docs: update to the streaming tutorial notebook in the lcel documentation (#18378)

**Description:** Update to the streaming tutorial notebook in the LCEL
documentation
**Issue:** Fixed an import and (minor) changes in documentation language
**Dependencies:** None

docs/docs/expression_language/streaming.ipynb

@@ -12,7 +12,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "bb7d49db-04d3-4399-bfe1-09f82bbe6015",
    "metadata": {},
@@ -28,7 +27,7 @@
     "1. sync `stream` and async `astream`: a **default implementation** of streaming that streams the **final output** from the chain.\n",
     "2. async `astream_events` and async `astream_log`: these provide a way to stream both **intermediate steps** and **final output** from the chain.\n",
     "\n",
-    "Let's take a look at both approaches, and try to understand a how to use them. 🥷\n",
+    "Let's take a look at both approaches, and try to understand how to use them. 🥷\n",
     "\n",
     "## Using Stream\n",
     "\n",
@@ -48,7 +47,25 @@
     "\n",
     "Large language models can take **several seconds** to generate a complete response to a query. This is far slower than the **~200-300 ms** threshold at which an application feels responsive to an end user.\n",
     "\n",
-    "The key strategy to make the application feel more responsive is to show intermediate progress; e.g., to stream the output from the model **token by token**."
+    "The key strategy to make the application feel more responsive is to show intermediate progress; viz., to stream the output from the model **token by token**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eb73e8b",
+   "metadata": {},
+   "source": [
+    "We will show examples of streaming using the chat model from [Anthropic](https://python.langchain.com/docs/integrations/platforms/anthropic). To use the model, you will need to install the `langchain-anthropic` package. You can do this with the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd351cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install -qU langchain-anthropic"
    ]
   },
   {
@@ -68,7 +85,7 @@
    "source": [
     "# Showing the example using anthropic, but you can use\n",
     "# your favorite chat model!\n",
-    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_anthropic import ChatAnthropic\n",
     "\n",
     "model = ChatAnthropic()\n",
     "\n",
@@ -152,7 +169,7 @@
     "We will use `StrOutputParser` to parse the output from the model. This is a simple parser that extracts the `content` field from an `AIMessageChunk`, giving us the `token` returned by the model.\n",
     "\n",
     ":::{.callout-tip}\n",
-    "LCEL is a *declarative* way to specify a \"program\" by chainining together different LangChain primitives. Chains created using LCEL benefit from an automatic implementation of `stream`, and `astream` allowing streaming of the final output. In fact, chains created with LCEL implement the entire standard Runnable interface.\n",
+    "LCEL is a *declarative* way to specify a \"program\" by chainining together different LangChain primitives. Chains created using LCEL benefit from an automatic implementation of `stream` and `astream` allowing streaming of the final output. In fact, chains created with LCEL implement the entire standard Runnable interface.\n",
     ":::"
    ]
   },
@@ -330,7 +347,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "cab6dca2-2027-414d-a196-2db6e3ebb8a5",
    "metadata": {},
@@ -1397,7 +1413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,