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docs: fix mdx codefences (#26802)

Browse Source 
```
git grep -l -E '"```\{=mdx\}\\n",' | xargs perl -0777 -i -pe 's/"```\{=mdx\}\\n",\n    (\W.*?),\n\s*"```\\?n?"/$1/s'
```
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6
docs/docs/how_to/agent_executor.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/chat_model_caching.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/chat_token_usage_tracking.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/convert_runnable_to_tool.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/debugging.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/dynamic_chain.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/extraction_examples.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/extraction_long_text.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/extraction_parse.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/function_calling.ipynb
View File

@ -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",

4
docs/docs/how_to/message_history.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/multi_vector.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/qa_chat_history_how_to.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/qa_citations.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/qa_per_user.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/qa_sources.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/qa_streaming.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/sequence.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/sql_csv.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/sql_large_db.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/sql_prompting.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/sql_query_checking.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/streaming.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/structured_output.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/summarize_map_reduce.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/summarize_refine.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/summarize_stuff.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tool_artifacts.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tool_calling.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tool_results_pass_to_model.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tool_runtime.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tool_stream_events.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tools_chain.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tools_error.ipynb
View File

@ -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"
]
},
{

4
docs/docs/how_to/tools_human.ipynb
View File

@ -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"
]
},
{

2
docs/docs/how_to/tools_prompting.ipynb
View File

@ -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/)."
]

4
docs/docs/integrations/retrievers/arxiv.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/retrievers/box.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/retrievers/wikipedia.ipynb
View File

@ -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"
]
},
{

2
docs/docs/integrations/tools/chatgpt_plugins.ipynb
View File

@ -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",

4
docs/docs/integrations/tools/github.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/tools/gmail.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/tools/sql_database.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/tools/tavily_search.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/astradb.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/chroma.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/clickhouse.ipynb
View File

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

4
docs/docs/integrations/vectorstores/couchbase.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/elasticsearch.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/faiss.ipynb
View File

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

4
docs/docs/integrations/vectorstores/milvus.ipynb
View File

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

4
docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
View File

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

4
docs/docs/integrations/vectorstores/pgvector.ipynb
View File

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

4
docs/docs/integrations/vectorstores/pinecone.ipynb
View File

@ -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"
]
},
{

4
docs/docs/integrations/vectorstores/qdrant.ipynb
View File

@ -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"
]
},
{

8
docs/docs/integrations/vectorstores/redis.ipynb
View File

@ -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"
]
},
{

4
docs/docs/tutorials/agents.ipynb
View File

@ -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"
]
},
{

6
docs/docs/tutorials/chatbot.ipynb
View File

@ -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"
]
},
{

2
docs/docs/tutorials/extraction.ipynb
View File

@ -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",

6
docs/docs/tutorials/llm_chain.ipynb
View File

@ -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"
]
},
{

4
docs/docs/tutorials/pdf_qa.ipynb
View File

@ -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"
]
},
{

4
docs/docs/tutorials/qa_chat_history.ipynb
View File

@ -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"
]
},
{

10
docs/docs/tutorials/rag.ipynb
View File

@ -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,

6
docs/docs/tutorials/retrievers.ipynb
View File

@ -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"
]
},
{

4
docs/docs/tutorials/sql_qa.ipynb
View File

@ -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"
]
},
{

6
docs/docs/tutorials/summarization.ipynb
View File

@ -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"
]
},
{

5
docs/scripts/notebook_convert.py
View File

@ -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\)",