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PromptLayer

PromptLayer is a platform for prompt engineering. It also helps with the LLM observability to visualize requests, version prompts, and track usage.

While PromptLayer does have LLMs that integrate directly with LangChain (e.g. PromptLayerOpenAI), using a callback is the recommended way to integrate PromptLayer with LangChain.

In this guide, we will go over how to setup the PromptLayerCallbackHandler.

See PromptLayer docs for more information.

Installation and Setup​

%pip install --upgrade --quiet  promptlayer --upgrade

Getting API Credentials​

If you do not have a PromptLayer account, create one on promptlayer.com. Then get an API key by clicking on the settings cog in the navbar and set it as an environment variabled called PROMPTLAYER_API_KEY

Usage​

Getting started with PromptLayerCallbackHandler is fairly simple, it takes two optional arguments:

  1. pl_tags - an optional list of strings that will be tracked as tags on PromptLayer.
  2. pl_id_callback - an optional function that will take promptlayer_request_id as an argument. This ID can be used with all of PromptLayer's tracking features to track, metadata, scores, and prompt usage.

Simple OpenAI Example​

In this simple example we use PromptLayerCallbackHandler with ChatOpenAI. We add a PromptLayer tag named chatopenai

import promptlayer  # Don't forget this 🍰
from langchain_community.callbacks.promptlayer_callback import (
    PromptLayerCallbackHandler,
)
from langchain.schema import (
    HumanMessage,
)
from langchain_openai import ChatOpenAI

chat_llm = ChatOpenAI(
    temperature=0,
    callbacks=[PromptLayerCallbackHandler(pl_tags=["chatopenai"])],
)
llm_results = chat_llm.invoke(
    [
        HumanMessage(content="What comes after 1,2,3 ?"),
        HumanMessage(content="Tell me another joke?"),
    ]
)
print(llm_results)

GPT4All Example​

from langchain_community.llms import GPT4All

model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
callbacks = [PromptLayerCallbackHandler(pl_tags=["langchain", "gpt4all"])]

response = model.invoke(
    "Once upon a time, ",
    config={"callbacks": callbacks},
)

API Reference:

In 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 programmatically 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.

from langchain_openai import OpenAI


def 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 template


openai_llm = OpenAI(
    model_name="gpt-3.5-turbo-instruct",
    callbacks=[PromptLayerCallbackHandler(pl_id_callback=pl_id_callback)],
)

example_prompt = promptlayer.prompts.get("example", version=1, langchain=True)
openai_llm.invoke(example_prompt.format(product="toasters"))

API Reference:

That is all it takes! After setup all your requests will show up on the PromptLayer dashboard. This callback also works with any LLM implemented on LangChain.

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