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# PromptLayer

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`)

## Wrappers

### LLM

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`