Update Modal.com integration docs (#8014)

Hey, I'm a Modal Labs engineer and I'm making this docs update after
getting a user question in [our beta Slack
space](https://join.slack.com/t/modalbetatesters/shared_invite/zt-1xl9gbob8-1QDgUY7_PRPg6dQ49hqEeQ)
about the Langchain integration docs.

🔗 [Modal beta-testers link to docs discussion
thread](https://modalbetatesters.slack.com/archives/C031Z7DBQFL/p1689777700594819?thread_ts=1689775859.855849&cid=C031Z7DBQFL)

docs/extras/ecosystem/integrations/modal.mdx

@@ -1,66 +1,95 @@
 # Modal
 
-This page covers how to use the Modal ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
+This page covers how to use the Modal ecosystem to run LangChain custom LLMs.
+It is broken into two parts: 
+
+1. Modal installation and web endpoint deployment
+2. Using deployed web endpoint with `LLM` wrapper class.
 
 ## Installation and Setup
-- Install with `pip install modal-client`
+
+- Install with `pip install modal`
 - Run `modal token new`
 
 ## Define your Modal Functions and Webhooks
 
-You must include a prompt. There is a rigid response structure.
+You must include a prompt. There is a rigid response structure:
 
 ```python
 class Item(BaseModel):
     prompt: str
 
-@stub.webhook(method="POST")
-def my_webhook(item: Item):
-    return {"prompt": my_function.call(item.prompt)}
+@stub.function()
+@modal.web_endpoint(method="POST")
+def get_text(item: Item):
+    return {"prompt": run_gpt2.call(item.prompt)}
 ```
 
-An example with GPT2:
+The following is an example with the GPT2 model:
 
 ```python
 from pydantic import BaseModel
 
 import modal
 
-stub = modal.Stub("example-get-started")
-
-volume = modal.SharedVolume().persist("gpt2_model_vol")
 CACHE_PATH = "/root/model_cache"
 
-@stub.function(
-    gpu="any",
-    image=modal.Image.debian_slim().pip_install(
-        "tokenizers", "transformers", "torch", "accelerate"
-    ),
-    shared_volumes={CACHE_PATH: volume},
-    retries=3,
-)
-def run_gpt2(text: str):
-    from transformers import GPT2Tokenizer, GPT2LMHeadModel
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2LMHeadModel.from_pretrained('gpt2')
-    encoded_input = tokenizer(text, return_tensors='pt').input_ids
-    output = model.generate(encoded_input, max_length=50, do_sample=True)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
 class Item(BaseModel):
     prompt: str
 
-@stub.webhook(method="POST")
+stub = modal.Stub(name="example-get-started-with-langchain")
+
+def download_model():
+    from transformers import GPT2Tokenizer, GPT2LMHeadModel
+    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+    model = GPT2LMHeadModel.from_pretrained('gpt2')
+    tokenizer.save_pretrained(CACHE_PATH)
+    model.save_pretrained(CACHE_PATH)
+
+# Define a container image for the LLM function below, which
+# downloads and stores the GPT-2 model.
+image = modal.Image.debian_slim().pip_install(
+    "tokenizers", "transformers", "torch", "accelerate"
+).run_function(download_model)
+
+@stub.function(
+    gpu="any",
+    image=image,
+    retries=3,
+)
+def run_gpt2(text: str):
+    from transformers import GPT2Tokenizer, GPT2LMHeadModel
+    tokenizer = GPT2Tokenizer.from_pretrained(CACHE_PATH)
+    model = GPT2LMHeadModel.from_pretrained(CACHE_PATH)
+    encoded_input = tokenizer(text, return_tensors='pt').input_ids
+    output = model.generate(encoded_input, max_length=50, do_sample=True)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+@stub.function()
+@modal.web_endpoint(method="POST")
 def get_text(item: Item):
     return {"prompt": run_gpt2.call(item.prompt)}
 ```
 
-## Wrappers
+### Deploy the web endpoint
 
-### LLM
+Deploy the web endpoint to Modal cloud with the [`modal deploy`](https://modal.com/docs/reference/cli/deploy) CLI command.
+Your web endpoint will acquire a persistent URL under the `modal.run` domain.
+
+## LLM wrapper around Modal web endpoint
+
+The  `Modal` LLM wrapper class which will accept your deployed web endpoint's URL.
 
-There exists an Modal LLM wrapper, which you can access with 
 ```python
 from langchain.llms import Modal
-```
+
+endpoint_url = "https://ecorp--custom-llm-endpoint.modal.run"  # REPLACE ME with your deployed Modal web endpoint's URL
+
+llm = Modal(endpoint_url=endpoint_url)
+llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
+
+llm_chain.run(question)
+```
+