This page covers how to use PromptLayer within LangChain. It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.

Installation and Setup#

If you want to work with PromptLayer:

  • Install the promptlayer python library pip install promptlayer

  • Create a PromptLayer account

  • Create an api token and set it as an environment variable (PROMPTLAYER_API_KEY)



There exists an PromptLayer OpenAI LLM wrapper, which you can access with

from langchain.llms import PromptLayerOpenAI

To tag your requests, use the argument pl_tags when instanializing the LLM

from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])

To get the PromptLayer request id, use the argument return_pl_id when instanializing the LLM

from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(return_pl_id=True)

This will add the PromptLayer request ID in the generation_info field of the Generation returned when using .generate or .agenerate

For example:

llm_results = llm.generate(["hello world"])
for res in llm_results.generations:
    print("pl request id: ", res[0].generation_info["pl_request_id"])

You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. Read more about it here.

This LLM is identical to the OpenAI LLM, except that

  • all your requests will be logged to your PromptLayer account

  • you can add pl_tags when instantializing to tag your requests on PromptLayer

  • you can add return_pl_id when instantializing to return a PromptLayer request id to use while tracking requests.

PromptLayer also provides native wrappers for PromptLayerChatOpenAI and PromptLayerOpenAIChat