fix(docs): squash js docs build errors (#5723)

docs/docs/tutorials/auth/resource_auth.md

@@ -212,7 +212,7 @@ The handler receives two parameters:
 :::js
 The handler receives an object with the following properties:
 
-1. `user` ([ProxyUser](../../cloud/reference/sdk/js_ts_sdk_ref.md#langgraph_sdk.auth.types.ProxyUser)): contains info about the current `user`, the user's `permissions`, the `resource` ("threads", "crons", "assistants")
+1. `user` contains info about the current `user`, the user's `permissions`, the `resource` ("threads", "crons", "assistants")
 2. `action` contains information about the action being taken ("create", "read", "update", "delete", "search", "create_run")
 3. `value` (`Record<string, any>`): data that is being created or accessed. The contents of this object depend on the resource and action being accessed. See [adding scoped authorization handlers](#scoped-authorization) below for information on how to get more tightly scoped access control.
    :::
@@ -588,10 +588,14 @@ Now that you can control access to resources, you might want to:
 1. Move on to [Connect an authentication provider](add_auth_server.md) to add real user accounts.
 2. Read more about [authorization patterns](../../concepts/auth.md#authorization).
 
-:::python 
+:::python
+
 3. Check out the [API reference](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) for details about the interfaces and methods used in this tutorial.
+
 :::
 
-:::js 
+:::js
+
 3. Check out the [API reference](../../cloud/reference/sdk/js_sdk_ref.md#langgraph_sdk.auth.Auth) for details about the interfaces and methods used in this tutorial.
+
 :::

docs/docs/tutorials/rag/langgraph_agentic_rag.md

@@ -109,7 +109,15 @@ Now that we have our split documents, we can index them into a vector store that
 
 ## 3. Generate query
 
-Now we will start building components ([nodes](../../concepts/low_level.md#nodes) and [edges](../../concepts/low_level.md#edges)) for our agentic RAG graph. Note that the components will operate on the [`MessagesState`](../../concepts/low_level.md#messagesstate) — graph state that contains a `messages` key with a list of [chat messages](https://python.langchain.com/docs/concepts/messages/).
+Now we will start building components ([nodes](../../concepts/low_level.md#nodes) and [edges](../../concepts/low_level.md#edges)) for our agentic RAG graph.
+
+:::python
+Note that the components will operate on the [`MessagesState`](../../concepts/low_level.md#messagesstate) — graph state that contains a `messages` key with a list of [chat messages](https://python.langchain.com/docs/concepts/messages/).
+:::
+
+:::js
+Note that the components will operate on the `MessagesZodState` — graph state that contains a `messages` key with a list of [chat messages](https://js.langchain.com/docs/concepts/messages/).
+:::
 
 1. Build a `generate_query_or_respond` node. It will call an LLM to generate a response based on the current graph state (list of messages). Given the input messages, it will decide to retrieve using the retriever tool, or respond directly to the user. Note that we're giving the chat model access to the `retriever_tool` we created earlier via `.bind_tools`:
 

docs/docs/tutorials/workflows.md

@@ -1274,7 +1274,7 @@ With orchestrator-worker, an orchestrator breaks down a task and delegates each
 
     **Creating Workers in LangGraph**
 
-    Because orchestrator-worker workflows are common, LangGraph **has the `Send` API to support this**. It lets you dynamically create worker nodes and send each one a specific input. Each worker has its own state, and all worker outputs are written to a *shared state key* that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. As you can see below, we iterate over a list of sections and `Send` each to a worker node. See further documentation [here](../how-tos/map-reduce/) and [here](../concepts/low_level/#send).
+    Because orchestrator-worker workflows are common, LangGraph **has the `Send` API to support this**. It lets you dynamically create worker nodes and send each one a specific input. Each worker has its own state, and all worker outputs are written to a *shared state key* that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. As you can see below, we iterate over a list of sections and `Send` each to a worker node. See further documentation [here](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) and [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#send).
 
     ```typescript
     import { withLangGraph } from "@langchain/langgraph/zod";