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.

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Installation and Setup

%pip install -qU  langchain-community promptlayer --upgrade

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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 variable called PROMPTLAYER_API_KEY

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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.

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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_core.messages 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)

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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},
)
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"))
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.