refactor: mdx lint (#32282)

Author: mdrxy

docs/docs/additional_resources/arxiv_references.mdx

@@ -7,4 +7,4 @@
 - `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
 - `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
 - `BaseLLM` methods `__call__`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
-- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.
+- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.

docs/docs/changes/changelog/core.mdx

@@ -90,4 +90,4 @@ Deprecated classes and methods will be removed in 0.2.0
 | OpenAIMultiFunctionsAgent       | create_openai_tools_agent         | Use LCEL builder over a class                  |
 | SelfAskWithSearchAgent          | create_self_ask_with_search       | Use LCEL builder over a class                  |
 | StructuredChatAgent             | create_structured_chat_agent      | Use LCEL builder over a class                  |
-| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |
+| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |

docs/docs/changes/changelog/langchain.mdx

@@ -11,8 +11,8 @@ Please see the following resources for more information:
 
 ## Legacy agent concept: AgentExecutor
 
-LangChain previously introduced the `AgentExecutor` as a runtime for agents. 
-While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. 
+LangChain previously introduced the `AgentExecutor` as a runtime for agents.
+While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents.
 As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph.
 
 ### Transitioning from AgentExecutor to LangGraph

docs/docs/concepts/agents.mdx

@@ -70,4 +70,4 @@ This is a common reason why you may fail to see events being emitted from custom
 runnables or tools.
 :::
 
-For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks).
+For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks).

docs/docs/concepts/callbacks.mdx

@@ -26,7 +26,7 @@ A full conversation often involves a combination of two patterns of alternating
 
 Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models/#context-window).
 
-While processing chat history, it's essential to preserve a correct conversation structure. 
+While processing chat history, it's essential to preserve a correct conversation structure.
 
 Key guidelines for managing chat history:
 

docs/docs/concepts/chat_history.mdx

@@ -127,7 +127,7 @@ If the input exceeds the context window, the model may not be able to process th
 The size of the input is measured in [tokens](/docs/concepts/tokens) which are the unit of processing that the model uses.
 
 ## Advanced topics
- 
+
 ### Rate-limiting
 
 Many chat model providers impose a limit on the number of requests that can be made in a given time period.

docs/docs/concepts/chat_models.mdx

@@ -15,9 +15,9 @@ Embedding models can also be [multimodal](/docs/concepts/multimodality) though s
 
 Imagine being able to capture the essence of any text - a tweet, document, or book - in a single, compact representation.
 This is the power of embedding models, which lie at the heart of many retrieval systems.
-Embedding models transform human language into a format that machines can understand and compare with speed and accuracy. 
+Embedding models transform human language into a format that machines can understand and compare with speed and accuracy.
 These models take text as input and produce a fixed-length array of numbers, a numerical fingerprint of the text's semantic meaning.
-Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding. 
+Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding.
 
 ## Key concepts
 
@@ -27,16 +27,16 @@ Embeddings allow search system to find relevant documents not just based on keyw
 
 (2) **Measure similarity**: Embedding vectors can be compared using simple mathematical operations.
 
-## Embedding 
+## Embedding
 
-### Historical context 
+### Historical context
 
-The landscape of embedding models has evolved significantly over the years. 
-A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/). 
+The landscape of embedding models has evolved significantly over the years.
+A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/).
 BERT applied transformer models to embed text as a simple vector representation, which lead to unprecedented performance across various NLP tasks.
-However, BERT wasn't optimized for generating sentence embeddings efficiently. 
+However, BERT wasn't optimized for generating sentence embeddings efficiently.
 This limitation spurred the creation of [SBERT (Sentence-BERT)](https://www.sbert.net/examples/training/sts/README.html), which adapted the BERT architecture to generate semantically rich sentence embeddings, easily comparable via similarity metrics like cosine similarity, dramatically reduced the computational overhead for tasks like finding similar sentences.
-Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations. 
+Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations.
 To navigate this variety, researchers and practitioners often turn to benchmarks like the Massive Text Embedding Benchmark (MTEB) [here](https://huggingface.co/blog/mteb) for objective comparisons.
 
 :::info[Further reading]
@@ -93,9 +93,9 @@ LangChain offers many embedding model integrations which you can find [on the em
 
 ## Measure similarity
 
-Each embedding is essentially a set of coordinates, often in a high-dimensional space. 
+Each embedding is essentially a set of coordinates, often in a high-dimensional space.
 In this space, the position of each point (embedding) reflects the meaning of its corresponding text.
-Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space. 
+Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space.
 This allows for intuitive comparisons between different pieces of text.
 By reducing text to these numerical representations, we can use simple mathematical operations to quickly measure how alike two pieces of text are, regardless of their original length or structure.
 Some common similarity metrics include:
@@ -118,7 +118,7 @@ def cosine_similarity(vec1, vec2):
 
 similarity = cosine_similarity(query_result, document_result)
 print("Cosine Similarity:", similarity)
-```  
+```
 
 :::info[Further reading]
 
@@ -127,4 +127,4 @@ print("Cosine Similarity:", similarity)
 * See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics.
 * See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings.
 
-::: 
+:::

docs/docs/concepts/embedding_models.mdx

@@ -14,4 +14,3 @@ This process is vital for building reliable applications.
 - It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code
 
 To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation).
-

docs/docs/concepts/evaluation.mdx

@@ -17,4 +17,4 @@ Sometimes these examples are hardcoded into the prompt, but for more advanced si
 
 ## Related resources
 
-* [Example selector how-to guides](/docs/how_to/#example-selectors)
+* [Example selector how-to guides](/docs/how_to/#example-selectors)