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# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
50 lines
2.0 KiB
Markdown
50 lines
2.0 KiB
Markdown
# PromptLayer
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This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
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It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
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## Installation and Setup
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If you want to work with PromptLayer:
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- Install the promptlayer python library `pip install promptlayer`
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- Create a PromptLayer account
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- Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
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## Wrappers
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### LLM
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There exists an PromptLayer OpenAI LLM wrapper, which you can access with
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```python
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from langchain.llms import PromptLayerOpenAI
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```
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To tag your requests, use the argument `pl_tags` when instanializing the LLM
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```python
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from langchain.llms import PromptLayerOpenAI
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llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
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```
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To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
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```python
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from langchain.llms import PromptLayerOpenAI
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llm = PromptLayerOpenAI(return_pl_id=True)
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```
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This will add the PromptLayer request ID in the `generation_info` field of the `Generation` returned when using `.generate` or `.agenerate`
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For example:
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```python
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llm_results = llm.generate(["hello world"])
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for res in llm_results.generations:
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print("pl request id: ", res[0].generation_info["pl_request_id"])
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```
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You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
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This LLM is identical to the [OpenAI LLM](./openai.md), except that
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- all your requests will be logged to your PromptLayer account
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- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
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- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
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PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb) and `PromptLayerOpenAIChat`
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