Update docstrings, imports

src/main.py

@@ -4,7 +4,6 @@ import streamlit as st
 from reasoning import ReasoningRouter, default_reasoning_router_config
 load_dotenv(find_dotenv())
 
-openai_api_key = os.getenv("OPENAI_API_KEY")
 
 def run_app():
     """

src/reasoning/__init__.py

@@ -1,5 +1,5 @@
-from .react import ReactStrategy
-from .tree_of_thought import TreeOfThoughtStrategy
-from .chain_of_thought import ChainOfThoughtStrategy
+from .react import ReactStrategy, default_react_config
+from .tree_of_thought import TreeOfThoughtStrategy, default_tot_config
+from .chain_of_thought import ChainOfThoughtStrategy, default_cot_confg
 from .reasoning_router import ReasoningRouter, default_reasoning_router_config
-from .reasoning_strategy import ReasoningStrategy, ReasoningConfig
+from .reasoning_strategy import ReasoningStrategy, ReasoningConfig, default_reasoning_config

src/reasoning/chain_of_thought.py

@@ -12,10 +12,10 @@ class ChainOfThoughtStrategy(ReasoningStrategy):
         self.display("Using 'Chain of Thought'")
 
         template_cot = """You are asked a question and rather than simply guessing the right answer break down the solution into a series of staps
-    The question is {question}
+        The question is {question}
 
-    Write out your step by step reasoning and after considering all of the facts and applying this reasoning write out your final answer
-    """
+        Write out your step by step reasoning and after considering all of the facts and applying this reasoning write out your final answer
+        """
         prompt = PromptTemplate(template=template_cot, input_variables=["question"])
         llm_chain = LLMChain(prompt=prompt, llm=self.llm)
         response_cot = llm_chain.run(question)

src/reasoning/reasoning_router.py

@@ -3,7 +3,7 @@ from .react import ReactStrategy, default_react_config
 from .tree_of_thought import TreeOfThoughtStrategy, default_tot_config
 from .chain_of_thought import ChainOfThoughtStrategy, default_cot_confg
 from .reasoning_strategy import ReasoningConfig
-from typing import Tuple, Callable
+from typing import Tuple, Callable, Optional
 import re
 import os
 
@@ -36,17 +36,25 @@ class ReasoningRouter:
 
         """
         self.template = """
-    Consider the following problem or puzzle: {question}. Based on the characteristics of the problem,
-      identify the most suitable approach among the three techniques described below. consider each carefully 
-      in the context of the question, write out the likelihood of success of each, and then select the most 
-      appropriate approach:""" + self.usage_block + """
-        Based on the characteristics of the given problem or puzzle, select the technique that aligns most closely with the nature of the problem. It is important to first provide the number of the technique that best solves the problem, followed by a period. Then you may provide your reason why you have chosen this technique. 
-
-    The number of the selected technique is...
-    """
+        Consider the following problem or puzzle: {question}. Based on the characteristics of the problem,
+        identify the most suitable approach among the three techniques described below. consider each carefully 
+        in the context of the question, write out the likelihood of success of each, and then select the most 
+        appropriate approach:""" + self.usage_block + """
+            Based on the characteristics of the given problem or puzzle, select the technique that aligns most closely with the nature of the problem. It is important to first provide the number of the technique that best solves the problem, followed by a period. Then you may provide your reason why you have chosen this technique. 
 
+        The number of the selected technique is...
+        """
+        
     @staticmethod
-    def find_first_integer(text):
+    def find_first_integer(text: str) -> Optional[int]:
+        """Finds the first integer in a string.
+
+        Args:
+            text (str): The string to search for an integer.
+
+        Returns:
+            int or None: The first integer found in the string, or None if no integer is found.
+        """
         match = re.search(r'\d+', text)
         if match:
             return int(match.group())

src/reasoning/reasoning_strategy.py

@@ -9,15 +9,13 @@ import os
 
 class ReasoningConfig(BaseModel):
     """
-    A class representing a reasoning strategy for answering questions.
-
-    Args:
-        config (ReasoningConfig): The configuration for the reasoning strategy.
-        display (Callable): A function for displaying output.
+    A configuration class for the reasoning strategy.
 
     Attributes:
-        llm (BaseLLM): The language model used for reasoning.
-        display (Callable): The function for displaying output.
+        temperature (float): The temperature parameter for the language model.
+        max_tokens (int): The maximum number of tokens to generate.
+        llm_class (Type[BaseLLM]): The language model class to use for reasoning.
+        usage (str): String describing when it is appropriate to use this reasoning strategy. 
     """
     temperature: float = 0.7
     max_tokens: int = 1500
@@ -25,6 +23,7 @@ class ReasoningConfig(BaseModel):
     usage: str
 
 class ReasoningStrategy:
+    """Base class for Reasoning Strategies"""
     def __init__(self, config: ReasoningConfig, display: Callable):
         self.llm = config.llm_class(temperature=config.temperature, max_tokens=config.max_tokens) # ign
         self.display = display