feat: agent node add memory (#15976)

api/core/workflow/nodes/agent/agent_node.py

@@ -1,15 +1,18 @@
 import json
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, cast
+from typing import Any, Optional, cast
 
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
-from core.model_manager import ModelManager
-from core.model_runtime.entities.model_entities import ModelType
+from core.memory.token_buffer_memory import TokenBufferMemory
+from core.model_manager import ModelInstance, ModelManager
+from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
 from core.plugin.manager.exc import PluginDaemonClientSideError
 from core.plugin.manager.plugin import PluginInstallationManager
+from core.provider_manager import ProviderManager
 from core.tools.entities.tool_entities import ToolParameter, ToolProviderType
 from core.tools.tool_manager import ToolManager
+from core.variables.segments import StringSegment
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
@@ -19,7 +22,9 @@ from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event.event import RunCompletedEvent
 from core.workflow.nodes.tool.tool_node import ToolNode
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
+from extensions.ext_database import db
 from factories.agent_factory import get_plugin_agent_strategy
+from models.model import Conversation
 from models.workflow import WorkflowNodeExecutionStatus
 
 
@@ -233,17 +238,20 @@ class AgentNode(ToolNode):
                     value = tool_value
                 if parameter.type == "model-selector":
                     value = cast(dict[str, Any], value)
-                    model_instance = ModelManager().get_model_instance(
-                        tenant_id=self.tenant_id,
-                        provider=value.get("provider", ""),
-                        model_type=ModelType(value.get("model_type", "")),
-                        model=value.get("model", ""),
-                    )
-                    models = model_instance.model_type_instance.plugin_model_provider.declaration.models
-                    finded_model = next((model for model in models if model.model == value.get("model", "")), None)
-
-                    value["entity"] = finded_model.model_dump(mode="json") if finded_model else None
-
+                    model_instance, model_schema = self._fetch_model(value)
+                    # memory config
+                    history_prompt_messages = []
+                    if node_data.memory:
+                        memory = self._fetch_memory(model_instance)
+                        if memory:
+                            prompt_messages = memory.get_history_prompt_messages(
+                                message_limit=node_data.memory.window.size if node_data.memory.window.size else None
+                            )
+                            history_prompt_messages = [
+                                prompt_message.model_dump(mode="json") for prompt_message in prompt_messages
+                            ]
+                    value["history_prompt_messages"] = history_prompt_messages
+                    value["entity"] = model_schema.model_dump(mode="json") if model_schema else None
             result[parameter_name] = value
 
         return result
@@ -297,3 +305,46 @@ class AgentNode(ToolNode):
         except StopIteration:
             icon = None
         return icon
+
+    def _fetch_memory(self, model_instance: ModelInstance) -> Optional[TokenBufferMemory]:
+        # get conversation id
+        conversation_id_variable = self.graph_runtime_state.variable_pool.get(
+            ["sys", SystemVariableKey.CONVERSATION_ID.value]
+        )
+        if not isinstance(conversation_id_variable, StringSegment):
+            return None
+        conversation_id = conversation_id_variable.value
+
+        # get conversation
+        conversation = (
+            db.session.query(Conversation)
+            .filter(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
+            .first()
+        )
+
+        if not conversation:
+            return None
+
+        memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+
+        return memory
+
+    def _fetch_model(self, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(
+            tenant_id=self.tenant_id, provider=value.get("provider", ""), model_type=ModelType.LLM
+        )
+        model_name = value.get("model", "")
+        model_credentials = provider_model_bundle.configuration.get_current_credentials(
+            model_type=ModelType.LLM, model=model_name
+        )
+        provider_name = provider_model_bundle.configuration.provider.provider
+        model_type_instance = provider_model_bundle.model_type_instance
+        model_instance = ModelManager().get_model_instance(
+            tenant_id=self.tenant_id,
+            provider=provider_name,
+            model_type=ModelType(value.get("model_type", "")),
+            model=model_name,
+        )
+        model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
+        return model_instance, model_schema

api/core/workflow/nodes/agent/entities.py

@@ -3,6 +3,7 @@ from typing import Any, Literal, Union
 
 from pydantic import BaseModel
 
+from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.tools.entities.tool_entities import ToolSelector
 from core.workflow.nodes.base.entities import BaseNodeData
 
@@ -11,6 +12,7 @@ class AgentNodeData(BaseNodeData):
     agent_strategy_provider_name: str  # redundancy
     agent_strategy_name: str
     agent_strategy_label: str  # redundancy
+    memory: MemoryConfig | None = None
 
     class AgentInput(BaseModel):
         value: Union[list[str], list[ToolSelector], Any]