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Model Runtime (#1858)

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Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: Garfield Dai <dai.hai@foxmail.com>
Co-authored-by: chenhe <guchenhe@gmail.com>
Co-authored-by: jyong <jyong@dify.ai>
Co-authored-by: Joel <iamjoel007@gmail.com>
Co-authored-by: Yeuoly <admin@srmxy.cn>
This commit is contained in:
takatost
2024-01-02 23:42:00 +08:00
committed by GitHub
parent e91dd28a76
commit d069c668f8
807 changed files with 171310 additions and 23806 deletions
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56
api/core/agent/agent_executor.py
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@@ -4,10 +4,10 @@ from typing import Union, Optional
from langchain.agents import BaseSingleActionAgent, BaseMultiActionAgent
from langchain.callbacks.manager import Callbacks
from langchain.memory.chat_memory import BaseChatMemory
from langchain.tools import BaseTool
from pydantic import BaseModel, Extra
from core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
@@ -15,9 +15,11 @@ from core.agent.agent.structed_multi_dataset_router_agent import StructuredMulti
from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
from langchain.agents import AgentExecutor as LCAgentExecutor
from core.entities.application_entities import ModelConfigEntity
from core.entities.message_entities import prompt_messages_to_lc_messages
from core.helper import moderation
from core.model_providers.error import LLMError
from core.model_providers.models.llm.base import BaseLLM
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.errors.invoke import InvokeError
from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
@@ -31,14 +33,15 @@ class PlanningStrategy(str, enum.Enum):
class AgentConfiguration(BaseModel):
strategy: PlanningStrategy
model_instance: BaseLLM
model_config: ModelConfigEntity
tools: list[BaseTool]
summary_model_instance: BaseLLM = None
memory: Optional[BaseChatMemory] = None
summary_model_config: Optional[ModelConfigEntity] = None
memory: Optional[TokenBufferMemory] = None
callbacks: Callbacks = None
max_iterations: int = 6
max_execution_time: Optional[float] = None
early_stopping_method: str = "generate"
agent_llm_callback: Optional[AgentLLMCallback] = None
# `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
class Config:
@@ -62,34 +65,42 @@ class AgentExecutor:
def _init_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
if self.configuration.strategy == PlanningStrategy.REACT:
agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools(
model_instance=self.configuration.model_instance,
model_config=self.configuration.model_config,
tools=self.configuration.tools,
output_parser=StructuredChatOutputParser(),
summary_model_instance=self.configuration.summary_model_instance
if self.configuration.summary_model_instance else None,
summary_model_config=self.configuration.summary_model_config
if self.configuration.summary_model_config else None,
agent_llm_callback=self.configuration.agent_llm_callback,
verbose=True
)
elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools(
model_instance=self.configuration.model_instance,
model_config=self.configuration.model_config,
tools=self.configuration.tools,
extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None, # used for read chat histories memory
summary_model_instance=self.configuration.summary_model_instance
if self.configuration.summary_model_instance else None,
extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages())
if self.configuration.memory else None, # used for read chat histories memory
summary_model_config=self.configuration.summary_model_config
if self.configuration.summary_model_config else None,
agent_llm_callback=self.configuration.agent_llm_callback,
verbose=True
)
elif self.configuration.strategy == PlanningStrategy.ROUTER:
self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool) or isinstance(t, DatasetMultiRetrieverTool)]
self.configuration.tools = [t for t in self.configuration.tools
if isinstance(t, DatasetRetrieverTool)
or isinstance(t, DatasetMultiRetrieverTool)]
agent = MultiDatasetRouterAgent.from_llm_and_tools(
model_instance=self.configuration.model_instance,
model_config=self.configuration.model_config,
tools=self.configuration.tools,
extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,
extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages())
if self.configuration.memory else None,
verbose=True
)
elif self.configuration.strategy == PlanningStrategy.REACT_ROUTER:
self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool) or isinstance(t, DatasetMultiRetrieverTool)]
self.configuration.tools = [t for t in self.configuration.tools
if isinstance(t, DatasetRetrieverTool)
or isinstance(t, DatasetMultiRetrieverTool)]
agent = StructuredMultiDatasetRouterAgent.from_llm_and_tools(
model_instance=self.configuration.model_instance,
model_config=self.configuration.model_config,
tools=self.configuration.tools,
output_parser=StructuredChatOutputParser(),
verbose=True
@@ -104,11 +115,11 @@ class AgentExecutor:
def run(self, query: str) -> AgentExecuteResult:
moderation_result = moderation.check_moderation(
self.configuration.model_instance.model_provider,
self.configuration.model_config,
query
)
if not moderation_result:
if moderation_result:
return AgentExecuteResult(
output="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.",
strategy=self.configuration.strategy,
@@ -118,7 +129,6 @@ class AgentExecutor:
agent_executor = LCAgentExecutor.from_agent_and_tools(
agent=self.agent,
tools=self.configuration.tools,
memory=self.configuration.memory,
max_iterations=self.configuration.max_iterations,
max_execution_time=self.configuration.max_execution_time,
early_stopping_method=self.configuration.early_stopping_method,
@@ -126,8 +136,8 @@ class AgentExecutor:
)
try:
output = agent_executor.run(query)
except LLMError as ex:
output = agent_executor.run(input=query)
except InvokeError as ex:
raise ex
except Exception as ex:
logging.exception("agent_executor run failed")