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

* 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>

knowledgehub/pipelines/tools/__init__.py

@@ -1,5 +1,6 @@
 from .base import BaseTool, ComponentTool
 from .google import GoogleSearchTool
+from .llm import LLMTool
 from .wikipedia import WikipediaTool
 
-__all__ = ["BaseTool", "ComponentTool", "GoogleSearchTool", "WikipediaTool"]
+__all__ = ["BaseTool", "ComponentTool", "GoogleSearchTool", "WikipediaTool", "LLMTool"]

knowledgehub/pipelines/tools/llm.py

@@ -0,0 +1,36 @@
+from typing import AnyStr, Optional, Type, Union
+
+from pydantic import BaseModel, Field
+
+from kotaemon.llms.chats.base import ChatLLM
+from kotaemon.llms.chats.openai import AzureChatOpenAI
+from kotaemon.llms.completions.base import LLM
+
+from .base import BaseTool, ToolException
+
+BaseLLM = Union[ChatLLM, LLM]
+
+
+class LLMArgs(BaseModel):
+    query: str = Field(..., description="a search question or prompt")
+
+
+class LLMTool(BaseTool):
+    name = "llm"
+    description = (
+        "A pretrained LLM like yourself. Useful when you need to act with "
+        "general world knowledge and common sense. Prioritize it when you "
+        "are confident in solving the problem "
+        "yourself. Input can be any instruction."
+    )
+    llm: BaseLLM = AzureChatOpenAI()
+    args_schema: Optional[Type[BaseModel]] = LLMArgs
+
+    def _run_tool(self, query: AnyStr) -> str:
+        output = None
+        try:
+            response = self.llm(query)
+        except ValueError:
+            raise ToolException("LLM Tool call failed")
+        output = response.text[0]
+        return output