docs[patch]: Adds callout in LLM concept docs, remove deprecated code (#23361)

CC @baskaryan @hwchase17

docs/docs/concepts.mdx

@@ -179,8 +179,15 @@ For a full list of LangChain model providers with multimodal models, [check out
 ### LLMs
 <span data-heading-keywords="llm,llms"></span>
 
+:::caution
+Pure text-in/text-out LLMs tend to be older or lower-level. Many popular models are best used as [chat completion models](/docs/concepts/#chat-models),
+even for non-chat use cases.
+
+You are probably looking for [the section above instead](/docs/concepts/#chat-models).
+:::
+
 Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see below).
+These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see above).
 
 Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input.
 This gives them the same interface as [Chat Models](/docs/concepts/#chat-models).

docs/docs/integrations/llms/bedrock.ipynb

@@ -52,67 +52,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using in a conversation chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "\n",
-    "conversation = ConversationChain(\n",
-    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
-    ")\n",
-    "\n",
-    "conversation.predict(input=\"Hi there!\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Conversation Chain With Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import Bedrock\n",
-    "from langchain_core.callbacks import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "llm = Bedrock(\n",
-    "    credentials_profile_name=\"bedrock-admin\",\n",
-    "    model_id=\"amazon.titan-text-express-v1\",\n",
-    "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "conversation = ConversationChain(\n",
-    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
-    ")\n",
-    "\n",
-    "conversation.predict(input=\"Hi there!\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -132,22 +71,17 @@
     "    model_id=\"<Custom model ARN>\",  # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n",
     "    model_kwargs={\"temperature\": 1},\n",
     "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
     ")\n",
     "\n",
-    "conversation = ConversationChain(\n",
-    "    llm=custom_llm, verbose=True, memory=ConversationBufferMemory()\n",
-    ")\n",
-    "conversation.predict(input=\"What is the recipe of mayonnaise?\")"
+    "custom_llm.invoke(input=\"What is the recipe of mayonnaise?\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Guardrails for Amazon Bedrock example \n",
+    "## Guardrails for Amazon Bedrock\n",
     "\n",
-    "## Guardrails for Amazon Bedrock (Preview) \n",
     "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n",
     "**Note**: Guardrails for Amazon Bedrock is currently in preview and not generally available. Reach out through your usual AWS Support contacts if you’d like access to this feature.\n",
     "In this section, we are going to set up a Bedrock language model with specific guardrails that include tracing capabilities.   "