Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]][source]#
Call predict and then parse the results.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[langchain.callbacks.manager.CallbackManagerForChainRun] = None) → Tuple[List[langchain.schema.PromptValue], Optional[List[str]]][source]#
Prepare prompts from inputs.
property lc_serializable: bool#
Return whether or not the class is serializable.
pydantic model langchain.chains.LLMCheckerChain[source]#
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMCheckerChain
llm = OpenAI(temperature=0.7)
checker_chain = LLMCheckerChain.from_llm(llm)
Validators
raise_deprecation » all fields
raise_deprecation » all fields
set_verbose » verbose
field check_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='Here is a bullet point list of assertions:\n{assertions}\nFor each assertion, determine whether it is true or false. If it is false, explain why.\n\n', template_format='f-string', validate_template=True)#
[Deprecated]
field create_draft_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True)#
[Deprecated]
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html