experimental: docstrings update (#18048)

Added missed docstrings. Formatted docsctrings to the consistent format.

libs/experimental/langchain_experimental/tot/__init__.py

@@ -1,3 +1,12 @@
+"""Implementation of a Tree of Thought (ToT) chain based on the paper
+"Large Language Model Guided Tree-of-Thought"
+
+https://arxiv.org/pdf/2305.08291.pdf
+
+The Tree of Thought (ToT) chain uses a tree structure to explore the space of
+possible solutions to a problem.
+
+"""
 from langchain_experimental.tot.base import ToTChain
 from langchain_experimental.tot.checker import ToTChecker
 

libs/experimental/langchain_experimental/tot/base.py

@@ -1,14 +1,3 @@
-"""
-This a Tree of Thought (ToT) chain based on the paper "Large Language Model
-Guided Tree-of-Thought"
-
-https://arxiv.org/pdf/2305.08291.pdf
-
-The Tree of Thought (ToT) chain uses a tree structure to explore the space of
-possible solutions to a problem.
-
-"""
-
 from __future__ import annotations
 
 from textwrap import indent
@@ -34,7 +23,7 @@ from langchain_experimental.tot.thought_generation import (
 
 class ToTChain(Chain):
     """
-    A Chain implementing the Tree of Thought (ToT).
+    Chain implementing the Tree of Thought (ToT).
     """
 
     llm: BaseLanguageModel

libs/experimental/langchain_experimental/tot/memory.py

@@ -7,7 +7,9 @@ from langchain_experimental.tot.thought import Thought
 
 class ToTDFSMemory:
     """
-    Memory for the Tree of Thought (ToT) chain. Implemented as a stack of
+    Memory for the Tree of Thought (ToT) chain.
+
+    It is implemented as a stack of
     thoughts. This allows for a depth first search (DFS) of the ToT.
     """
 

libs/experimental/langchain_experimental/tot/prompts.py

@@ -9,6 +9,8 @@ from langchain_experimental.tot.thought import ThoughtValidity
 
 
 def get_cot_prompt() -> PromptTemplate:
+    """Get the prompt for the Chain of Thought (CoT) chain."""
+
     return PromptTemplate(
         template_format="jinja2",
         input_variables=["problem_description", "thoughts"],
@@ -36,7 +38,7 @@ def get_cot_prompt() -> PromptTemplate:
 
 
 class JSONListOutputParser(BaseOutputParser):
-    """Class to parse the output of a PROPOSE_PROMPT response."""
+    """Parse the output of a PROPOSE_PROMPT response."""
 
     @property
     def _type(self) -> str:
@@ -53,6 +55,8 @@ class JSONListOutputParser(BaseOutputParser):
 
 
 def get_propose_prompt() -> PromptTemplate:
+    """Get the prompt for the PROPOSE_PROMPT chain."""
+
     return PromptTemplate(
         template_format="jinja2",
         input_variables=["problem_description", "thoughts", "n"],
@@ -95,6 +99,8 @@ def get_propose_prompt() -> PromptTemplate:
 
 
 class CheckerOutputParser(BaseOutputParser):
+    """Parse and check the output of the language model."""
+
     def parse(self, text: str) -> ThoughtValidity:
         """Parse the output of the language model."""
         text = text.upper()

libs/experimental/langchain_experimental/tot/thought.py

@@ -7,12 +7,16 @@ from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
 class ThoughtValidity(Enum):
+    """Enum for the validity of a thought."""
+
     VALID_INTERMEDIATE = 0
     VALID_FINAL = 1
     INVALID = 2
 
 
 class Thought(BaseModel):
+    """A thought in the ToT."""
+
     text: str
     validity: ThoughtValidity
     children: Set[Thought] = Field(default_factory=set)

libs/experimental/langchain_experimental/tot/thought_generation.py

@@ -39,7 +39,7 @@ class BaseThoughtGenerationStrategy(LLMChain):
 
 class SampleCoTStrategy(BaseThoughtGenerationStrategy):
     """
-    Sample thoughts from a Chain-of-Thought (CoT) prompt.
+    Sample strategy from a Chain-of-Thought (CoT) prompt.
 
     This strategy works better when the thought space is rich, such as when each
     thought is a paragraph. Independent and identically distributed samples
@@ -62,7 +62,7 @@ class SampleCoTStrategy(BaseThoughtGenerationStrategy):
 
 class ProposePromptStrategy(BaseThoughtGenerationStrategy):
     """
-    Propose thoughts sequentially using a "propose prompt".
+    Strategy that is sequentially using a "propose prompt".
 
     This strategy works better when the thought space is more constrained, such
     as when each thought is just a word or a line. Proposing different thoughts