update the documentation strings

lightrag/lightrag/components/reasoning/chain_of_thought.py

@@ -1,4 +1,6 @@
 """
+Chain of the thought(CoT) is to mimic a step-by-step thought process for arriving at the answer.
+
 https://arxiv.org/abs/2201.11903, published in Jan, 2023
 
 Chain of the thought(CoT) is to mimic a step-by-step thought process for arriving at the answer. You can achieve it in two ways:

lightrag/lightrag/core/component.py

@@ -1,3 +1,5 @@
+"""Component is to LLM task pipelines what nn.Module is to PyTorch models."""
+
 from collections import OrderedDict, namedtuple
 from typing import (
     Callable,

lightrag/lightrag/core/default_prompt_template.py

@@ -1,3 +1,8 @@
+"""This is the default system prompt template used in the LightRAG.
+
+Use :ref:`Prompt <core-prompt_builder>` class  to manage it.
+"""
+
 # TODO: potentially make a data class for this
 LIGHTRAG_DEFAULT_PROMPT_ARGS = [
     "task_desc_str",  # task description
@@ -75,5 +80,5 @@
 """
 """This is the default system prompt template used in the LightRAG.
 
-Use :ref:`Prompt<core-prompt_builder>` class  to manage it.
+Use :ref:`Prompt <core-prompt_builder>` class  to manage it.
 """

lightrag/lightrag/core/generator.py

@@ -1,3 +1,6 @@
+"""Generator is a user-facing orchestration component with a simple and unified interface for LLM prediction. 
+
+It is a pipeline that consists of three subcomponents."""
 from typing import Any, Dict, List, Optional, Union
 from copy import deepcopy
 import logging

lightrag/lightrag/core/parameter.py

@@ -1,3 +1,4 @@
+"""WIP"""
 from typing import Generic, TypeVar, Any
 
 T = TypeVar("T")  # covariant set to False to allow for in-place updates

lightrag/lightrag/core/tokenizer.py

@@ -1,3 +1,6 @@
+"""
+Tokenizer from tiktoken.
+"""
 import tiktoken
 from typing import List
 

lightrag/lightrag/core/tool_manager.py

@@ -1,3 +1,6 @@
+"""
+The ToolManager manages a list of tools, context, and all ways to execute functions.
+"""
 from typing import List, Dict, Optional, Any, Callable, Awaitable, Union
 import logging
 from copy import deepcopy

lightrag/lightrag/optim/llm_optimizer.py

@@ -1,5 +1,7 @@
 r"""
-Based and optimized from ORPO llm optimizer: https://arxiv.org/abs/2309.03409
+Based and optimized from ORPO llm optimizer.
+
+https://arxiv.org/abs/2309.03409
 Source code: https://github.com/google-deepmind/opro
 """