text: this example we unlock more of the power of PromptLayer.PromptLayer allows you to visually create, version, and track prompt templates. Using the Prompt Registry, we can programatically fetch the prompt template called example.We also define a pl_id_callback function which takes in the promptlayer_request_id and logs a score, metadata and links the prompt template used. Read more about tracking on our docs.import promptlayer  # Don't forget this ğŸ�°from langchain.callbacks import PromptLayerCallbackHandlerfrom langchain.llms import OpenAIdef pl_id_callback(promptlayer_request_id):    print("prompt layer id ", promptlayer_request_id)    promptlayer.track.score(        request_id=promptlayer_request_id, score=100    )  # score is an integer 0-100    promptlayer.track.metadata(        request_id=promptlayer_request_id, metadata={"foo": "bar"}    )  # metadata is a dictionary of key value pairs that is tracked on PromptLayer    promptlayer.track.prompt(        request_id=promptlayer_request_id,        prompt_name="example",        prompt_input_variables={"product": "toasters"},        version=1,    )  # link the request to a prompt templateopenai_llm = OpenAI(    model_name="text-davinci-002",    callbacks=[PromptLayerCallbackHandler(pl_id_callback=pl_id_callback)],)example_prompt = promptlayer.prompts.get("example", version=1, langchain=True)openai_llm(example_prompt.format(product="toasters"))That is all it takes! After setup all your requests will show up on the PromptLayer dashboard.