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PromptLayer OpenAI

PromptLayer is the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware between your code and OpenAI’s python library.

PromptLayer records all your OpenAI API requests, allowing you to search and explore request history in the PromptLayer dashboard.

This example showcases how to connect to PromptLayer to start recording your OpenAI requests.

Another example is here.

Install PromptLayer

The promptlayer package is required to use PromptLayer with OpenAI. Install promptlayer using pip.

pip install promptlayer


import os
from langchain.llms import PromptLayerOpenAI
import promptlayer

API Reference:

Set the Environment API Key

You can create a PromptLayer API Key at by clicking the settings cog in the navbar.

Set it as an environment variable called PROMPTLAYER_API_KEY.

You also need an OpenAI Key, called OPENAI_API_KEY.

from getpass import getpass

from getpass import getpass

OPENAI_API_KEY = getpass()

Use the PromptLayerOpenAI LLM like normal

You can optionally pass in pl_tags to track your requests with PromptLayer's tagging feature.

llm = PromptLayerOpenAI(pl_tags=["langchain"])
llm("I am a cat and I want")

The above request should now appear on your PromptLayer dashboard.

Using PromptLayer Track

If you would like to use any of the PromptLayer tracking features, you need to pass the argument return_pl_id when instantializing the PromptLayer LLM to get the request id.

llm = PromptLayerOpenAI(return_pl_id=True)
llm_results = llm.generate(["Tell me a joke"])

for res in llm_results.generations:
pl_request_id = res[0].generation_info["pl_request_id"]
promptlayer.track.score(request_id=pl_request_id, score=100)

Using this allows you to track the performance of your model in the PromptLayer dashboard. If you are using a prompt template, you can attach a template to a request as well. Overall, this gives you the opportunity to track the performance of different templates and models in the PromptLayer dashboard.