mirror of
http://112.124.100.131/huang.ze/ebiz-dify-ai.git
synced 2025-12-10 03:16:51 +08:00
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
@@ -9,7 +9,7 @@ import pandas as pd
|
||||
from flask import Flask, current_app
|
||||
from werkzeug.datastructures import FileStorage
|
||||
|
||||
from core.generator.llm_generator import LLMGenerator
|
||||
from core.llm_generator.llm_generator import LLMGenerator
|
||||
from core.rag.cleaner.clean_processor import CleanProcessor
|
||||
from core.rag.datasource.retrieval_service import RetrievalService
|
||||
from core.rag.datasource.vdb.vector_factory import Vector
|
||||
|
||||
0
api/core/rag/retrieval/__init__.py
Normal file
0
api/core/rag/retrieval/__init__.py
Normal file
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 @@
|
||||
import time
|
||||
from collections.abc import Mapping
|
||||
from typing import Any, Optional
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.chat_models.base import SimpleChatModel
|
||||
from langchain.schema import AIMessage, BaseMessage, ChatGeneration, ChatResult
|
||||
|
||||
|
||||
class FakeLLM(SimpleChatModel):
|
||||
"""Fake ChatModel for testing purposes."""
|
||||
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results or not."""
|
||||
response: str
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "fake-chat-model"
|
||||
|
||||
def _call(
|
||||
self,
|
||||
messages: list[BaseMessage],
|
||||
stop: Optional[list[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""First try to lookup in queries, else return 'foo' or 'bar'."""
|
||||
return self.response
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
return {"response": self.response}
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
return 0
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: list[BaseMessage],
|
||||
stop: Optional[list[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
|
||||
if self.streaming:
|
||||
for token in output_str:
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token)
|
||||
time.sleep(0.01)
|
||||
|
||||
message = AIMessage(content=output_str)
|
||||
generation = ChatGeneration(message=message)
|
||||
llm_output = {"token_usage": {
|
||||
'prompt_tokens': 0,
|
||||
'completion_tokens': 0,
|
||||
'total_tokens': 0,
|
||||
}}
|
||||
return ChatResult(generations=[generation], llm_output=llm_output)
|
||||
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 @@
|
||||
from typing import Any, Optional
|
||||
|
||||
from langchain import LLMChain as LCLLMChain
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
from langchain.schema import Generation, LLMResult
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.entities.message_entities import lc_messages_to_prompt_messages
|
||||
from core.model_manager import ModelInstance
|
||||
from core.rag.retrieval.agent.fake_llm import FakeLLM
|
||||
|
||||
|
||||
class LLMChain(LCLLMChain):
|
||||
model_config: ModelConfigWithCredentialsEntity
|
||||
"""The language model instance to use."""
|
||||
llm: BaseLanguageModel = FakeLLM(response="")
|
||||
parameters: dict[str, Any] = {}
|
||||
|
||||
def generate(
|
||||
self,
|
||||
input_list: list[dict[str, Any]],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> LLMResult:
|
||||
"""Generate LLM result from inputs."""
|
||||
prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
|
||||
messages = prompts[0].to_messages()
|
||||
prompt_messages = lc_messages_to_prompt_messages(messages)
|
||||
|
||||
model_instance = ModelInstance(
|
||||
provider_model_bundle=self.model_config.provider_model_bundle,
|
||||
model=self.model_config.model,
|
||||
)
|
||||
|
||||
result = model_instance.invoke_llm(
|
||||
prompt_messages=prompt_messages,
|
||||
stream=False,
|
||||
stop=stop,
|
||||
model_parameters=self.parameters
|
||||
)
|
||||
|
||||
generations = [
|
||||
[Generation(text=result.message.content)]
|
||||
]
|
||||
|
||||
return LLMResult(generations=generations)
|
||||
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 @@
|
||||
from collections.abc import Sequence
|
||||
from typing import Any, Optional, Union
|
||||
|
||||
from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
|
||||
from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.prompts.chat import BaseMessagePromptTemplate
|
||||
from langchain.schema import AgentAction, AgentFinish, AIMessage, SystemMessage
|
||||
from langchain.tools import BaseTool
|
||||
from pydantic import root_validator
|
||||
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.entities.message_entities import lc_messages_to_prompt_messages
|
||||
from core.model_manager import ModelInstance
|
||||
from core.model_runtime.entities.message_entities import PromptMessageTool
|
||||
from core.rag.retrieval.agent.fake_llm import FakeLLM
|
||||
|
||||
|
||||
class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
|
||||
"""
|
||||
An Multi Dataset Retrieve Agent driven by Router.
|
||||
"""
|
||||
model_config: ModelConfigWithCredentialsEntity
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator
|
||||
def validate_llm(cls, values: dict) -> dict:
|
||||
return values
|
||||
|
||||
def should_use_agent(self, query: str):
|
||||
"""
|
||||
return should use agent
|
||||
|
||||
: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
|
||||
**kwargs: User inputs.
|
||||
|
||||
Returns:
|
||||
Action specifying what tool to use.
|
||||
"""
|
||||
if len(self.tools) == 0:
|
||||
return AgentFinish(return_values={"output": ''}, log='')
|
||||
elif len(self.tools) == 1:
|
||||
tool = next(iter(self.tools))
|
||||
rst = tool.run(tool_input={'query': kwargs['input']})
|
||||
# output = ''
|
||||
# rst_json = json.loads(rst)
|
||||
# for item in rst_json:
|
||||
# output += f'{item["content"]}\n'
|
||||
return AgentFinish(return_values={"output": rst}, log=rst)
|
||||
|
||||
if intermediate_steps:
|
||||
_, observation = intermediate_steps[-1]
|
||||
return AgentFinish(return_values={"output": observation}, log=observation)
|
||||
|
||||
try:
|
||||
agent_decision = self.real_plan(intermediate_steps, callbacks, **kwargs)
|
||||
if isinstance(agent_decision, AgentAction):
|
||||
tool_inputs = agent_decision.tool_input
|
||||
if isinstance(tool_inputs, dict) and 'query' in tool_inputs and 'chat_history' not in kwargs:
|
||||
tool_inputs['query'] = kwargs['input']
|
||||
agent_decision.tool_input = tool_inputs
|
||||
else:
|
||||
agent_decision.return_values['output'] = ''
|
||||
return agent_decision
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def real_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
|
||||
**kwargs: User inputs.
|
||||
|
||||
Returns:
|
||||
Action specifying what tool to use.
|
||||
"""
|
||||
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
|
||||
selected_inputs = {
|
||||
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
|
||||
}
|
||||
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
|
||||
prompt = self.prompt.format_prompt(**full_inputs)
|
||||
messages = prompt.to_messages()
|
||||
prompt_messages = lc_messages_to_prompt_messages(messages)
|
||||
|
||||
model_instance = ModelInstance(
|
||||
provider_model_bundle=self.model_config.provider_model_bundle,
|
||||
model=self.model_config.model,
|
||||
)
|
||||
|
||||
tools = []
|
||||
for function in self.functions:
|
||||
tool = PromptMessageTool(
|
||||
**function
|
||||
)
|
||||
|
||||
tools.append(tool)
|
||||
|
||||
result = model_instance.invoke_llm(
|
||||
prompt_messages=prompt_messages,
|
||||
tools=tools,
|
||||
stream=False,
|
||||
model_parameters={
|
||||
'temperature': 0.2,
|
||||
'top_p': 0.3,
|
||||
'max_tokens': 1500
|
||||
}
|
||||
)
|
||||
|
||||
ai_message = AIMessage(
|
||||
content=result.message.content or "",
|
||||
additional_kwargs={
|
||||
'function_call': {
|
||||
'id': result.message.tool_calls[0].id,
|
||||
**result.message.tool_calls[0].function.dict()
|
||||
} if result.message.tool_calls else None
|
||||
}
|
||||
)
|
||||
|
||||
agent_decision = _parse_ai_message(ai_message)
|
||||
return agent_decision
|
||||
|
||||
async def aplan(
|
||||
self,
|
||||
intermediate_steps: list[tuple[AgentAction, str]],
|
||||
callbacks: Callbacks = None,
|
||||
**kwargs: Any,
|
||||
) -> Union[AgentAction, AgentFinish]:
|
||||
raise NotImplementedError()
|
||||
|
||||
@classmethod
|
||||
def from_llm_and_tools(
|
||||
cls,
|
||||
model_config: ModelConfigWithCredentialsEntity,
|
||||
tools: Sequence[BaseTool],
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
|
||||
system_message: Optional[SystemMessage] = SystemMessage(
|
||||
content="You are a helpful AI assistant."
|
||||
),
|
||||
**kwargs: Any,
|
||||
) -> BaseSingleActionAgent:
|
||||
prompt = cls.create_prompt(
|
||||
extra_prompt_messages=extra_prompt_messages,
|
||||
system_message=system_message,
|
||||
)
|
||||
return cls(
|
||||
model_config=model_config,
|
||||
llm=FakeLLM(response=''),
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
callback_manager=callback_manager,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -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,
|
||||
)
|
||||
117
api/core/rag/retrieval/agent_based_dataset_executor.py
Normal file
117
api/core/rag/retrieval/agent_based_dataset_executor.py
Normal file
@@ -0,0 +1,117 @@
|
||||
import logging
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain.agents import AgentExecutor as LCAgentExecutor
|
||||
from langchain.agents import BaseMultiActionAgent, BaseSingleActionAgent
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.tools import BaseTool
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
from core.entities.agent_entities import PlanningStrategy
|
||||
from core.entities.message_entities import prompt_messages_to_lc_messages
|
||||
from core.helper import moderation
|
||||
from core.memory.token_buffer_memory import TokenBufferMemory
|
||||
from core.model_runtime.errors.invoke import InvokeError
|
||||
from core.rag.retrieval.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
|
||||
from core.rag.retrieval.agent.output_parser.structured_chat import StructuredChatOutputParser
|
||||
from core.rag.retrieval.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
|
||||
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
|
||||
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
|
||||
|
||||
|
||||
class AgentConfiguration(BaseModel):
|
||||
strategy: PlanningStrategy
|
||||
model_config: ModelConfigWithCredentialsEntity
|
||||
tools: list[BaseTool]
|
||||
summary_model_config: Optional[ModelConfigWithCredentialsEntity] = None
|
||||
memory: Optional[TokenBufferMemory] = None
|
||||
callbacks: Callbacks = None
|
||||
max_iterations: int = 6
|
||||
max_execution_time: Optional[float] = None
|
||||
early_stopping_method: str = "generate"
|
||||
# `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
|
||||
class AgentExecuteResult(BaseModel):
|
||||
strategy: PlanningStrategy
|
||||
output: Optional[str]
|
||||
configuration: AgentConfiguration
|
||||
|
||||
|
||||
class AgentExecutor:
|
||||
def __init__(self, configuration: AgentConfiguration):
|
||||
self.configuration = configuration
|
||||
self.agent = self._init_agent()
|
||||
|
||||
def _init_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
|
||||
if self.configuration.strategy == PlanningStrategy.ROUTER:
|
||||
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_config=self.configuration.model_config,
|
||||
tools=self.configuration.tools,
|
||||
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)]
|
||||
agent = StructuredMultiDatasetRouterAgent.from_llm_and_tools(
|
||||
model_config=self.configuration.model_config,
|
||||
tools=self.configuration.tools,
|
||||
output_parser=StructuredChatOutputParser(),
|
||||
verbose=True
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown Agent Strategy: {self.configuration.strategy}")
|
||||
|
||||
return agent
|
||||
|
||||
def should_use_agent(self, query: str) -> bool:
|
||||
return self.agent.should_use_agent(query)
|
||||
|
||||
def run(self, query: str) -> AgentExecuteResult:
|
||||
moderation_result = moderation.check_moderation(
|
||||
self.configuration.model_config,
|
||||
query
|
||||
)
|
||||
|
||||
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,
|
||||
configuration=self.configuration
|
||||
)
|
||||
|
||||
agent_executor = LCAgentExecutor.from_agent_and_tools(
|
||||
agent=self.agent,
|
||||
tools=self.configuration.tools,
|
||||
max_iterations=self.configuration.max_iterations,
|
||||
max_execution_time=self.configuration.max_execution_time,
|
||||
early_stopping_method=self.configuration.early_stopping_method,
|
||||
callbacks=self.configuration.callbacks
|
||||
)
|
||||
|
||||
try:
|
||||
output = agent_executor.run(input=query)
|
||||
except InvokeError as ex:
|
||||
raise ex
|
||||
except Exception as ex:
|
||||
logging.exception("agent_executor run failed")
|
||||
output = None
|
||||
|
||||
return AgentExecuteResult(
|
||||
output=output,
|
||||
strategy=self.configuration.strategy,
|
||||
configuration=self.configuration
|
||||
)
|
||||
182
api/core/rag/retrieval/dataset_retrieval.py
Normal file
182
api/core/rag/retrieval/dataset_retrieval.py
Normal file
@@ -0,0 +1,182 @@
|
||||
from typing import Optional, cast
|
||||
|
||||
from langchain.tools import BaseTool
|
||||
|
||||
from core.app.app_config.entities import DatasetEntity, DatasetRetrieveConfigEntity
|
||||
from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
|
||||
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
|
||||
from core.entities.agent_entities import PlanningStrategy
|
||||
from core.memory.token_buffer_memory import TokenBufferMemory
|
||||
from core.model_runtime.entities.model_entities import ModelFeature
|
||||
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
|
||||
from core.rag.retrieval.agent_based_dataset_executor import AgentConfiguration, AgentExecutor
|
||||
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
|
||||
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
|
||||
from extensions.ext_database import db
|
||||
from models.dataset import Dataset
|
||||
|
||||
|
||||
class DatasetRetrieval:
|
||||
def retrieve(self, tenant_id: str,
|
||||
model_config: ModelConfigWithCredentialsEntity,
|
||||
config: DatasetEntity,
|
||||
query: str,
|
||||
invoke_from: InvokeFrom,
|
||||
show_retrieve_source: bool,
|
||||
hit_callback: DatasetIndexToolCallbackHandler,
|
||||
memory: Optional[TokenBufferMemory] = None) -> Optional[str]:
|
||||
"""
|
||||
Retrieve dataset.
|
||||
:param tenant_id: tenant id
|
||||
:param model_config: model config
|
||||
:param config: dataset config
|
||||
:param query: query
|
||||
:param invoke_from: invoke from
|
||||
:param show_retrieve_source: show retrieve source
|
||||
:param hit_callback: hit callback
|
||||
:param memory: memory
|
||||
:return:
|
||||
"""
|
||||
dataset_ids = config.dataset_ids
|
||||
retrieve_config = config.retrieve_config
|
||||
|
||||
# check model is support tool calling
|
||||
model_type_instance = model_config.provider_model_bundle.model_type_instance
|
||||
model_type_instance = cast(LargeLanguageModel, model_type_instance)
|
||||
|
||||
# get model schema
|
||||
model_schema = model_type_instance.get_model_schema(
|
||||
model=model_config.model,
|
||||
credentials=model_config.credentials
|
||||
)
|
||||
|
||||
if not model_schema:
|
||||
return None
|
||||
|
||||
planning_strategy = PlanningStrategy.REACT_ROUTER
|
||||
features = model_schema.features
|
||||
if features:
|
||||
if ModelFeature.TOOL_CALL in features \
|
||||
or ModelFeature.MULTI_TOOL_CALL in features:
|
||||
planning_strategy = PlanningStrategy.ROUTER
|
||||
|
||||
dataset_retriever_tools = self.to_dataset_retriever_tool(
|
||||
tenant_id=tenant_id,
|
||||
dataset_ids=dataset_ids,
|
||||
retrieve_config=retrieve_config,
|
||||
return_resource=show_retrieve_source,
|
||||
invoke_from=invoke_from,
|
||||
hit_callback=hit_callback
|
||||
)
|
||||
|
||||
if len(dataset_retriever_tools) == 0:
|
||||
return None
|
||||
|
||||
agent_configuration = AgentConfiguration(
|
||||
strategy=planning_strategy,
|
||||
model_config=model_config,
|
||||
tools=dataset_retriever_tools,
|
||||
memory=memory,
|
||||
max_iterations=10,
|
||||
max_execution_time=400.0,
|
||||
early_stopping_method="generate"
|
||||
)
|
||||
|
||||
agent_executor = AgentExecutor(agent_configuration)
|
||||
|
||||
should_use_agent = agent_executor.should_use_agent(query)
|
||||
if not should_use_agent:
|
||||
return None
|
||||
|
||||
result = agent_executor.run(query)
|
||||
|
||||
return result.output
|
||||
|
||||
def to_dataset_retriever_tool(self, tenant_id: str,
|
||||
dataset_ids: list[str],
|
||||
retrieve_config: DatasetRetrieveConfigEntity,
|
||||
return_resource: bool,
|
||||
invoke_from: InvokeFrom,
|
||||
hit_callback: DatasetIndexToolCallbackHandler) \
|
||||
-> Optional[list[BaseTool]]:
|
||||
"""
|
||||
A dataset tool is a tool that can be used to retrieve information from a dataset
|
||||
:param tenant_id: tenant id
|
||||
:param dataset_ids: dataset ids
|
||||
:param retrieve_config: retrieve config
|
||||
:param return_resource: return resource
|
||||
:param invoke_from: invoke from
|
||||
:param hit_callback: hit callback
|
||||
"""
|
||||
tools = []
|
||||
available_datasets = []
|
||||
for dataset_id in dataset_ids:
|
||||
# get dataset from dataset id
|
||||
dataset = db.session.query(Dataset).filter(
|
||||
Dataset.tenant_id == tenant_id,
|
||||
Dataset.id == dataset_id
|
||||
).first()
|
||||
|
||||
# pass if dataset is not available
|
||||
if not dataset:
|
||||
continue
|
||||
|
||||
# pass if dataset is not available
|
||||
if (dataset and dataset.available_document_count == 0
|
||||
and dataset.available_document_count == 0):
|
||||
continue
|
||||
|
||||
available_datasets.append(dataset)
|
||||
|
||||
if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE:
|
||||
# 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
|
||||
}
|
||||
|
||||
for dataset in available_datasets:
|
||||
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=return_resource,
|
||||
retriever_from=invoke_from.to_source()
|
||||
)
|
||||
|
||||
tools.append(tool)
|
||||
elif retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE:
|
||||
tool = DatasetMultiRetrieverTool.from_dataset(
|
||||
dataset_ids=[dataset.id for dataset in available_datasets],
|
||||
tenant_id=tenant_id,
|
||||
top_k=retrieve_config.top_k or 2,
|
||||
score_threshold=retrieve_config.score_threshold,
|
||||
hit_callbacks=[hit_callback],
|
||||
return_resource=return_resource,
|
||||
retriever_from=invoke_from.to_source(),
|
||||
reranking_provider_name=retrieve_config.reranking_model.get('reranking_provider_name'),
|
||||
reranking_model_name=retrieve_config.reranking_model.get('reranking_model_name')
|
||||
)
|
||||
|
||||
tools.append(tool)
|
||||
|
||||
return tools
|
||||
Reference in New Issue
Block a user