This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: langchain.schema.prompt_template.BasePromptTemplate = PromptTemplate(input_variables=['problem_description', 'thoughts', 'n'], output_parser=JSONListOutputParser(), template='You are an intelligent agent that is generating thoughts in a tree of\nthoughts setting.\n\nThe output should be a markdown code snippet formatted as a JSON list of\nstrings, including the leading and trailing "```json" and "```":\n\n```json\n[\n    "<thought-1>",\n    "<thought-2>",\n    "<thought-3>"\n]\n```\n\nPROBLEM\n\n{{ problem_description }}\n\n{% if thoughts %}\nVALID THOUGHTS\n\n{% for thought in thoughts %}\n{{ thought }}\n{% endfor %}\n\nPossible next {{ n }} valid thoughts based on the last valid thought:\n{% else %}\n\nPossible next {{ n }} valid thoughts based on the PROBLEM:\n{%- endif -%}', template_format='jinja2')¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,