langchain-experimental: 0.3.3

agents

Functions

agents.agent_toolkits.csv.base.create_csv_agent(...)	Create pandas dataframe agent by loading csv to a dataframe.
agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent(llm, df)	Construct a Pandas agent from an LLM and dataframe(s).
agents.agent_toolkits.python.base.create_python_agent(...)	Construct a python agent from an LLM and tool.
agents.agent_toolkits.spark.base.create_spark_dataframe_agent(llm, df)	Construct a Spark agent from an LLM and dataframe.
agents.agent_toolkits.xorbits.base.create_xorbits_agent(...)	Construct a xorbits agent from an LLM and dataframe.

autonomous_agents

Classes

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.

Functions

autonomous_agents.autogpt.output_parser.preprocess_json_input(...)	Preprocesses a string to be parsed as json.
autonomous_agents.autogpt.prompt_generator.get_prompt(tools)	Generates a prompt string.
autonomous_agents.hugginggpt.repsonse_generator.load_response_generator(llm)	Load the ResponseGenerator.
autonomous_agents.hugginggpt.task_planner.load_chat_planner(llm)	Load the chat planner.

chat_models

Classes

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

Classes

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

Classes

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

Classes

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.

Functions

data_anonymizer.deanonymizer_mapping.create_anonymizer_mapping(...)	Create or update the mapping used to anonymize and/or
data_anonymizer.deanonymizer_mapping.format_duplicated_operator(...)	Format the operator name with the count.
data_anonymizer.deanonymizer_matching_strategies.case_insensitive_matching_strategy(...)	Case insensitive matching strategy for deanonymization.
data_anonymizer.deanonymizer_matching_strategies.combined_exact_fuzzy_matching_strategy(...)	Combined exact and fuzzy matching strategy for deanonymization.
data_anonymizer.deanonymizer_matching_strategies.exact_matching_strategy(...)	Exact matching strategy for deanonymization.
data_anonymizer.deanonymizer_matching_strategies.fuzzy_matching_strategy(...)	Fuzzy matching strategy for deanonymization.
data_anonymizer.deanonymizer_matching_strategies.ngram_fuzzy_matching_strategy(...)	N-gram fuzzy matching strategy for deanonymization.
data_anonymizer.faker_presidio_mapping.get_pseudoanonymizer_mapping([seed])	Get a mapping of entities to pseudo anonymize them.

fallacy_removal

Classes

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

generative_agents

Classes

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

Classes

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.

Functions

graph_transformers.diffbot.format_property_key(s)	Formats a string to be used as a property key.
graph_transformers.llm.create_simple_model([...])	Create a simple graph model with optional constraints on node and relationship types.
graph_transformers.llm.create_unstructured_prompt([...])
graph_transformers.llm.format_property_key(s)
graph_transformers.llm.map_to_base_node(node)	Map the SimpleNode to the base Node.
graph_transformers.llm.map_to_base_relationship(rel)	Map the SimpleRelationship to the base Relationship.
graph_transformers.llm.optional_enum_field([...])	Utility function to conditionally create a field with an enum constraint.
graph_transformers.llm.validate_and_get_relationship_type(...)

llm_bash

Classes

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

Classes

llm_symbolic_math.base.LLMSymbolicMathChain

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

llms

Classes

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.

Functions

llms.jsonformer_decoder.import_jsonformer()	Lazily import of the jsonformer package.
llms.lmformatenforcer_decoder.import_lmformatenforcer()	Lazily import of the lmformatenforcer package.
llms.ollama_functions.convert_to_ollama_tool(tool)	Convert a tool to an Ollama tool.
llms.ollama_functions.parse_response(message)	Extract function_call from AIMessage.
llms.rellm_decoder.import_rellm()	Lazily import of the rellm package.

Deprecated classes

llms.anthropic_functions.AnthropicFunctions
llms.ollama_functions.OllamaFunctions

open_clip

Classes

open_clip.open_clip.OpenCLIPEmbeddings

OpenCLIP Embeddings model.

pal_chain

Classes

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

plan_and_execute

Classes

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.

Functions

plan_and_execute.executors.agent_executor.load_agent_executor(...)	Load an agent executor.
plan_and_execute.planners.chat_planner.load_chat_planner(llm)	Load a chat planner.

prompt_injection_identifier

Classes

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

Classes

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

Classes

retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever

Retriever that uses Vector SQL Database.

rl_chain

Classes

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.RLChain[PickBestEvent]	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.

Functions

rl_chain.base.BasedOn(anything)	Wrap a value to indicate that it should be based on.
rl_chain.base.Embed(anything[, keep])	Wrap a value to indicate that it should be embedded.
rl_chain.base.EmbedAndKeep(anything)	Wrap a value to indicate that it should be embedded and kept.
rl_chain.base.ToSelectFrom(anything)	Wrap a value to indicate that it should be selected from.
rl_chain.base.get_based_on_and_to_select_from(inputs)	Get the BasedOn and ToSelectFrom from the inputs.
rl_chain.base.parse_lines(parser, input_str)	Parse the input string into a list of examples.
rl_chain.base.prepare_inputs_for_autoembed(inputs)	Prepare the inputs for auto embedding.
rl_chain.helpers.embed(to_embed, model[, ...])	Embed the actions or context using the SentenceTransformer model (or a model that has an encode function).
rl_chain.helpers.embed_dict_type(item, model)	Embed a dictionary item.
rl_chain.helpers.embed_list_type(item, model)	Embed a list item.
rl_chain.helpers.embed_string_type(item, model)	Embed a string or an _Embed object.
rl_chain.helpers.is_stringtype_instance(item)	Check if an item is a string.
rl_chain.helpers.stringify_embedding(embedding)	Convert an embedding to a string.

smart_llm

Classes

smart_llm.base.SmartLLMChain

Chain for applying self-critique using the SmartGPT workflow.

sql

Classes

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.

Functions

sql.vector_sql.get_result_from_sqldb(db, cmd)

Get result from SQL Database.

tabular_synthetic_data

Classes

tabular_synthetic_data.base.SyntheticDataGenerator

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

Functions

tabular_synthetic_data.openai.create_openai_data_generator(...)

Create an instance of SyntheticDataGenerator tailored for OpenAI models.

text_splitter

Classes

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

Split the text based on semantic similarity.

Functions

text_splitter.calculate_cosine_distances(...)	Calculate cosine distances between sentences.
text_splitter.combine_sentences(sentences[, ...])	Combine sentences based on buffer size.

tools

Classes

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.

Functions

tools.python.tool.sanitize_input(query)

Sanitize input to the python REPL.

tot

Classes

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.

Functions

tot.prompts.get_cot_prompt()	Get the prompt for the Chain of Thought (CoT) chain.
tot.prompts.get_propose_prompt()	Get the prompt for the PROPOSE_PROMPT chain.

utilities

Classes

utilities.python.PythonREPL

Simulates a standalone Python REPL.

video_captioning

Classes

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()