experimental[patch]: update module doc strings (#19539)

Added missed module descriptions. Fixed format.

libs/experimental/langchain_experimental/agents/__init__.py

@@ -1,3 +1,12 @@
+"""**Agent** is a class that uses an LLM to choose 
+a sequence of actions to take.
+
+In Chains, a sequence of actions is hardcoded. In Agents,
+a language model is used as a reasoning engine to determine which actions
+to take and in which order.
+
+Agents select and use **Tools** and **Toolkits** for actions.
+"""  # noqa: E501
 from langchain_experimental.agents.agent_toolkits import (
     create_csv_agent,
     create_pandas_dataframe_agent,

libs/experimental/langchain_experimental/autonomous_agents/__init__.py

@@ -1,3 +1,15 @@
+"""**Autonomous agents** in the Langchain experimental package include
+[AutoGPT](https://github.com/Significant-Gravitas/AutoGPT),
+[BabyAGI](https://github.com/yoheinakajima/babyagi),
+and [HuggingGPT](https://arxiv.org/abs/2303.17580) agents that
+interact with language models autonomously.
+
+These agents have specific functionalities like memory management,
+task creation, execution chains, and response generation.
+
+They differ from ordinary agents by their autonomous decision-making capabilities,
+memory handling, and specialized functionalities for tasks and response.
+"""
 from langchain_experimental.autonomous_agents.autogpt.agent import AutoGPT
 from langchain_experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
 from langchain_experimental.autonomous_agents.hugginggpt.hugginggpt import HuggingGPT

libs/experimental/langchain_experimental/comprehend_moderation/__init__.py

@@ -1,3 +1,23 @@
+"""
+**Comprehend Moderation** is used to detect and handle `Personally Identifiable Information (PII)`,
+`toxicity`, and `prompt safety` in text.
+
+The Langchain experimental package includes the **AmazonComprehendModerationChain** class
+for the comprehend moderation tasks. It is based on `Amazon Comprehend` service.
+This class can be configured with specific moderation settings like PII labels, redaction,
+toxicity thresholds, and prompt safety thresholds.
+
+See more at https://aws.amazon.com/comprehend/
+
+`Amazon Comprehend` service is used by several other classes:
+- **ComprehendToxicity** class is used to check the toxicity of text prompts using
+  `AWS Comprehend service` and take actions based on the configuration
+- **ComprehendPromptSafety** class is used to validate the safety of given prompt
+  text, raising an error if unsafe content is detected based on the specified threshold
+- **ComprehendPII** class is designed to handle 
+  `Personally Identifiable Information (PII)` moderation tasks,
+  detecting and managing PII entities in text inputs
+"""  # noqa: E501
 from langchain_experimental.comprehend_moderation.amazon_comprehend_moderation import (
     AmazonComprehendModerationChain,
 )

libs/experimental/langchain_experimental/cpal/__init__.py

@@ -0,0 +1,17 @@
+"""
+**Causal program-aided language (CPAL)** is a concept implemented in LangChain as
+a chain for causal modeling and narrative decomposition.
+
+CPAL improves upon the program-aided language (**PAL**) by incorporating
+causal structure to prevent hallucination in language models,
+particularly when dealing with complex narratives and math
+problems with nested dependencies.
+
+CPAL involves translating causal narratives into a stack of operations,
+setting hypothetical conditions for causal models, and decomposing
+narratives into story elements.
+
+It allows for the creation of causal chains that define the relationships
+between different elements in a narrative, enabling the modeling and analysis
+of causal relationships within a given context.
+"""

libs/experimental/langchain_experimental/data_anonymizer/__init__.py

@@ -1,4 +1,13 @@
-"""Data anonymizer package"""
+"""**Data anonymizer** contains both Anonymizers and Deanonymizers.
+It uses the [Microsoft Presidio](https://microsoft.github.io/presidio/) library.
+
+**Anonymizers** are used to replace a `Personally Identifiable Information (PII)`
+entity text with some other
+value by applying a certain operator (e.g. replace, mask, redact, encrypt).
+
+**Deanonymizers** are used to revert the anonymization operation
+(e.g. to decrypt an encrypted text).
+"""
 from langchain_experimental.data_anonymizer.presidio import (
     PresidioAnonymizer,
     PresidioReversibleAnonymizer,

libs/experimental/langchain_experimental/fallacy_removal/__init__.py

@@ -1,4 +1,7 @@
-"""The Chain runs a self-review of logical fallacies as determined by this paper  \
-categorizing and defining logical fallacies https://arxiv.org/pdf/2212.07425.pdf. \
-Modeled after Constitutional AI and in same format, but applying logical \
-fallacies as generalized rules to remove in output"""
+"""**Fallacy Removal** Chain runs a self-review of logical fallacies
+as determined by paper
+[Robust and Explainable Identification of Logical Fallacies in Natural
+Language Arguments](https://arxiv.org/pdf/2212.07425.pdf).
+It is modeled after `Constitutional AI` and in the same format, but applying logical
+fallacies as generalized rules to remove them in output.
+"""

libs/experimental/langchain_experimental/generative_agents/__init__.py

@@ -1,4 +1,4 @@
-"""Generative Agents primitives."""
+"""**Generative Agent** primitives."""
 from langchain_experimental.generative_agents.generative_agent import GenerativeAgent
 from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
 

libs/experimental/langchain_experimental/graph_transformers/__init__.py

@@ -1,3 +1,4 @@
+"""**Graph Transformers** transform Documents into Graph Documents."""
 from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer
 from langchain_experimental.graph_transformers.llm import LLMGraphTransformer
 

libs/experimental/langchain_experimental/graph_transformers/llm.py

@@ -143,24 +143,26 @@ def map_to_base_relationship(rel: Any) -> Relationship:
 
 
 class LLMGraphTransformer:
-    """
-    A class designed to transform documents into graph-based documents using a LLM.
+    """Transform documents into graph-based documents using a LLM.
+
     It allows specifying constraints on the types of nodes and relationships to include
     in the output graph. The class doesn't support neither extract and node or
     relationship properties
 
     Args:
         llm (BaseLanguageModel): An instance of a language model supporting structured
-        output. allowed_nodes (List[str], optional): Specifies which node types are
-        allowed in the graph. Defaults to an empty list, allowing all node types.
+          output.
+        allowed_nodes (List[str], optional): Specifies which node types are
+          allowed in the graph. Defaults to an empty list, allowing all node types.
         allowed_relationships (List[str], optional): Specifies which relationship types
-        are allowed in the graph. Defaults to an empty list, allowing all relationship
-        types.
-        prompt (Optional[ChatPromptTemplate], optional): The prompt to pass to the to
-        the LLM with additional instructions.
+          are allowed in the graph. Defaults to an empty list, allowing all relationship
+          types.
+        prompt (Optional[ChatPromptTemplate], optional): The prompt to pass to
+          the LLM with additional instructions.
         strict_mode (bool, optional): Determines whether the transformer should apply
-        filtering to strictly adhere to `allowed_nodes` and `allowed_relationships`.
-        Defaults to True.
+          filtering to strictly adhere to `allowed_nodes` and `allowed_relationships`.
+          Defaults to True.
+
     Example:
         .. code-block:: python
             from langchain_experimental.graph_transformers import LLMGraphTransformer

libs/experimental/langchain_experimental/llm_bash/__init__.py

@@ -1 +1,2 @@
-"""Chain that interprets a prompt and executes bash code to perform bash operations."""
+"""**LLM bash** is a chain that uses LLM to interpret a prompt and
+executes **bash** code."""

libs/experimental/langchain_experimental/llm_symbolic_math/__init__.py

@@ -1,4 +1,4 @@
-"""Chain that interprets a prompt and executes python code to do math.
+"""Chain that interprets a prompt and **executes python code to do math**.
 
-Heavily borrowed from llm_math, wrapper for SymPy
+Heavily borrowed from `llm_math`, uses the [SymPy](https://www.sympy.org/) package.
 """

libs/experimental/langchain_experimental/llms/__init__.py

@@ -1,5 +1,6 @@
-"""Experimental LLM wrappers."""
-
+"""Experimental **LLM** classes provide
+access to the large language model (**LLM**) APIs and services.
+"""
 from langchain_experimental.llms.jsonformer_decoder import JsonFormer
 from langchain_experimental.llms.llamaapi import ChatLlamaAPI
 from langchain_experimental.llms.lmformatenforcer_decoder import LMFormatEnforcer

libs/experimental/langchain_experimental/open_clip/__init__.py

@@ -1,3 +1,11 @@
+"""**OpenCLIP Embeddings** model.
+
+OpenCLIP is a multimodal model that can encode text and images into a shared space.
+
+See this paper for more details: https://arxiv.org/abs/2103.00020
+and [this repository](https://github.com/mlfoundations/open_clip) for details.
+
+"""
 from .open_clip import OpenCLIPEmbeddings
 
 __all__ = ["OpenCLIPEmbeddings"]

libs/experimental/langchain_experimental/pal_chain/__init__.py

@@ -1,10 +1,9 @@
-"""Implements Program-Aided Language Models.
+"""**PAL Chain** implements **Program-Aided Language** Models.
 
-As in https://arxiv.org/pdf/2211.10435.pdf.
+See the paper: https://arxiv.org/pdf/2211.10435.pdf.
 
-This is vulnerable to arbitrary code execution:
-https://github.com/langchain-ai/langchain/issues/5872
-"""
+This chain is vulnerable to [arbitrary code execution](https://github.com/langchain-ai/langchain/issues/5872).
+"""  # noqa: E501
 from langchain_experimental.pal_chain.base import PALChain
 
 __all__ = ["PALChain"]

libs/experimental/langchain_experimental/plan_and_execute/__init__.py

@@ -1,3 +1,7 @@
+"""**Plan-and-execute agents** are planning tasks with a language model (LLM) and
+executing them with a separate agent.
+
+"""
 from langchain_experimental.plan_and_execute.agent_executor import PlanAndExecute
 from langchain_experimental.plan_and_execute.executors.agent_executor import (
     load_agent_executor,

libs/experimental/langchain_experimental/prompt_injection_identifier/__init__.py

@@ -1,4 +1,7 @@
-"""HuggingFace Security toolkit."""
+"""**HuggingFace Injection Identifier** is a tool that uses
+[HuggingFace Prompt Injection model](https://huggingface.co/deepset/deberta-v3-base-injection)
+to detect prompt injection attacks.
+"""
 
 from langchain_experimental.prompt_injection_identifier.hugging_face_identifier import (
     HuggingFaceInjectionIdentifier,

libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py

@@ -50,7 +50,7 @@ def _model_default_factory(
 
 
 class HuggingFaceInjectionIdentifier(BaseTool):
-    """Tool that uses HuggingFace Prompt Injection to
+    """Tool that uses HuggingFace Prompt Injection model to
     detect prompt injection attacks."""
 
     name: str = "hugging_face_injection_identifier"

libs/experimental/langchain_experimental/prompts/__init__.py

@@ -1,3 +1,5 @@
+"""Unified method for **loading a prompt** from LangChainHub or local file system.
+"""
 from langchain_experimental.prompts.load import load_prompt
 
 __all__ = ["load_prompt"]

libs/experimental/langchain_experimental/recommenders/__init__.py

@@ -1,4 +1,9 @@
-"""Amazon Personalize primitives."""
+"""**Amazon Personalize** primitives.
+
+[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) 
+is a fully managed machine learning service that uses your data to generate 
+item recommendations for your users.
+"""  # noqa: E501
 from langchain_experimental.recommenders.amazon_personalize import AmazonPersonalize
 from langchain_experimental.recommenders.amazon_personalize_chain import (
     AmazonPersonalizeChain,

libs/experimental/langchain_experimental/recommenders/amazon_personalize.py

@@ -2,8 +2,9 @@ from typing import Any, List, Mapping, Optional, Sequence
 
 
 class AmazonPersonalize:
-    """Amazon Personalize Runtime wrapper for executing real-time operations:
-    https://docs.aws.amazon.com/personalize/latest/dg/API_Operations_Amazon_Personalize_Runtime.html
+    """Amazon Personalize Runtime wrapper for executing real-time operations.
+
+    See [this link for more details](https://docs.aws.amazon.com/personalize/latest/dg/API_Operations_Amazon_Personalize_Runtime.html).
 
     Args:
         campaign_arn: str, Optional: The Amazon Resource Name (ARN) of the campaign

libs/experimental/langchain_experimental/retrievers/__init__.py

@@ -0,0 +1,5 @@
+"""**Retriever** class returns Documents given a text **query**.
+
+It is more general than a vector store. A retriever does not need to be able to
+store documents, only to return (or retrieve) it.
+"""

libs/experimental/langchain_experimental/rl_chain/__init__.py

@@ -1,3 +1,12 @@
+"""
+**RL (Reinforcement Learning) Chain** leverages the `Vowpal Wabbit (VW)` models
+for reinforcement learning with a context, with the goal of modifying
+the prompt before the LLM call.
+
+[Vowpal Wabbit](https://vowpalwabbit.org/) provides fast, efficient,
+and flexible online machine learning techniques for reinforcement learning,
+supervised learning, and more.
+"""
 import logging
 
 from langchain_experimental.rl_chain.base import (

libs/experimental/langchain_experimental/smart_llm/__init__.py

@@ -1,13 +1,13 @@
-"""Chain for applying self-critique using the SmartGPT workflow.
+"""**SmartGPT** chain is applying self-critique using the `SmartGPT` workflow.
 
 See details at https://youtu.be/wVzuvf9D9BU
 
 The workflow performs these 3 steps:
-1. **Ideate**: Pass the user prompt to an ideation LLM n_ideas times,
+1. **Ideate**: Pass the user prompt to an `Ideation LLM` n_ideas times,
    each result is an "idea"
-2. **Critique**: Pass the ideas to a critique LLM which looks for flaws in the ideas
+2. **Critique**: Pass the ideas to a `Critique LLM` which looks for flaws in the ideas
    & picks the best one
-3. **Resolve**: Pass the critique to a resolver LLM which improves upon the best idea
+3. **Resolve**: Pass the critique to a `Resolver LLM` which improves upon the best idea
    & outputs only the (improved version of) the best output
 
 In total, the SmartGPT workflow will use n_ideas+2 LLM calls

libs/experimental/langchain_experimental/sql/__init__.py

@@ -1,4 +1,4 @@
-"""Chain for interacting with SQL Database."""
+"""**SQL Chain** interacts with `SQL` Database."""
 from langchain_experimental.sql.base import SQLDatabaseChain, SQLDatabaseSequentialChain
 
 __all__ = ["SQLDatabaseChain", "SQLDatabaseSequentialChain"]

libs/experimental/langchain_experimental/synthetic_data/__init__.py

@@ -1,3 +1,4 @@
+"""Generate **synthetic data** using LLM and few-shot template."""
 from typing import Any, Dict, List, Optional
 
 from langchain.chains.base import Chain

libs/experimental/langchain_experimental/tabular_synthetic_data/__init__.py

@@ -0,0 +1 @@
+"""Generate **tabular synthetic data** using LLM and few-shot template."""

libs/experimental/langchain_experimental/text_splitter.py

@@ -1,3 +1,4 @@
+"""Experimental **text splitter** based on semantic similarity."""
 import copy
 import re
 from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, cast

libs/experimental/langchain_experimental/tools/__init__.py

@@ -1,3 +1,4 @@
+"""Experimental **Python REPL** tools."""
 from langchain_experimental.tools.python.tool import PythonAstREPLTool, PythonREPLTool
 
 __all__ = ["PythonREPLTool", "PythonAstREPLTool"]

libs/experimental/langchain_experimental/tot/__init__.py

@@ -1,7 +1,5 @@
-"""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
+"""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.

libs/experimental/langchain_experimental/utilities/__init__.py

@@ -1,3 +1,4 @@
+"""Utility that simulates a standalone **Python REPL**."""
 from langchain_experimental.utilities.python import PythonREPL
 
 __all__ = ["PythonREPL"]