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feat: optimize completion model agent (#1364)

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pull/1366/head
takatost 3 years ago committed by GitHub
parent 16d80ebab3
commit 07285e5f8b
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 156 additions and 22 deletions
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2
api/core/agent/agent/multi_dataset_router_agent.py
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@ -76,7 +76,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
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:
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:

90
api/core/agent/agent/structed_multi_dataset_router_agent.py
Unescape Escape View File

@ -1,7 +1,7 @@
import re
from typing import List, Tuple, Any, Union, Sequence, Optional, cast
from langchain import BasePromptTemplate
from langchain import BasePromptTemplate, PromptTemplate
from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
from langchain.callbacks.base import BaseCallbackManager
@ -12,6 +12,7 @@ from langchain.tools import BaseTool
from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
from core.chain.llm_chain import LLMChain
from core.model_providers.models.entity.model_params import ModelMode
from core.model_providers.models.llm.base import BaseLLM
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
@ -92,6 +93,10 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
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:
@ -107,6 +112,8 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
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
@ -143,6 +150,61 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
]
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_instance.model_mode == ModelMode.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,
@ -160,15 +222,23 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
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,
)
if model_instance.model_mode == ModelMode.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_instance=model_instance,
prompt=prompt,

86
api/core/agent/agent/structured_chat.py
Unescape Escape View File

@ -1,7 +1,7 @@
import re
from typing import List, Tuple, Any, Union, Sequence, Optional
from typing import List, Tuple, Any, Union, Sequence, Optional, cast
from langchain import BasePromptTemplate
from langchain import BasePromptTemplate, PromptTemplate
from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
from langchain.callbacks.base import BaseCallbackManager
@ -15,6 +15,7 @@ from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
from core.chain.llm_chain import LLMChain
from core.model_providers.models.entity.model_params import ModelMode
from core.model_providers.models.llm.base import BaseLLM
FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
@ -184,6 +185,61 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
]
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_instance.model_mode == ModelMode.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,
@ -201,15 +257,23 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
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,
)
if model_instance.model_mode == ModelMode.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_instance=model_instance,
prompt=prompt,

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