Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]][source]#
Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[langchain.callbacks.manager.AsyncCallbackManagerForChainRun] = None) → Tuple[List[langchain.schema.PromptValue], Optional[List[str]]][source]#
Prepare prompts from inputs.
create_outputs(response: langchain.schema.LLMResult) → List[Dict[str, str]][source]#
Create outputs from response.
classmethod from_string(llm: langchain.base_language.BaseLanguageModel, template: str) → langchain.chains.llm.LLMChain[source]#
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[langchain.callbacks.manager.CallbackManagerForChainRun] = None) → langchain.schema.LLMResult[source]#
Generate LLM result from inputs.
predict(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → str[source]#
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html