docs[patch]: promptlayer pages update (#14416)

Updated provider page by adding LLM and ChatLLM references; removed a
content that is duplicate text from the LLM referenced page.
Updated the collback page

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -7,12 +7,13 @@
    "source": [
     "# PromptLayer\n",
     "\n",
+    ">[PromptLayer](https://docs.promptlayer.com/introduction) is a platform for prompt engineering. It also helps with the LLM observability to visualize requests, version prompts, and track usage.\n",
+    ">\n",
+    ">While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), using a callback is the recommended way to integrate `PromptLayer` with LangChain.\n",
     "\n",
-    ">[PromptLayer](https://promptlayer.com) is a an LLM observability platform that lets you visualize requests, version prompts, and track usage. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. \n",
+    "In this guide, we will go over how to setup the `PromptLayerCallbackHandler`. \n",
     "\n",
-    "While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
-    "\n",
-    "See [our docs](https://docs.promptlayer.com/languages/langchain) for more information."
+    "See [PromptLayer docs](https://docs.promptlayer.com/languages/langchain) for more information."
    ]
   },
   {

docs/docs/integrations/providers/promptlayer.mdx

@@ -1,49 +1,49 @@
 # PromptLayer
 
-This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
+>[PromptLayer](https://docs.promptlayer.com/introduction) 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`](https://docs.promptlayer.com/languages/langchain)), 
+> using a callback is the recommended way to integrate `PromptLayer` with LangChain.
 
 ## Installation and Setup
 
-If you want to work with PromptLayer:
-- Install the promptlayer python library `pip install promptlayer`
-- Create a PromptLayer account
+To work with `PromptLayer`, we have to:
+- Create a `PromptLayer` account
 - Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
 
-## Wrappers
+Install a Python package:
 
-### LLM
-
-There exists an PromptLayer OpenAI LLM wrapper, which you can access with
-```python
-from langchain.llms import PromptLayerOpenAI
+```bash
+pip install promptlayer
 ```
 
-To tag your requests, use the argument `pl_tags` when initializing the LLM
+
+## Callback
+
+See a [usage example](/docs/integrations/callbacks/promptlayer).
+
 ```python
-from langchain.llms import PromptLayerOpenAI
-llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
+import promptlayer  # Don't forget this import!
+from langchain.callbacks import PromptLayerCallbackHandler
 ```
 
-To get the PromptLayer request id, use the argument `return_pl_id` when initializing the LLM
+
+## LLM
+
+See a [usage example](/docs/integrations/llms/promptlayer_openai).
+
 ```python
 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:
-```python
-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](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
 
-This LLM is identical to the [OpenAI](/docs/ecosystem/integrations/openai) LLM, except that
-- all your requests will be logged to your PromptLayer account
-- you can add `pl_tags` when instantiating to tag your requests on PromptLayer
-- you can add `return_pl_id` when instantiating to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
+## Chat Models
 
+See a [usage example](/docs/integrations/chat/promptlayer_chatopenai).
+
+```python
+from langchain.chat_models import PromptLayerChatOpenAI
+```
 
-PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](/docs/integrations/chat/promptlayer_chatopenai) and `PromptLayerOpenAIChat`