from chainlite import load_config_file
load_config_file("./llm_config.yaml") # The path should be relative to the directory you run the script from, usually the root directory of your project

llm_generation_chain(
    template_file: str,
    engine: str,
    max_tokens: int,
    temperature: float = 0.0,
    stop_tokens=None,
    top_p: float = 0.9,
    output_json: bool = False,
    template_blocks: list[tuple[str]]=None,
    keep_indentation: bool = False,
    progress_bar_desc: Optional[str] = None,
    additional_postprocessing_runnable: Runnable = None,
    tools: Optional[List[Callable]] = None,
    force_tool_calling: bool = False,
    return_top_logprobs: int = 0,
    bind_prompt_values: Dict = {},
) # returns a LangChain chain the accepts inputs and returns a string as output
load_config_from_file(config_file: str)
pprint_chain() # can be used to print inputs or outputs of a LangChain chain.
write_prompt_logs_to_file(log_file: Optional[str]) # writes all instructions, inputs and outputs of all your LLM API calls to a jsonl file. Good for debugging or collecting data using LLMs
get_total_cost() # returns the total cost of all LLM API calls you have made. Resets each time you run your code.

from chainlite import load_config_file
load_config_file("./chainlite_config.yaml")

async def tell_joke(topic: str):
    response = await llm_generation_chain(
        template_file="joke.prompt",
        engine="gpt-35-turbo",
        max_tokens=100,
    ).ainvoke({"topic": topic})
    print(response)

asyncio.run(tell_joke("Life as a PhD student")) # prints "Why did the PhD student bring a ladder to the library?\nTo take their research to the next level!"
write_prompt_logs_to_file("llm_input_outputs.jsonl")