From c056ec9f54f935b57f4dacb7abd08d8277c555a3 Mon Sep 17 00:00:00 2001
From: William FH <13333726+hinthornw@users.noreply.github.com>
Date: Fri, 17 May 2024 08:15:54 -0700
Subject: [PATCH] [Docs] Capitalization Nits (#485)

---
 docs/docs/how-tos/index.md      | 34 ++++++++++++++++-----------------
 docs/docs/index.md              |  4 ++++
 docs/mkdocs.yml                 |  2 +-
 examples/persistence.ipynb      | 25 +++++++++++++-----------
 examples/streaming-tokens.ipynb |  2 +-
 5 files changed, 37 insertions(+), 30 deletions(-)

diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md
index 7e277c3de..b7756b065 100644
--- a/docs/docs/how-tos/index.md
+++ b/docs/docs/how-tos/index.md
@@ -1,37 +1,37 @@
-# How-To Guides
+# How-to guides
 
-Welcome to the LangGraph How-To Guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph. 
+Welcome to the LangGraph how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph.
 
 ## Core
 
-The core guides show how to address common needs when building a out AI workflows, with special focus placed on [ReAct](https://arxiv.org/abs/2210.03629)-style agents with [tool calling](https://python.langchain.com/docs/modules/model_io/chat/function_calling/).
+The core guides show how to address common needs when building out AI workflows, with special focus placed on [ReAct](https://arxiv.org/abs/2210.03629)-style agents with [tool calling](https://python.langchain.com/docs/modules/model_io/chat/function_calling/).
 
-- [Persistence](persistence.ipynb): How to give your graph "memory" and resiliance by saving and loading state
-- [Time Travel](time-travel.ipynb): How to navigate and manipulate graph state history once it's persisted
-- [Async Execution](async.ipynb): How to run nodes asynchronously for improved performance
-- [Streaming Responses](streaming-tokens.ipynb): How to stream agent responses in real-time
+- [Persistence](persistence.ipynb): How to give your graph "memory" and resilience by saving and loading state
+- [Time travel](time-travel.ipynb): How to navigate and manipulate graph state history once it's persisted
+- [Async execution](async.ipynb): How to run nodes asynchronously for improved performance
+- [Streaming responses](streaming-tokens.ipynb): How to stream agent responses in real-time
 - [Visualization](visualization.ipynb): How to visualize your graphs
 - [Configuration](configuration.ipynb): How to indicate that a graph can swap out configurable components
 
-### Design Patterns
+### Design patterns
 
 Recipes showing how to apply common design patterns in your workflows:
 
 - [Subgraphs](subgraph.ipynb): How to compose subgraphs within a larger graph
 - [Branching](branching.ipynb): How to create branching logic in your graphs for parallel node execution
-- [Human-in-the-Loop](human-in-the-loop.ipynb): How to incorporate human feedback and intervention
+- [Human-in-the-loop](human-in-the-loop.ipynb): How to incorporate human feedback and intervention
 
 The following examples are useful especially if you are used to LangChain's AgentExecutor configurations.
 
-- [Force Calling a Tool First](force-calling-a-tool-first.ipynb): Define a fixed workflow before ceding control to the ReAct agent
-- [Dynamic Direct Return](dynamically-returning-directly.ipynb): Let the LLM to decide whether the graph should finish after a tool is run or whether the LLM should be able to review the output and keep going.
-- [Respond in Structured Format](respond-in-format.ipynb): Let the LLM use tools or populate schema to provide the user. Useful if your agent should generate structured content.
-- [Managing Agent Steps](managing-agent-steps.ipynb): How to format the intermediate steps of your workflow for the agent.
+- [Force calling a tool first](force-calling-a-tool-first.ipynb): Define a fixed workflow before ceding control to the ReAct agent
+- [Dynamic direct return](dynamically-returning-directly.ipynb): Let the LLM decide whether the graph should finish after a tool is run or whether the LLM should be able to review the output and keep going
+- [Respond in structured format](respond-in-format.ipynb): Let the LLM use tools or populate schema to provide the user. Useful if your agent should generate structured content
+- [Managing agent steps](managing-agent-steps.ipynb): How to format the intermediate steps of your workflow for the agent
 
-### Alternative ways to define State
+### Alternative ways to define state
 
-- [Pydantic State](state-model.ipynb): Use a pydantic model as your state
+- [Pydantic state](state-model.ipynb): Use a Pydantic model as your state
 
-### Structured Output
+### Structured output
 
-- [Extraction with Re-prompting](./extraction/retries.ipynb): how to generate complex nested schemas using JSONPatch retries, for when function calling is insufficient, and regular reprompting still fails to generate valid results.
\ No newline at end of file
+- [Extraction with re-prompting](./extraction/retries.ipynb): How to generate complex nested schemas using JSONPatch retries, for when function calling is insufficient, and regular reprompting still fails to generate valid results
\ No newline at end of file
diff --git a/docs/docs/index.md b/docs/docs/index.md
index 0dcefe428..8b627a016 100644
--- a/docs/docs/index.md
+++ b/docs/docs/index.md
@@ -7,6 +7,10 @@ hide_comments: true
 
 ⚡ Build language agents as graphs ⚡
 
+!!! note "Python version :material-language-python:"
+
+    Looking for the JS version? Click [:fontawesome-brands-square-js: here](https://github.com/langchain-ai/langgraphjs) ([:simple-readme: JS docs](https://langchain-ai.github.io/langgraphjs/)).
+
 ## Overview
 
 Suppose you're building a customer support assistant. You want your assistant to be able to:
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 49119e19c..e3593a21c 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -208,6 +208,6 @@ extra:
     - icon: fontawesome/brands/js 
       link: https://langchain-ai.github.io/langgraphjs/
     - icon: fontawesome/brands/github
-      link: https://github.com/langchain-ai/langgraphjs
+      link: https://github.com/langchain-ai/langgraph
     - icon: fontawesome/brands/twitter
       link: https://twitter.com/LangChainAI
\ No newline at end of file
diff --git a/examples/persistence.ipynb b/examples/persistence.ipynb
index 37edf6afb..c8f8bf913 100644
--- a/examples/persistence.ipynb
+++ b/examples/persistence.ipynb
@@ -173,9 +173,8 @@
    "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
    "metadata": {},
    "source": [
-    "We can now wrap these tools in a simple ToolExecutor.\n",
-    "This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n",
-    "A ToolInvocation is any class with `tool` and `tool_input` attribute.\n"
+    "Now we can create our [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/?h=tool+node#toolnode). This \n",
+    "object actually **runs** the tools (aka functions)  that the LLM has asked to use."
    ]
   },
   {
@@ -197,13 +196,19 @@
    "source": [
     "## Set up the model\n",
     "\n",
-    "Now we need to load the chat model we want to use.\n",
-    "Importantly, this should satisfy two criteria:\n",
+    "Now we need to load the [chat model](https://python.langchain.com/v0.2/docs/concepts/#chat-models) to power our agent.\n",
+    "For the design below, it must satisfy two criteria:\n",
     "\n",
-    "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
-    "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
+    "1. It should work with **messages** (since our state contains a list of chat messages)\n",
+    "2. It should work with [**tool calling**](https://python.langchain.com/v0.1/docs/modules/model_io/chat/function_calling/).\n",
     "\n",
-    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example."
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Note</p>\n",
+    "    <p>\n",
+    "        These model requirements are not general requirements for using LangGraph - they are just requirements for this one example.\n",
+    "    </p>\n",
+    "</div>    \n",
+    "    "
    ]
   },
   {
@@ -245,7 +250,7 @@
    "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
    "metadata": {},
    "source": [
-    "## Define the nodes\n",
+    "## Define the graph \n",
     "\n",
     "We now need to define a few different nodes in our graph.\n",
     "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n",
@@ -300,8 +305,6 @@
    "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
    "metadata": {},
    "source": [
-    "## Define the graph\n",
-    "\n",
     "We can now put it all together and define the graph!"
    ]
   },
diff --git a/examples/streaming-tokens.ipynb b/examples/streaming-tokens.ipynb
index 232cc5e12..40ef406a9 100644
--- a/examples/streaming-tokens.ipynb
+++ b/examples/streaming-tokens.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Streaming Tokens\n",
     "\n",
-    "In this example we will stream tokens from the language model powering an agent. We will use a ReAct agent as an example. The main thing to bear in mind here is that using [async nodes](./async.ipynb) typically offers the best behavior for this, since we will be using the `async_log` method.\n",
+    "In this example we will stream tokens from the language model powering an agent. We will use a ReAct agent as an example. The main thing to bear in mind here is that using [async nodes](./async.ipynb) typically offers the best behavior for this, since we will be using the `astream_events` method.\n",
     "\n",
     "This how-to guide closely follows the others in this directory, so we will call out differences with the **STREAMING** tag below (if you just want to search for those).\n",
     "\n",