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[AUR-431] Add ReAct Agent (#34)

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* add base Tool

* minor update test_tool

* update test dependency

* update test dependency

* Fix namespace conflict

* update test

* add base Agent Interface, add ReWoo Agent

* minor update

* update test

* fix typo

* remove unneeded print

* update rewoo agent

* add LLMTool

* update BaseAgent type

* add ReAct agent

* add ReAct agent

* minor update

* minor update

* minor update

* minor update

* update docstring

* fix max_iteration

---------

Co-authored-by: trducng <trungduc1992@gmail.com>
This commit is contained in:
Tuan Anh Nguyen Dang (Tadashi_Cin)
2023-10-02 11:29:12 +07:00
committed by GitHub
parent 91048770fa
commit 3cceec63ef
9 changed files with 340 additions and 27 deletions
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22
knowledgehub/llms/chats/base.py
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@@ -34,16 +34,16 @@ class LangchainChatLLM(ChatLLM):
def agent(self) -> BaseLanguageModel:
return self._lc_class(**self._kwargs)
def run_raw(self, text: str) -> LLMInterface:
def run_raw(self, text: str, **kwargs) -> LLMInterface:
message = HumanMessage(content=text)
return self.run_document([message])
return self.run_document([message], **kwargs)
def run_batch_raw(self, text: List[str]) -> List[LLMInterface]:
def run_batch_raw(self, text: List[str], **kwargs) -> List[LLMInterface]:
inputs = [[HumanMessage(content=each)] for each in text]
return self.run_batch_document(inputs)
return self.run_batch_document(inputs, **kwargs)
def run_document(self, text: List[Message]) -> LLMInterface:
pred = self.agent.generate([text]) # type: ignore
def run_document(self, text: List[Message], **kwargs) -> LLMInterface:
pred = self.agent.generate([text], **kwargs) # type: ignore
return LLMInterface(
text=[each.text for each in pred.generations[0]],
completion_tokens=pred.llm_output["token_usage"]["completion_tokens"],
@@ -52,20 +52,22 @@ class LangchainChatLLM(ChatLLM):
logits=[],
)
def run_batch_document(self, text: List[List[Message]]) -> List[LLMInterface]:
def run_batch_document(
self, text: List[List[Message]], **kwargs
) -> List[LLMInterface]:
outputs = []
for each_text in text:
outputs.append(self.run_document(each_text))
outputs.append(self.run_document(each_text, **kwargs))
return outputs
def is_document(self, text) -> bool:
def is_document(self, text, **kwargs) -> bool:
if isinstance(text, str):
return False
elif isinstance(text, List) and isinstance(text[0], str):
return False
return True
def is_batch(self, text) -> bool:
def is_batch(self, text, **kwargs) -> bool:
if isinstance(text, str):
return False
elif isinstance(text, List):