langchain-experimental: 0.0.64

agents

autonomous_agents

autonomous_agents.autogpt.agent.AutoGPT(...)	Agent for interacting with AutoGPT.
autonomous_agents.autogpt.memory.AutoGPTMemory	Memory for AutoGPT.
autonomous_agents.autogpt.output_parser.AutoGPTAction(...)	Action returned by AutoGPTOutputParser.
autonomous_agents.autogpt.output_parser.AutoGPTOutputParser	Output parser for AutoGPT.
autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser	Base Output parser for AutoGPT.
autonomous_agents.autogpt.prompt.AutoGPTPrompt	Prompt for AutoGPT.
autonomous_agents.autogpt.prompt_generator.PromptGenerator()	Generator of custom prompt strings.
autonomous_agents.baby_agi.baby_agi.BabyAGI	Controller model for the BabyAGI agent.
autonomous_agents.baby_agi.task_creation.TaskCreationChain	Chain generating tasks.
autonomous_agents.baby_agi.task_execution.TaskExecutionChain	Chain to execute tasks.
autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain	Chain to prioritize tasks.
autonomous_agents.hugginggpt.hugginggpt.HuggingGPT(...)	Agent for interacting with HuggingGPT.
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain	Chain to execute tasks.
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerator(...)	Generates a response based on the input.
autonomous_agents.hugginggpt.task_executor.Task(...)	Task to be executed.
autonomous_agents.hugginggpt.task_executor.TaskExecutor(plan)	Load tools and execute tasks.
autonomous_agents.hugginggpt.task_planner.BasePlanner	Base class for a planner.
autonomous_agents.hugginggpt.task_planner.Plan(steps)	A plan to execute.
autonomous_agents.hugginggpt.task_planner.PlanningOutputParser	Parses the output of the planning stage.
autonomous_agents.hugginggpt.task_planner.Step(...)	A step in the plan.
autonomous_agents.hugginggpt.task_planner.TaskPlaningChain	Chain to execute tasks.
autonomous_agents.hugginggpt.task_planner.TaskPlanner	Planner for tasks.

chat_models

chat_models.llm_wrapper.ChatWrapper	Wrapper for chat LLMs.
chat_models.llm_wrapper.Llama2Chat	Wrapper for Llama-2-chat model.
chat_models.llm_wrapper.Mixtral	See https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format
chat_models.llm_wrapper.Orca	Wrapper for Orca-style models.
chat_models.llm_wrapper.Vicuna	Wrapper for Vicuna-style models.

comprehend_moderation

comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain	Moderation Chain, based on Amazon Comprehend service.
comprehend_moderation.base_moderation.BaseModeration(client)	Base class for moderation.
comprehend_moderation.base_moderation_callbacks.BaseModerationCallbackHandler()	Base class for moderation callback handlers.
comprehend_moderation.base_moderation_config.BaseModerationConfig	Base configuration settings for moderation.
comprehend_moderation.base_moderation_config.ModerationPiiConfig	Configuration for PII moderation filter.
comprehend_moderation.base_moderation_config.ModerationPromptSafetyConfig	Configuration for Prompt Safety moderation filter.
comprehend_moderation.base_moderation_config.ModerationToxicityConfig	Configuration for Toxicity moderation filter.
comprehend_moderation.base_moderation_exceptions.ModerationPiiError([...])	Exception raised if PII entities are detected.
comprehend_moderation.base_moderation_exceptions.ModerationPromptSafetyError([...])	Exception raised if Unsafe prompts are detected.
comprehend_moderation.base_moderation_exceptions.ModerationToxicityError([...])	Exception raised if Toxic entities are detected.
comprehend_moderation.pii.ComprehendPII(client)	Class to handle Personally Identifiable Information (PII) moderation.
comprehend_moderation.prompt_safety.ComprehendPromptSafety(client)	Class to handle prompt safety moderation.
comprehend_moderation.toxicity.ComprehendToxicity(client)	Class to handle toxicity moderation.

cpal

cpal.base.CPALChain	Causal program-aided language (CPAL) chain implementation.
cpal.base.CausalChain	Translate the causal narrative into a stack of operations.
cpal.base.InterventionChain	Set the hypothetical conditions for the causal model.
cpal.base.NarrativeChain	Decompose the narrative into its story elements.
cpal.base.QueryChain	Query the outcome table using SQL.
cpal.constants.Constant(value[, names, ...])	Enum for constants used in the CPAL.
cpal.models.CausalModel	Casual data.
cpal.models.EntityModel	Entity in the story.
cpal.models.EntitySettingModel	Entity initial conditions.
cpal.models.InterventionModel	Intervention data of the story aka initial conditions.
cpal.models.NarrativeModel	Narrative input as three story elements.
cpal.models.QueryModel	Query data of the story.
cpal.models.ResultModel	Result of the story query.
cpal.models.StoryModel	Story data.
cpal.models.SystemSettingModel	System initial conditions.

data_anonymizer

data_anonymizer.base.AnonymizerBase()	Base abstract class for anonymizers.
data_anonymizer.base.ReversibleAnonymizerBase()	Base abstract class for reversible anonymizers.
data_anonymizer.deanonymizer_mapping.DeanonymizerMapping(...)	Deanonymizer mapping.
data_anonymizer.presidio.PresidioAnonymizer([...])	Anonymizer using Microsoft Presidio.
data_anonymizer.presidio.PresidioAnonymizerBase([...])	Base Anonymizer using Microsoft Presidio.
data_anonymizer.presidio.PresidioReversibleAnonymizer([...])	Reversible Anonymizer using Microsoft Presidio.

fallacy_removal

fallacy_removal.base.FallacyChain	Chain for applying logical fallacy evaluations.
fallacy_removal.models.LogicalFallacy	Logical fallacy.

generative_agents

generative_agents.generative_agent.GenerativeAgent	Agent as a character with memory and innate characteristics.
generative_agents.memory.GenerativeAgentMemory	Memory for the generative agent.

graph_transformers

graph_transformers.diffbot.DiffbotGraphTransformer(...)	Transform documents into graph documents using Diffbot NLP API.
graph_transformers.diffbot.NodesList()	List of nodes with associated properties.
graph_transformers.diffbot.SimplifiedSchema()	Simplified schema mapping.
graph_transformers.diffbot.TypeOption(value)
graph_transformers.gliner.GlinerGraphTransformer(...)	A transformer class for converting documents into graph structures using the GLiNER and GLiREL models.
graph_transformers.llm.LLMGraphTransformer(llm)	Transform documents into graph-based documents using a LLM.
graph_transformers.llm.UnstructuredRelation	Create a new model by parsing and validating input data from keyword arguments.
graph_transformers.relik.RelikGraphTransformer([...])	A transformer class for converting documents into graph structures using the Relik library and models.

llm_bash

llm_bash.base.LLMBashChain	Chain that interprets a prompt and executes bash operations.
llm_bash.bash.BashProcess([strip_newlines, ...])	Wrapper for starting subprocesses.
llm_bash.prompt.BashOutputParser	Parser for bash output.

llm_symbolic_math

llm_symbolic_math.base.LLMSymbolicMathChain

Chain that interprets a prompt and executes python code to do symbolic math.

llms

llms.anthropic_functions.TagParser()	Parser for the tool tags.
llms.jsonformer_decoder.JsonFormer	Jsonformer wrapped LLM using HuggingFace Pipeline API.
llms.llamaapi.ChatLlamaAPI	Chat model using the Llama API.
llms.lmformatenforcer_decoder.LMFormatEnforcer	LMFormatEnforcer wrapped LLM using HuggingFace Pipeline API.
llms.rellm_decoder.RELLM	RELLM wrapped LLM using HuggingFace Pipeline API.

open_clip

open_clip.open_clip.OpenCLIPEmbeddings

OpenCLIP Embeddings model.

pal_chain

pal_chain.base.PALChain	Chain that implements Program-Aided Language Models (PAL).
pal_chain.base.PALValidation([...])	Validation for PAL generated code.

plan_and_execute

plan_and_execute.agent_executor.PlanAndExecute	Plan and execute a chain of steps.
plan_and_execute.executors.base.BaseExecutor	Base executor.
plan_and_execute.executors.base.ChainExecutor	Chain executor.
plan_and_execute.planners.base.BasePlanner	Base planner.
plan_and_execute.planners.base.LLMPlanner	LLM planner.
plan_and_execute.planners.chat_planner.PlanningOutputParser	Planning output parser.
plan_and_execute.schema.BaseStepContainer	Base step container.
plan_and_execute.schema.ListStepContainer	Container for List of steps.
plan_and_execute.schema.Plan	Plan.
plan_and_execute.schema.PlanOutputParser	Plan output parser.
plan_and_execute.schema.Step	Step.
plan_and_execute.schema.StepResponse	Step response.

prompt_injection_identifier

prompt_injection_identifier.hugging_face_identifier.HuggingFaceInjectionIdentifier	Tool that uses HuggingFace Prompt Injection model to detect prompt injection attacks.
prompt_injection_identifier.hugging_face_identifier.PromptInjectionException([...])	Exception raised when prompt injection attack is detected.

recommenders

recommenders.amazon_personalize.AmazonPersonalize([...])	Amazon Personalize Runtime wrapper for executing real-time operations.
recommenders.amazon_personalize_chain.AmazonPersonalizeChain	Chain for retrieving recommendations from Amazon Personalize,

retrievers

retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever

Retriever that uses Vector SQL Database.

rl_chain

rl_chain.base.AutoSelectionScorer	Auto selection scorer.
rl_chain.base.Embedder(*args, **kwargs)	Abstract class to represent an embedder.
rl_chain.base.Event(inputs[, selected])	Abstract class to represent an event.
rl_chain.base.Policy(**kwargs)	Abstract class to represent a policy.
rl_chain.base.RLChain	Chain that leverages the Vowpal Wabbit (VW) model as a learned policy for reinforcement learning.
rl_chain.base.Selected()	Abstract class to represent the selected item.
rl_chain.base.SelectionScorer	Abstract class to grade the chosen selection or the response of the llm.
rl_chain.base.VwPolicy(model_repo, vw_cmd, ...)	Vowpal Wabbit policy.
rl_chain.metrics.MetricsTrackerAverage(step)	Metrics Tracker Average.
rl_chain.metrics.MetricsTrackerRollingWindow(...)	Metrics Tracker Rolling Window.
rl_chain.model_repository.ModelRepository(folder)	Model Repository.
rl_chain.pick_best_chain.PickBest	Chain that leverages the Vowpal Wabbit (VW) model for reinforcement learning with a context, with the goal of modifying the prompt before the LLM call.
rl_chain.pick_best_chain.PickBestEvent(...)	Event class for PickBest chain.
rl_chain.pick_best_chain.PickBestFeatureEmbedder(...)	Embed the BasedOn and ToSelectFrom inputs into a format that can be used by the learning policy.
rl_chain.pick_best_chain.PickBestRandomPolicy(...)	Random policy for PickBest chain.
rl_chain.pick_best_chain.PickBestSelected([...])	Selected class for PickBest chain.
rl_chain.vw_logger.VwLogger(path)	Vowpal Wabbit custom logger.

smart_llm

smart_llm.base.SmartLLMChain

Chain for applying self-critique using the SmartGPT workflow.

sql

sql.base.SQLDatabaseChain	Chain for interacting with SQL Database.
sql.base.SQLDatabaseSequentialChain	Chain for querying SQL database that is a sequential chain.
sql.vector_sql.VectorSQLDatabaseChain	Chain for interacting with Vector SQL Database.
sql.vector_sql.VectorSQLOutputParser	Output Parser for Vector SQL.
sql.vector_sql.VectorSQLRetrieveAllOutputParser	Parser based on VectorSQLOutputParser.

tabular_synthetic_data

tabular_synthetic_data.base.SyntheticDataGenerator

Generate synthetic data using the given LLM and few-shot template.

text_splitter

text_splitter.SemanticChunker(embeddings[, ...])

Split the text based on semantic similarity.

tools

tools.python.tool.PythonAstREPLTool	Tool for running python code in a REPL.
tools.python.tool.PythonInputs	Python inputs.
tools.python.tool.PythonREPLTool	Tool for running python code in a REPL.

tot

tot.base.ToTChain	Chain implementing the Tree of Thought (ToT).
tot.checker.ToTChecker	Tree of Thought (ToT) checker.
tot.controller.ToTController([c])	Tree of Thought (ToT) controller.
tot.memory.ToTDFSMemory([stack])	Memory for the Tree of Thought (ToT) chain.
tot.prompts.CheckerOutputParser	Parse and check the output of the language model.
tot.prompts.JSONListOutputParser	Parse the output of a PROPOSE_PROMPT response.
tot.thought.Thought	A thought in the ToT.
tot.thought.ThoughtValidity(value[, names, ...])	Enum for the validity of a thought.
tot.thought_generation.BaseThoughtGenerationStrategy	Base class for a thought generation strategy.
tot.thought_generation.ProposePromptStrategy	Strategy that is sequentially using a "propose prompt".
tot.thought_generation.SampleCoTStrategy	Sample strategy from a Chain-of-Thought (CoT) prompt.

utilities

utilities.python.PythonREPL

Simulates a standalone Python REPL.

video_captioning

video_captioning.base.VideoCaptioningChain	Video Captioning Chain.
video_captioning.models.AudioModel(...)
video_captioning.models.BaseModel(...)
video_captioning.models.CaptionModel(...)
video_captioning.models.VideoModel(...)
video_captioning.services.audio_service.AudioProcessor(api_key)
video_captioning.services.caption_service.CaptionProcessor(llm)
video_captioning.services.combine_service.CombineProcessor(llm)
video_captioning.services.image_service.ImageProcessor([...])
video_captioning.services.srt_service.SRTProcessor()