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FEAT: NEW WORKFLOW ENGINE (#3160)
Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn> Co-authored-by: JzoNg <jzongcode@gmail.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: nite-knite <nkCoding@gmail.com> Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
0
api/core/rag/retrieval/agent/__init__.py
Normal file
0
api/core/rag/retrieval/agent/__init__.py
Normal file
59
api/core/rag/retrieval/agent/fake_llm.py
Normal file
59
api/core/rag/retrieval/agent/fake_llm.py
Normal file
@@ -0,0 +1,59 @@
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import time
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from collections.abc import Mapping
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from typing import Any, Optional
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.chat_models.base import SimpleChatModel
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from langchain.schema import AIMessage, BaseMessage, ChatGeneration, ChatResult
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class FakeLLM(SimpleChatModel):
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"""Fake ChatModel for testing purposes."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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response: str
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@property
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def _llm_type(self) -> str:
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return "fake-chat-model"
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def _call(
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self,
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messages: list[BaseMessage],
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stop: Optional[list[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""First try to lookup in queries, else return 'foo' or 'bar'."""
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return self.response
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {"response": self.response}
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def get_num_tokens(self, text: str) -> int:
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return 0
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def _generate(
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self,
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messages: list[BaseMessage],
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stop: Optional[list[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
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if self.streaming:
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for token in output_str:
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if run_manager:
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run_manager.on_llm_new_token(token)
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time.sleep(0.01)
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message = AIMessage(content=output_str)
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generation = ChatGeneration(message=message)
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llm_output = {"token_usage": {
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'prompt_tokens': 0,
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'completion_tokens': 0,
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'total_tokens': 0,
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}}
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return ChatResult(generations=[generation], llm_output=llm_output)
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46
api/core/rag/retrieval/agent/llm_chain.py
Normal file
46
api/core/rag/retrieval/agent/llm_chain.py
Normal file
@@ -0,0 +1,46 @@
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from typing import Any, Optional
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from langchain import LLMChain as LCLLMChain
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.schema import Generation, LLMResult
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from langchain.schema.language_model import BaseLanguageModel
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.entities.message_entities import lc_messages_to_prompt_messages
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from core.model_manager import ModelInstance
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from core.rag.retrieval.agent.fake_llm import FakeLLM
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class LLMChain(LCLLMChain):
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model_config: ModelConfigWithCredentialsEntity
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"""The language model instance to use."""
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llm: BaseLanguageModel = FakeLLM(response="")
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parameters: dict[str, Any] = {}
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def generate(
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self,
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input_list: list[dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
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messages = prompts[0].to_messages()
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prompt_messages = lc_messages_to_prompt_messages(messages)
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model_instance = ModelInstance(
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provider_model_bundle=self.model_config.provider_model_bundle,
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model=self.model_config.model,
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)
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result = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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stream=False,
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stop=stop,
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model_parameters=self.parameters
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)
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generations = [
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[Generation(text=result.message.content)]
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]
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return LLMResult(generations=generations)
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179
api/core/rag/retrieval/agent/multi_dataset_router_agent.py
Normal file
179
api/core/rag/retrieval/agent/multi_dataset_router_agent.py
Normal file
@@ -0,0 +1,179 @@
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from collections.abc import Sequence
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from typing import Any, Optional, Union
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from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
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from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.callbacks.manager import Callbacks
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from langchain.prompts.chat import BaseMessagePromptTemplate
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from langchain.schema import AgentAction, AgentFinish, AIMessage, SystemMessage
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from langchain.tools import BaseTool
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from pydantic import root_validator
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.entities.message_entities import lc_messages_to_prompt_messages
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from core.model_manager import ModelInstance
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from core.model_runtime.entities.message_entities import PromptMessageTool
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from core.rag.retrieval.agent.fake_llm import FakeLLM
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class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
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"""
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An Multi Dataset Retrieve Agent driven by Router.
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"""
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model_config: ModelConfigWithCredentialsEntity
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@root_validator
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def validate_llm(cls, values: dict) -> dict:
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return values
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def should_use_agent(self, query: str):
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"""
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return should use agent
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:param query:
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:return:
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"""
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return True
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def plan(
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self,
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intermediate_steps: list[tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
|
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date, along with observations
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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if len(self.tools) == 0:
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return AgentFinish(return_values={"output": ''}, log='')
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elif len(self.tools) == 1:
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tool = next(iter(self.tools))
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rst = tool.run(tool_input={'query': kwargs['input']})
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# output = ''
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# rst_json = json.loads(rst)
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# for item in rst_json:
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# output += f'{item["content"]}\n'
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return AgentFinish(return_values={"output": rst}, log=rst)
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if intermediate_steps:
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_, observation = intermediate_steps[-1]
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return AgentFinish(return_values={"output": observation}, log=observation)
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try:
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agent_decision = self.real_plan(intermediate_steps, callbacks, **kwargs)
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if isinstance(agent_decision, AgentAction):
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tool_inputs = agent_decision.tool_input
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if isinstance(tool_inputs, dict) and 'query' in tool_inputs and 'chat_history' not in kwargs:
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tool_inputs['query'] = kwargs['input']
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agent_decision.tool_input = tool_inputs
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else:
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agent_decision.return_values['output'] = ''
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return agent_decision
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except Exception as e:
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raise e
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def real_plan(
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self,
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intermediate_steps: list[tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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||||
) -> Union[AgentAction, AgentFinish]:
|
||||
"""Given input, decided what to do.
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||||
|
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Args:
|
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intermediate_steps: Steps the LLM has taken to date, along with observations
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||||
**kwargs: User inputs.
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||||
|
||||
Returns:
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Action specifying what tool to use.
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||||
"""
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agent_scratchpad = _format_intermediate_steps(intermediate_steps)
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selected_inputs = {
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k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
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}
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full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
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prompt = self.prompt.format_prompt(**full_inputs)
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messages = prompt.to_messages()
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prompt_messages = lc_messages_to_prompt_messages(messages)
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model_instance = ModelInstance(
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provider_model_bundle=self.model_config.provider_model_bundle,
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model=self.model_config.model,
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)
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tools = []
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for function in self.functions:
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tool = PromptMessageTool(
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**function
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)
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tools.append(tool)
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result = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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tools=tools,
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||||
stream=False,
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model_parameters={
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'temperature': 0.2,
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'top_p': 0.3,
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'max_tokens': 1500
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||||
}
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)
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ai_message = AIMessage(
|
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content=result.message.content or "",
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additional_kwargs={
|
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'function_call': {
|
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'id': result.message.tool_calls[0].id,
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**result.message.tool_calls[0].function.dict()
|
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} if result.message.tool_calls else None
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||||
}
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)
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agent_decision = _parse_ai_message(ai_message)
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return agent_decision
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async def aplan(
|
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self,
|
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intermediate_steps: list[tuple[AgentAction, str]],
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||||
callbacks: Callbacks = None,
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||||
**kwargs: Any,
|
||||
) -> Union[AgentAction, AgentFinish]:
|
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raise NotImplementedError()
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||||
|
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@classmethod
|
||||
def from_llm_and_tools(
|
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cls,
|
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model_config: ModelConfigWithCredentialsEntity,
|
||||
tools: Sequence[BaseTool],
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
|
||||
system_message: Optional[SystemMessage] = SystemMessage(
|
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content="You are a helpful AI assistant."
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||||
),
|
||||
**kwargs: Any,
|
||||
) -> BaseSingleActionAgent:
|
||||
prompt = cls.create_prompt(
|
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extra_prompt_messages=extra_prompt_messages,
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||||
system_message=system_message,
|
||||
)
|
||||
return cls(
|
||||
model_config=model_config,
|
||||
llm=FakeLLM(response=''),
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
callback_manager=callback_manager,
|
||||
**kwargs,
|
||||
)
|
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@@ -0,0 +1,29 @@
|
||||
import json
|
||||
import re
|
||||
from typing import Union
|
||||
|
||||
from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser
|
||||
from langchain.agents.structured_chat.output_parser import logger
|
||||
from langchain.schema import AgentAction, AgentFinish, OutputParserException
|
||||
|
||||
|
||||
class StructuredChatOutputParser(LCStructuredChatOutputParser):
|
||||
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
|
||||
try:
|
||||
action_match = re.search(r"```(\w*)\n?({.*?)```", text, re.DOTALL)
|
||||
if action_match is not None:
|
||||
response = json.loads(action_match.group(2).strip(), strict=False)
|
||||
if isinstance(response, list):
|
||||
# gpt turbo frequently ignores the directive to emit a single action
|
||||
logger.warning("Got multiple action responses: %s", response)
|
||||
response = response[0]
|
||||
if response["action"] == "Final Answer":
|
||||
return AgentFinish({"output": response["action_input"]}, text)
|
||||
else:
|
||||
return AgentAction(
|
||||
response["action"], response.get("action_input", {}), text
|
||||
)
|
||||
else:
|
||||
return AgentFinish({"output": text}, text)
|
||||
except Exception as e:
|
||||
raise OutputParserException(f"Could not parse LLM output: {text}")
|
||||
@@ -0,0 +1,259 @@
|
||||
import re
|
||||
from collections.abc import Sequence
|
||||
from typing import Any, Optional, Union, cast
|
||||
|
||||
from langchain import BasePromptTemplate, PromptTemplate
|
||||
from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
|
||||
from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
|
||||
from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
|
||||
from langchain.schema import AgentAction, AgentFinish, OutputParserException
|
||||
from langchain.tools import BaseTool
|
||||
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.rag.retrieval.agent.llm_chain import LLMChain
|
||||
|
||||
FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||
The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
|
||||
Valid "action" values: "Final Answer" or {tool_names}
|
||||
|
||||
Provide only ONE action per $JSON_BLOB, as shown:
|
||||
|
||||
```
|
||||
{{{{
|
||||
"action": $TOOL_NAME,
|
||||
"action_input": $INPUT
|
||||
}}}}
|
||||
```
|
||||
|
||||
Follow this format:
|
||||
|
||||
Question: input question to answer
|
||||
Thought: consider previous and subsequent steps
|
||||
Action:
|
||||
```
|
||||
$JSON_BLOB
|
||||
```
|
||||
Observation: action result
|
||||
... (repeat Thought/Action/Observation N times)
|
||||
Thought: I know what to respond
|
||||
Action:
|
||||
```
|
||||
{{{{
|
||||
"action": "Final Answer",
|
||||
"action_input": "Final response to human"
|
||||
}}}}
|
||||
```"""
|
||||
|
||||
|
||||
class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
|
||||
dataset_tools: Sequence[BaseTool]
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def should_use_agent(self, query: str):
|
||||
"""
|
||||
return should use agent
|
||||
Using the ReACT mode to determine whether an agent is needed is costly,
|
||||
so it's better to just use an Agent for reasoning, which is cheaper.
|
||||
|
||||
:param query:
|
||||
:return:
|
||||
"""
|
||||
return True
|
||||
|
||||
def plan(
|
||||
self,
|
||||
intermediate_steps: list[tuple[AgentAction, str]],
|
||||
callbacks: Callbacks = None,
|
||||
**kwargs: Any,
|
||||
) -> Union[AgentAction, AgentFinish]:
|
||||
"""Given input, decided what to do.
|
||||
|
||||
Args:
|
||||
intermediate_steps: Steps the LLM has taken to date,
|
||||
along with observations
|
||||
callbacks: Callbacks to run.
|
||||
**kwargs: User inputs.
|
||||
|
||||
Returns:
|
||||
Action specifying what tool to use.
|
||||
"""
|
||||
if len(self.dataset_tools) == 0:
|
||||
return AgentFinish(return_values={"output": ''}, log='')
|
||||
elif len(self.dataset_tools) == 1:
|
||||
tool = next(iter(self.dataset_tools))
|
||||
rst = tool.run(tool_input={'query': kwargs['input']})
|
||||
return AgentFinish(return_values={"output": rst}, log=rst)
|
||||
|
||||
if intermediate_steps:
|
||||
_, observation = intermediate_steps[-1]
|
||||
return AgentFinish(return_values={"output": observation}, log=observation)
|
||||
|
||||
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
|
||||
|
||||
try:
|
||||
full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
try:
|
||||
agent_decision = self.output_parser.parse(full_output)
|
||||
if isinstance(agent_decision, AgentAction):
|
||||
tool_inputs = agent_decision.tool_input
|
||||
if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
|
||||
tool_inputs['query'] = kwargs['input']
|
||||
agent_decision.tool_input = tool_inputs
|
||||
elif isinstance(tool_inputs, str):
|
||||
agent_decision.tool_input = kwargs['input']
|
||||
else:
|
||||
agent_decision.return_values['output'] = ''
|
||||
return agent_decision
|
||||
except OutputParserException:
|
||||
return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
|
||||
"I don't know how to respond to that."}, "")
|
||||
|
||||
@classmethod
|
||||
def create_prompt(
|
||||
cls,
|
||||
tools: Sequence[BaseTool],
|
||||
prefix: str = PREFIX,
|
||||
suffix: str = SUFFIX,
|
||||
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
|
||||
format_instructions: str = FORMAT_INSTRUCTIONS,
|
||||
input_variables: Optional[list[str]] = None,
|
||||
memory_prompts: Optional[list[BasePromptTemplate]] = None,
|
||||
) -> BasePromptTemplate:
|
||||
tool_strings = []
|
||||
for tool in tools:
|
||||
args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
|
||||
tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
|
||||
formatted_tools = "\n".join(tool_strings)
|
||||
unique_tool_names = set(tool.name for tool in tools)
|
||||
tool_names = ", ".join('"' + name + '"' for name in unique_tool_names)
|
||||
format_instructions = format_instructions.format(tool_names=tool_names)
|
||||
template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
|
||||
if input_variables is None:
|
||||
input_variables = ["input", "agent_scratchpad"]
|
||||
_memory_prompts = memory_prompts or []
|
||||
messages = [
|
||||
SystemMessagePromptTemplate.from_template(template),
|
||||
*_memory_prompts,
|
||||
HumanMessagePromptTemplate.from_template(human_message_template),
|
||||
]
|
||||
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
|
||||
|
||||
@classmethod
|
||||
def create_completion_prompt(
|
||||
cls,
|
||||
tools: Sequence[BaseTool],
|
||||
prefix: str = PREFIX,
|
||||
format_instructions: str = FORMAT_INSTRUCTIONS,
|
||||
input_variables: Optional[list[str]] = None,
|
||||
) -> PromptTemplate:
|
||||
"""Create prompt in the style of the zero shot agent.
|
||||
|
||||
Args:
|
||||
tools: List of tools the agent will have access to, used to format the
|
||||
prompt.
|
||||
prefix: String to put before the list of tools.
|
||||
input_variables: List of input variables the final prompt will expect.
|
||||
|
||||
Returns:
|
||||
A PromptTemplate with the template assembled from the pieces here.
|
||||
"""
|
||||
suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||
Question: {input}
|
||||
Thought: {agent_scratchpad}
|
||||
"""
|
||||
|
||||
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
|
||||
tool_names = ", ".join([tool.name for tool in tools])
|
||||
format_instructions = format_instructions.format(tool_names=tool_names)
|
||||
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
|
||||
if input_variables is None:
|
||||
input_variables = ["input", "agent_scratchpad"]
|
||||
return PromptTemplate(template=template, input_variables=input_variables)
|
||||
|
||||
def _construct_scratchpad(
|
||||
self, intermediate_steps: list[tuple[AgentAction, str]]
|
||||
) -> str:
|
||||
agent_scratchpad = ""
|
||||
for action, observation in intermediate_steps:
|
||||
agent_scratchpad += action.log
|
||||
agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
|
||||
|
||||
if not isinstance(agent_scratchpad, str):
|
||||
raise ValueError("agent_scratchpad should be of type string.")
|
||||
if agent_scratchpad:
|
||||
llm_chain = cast(LLMChain, self.llm_chain)
|
||||
if llm_chain.model_config.mode == "chat":
|
||||
return (
|
||||
f"This was your previous work "
|
||||
f"(but I haven't seen any of it! I only see what "
|
||||
f"you return as final answer):\n{agent_scratchpad}"
|
||||
)
|
||||
else:
|
||||
return agent_scratchpad
|
||||
else:
|
||||
return agent_scratchpad
|
||||
|
||||
@classmethod
|
||||
def from_llm_and_tools(
|
||||
cls,
|
||||
model_config: ModelConfigWithCredentialsEntity,
|
||||
tools: Sequence[BaseTool],
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
output_parser: Optional[AgentOutputParser] = None,
|
||||
prefix: str = PREFIX,
|
||||
suffix: str = SUFFIX,
|
||||
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
|
||||
format_instructions: str = FORMAT_INSTRUCTIONS,
|
||||
input_variables: Optional[list[str]] = None,
|
||||
memory_prompts: Optional[list[BasePromptTemplate]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Agent:
|
||||
"""Construct an agent from an LLM and tools."""
|
||||
cls._validate_tools(tools)
|
||||
if model_config.mode == "chat":
|
||||
prompt = cls.create_prompt(
|
||||
tools,
|
||||
prefix=prefix,
|
||||
suffix=suffix,
|
||||
human_message_template=human_message_template,
|
||||
format_instructions=format_instructions,
|
||||
input_variables=input_variables,
|
||||
memory_prompts=memory_prompts,
|
||||
)
|
||||
else:
|
||||
prompt = cls.create_completion_prompt(
|
||||
tools,
|
||||
prefix=prefix,
|
||||
format_instructions=format_instructions,
|
||||
input_variables=input_variables
|
||||
)
|
||||
|
||||
llm_chain = LLMChain(
|
||||
model_config=model_config,
|
||||
prompt=prompt,
|
||||
callback_manager=callback_manager,
|
||||
parameters={
|
||||
'temperature': 0.2,
|
||||
'top_p': 0.3,
|
||||
'max_tokens': 1500
|
||||
}
|
||||
)
|
||||
tool_names = [tool.name for tool in tools]
|
||||
_output_parser = output_parser
|
||||
return cls(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
output_parser=_output_parser,
|
||||
dataset_tools=tools,
|
||||
**kwargs,
|
||||
)
|
||||
Reference in New Issue
Block a user