import logging
from typing import cast, Optional, List
from langchain import WikipediaAPIWrapper
from langchain.callbacks.base import BaseCallbackHandler
from langchain.tools import BaseTool, WikipediaQueryRun, Tool
from pydantic import BaseModel, Field
from core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor
from core.application_queue_manager import ApplicationQueueManager
from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
from core.entities.application_entities import ModelConfigEntity, InvokeFrom, \
AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.model_entities import ModelFeature, ModelType
from core.model_runtime.model_providers import model_provider_factory
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tool.current_datetime_tool import DatetimeTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tool.provider.serpapi_provider import SerpAPIToolProvider
from core.tool.serpapi_wrapper import OptimizedSerpAPIWrapper, OptimizedSerpAPIInput
from core.tool.web_reader_tool import WebReaderTool
from extensions.ext_database import db
from models.dataset import Dataset
from models.model import Message
logger = logging.getLogger(__name__)
class AgentRunnerFeature:
def __init__(self, tenant_id: str,
app_orchestration_config: AppOrchestrationConfigEntity,
model_config: ModelConfigEntity,
config: AgentEntity,
queue_manager: ApplicationQueueManager,
message: Message,
user_id: str,
agent_llm_callback: AgentLLMCallback,
callback: AgentLoopGatherCallbackHandler,
memory: Optional[TokenBufferMemory] = None,) -> None:
"""
Agent runner
:param tenant_id: tenant id
:param app_orchestration_config: app orchestration config
:param model_config: model config
:param config: dataset config
:param queue_manager: queue manager
:param message: message
:param user_id: user id
:param agent_llm_callback: agent llm callback
:param callback: callback
:param memory: memory
"""
self.tenant_id = tenant_id
self.app_orchestration_config = app_orchestration_config
self.model_config = model_config
self.config = config
self.queue_manager = queue_manager
self.message = message
self.user_id = user_id
self.agent_llm_callback = agent_llm_callback
self.callback = callback
self.memory = memory
def run(self, query: str,
invoke_from: InvokeFrom) -> Optional[str]:
"""
Retrieve agent loop result.
:param query: query
:param invoke_from: invoke from
:return:
"""
provider = self.config.provider
model = self.config.model
tool_configs = self.config.tools
# check model is support tool calling
provider_instance = model_provider_factory.get_provider_instance(provider=provider)
model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
model_type_instance = cast(LargeLanguageModel, model_type_instance)
# get model schema
model_schema = model_type_instance.get_model_schema(
model=model,
credentials=self.model_config.credentials
)
if not model_schema:
return None
planning_strategy = PlanningStrategy.REACT
features = model_schema.features
if features:
if ModelFeature.TOOL_CALL in features \
or ModelFeature.MULTI_TOOL_CALL in features:
planning_strategy = PlanningStrategy.FUNCTION_CALL
tools = self.to_tools(
tool_configs=tool_configs,
invoke_from=invoke_from,
callbacks=[self.callback, DifyStdOutCallbackHandler()],
)
if len(tools) == 0:
return None
agent_configuration = AgentConfiguration(
strategy=planning_strategy,
model_config=self.model_config,
tools=tools,
memory=self.memory,
max_iterations=10,
max_execution_time=400.0,
early_stopping_method="generate",
agent_llm_callback=self.agent_llm_callback,
callbacks=[self.callback, DifyStdOutCallbackHandler()]
)
agent_executor = AgentExecutor(agent_configuration)
try:
# check if should use agent
should_use_agent = agent_executor.should_use_agent(query)
if not should_use_agent:
return None
result = agent_executor.run(query)
return result.output
except Exception as ex:
logger.exception("agent_executor run failed")
return None
def to_tools(self, tool_configs: list[AgentToolEntity],
invoke_from: InvokeFrom,
callbacks: list[BaseCallbackHandler]) \
-> Optional[List[BaseTool]]:
"""
Convert tool configs to tools
:param tool_configs: tool configs
:param invoke_from: invoke from
:param callbacks: callbacks
"""
tools = []
for tool_config in tool_configs:
tool = None
if tool_config.tool_id == "dataset":
tool = self.to_dataset_retriever_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "web_reader":
tool = self.to_web_reader_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "google_search":
tool = self.to_google_search_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "wikipedia":
tool = self.to_wikipedia_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "current_datetime":
tool = self.to_current_datetime_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
if tool:
if tool.callbacks is not None:
tool.callbacks.extend(callbacks)
else:
tool.callbacks = callbacks
tools.append(tool)
return tools
def to_dataset_retriever_tool(self, tool_config: dict,
invoke_from: InvokeFrom) \
-> Optional[BaseTool]:
"""
A dataset tool is a tool that can be used to retrieve information from a dataset
:param tool_config: tool config
:param invoke_from: invoke from
"""
show_retrieve_source = self.app_orchestration_config.show_retrieve_source
hit_callback = DatasetIndexToolCallbackHandler(
queue_manager=self.queue_manager,
app_id=self.message.app_id,
message_id=self.message.id,
user_id=self.user_id,
invoke_from=invoke_from
)
# get dataset from dataset id
dataset = db.session.query(Dataset).filter(
Dataset.tenant_id == self.tenant_id,
Dataset.id == tool_config.get("id")
).first()
# pass if dataset is not available
if not dataset:
return None
# pass if dataset is not available
if (dataset and dataset.available_document_count == 0
and dataset.available_document_count == 0):
return None
# get retrieval model config
default_retrieval_model = {
'search_method': 'semantic_search',
'reranking_enable': False,
'reranking_model': {
'reranking_provider_name': '',
'reranking_model_name': ''
},
'top_k': 2,
'score_threshold_enabled': False
}
retrieval_model_config = dataset.retrieval_model \
if dataset.retrieval_model else default_retrieval_model
# get top k
top_k = retrieval_model_config['top_k']
# get score threshold
score_threshold = None
score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled")
if score_threshold_enabled:
score_threshold = retrieval_model_config.get("score_threshold")
tool = DatasetRetrieverTool.from_dataset(
dataset=dataset,
top_k=top_k,
score_threshold=score_threshold,
hit_callbacks=[hit_callback],
return_resource=show_retrieve_source,
retriever_from=invoke_from.to_source()
)
return tool
def to_web_reader_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for reading web pages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
model_parameters = {
"temperature": 0,
"max_tokens": 500
}
tool = WebReaderTool(
model_config=self.model_config,
model_parameters=model_parameters,
max_chunk_length=4000,
continue_reading=True
)
return tool
def to_google_search_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for performing a Google search and extracting snippets and webpages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
tool_provider = SerpAPIToolProvider(tenant_id=self.tenant_id)
func_kwargs = tool_provider.credentials_to_func_kwargs()
if not func_kwargs:
return None
tool = Tool(
name="google_search",
description="A tool for performing a Google search and extracting snippets and webpages "
"when you need to search for something you don't know or when your information "
"is not up to date. "
"Input should be a search query.",
func=OptimizedSerpAPIWrapper(**func_kwargs).run,
args_schema=OptimizedSerpAPIInput
)
return tool
def to_current_datetime_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for getting the current date and time
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
return DatetimeTool()
def to_wikipedia_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for searching Wikipedia
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
class WikipediaInput(BaseModel):
query: str = Field(..., description="search query.")
return WikipediaQueryRun(
name="wikipedia",
api_wrapper=WikipediaAPIWrapper(doc_content_chars_max=4000),
args_schema=WikipediaInput
)