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.
langchain_experimental.prompt_injection_identifier¶
HuggingFace Security toolkit.
Classes¶
prompt_injection_identifier.hugging_face_identifier.HuggingFaceInjectionIdentifier
Tool that uses HF model to detect prompt injection attacks.
Functions¶
langchain_experimental.prompts¶
Functions¶
prompts.load.load_prompt(path)
Unified method for loading a prompt from LangChainHub or local fs.
langchain_experimental.retrievers¶
Classes¶
retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever
Retriever that uses SQLDatabase as Retriever
langchain_experimental.rl_chain¶
Classes¶
rl_chain.base.AutoSelectionScorer
Create a new model by parsing and validating input data from keyword arguments.
rl_chain.base.Embedder(*args, **kwargs)
rl_chain.base.Event(inputs[, selected])
rl_chain.base.Policy(**kwargs)
rl_chain.base.RLChain
The RLChain class leverages the Vowpal Wabbit (VW) model as a learned policy for reinforcement learning.
rl_chain.base.Selected()
rl_chain.base.SelectionScorer
Abstract method to grade the chosen selection or the response of the llm
rl_chain.base.VwPolicy(model_repo, vw_cmd, ...)
rl_chain.metrics.MetricsTrackerAverage(step)
rl_chain.metrics.MetricsTrackerRollingWindow(...)
rl_chain.model_repository.ModelRepository(folder)
rl_chain.pick_best_chain.PickBest
PickBest is a class designed to leverage the Vowpal Wabbit (VW) model for reinforcement learning with a context, with the goal of modifying the prompt before the LLM call.