    def exposed_run_and_wait(
+        f: Callable[P, Coroutine[None, None, R]]
+    ) -> Callable[Concatenate[object, P], R]:
+        @expose
+        @wraps(f)
+        def wrapper(self, *args: P.args, **kwargs: P.kwargs) -> R:
+            coroutine = f(*args, **kwargs)
+            res = self.run_and_wait(coroutine)
+            return res
+
+        return wrapper
+
+    # Executions
+    create_graph_execution = exposed_run_and_wait(create_graph_execution)
+    get_execution_results = exposed_run_and_wait(get_execution_results)
+    get_incomplete_executions = exposed_run_and_wait(get_incomplete_executions)
+    get_latest_execution = exposed_run_and_wait(get_latest_execution)
+    update_execution_status = exposed_run_and_wait(update_execution_status)
+    update_graph_execution_stats = exposed_run_and_wait(update_graph_execution_stats)
+    update_node_execution_stats = exposed_run_and_wait(update_node_execution_stats)
+    upsert_execution_input = exposed_run_and_wait(upsert_execution_input)
+    upsert_execution_output = exposed_run_and_wait(upsert_execution_output)
+
+    # Graphs
+    get_node = exposed_run_and_wait(get_node)
+    get_graph = exposed_run_and_wait(get_graph)
+
+    # Credits
+    user_credit_model = get_user_credit_model()
+    get_or_refill_credit = cast(
+        Callable[[Any, str], int],
+        exposed_run_and_wait(user_credit_model.get_or_refill_credit),
+    )
+    spend_credits = cast(
+        Callable[[Any, str, int, str, dict[str, str], float, float], int],
+        exposed_run_and_wait(user_credit_model.spend_credits),
+    )