docs: fix mdx codefences (#26802)

```
git grep -l -E '"```\{=mdx\}\\n",' | xargs perl -0777 -i -pe 's/"```\{=mdx\}\\n",\n    (\W.*?),\n\s*"```\\?n?"/$1/s'
```

docs/docs/how_to/agent_executor.ipynb

@@ -49,7 +49,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -63,7 +62,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
@@ -270,11 +268,9 @@
     "\n",
     "Next, let's learn how to use a language model by to call tools. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
+    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/chat_model_caching.ipynb

@@ -28,11 +28,9 @@
    "id": "289b31de",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -155,11 +155,9 @@
     "\n",
     "For example, OpenAI will return a message [chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html) at the end of a stream with token usage information. This behavior is supported by `langchain-openai >= 0.1.9` and can be enabled by setting `stream_usage=True`. This attribute can also be set when `ChatOpenAI` is instantiated.\n",
     "\n",
-    "```{=mdx}\n",
     ":::note\n",
     "By default, the last message chunk in a stream will include a `\"finish_reason\"` in the message's `response_metadata` attribute. If we include token usage in streaming mode, an additional chunk containing usage metadata will be added to the end of the stream, such that `\"finish_reason\"` appears on the second to last message chunk.\n",
-    ":::\n",
+    ":::\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -266,11 +266,9 @@
     "\n",
     "We first instantiate a chat model that supports [tool calling](/docs/how_to/tool_calling/):\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/debugging.ipynb

@@ -49,13 +49,11 @@
     "\n",
     "Let's suppose we have an agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/dynamic_chain.ipynb

@@ -17,13 +17,11 @@
     "\n",
     "Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs ([routing](/docs/how_to/routing/) is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/extraction_examples.ipynb

@@ -350,14 +350,12 @@
     "\n",
     "Let's select an LLM. Because we are using tool-calling, we will need a model that supports a tool-calling feature. See [this table](/docs/integrations/chat) for available LLMs.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  openaiParams={`model=\"gpt-4-0125-preview\", temperature=0`}\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/extraction_long_text.ipynb

@@ -196,14 +196,12 @@
     "\n",
     "Let's select an LLM. Because we are using tool-calling, we will need a model that supports a tool-calling feature. See [this table](/docs/integrations/chat) for available LLMs.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  openaiParams={`model=\"gpt-4-0125-preview\", temperature=0`}\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/extraction_parse.ipynb

@@ -18,11 +18,9 @@
     "\n",
     "First we select a LLM:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"model\" />\n",
+    "<ChatModelTabs customVarName=\"model\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/function_calling.ipynb

@@ -17,14 +17,12 @@
    "source": [
     "# How to do tool/function calling\n",
     "\n",
-    "```{=mdx}\n",
     ":::info\n",
     "We use the term tool calling interchangeably with function calling. Although\n",
     "function calling is sometimes meant to refer to invocations of a single function,\n",
     "we treat all models as though they can return multiple tool or function calls in \n",
     "each message.\n",
     ":::\n",
-    "```\n",
     "\n",
     "Tool calling allows a model to respond to a given prompt by generating output that \n",
     "matches a user-defined schema. While the name implies that the model is performing \n",
@@ -165,14 +163,12 @@
    "source": [
     "We can bind them to chat models as follows:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
     "/>\n",
-    "```\n",
     "\n",
     "We can use the `bind_tools()` method to handle converting\n",
     "`Multiply` to a \"tool\" and binding it to the model (i.e.,\n",

docs/docs/how_to/message_history.ipynb

@@ -155,13 +155,11 @@
     "\n",
     "First we construct a runnable (which here accepts a dict as input and returns a message as output):\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/multi_vector.ipynb

@@ -246,11 +246,9 @@
     "\n",
     "We construct a simple [chain](/docs/how_to/sequence) that will receive an input [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) object and generate a summary using a LLM.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/qa_chat_history_how_to.ipynb

@@ -120,11 +120,9 @@
    "id": "646840fb-5212-48ea-8bc7-ec7be5ec727e",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/qa_citations.ipynb

@@ -67,11 +67,9 @@
    "source": [
     "Let's first select a LLM:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/qa_per_user.ipynb

@@ -124,11 +124,9 @@
     "We can now create the chain that we will use to do question-answering over.\n",
     "\n",
     "Let's first select a LLM.\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/qa_sources.ipynb

@@ -100,11 +100,9 @@
     "\n",
     "Let's first select a LLM:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/qa_streaming.ipynb

@@ -93,11 +93,9 @@
     "\n",
     "Let's first select a LLM:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/sequence.ipynb

@@ -37,13 +37,11 @@
     "\n",
     "To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/how_to#prompt-templates) to format input into a [chat model](/docs/how_to#chat-models), and finally converting the chat message output into a string with an [output parser](/docs/how_to#output-parsers).\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"model\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/sql_csv.ipynb

@@ -167,11 +167,9 @@
    "source": [
     "And create a [SQL agent](/docs/tutorials/sql_qa) to interact with it:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/sql_large_db.ipynb

@@ -94,11 +94,9 @@
     "\n",
     "One easy and reliable way to do this is using [tool-calling](/docs/how_to/tool_calling). Below, we show how we can use this feature to obtain output conforming to a desired format (in this case, a list of table names). We use the chat model's `.bind_tools` method to bind a tool in Pydantic format, and feed this into an output parser to reconstruct the object from the model's response.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/sql_prompting.ipynb

@@ -125,11 +125,9 @@
    "source": [
     "For example, using our current DB we can see that we'll get a SQLite-specific prompt.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/sql_query_checking.ipynb

@@ -91,11 +91,9 @@
     "\n",
     "Perhaps the simplest strategy is to ask the model itself to check the original query for common mistakes. Suppose we have the following SQL query chain:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/streaming.ipynb

@@ -67,13 +67,11 @@
     "\n",
     "We will show examples of streaming using a chat model. Choose one from the options below:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"model\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/structured_output.ipynb

@@ -47,13 +47,11 @@
     "\n",
     "As an example, let's get a model to generate a joke and separate the setup from the punchline:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/summarize_map_reduce.ipynb

@@ -41,13 +41,11 @@
     "## Load chat model\n",
     "\n",
     "Let's first load a chat model:\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/summarize_refine.ipynb

@@ -46,13 +46,11 @@
     "## Load chat model\n",
     "\n",
     "Let's first load a chat model:\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/summarize_stuff.ipynb

@@ -31,13 +31,11 @@
     "## Load chat model\n",
     "\n",
     "Let's first load a chat model:\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tool_artifacts.ipynb

@@ -145,13 +145,11 @@
     "\n",
     "With a [tool-calling model](/docs/how_to/tool_calling/), we can easily use a model to call our Tool and generate ToolMessages:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tool_calling.ipynb

@@ -196,14 +196,12 @@
     "To actually bind those schemas to a chat model, we'll use the `.bind_tools()` method. This handles converting\n",
     "the `add` and `multiply` schemas to the proper format for the model. The tool schema will then be passed it in each time the model is invoked.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -29,14 +29,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tool_runtime.ipynb

@@ -42,14 +42,12 @@
    "source": [
     "We can bind them to chat models as follows:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tool_stream_events.ipynb

@@ -31,11 +31,9 @@
     "\n",
     "Say you have a custom tool that calls a chain that condenses its input by prompting a chat model to return only 10 words, then reversing the output. First, define it in a naive way:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"model\" />\n",
+    "<ChatModelTabs customVarName=\"model\" />\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tools_chain.ipynb

@@ -147,11 +147,9 @@
     "\n",
     "First we'll define our model and tools. We'll start with just a single tool, `multiply`.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tools_error.ipynb

@@ -79,11 +79,9 @@
     "\n",
     "Suppose we have the following (dummy) tool and tool-calling chain. We'll make our tool intentionally convoluted to try and trip up the model.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tools_human.ipynb

@@ -77,11 +77,9 @@
    "id": "43721981-4595-4721-bea0-5c67696426d3",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n"
-    "```"
    ]
   },
   {

docs/docs/how_to/tools_prompting.ipynb

@@ -89,11 +89,9 @@
    "source": [
     "You can select any of the given models for this how-to guide. Keep in mind that most of these models already [support native tool calling](/docs/integrations/chat/), so using the prompting strategy shown here doesn't make sense for these models, and instead you should follow the [how to use a chat model to call tools](/docs/how_to/tool_calling) guide.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
-    "```\n",
     "\n",
     "To illustrate the idea, we'll use `phi3` via Ollama, which does **NOT** have native support for tool calling. If you'd like to use `Ollama` as well follow [these instructions](/docs/integrations/chat/ollama/)."
    ]

docs/docs/integrations/retrievers/arxiv.ipynb

@@ -215,11 +215,9 @@
     "\n",
     "We will need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/retrievers/box.ipynb

@@ -248,11 +248,9 @@
     "\n",
     "We will need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/retrievers/wikipedia.ipynb

@@ -152,11 +152,9 @@
     "\n",
     "We will need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/tools/chatgpt_plugins.ipynb

@@ -17,13 +17,11 @@
    "source": [
     "# ChatGPT Plugins\n",
     "\n",
-    "```{=mdx}\n",
     ":::warning Deprecated\n",
     "\n",
     "OpenAI has [deprecated plugins](https://openai.com/index/chatgpt-plugins/).\n",
     "\n",
     ":::\n",
-    "```\n",
     "\n",
     "This example shows how to use ChatGPT Plugins within LangChain abstractions.\n",
     "\n",

docs/docs/integrations/tools/github.ipynb

@@ -208,11 +208,9 @@
     "\n",
     "We will need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/tools/gmail.ipynb

@@ -159,11 +159,9 @@
     "\n",
     "We will need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/tools/sql_database.ipynb

@@ -133,11 +133,9 @@
    "source": [
     "We will also need a LLM or chat model:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/tools/tavily_search.ipynb

@@ -263,11 +263,9 @@
     "\n",
     "We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/astradb.ipynb

@@ -107,11 +107,9 @@
     "\n",
     "Below, we instantiate our vector store using the explicit embedding class:\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/chroma.ipynb

@@ -66,11 +66,9 @@
     "\n",
     "Below is a basic initialization, including the use of a directory to save the data locally.\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -80,11 +80,9 @@
    "source": [
     "## Instantiation\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -167,11 +167,9 @@
     "\n",
     "Below, we create the vector store object with the cluster information and the search index name. \n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/databricks_vector_search.ipynb

@@ -242,11 +242,9 @@
     "you also need to provide the embedding model and text column in your source table to\n",
     "use for the embeddings:\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/elasticsearch.ipynb

@@ -80,11 +80,9 @@
     "### Running with Authentication\n",
     "For production, we recommend you run with security enabled. To connect with login credentials, you can use the parameters `es_api_key` or `es_user` and `es_password`.\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/faiss.ipynb

@@ -66,11 +66,9 @@
    "source": [
     "## Initialization\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/milvus.ipynb

@@ -41,11 +41,9 @@
     "\n",
     "## Initialization\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -88,11 +88,9 @@
    "source": [
     "## Initialization\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/pgvector.ipynb

@@ -92,11 +92,9 @@
    "source": [
     "## Instantiation\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -130,11 +130,9 @@
    "source": [
     "Now that our Pinecone index is setup, we can initialize our vector store. \n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/qdrant.ipynb

@@ -77,11 +77,9 @@
     "For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the client is destroyed - usually at the end of your script/notebook.\n",
     "\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {

docs/docs/integrations/vectorstores/redis.ipynb

@@ -362,11 +362,9 @@
     "\n",
     "Below we will use the `RedisVectorStore.__init__` method using a `RedisConfig` instance.\n",
     "\n",
-    "```{=mdx}\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n",
+    "<EmbeddingTabs/>\n"
-    "```"
    ]
   },
   {
@@ -884,11 +882,9 @@
     "## Chain usage\n",
     "The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/agents.ipynb

@@ -223,11 +223,9 @@
     "\n",
     "Next, let's learn how to use a language model by to call tools. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
+    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/chatbot.ipynb

@@ -61,7 +61,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -75,7 +74,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
@@ -107,11 +105,9 @@
     "\n",
     "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-3.5-turbo\"`} />\n",
+    "<ChatModelTabs openaiParams={`model=\"gpt-3.5-turbo\"`} />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/extraction.ipynb

@@ -51,7 +51,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -65,7 +64,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",

docs/docs/tutorials/llm_chain.ipynb

@@ -45,7 +45,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -59,7 +58,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
@@ -97,11 +95,9 @@
     "\n",
     "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
+    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/pdf_qa.ipynb

@@ -119,11 +119,9 @@
     "\n",
     "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/#text-splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/#vector-stores). You can then create a [retriever](/docs/concepts/#retrievers) from the vector store for use in our RAG chain:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" openaiParams={`model=\"gpt-4o\"`} />\n",
+    "<ChatModelTabs customVarName=\"llm\" openaiParams={`model=\"gpt-4o\"`} />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/qa_chat_history.ipynb

@@ -133,11 +133,9 @@
    "id": "646840fb-5212-48ea-8bc7-ec7be5ec727e",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/rag.ipynb

@@ -129,11 +129,9 @@
     "We can create a simple indexing pipeline and RAG chain to do this in ~20\n",
     "lines of code:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {
@@ -620,12 +618,10 @@
     "We’ll use the gpt-4o-mini OpenAI chat model, but any LangChain `LLM`\n",
     "or `ChatModel` could be substituted in.\n",
     "\n",
-    "```{=mdx}\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
     "  anthropicParams={`model=\"claude-3-sonnet-20240229\", temperature=0.2, max_tokens=1024`}\n",
     "/>\n",
-    "```\n",
     "\n",
     "We’ll use a prompt for RAG that is checked into the LangChain prompt hub\n",
     "([here](https://smith.langchain.com/hub/rlm/rag-prompt))."
@@ -959,7 +955,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -973,7 +969,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/retrievers.ipynb

@@ -27,7 +27,6 @@
     "\n",
     "This tutorial requires the `langchain`, `langchain-chroma`, and `langchain-openai` packages:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -41,7 +40,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
     "\n",
@@ -389,11 +387,9 @@
     "\n",
     "Retrievers can easily be incorporated into more complex applications, such as retrieval-augmented generation (RAG) applications that combine a given question with retrieved context into a prompt for a LLM. Below we show a minimal example.\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/sql_qa.ipynb

@@ -147,11 +147,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
-    "```"
    ]
   },
   {

docs/docs/tutorials/summarization.ipynb

@@ -74,7 +74,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -88,7 +87,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
@@ -206,13 +204,11 @@
    "source": [
     "Let's next select a LLM:\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
     "<ChatModelTabs\n",
     "  customVarName=\"llm\"\n",
-    "/>\n",
+    "/>\n"
-    "```"
    ]
   },
   {

docs/scripts/notebook_convert.py

@@ -13,11 +13,6 @@ from nbconvert.preprocessors import Preprocessor
 class EscapePreprocessor(Preprocessor):
     def preprocess_cell(self, cell, resources, cell_index):
         if cell.cell_type == "markdown":
-            # find all occurrences of ```{=mdx} blocks and remove wrapper
-            if "```{=mdx}\n" in cell.source:
-                cell.source = re.sub(
-                    r"```{=mdx}\n(.*?)\n```", r"\1", cell.source, flags=re.DOTALL
-                )
             # rewrite .ipynb links to .md
             cell.source = re.sub(
                 r"\[([^\]]*)\]\((?![^\)]*//)([^)]*)\.ipynb\)",