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Commit 02a42a7f authored by John Wang's avatar John Wang
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feat: add multi function call agent

parent 96cd7966
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Showing with 191 additions and 133 deletions
api/core/agent/agent/calc_token_mixin.py
View file @ 02a42a7f
...@@ -8,11 +8,11 @@ from langchain.schema import BaseMessage ...@@ -8,11 +8,11 @@ from langchain.schema import BaseMessage
class CalcTokenMixin: class CalcTokenMixin:
def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage]) -> int: def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
llm = cast(ChatOpenAI, llm) llm = cast(ChatOpenAI, llm)
return llm.get_num_tokens_from_messages(messages) return llm.get_num_tokens_from_messages(messages)
def get_message_rest_tokens(self, llm: BaseLanguageModel, messages: List[BaseMessage]) -> int: def get_message_rest_tokens(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
""" """
Got the rest tokens available for the model after excluding messages tokens and completion max tokens Got the rest tokens available for the model after excluding messages tokens and completion max tokens
...@@ -23,7 +23,7 @@ class CalcTokenMixin: ...@@ -23,7 +23,7 @@ class CalcTokenMixin:
llm = cast(ChatOpenAI, llm) llm = cast(ChatOpenAI, llm)
llm_max_tokens = OpenAI.modelname_to_contextsize(llm.model_name) llm_max_tokens = OpenAI.modelname_to_contextsize(llm.model_name)
completion_max_tokens = llm.max_tokens completion_max_tokens = llm.max_tokens
used_tokens = self.get_num_tokens_from_messages(llm, messages) used_tokens = self.get_num_tokens_from_messages(llm, messages, **kwargs)
rest_tokens = llm_max_tokens - completion_max_tokens - used_tokens rest_tokens = llm_max_tokens - completion_max_tokens - used_tokens
return rest_tokens return rest_tokens
......
api/core/agent/agent/openai_function_call.py
View file @ 02a42a7f
from typing import List, Tuple, Any, Union, cast from typing import List, Tuple, Any, Union
from langchain.agents import OpenAIFunctionsAgent from langchain.agents import OpenAIFunctionsAgent
from langchain.agents.openai_functions_agent.base import _parse_ai_message, \ from langchain.agents.openai_functions_agent.base import _parse_ai_message, \
_format_intermediate_steps _format_intermediate_steps
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import Callbacks from langchain.callbacks.manager import Callbacks
from langchain.chat_models import ChatOpenAI from langchain.schema import AgentAction, AgentFinish
from langchain.chat_models.openai import _convert_message_to_dict
from langchain.memory.summary import SummarizerMixin
from langchain.schema import AgentAction, AgentFinish, BaseMessage, SystemMessage, HumanMessage, AIMessage
from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin): class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
moving_summary_buffer: str = ""
moving_summary_index: int = 0
summary_llm: BaseLanguageModel
def plan( def plan(
self, self,
...@@ -41,128 +35,14 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi ...@@ -41,128 +35,14 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
prompt = self.prompt.format_prompt(**full_inputs) prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages() messages = prompt.to_messages()
# calculate rest tokens and summarize previous function observation messages if rest_tokens < 0 # summarize messages if rest_tokens < 0
rest_tokens = self.get_message_rest_tokens(self.llm, messages) try:
rest_tokens = rest_tokens - 20 # to deal with the inaccuracy of rest_tokens messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
if rest_tokens < 0: except ExceededLLMTokensLimitError as e:
try: return AgentFinish(return_values={"output": str(e)}, log=str(e))
messages = self.summarize_messages(messages)
except ExceededLLMTokensLimitError as e:
return AgentFinish(return_values={"output": str(e)}, log=str(e))
predicted_message = self.llm.predict_messages( predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks messages, functions=self.functions, callbacks=callbacks
) )
agent_decision = _parse_ai_message(predicted_message) agent_decision = _parse_ai_message(predicted_message)
return agent_decision return agent_decision
def summarize_messages(self, messages: List[BaseMessage]) -> List[BaseMessage]:
system_message = None
human_message = None
should_summary_messages = []
for message in messages:
if isinstance(message, SystemMessage):
system_message = message
elif isinstance(message, HumanMessage):
human_message = message
else:
should_summary_messages.append(message)
if len(should_summary_messages) > 2:
ai_message = should_summary_messages[-2]
function_message = should_summary_messages[-1]
should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
self.moving_summary_index = len(should_summary_messages)
else:
error_msg = "Exceeded LLM tokens limit, stopped."
raise ExceededLLMTokensLimitError(error_msg)
new_messages = [system_message, human_message]
if self.moving_summary_index == 0:
should_summary_messages.insert(0, human_message)
summary_handler = SummarizerMixin(llm=self.summary_llm)
self.moving_summary_buffer = summary_handler.predict_new_summary(
messages=should_summary_messages,
existing_summary=self.moving_summary_buffer
)
new_messages.append(AIMessage(content=self.moving_summary_buffer))
new_messages.append(ai_message)
new_messages.append(function_message)
return new_messages
def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage]) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
llm = cast(ChatOpenAI, llm)
model, encoding = llm._get_encoding_model()
if model.startswith("gpt-3.5-turbo"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}."
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
"information on how messages are converted to tokens."
)
num_tokens = 0
for m in messages:
message = _convert_message_to_dict(m)
num_tokens += tokens_per_message
for key, value in message.items():
if key == "function_call":
for f_key, f_value in value.items():
num_tokens += len(encoding.encode(f_key))
num_tokens += len(encoding.encode(f_value))
else:
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
if self.functions:
for function in self.functions:
num_tokens += len(encoding.encode('name'))
num_tokens += len(encoding.encode(function.get("name")))
num_tokens += len(encoding.encode('description'))
num_tokens += len(encoding.encode(function.get("description")))
parameters = function.get("parameters")
num_tokens += len(encoding.encode('parameters'))
if 'title' in parameters:
num_tokens += len(encoding.encode('title'))
num_tokens += len(encoding.encode(parameters.get("title")))
num_tokens += len(encoding.encode('type'))
num_tokens += len(encoding.encode(parameters.get("type")))
if 'properties' in parameters:
num_tokens += len(encoding.encode('properties'))
for key, value in parameters.get('properties').items():
num_tokens += len(encoding.encode(key))
for field_key, field_value in value.items():
num_tokens += len(encoding.encode(field_key))
if field_key == 'enum':
for enum_field in field_value:
num_tokens += 3
num_tokens += len(encoding.encode(enum_field))
else:
num_tokens += len(encoding.encode(field_key))
num_tokens += len(encoding.encode(str(field_value)))
if 'required' in parameters:
num_tokens += len(encoding.encode('required'))
for required_field in parameters['required']:
num_tokens += 3
num_tokens += len(encoding.encode(required_field))
return num_tokens
api/core/agent/agent/openai_function_call_summarize_mixin.py 0 → 100644
View file @ 02a42a7f
from typing import cast, List
from langchain.chat_models import ChatOpenAI
from langchain.chat_models.openai import _convert_message_to_dict
from langchain.memory.summary import SummarizerMixin
from langchain.schema import SystemMessage, HumanMessage, BaseMessage, AIMessage, BaseLanguageModel
from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError, CalcTokenMixin
class OpenAIFunctionCallSummarizeMixin(CalcTokenMixin):
moving_summary_buffer: str = ""
moving_summary_index: int = 0
summary_llm: BaseLanguageModel
def summarize_messages_if_needed(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> List[BaseMessage]:
# calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
rest_tokens = self.get_message_rest_tokens(llm, messages, **kwargs)
rest_tokens = rest_tokens - 20 # to deal with the inaccuracy of rest_tokens
if rest_tokens >= 0:
return messages
system_message = None
human_message = None
should_summary_messages = []
for message in messages:
if isinstance(message, SystemMessage):
system_message = message
elif isinstance(message, HumanMessage):
human_message = message
else:
should_summary_messages.append(message)
if len(should_summary_messages) > 2:
ai_message = should_summary_messages[-2]
function_message = should_summary_messages[-1]
should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
self.moving_summary_index = len(should_summary_messages)
else:
error_msg = "Exceeded LLM tokens limit, stopped."
raise ExceededLLMTokensLimitError(error_msg)
new_messages = [system_message, human_message]
if self.moving_summary_index == 0:
should_summary_messages.insert(0, human_message)
summary_handler = SummarizerMixin(llm=self.summary_llm)
self.moving_summary_buffer = summary_handler.predict_new_summary(
messages=should_summary_messages,
existing_summary=self.moving_summary_buffer
)
new_messages.append(AIMessage(content=self.moving_summary_buffer))
new_messages.append(ai_message)
new_messages.append(function_message)
return new_messages
def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
llm = cast(ChatOpenAI, llm)
model, encoding = llm._get_encoding_model()
if model.startswith("gpt-3.5-turbo"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}."
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
"information on how messages are converted to tokens."
)
num_tokens = 0
for m in messages:
message = _convert_message_to_dict(m)
num_tokens += tokens_per_message
for key, value in message.items():
if key == "function_call":
for f_key, f_value in value.items():
num_tokens += len(encoding.encode(f_key))
num_tokens += len(encoding.encode(f_value))
else:
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
if kwargs.get('functions'):
for function in kwargs.get('functions'):
num_tokens += len(encoding.encode('name'))
num_tokens += len(encoding.encode(function.get("name")))
num_tokens += len(encoding.encode('description'))
num_tokens += len(encoding.encode(function.get("description")))
parameters = function.get("parameters")
num_tokens += len(encoding.encode('parameters'))
if 'title' in parameters:
num_tokens += len(encoding.encode('title'))
num_tokens += len(encoding.encode(parameters.get("title")))
num_tokens += len(encoding.encode('type'))
num_tokens += len(encoding.encode(parameters.get("type")))
if 'properties' in parameters:
num_tokens += len(encoding.encode('properties'))
for key, value in parameters.get('properties').items():
num_tokens += len(encoding.encode(key))
for field_key, field_value in value.items():
num_tokens += len(encoding.encode(field_key))
if field_key == 'enum':
for enum_field in field_value:
num_tokens += 3
num_tokens += len(encoding.encode(enum_field))
else:
num_tokens += len(encoding.encode(field_key))
num_tokens += len(encoding.encode(str(field_value)))
if 'required' in parameters:
num_tokens += len(encoding.encode('required'))
for required_field in parameters['required']:
num_tokens += 3
num_tokens += len(encoding.encode(required_field))
return num_tokens
api/core/agent/agent/openai_multi_function_call.py 0 → 100644
View file @ 02a42a7f
from typing import List, Tuple, Any, Union
from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent, _format_intermediate_steps, \
_parse_ai_message
from langchain.callbacks.manager import Callbacks
from langchain.schema import AgentAction, AgentFinish
from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
class AutoSummarizingOpenMultiAIFunctionCallAgent(OpenAIMultiFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
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.
"""
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()
# summarize messages if rest_tokens < 0
try:
messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
except ExceededLLMTokensLimitError as e:
return AgentFinish(return_values={"output": str(e)}, log=str(e))
predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
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