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Introduce Plugins (#13836)
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Chen <52963600+GareArc@users.noreply.github.com>
This commit is contained in:
Yeuoly
@@ -1,7 +1,6 @@
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import json
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import logging
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import uuid
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from datetime import UTC, datetime
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from typing import Optional, Union, cast
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from core.agent.entities import AgentEntity, AgentToolEntity
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@@ -32,19 +31,16 @@ from core.model_runtime.entities import (
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from core.model_runtime.entities.message_entities import ImagePromptMessageContent
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from core.model_runtime.entities.model_entities import ModelFeature
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.prompt.utils.extract_thread_messages import extract_thread_messages
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from core.tools.__base.tool import Tool
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from core.tools.entities.tool_entities import (
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ToolParameter,
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ToolRuntimeVariablePool,
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)
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from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
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from core.tools.tool.tool import Tool
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from core.tools.tool_manager import ToolManager
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from core.tools.utils.dataset_retriever_tool import DatasetRetrieverTool
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from extensions.ext_database import db
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from factories import file_factory
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from models.model import Conversation, Message, MessageAgentThought, MessageFile
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from models.tools import ToolConversationVariables
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logger = logging.getLogger(__name__)
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@@ -62,11 +58,9 @@ class BaseAgentRunner(AppRunner):
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queue_manager: AppQueueManager,
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message: Message,
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user_id: str,
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model_instance: ModelInstance,
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memory: Optional[TokenBufferMemory] = None,
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prompt_messages: Optional[list[PromptMessage]] = None,
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variables_pool: Optional[ToolRuntimeVariablePool] = None,
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db_variables: Optional[ToolConversationVariables] = None,
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model_instance: ModelInstance,
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) -> None:
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self.tenant_id = tenant_id
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self.application_generate_entity = application_generate_entity
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@@ -79,8 +73,6 @@ class BaseAgentRunner(AppRunner):
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self.user_id = user_id
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self.memory = memory
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self.history_prompt_messages = self.organize_agent_history(prompt_messages=prompt_messages or [])
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self.variables_pool = variables_pool
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self.db_variables_pool = db_variables
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self.model_instance = model_instance
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# init callback
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@@ -141,11 +133,10 @@ class BaseAgentRunner(AppRunner):
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agent_tool=tool,
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invoke_from=self.application_generate_entity.invoke_from,
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)
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tool_entity.load_variables(self.variables_pool)
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assert tool_entity.entity.description
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message_tool = PromptMessageTool(
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name=tool.tool_name,
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description=tool_entity.description.llm if tool_entity.description else "",
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description=tool_entity.entity.description.llm,
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parameters={
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"type": "object",
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"properties": {},
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@@ -153,7 +144,7 @@ class BaseAgentRunner(AppRunner):
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},
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)
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parameters = tool_entity.get_all_runtime_parameters()
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parameters = tool_entity.get_merged_runtime_parameters()
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for parameter in parameters:
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if parameter.form != ToolParameter.ToolParameterForm.LLM:
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continue
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@@ -186,9 +177,11 @@ class BaseAgentRunner(AppRunner):
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"""
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convert dataset retriever tool to prompt message tool
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"""
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assert tool.entity.description
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prompt_tool = PromptMessageTool(
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name=tool.identity.name if tool.identity else "unknown",
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description=tool.description.llm if tool.description else "",
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name=tool.entity.identity.name,
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description=tool.entity.description.llm,
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parameters={
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"type": "object",
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"properties": {},
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@@ -234,8 +227,7 @@ class BaseAgentRunner(AppRunner):
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# save prompt tool
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prompt_messages_tools.append(prompt_tool)
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# save tool entity
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if dataset_tool.identity is not None:
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tool_instances[dataset_tool.identity.name] = dataset_tool
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tool_instances[dataset_tool.entity.identity.name] = dataset_tool
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return tool_instances, prompt_messages_tools
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@@ -320,24 +312,24 @@ class BaseAgentRunner(AppRunner):
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def save_agent_thought(
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self,
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agent_thought: MessageAgentThought,
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tool_name: str,
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tool_input: Union[str, dict],
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thought: str,
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tool_name: str | None,
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tool_input: Union[str, dict, None],
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thought: str | None,
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observation: Union[str, dict, None],
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tool_invoke_meta: Union[str, dict, None],
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answer: str,
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answer: str | None,
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messages_ids: list[str],
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llm_usage: LLMUsage | None = None,
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):
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"""
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Save agent thought
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"""
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queried_thought = (
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updated_agent_thought = (
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db.session.query(MessageAgentThought).filter(MessageAgentThought.id == agent_thought.id).first()
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)
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if not queried_thought:
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raise ValueError(f"Agent thought {agent_thought.id} not found")
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agent_thought = queried_thought
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if not updated_agent_thought:
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raise ValueError("agent thought not found")
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agent_thought = updated_agent_thought
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if thought:
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agent_thought.thought = thought
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@@ -349,39 +341,39 @@ class BaseAgentRunner(AppRunner):
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if isinstance(tool_input, dict):
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try:
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tool_input = json.dumps(tool_input, ensure_ascii=False)
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except Exception as e:
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except Exception:
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tool_input = json.dumps(tool_input)
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agent_thought.tool_input = tool_input
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updated_agent_thought.tool_input = tool_input
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if observation:
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if isinstance(observation, dict):
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try:
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observation = json.dumps(observation, ensure_ascii=False)
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except Exception as e:
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except Exception:
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observation = json.dumps(observation)
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agent_thought.observation = observation
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updated_agent_thought.observation = observation
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if answer:
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agent_thought.answer = answer
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if messages_ids is not None and len(messages_ids) > 0:
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agent_thought.message_files = json.dumps(messages_ids)
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updated_agent_thought.message_files = json.dumps(messages_ids)
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if llm_usage:
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agent_thought.message_token = llm_usage.prompt_tokens
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agent_thought.message_price_unit = llm_usage.prompt_price_unit
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agent_thought.message_unit_price = llm_usage.prompt_unit_price
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agent_thought.answer_token = llm_usage.completion_tokens
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agent_thought.answer_price_unit = llm_usage.completion_price_unit
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agent_thought.answer_unit_price = llm_usage.completion_unit_price
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agent_thought.tokens = llm_usage.total_tokens
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agent_thought.total_price = llm_usage.total_price
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updated_agent_thought.message_token = llm_usage.prompt_tokens
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updated_agent_thought.message_price_unit = llm_usage.prompt_price_unit
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updated_agent_thought.message_unit_price = llm_usage.prompt_unit_price
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updated_agent_thought.answer_token = llm_usage.completion_tokens
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updated_agent_thought.answer_price_unit = llm_usage.completion_price_unit
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updated_agent_thought.answer_unit_price = llm_usage.completion_unit_price
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updated_agent_thought.tokens = llm_usage.total_tokens
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updated_agent_thought.total_price = llm_usage.total_price
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# check if tool labels is not empty
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labels = agent_thought.tool_labels or {}
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tools = agent_thought.tool.split(";") if agent_thought.tool else []
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labels = updated_agent_thought.tool_labels or {}
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tools = updated_agent_thought.tool.split(";") if updated_agent_thought.tool else []
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for tool in tools:
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if not tool:
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continue
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@@ -392,42 +384,20 @@ class BaseAgentRunner(AppRunner):
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else:
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labels[tool] = {"en_US": tool, "zh_Hans": tool}
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agent_thought.tool_labels_str = json.dumps(labels)
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updated_agent_thought.tool_labels_str = json.dumps(labels)
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if tool_invoke_meta is not None:
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if isinstance(tool_invoke_meta, dict):
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try:
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tool_invoke_meta = json.dumps(tool_invoke_meta, ensure_ascii=False)
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except Exception as e:
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except Exception:
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tool_invoke_meta = json.dumps(tool_invoke_meta)
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agent_thought.tool_meta_str = tool_invoke_meta
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updated_agent_thought.tool_meta_str = tool_invoke_meta
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db.session.commit()
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db.session.close()
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def update_db_variables(self, tool_variables: ToolRuntimeVariablePool, db_variables: ToolConversationVariables):
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"""
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convert tool variables to db variables
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"""
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queried_variables = (
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db.session.query(ToolConversationVariables)
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.filter(
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ToolConversationVariables.conversation_id == self.message.conversation_id,
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)
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.first()
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)
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if not queried_variables:
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return
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db_variables = queried_variables
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db_variables.updated_at = datetime.now(UTC).replace(tzinfo=None)
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db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
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db.session.commit()
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db.session.close()
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def organize_agent_history(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
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"""
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Organize agent history
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@@ -464,11 +434,11 @@ class BaseAgentRunner(AppRunner):
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tool_call_response: list[ToolPromptMessage] = []
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try:
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tool_inputs = json.loads(agent_thought.tool_input)
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except Exception as e:
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except Exception:
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tool_inputs = {tool: {} for tool in tools}
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try:
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tool_responses = json.loads(agent_thought.observation)
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except Exception as e:
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except Exception:
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tool_responses = dict.fromkeys(tools, agent_thought.observation)
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for tool in tools:
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@@ -515,7 +485,11 @@ class BaseAgentRunner(AppRunner):
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files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
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if not files:
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return UserPromptMessage(content=message.query)
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file_extra_config = FileUploadConfigManager.convert(message.app_model_config.to_dict())
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if message.app_model_config:
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file_extra_config = FileUploadConfigManager.convert(message.app_model_config.to_dict())
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else:
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file_extra_config = None
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if not file_extra_config:
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return UserPromptMessage(content=message.query)
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