rm .html from local doc links (langchain-ai#12293)

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diff --git a/docs/docs/get_started/quickstart.mdx b/docs/docs/get_started/quickstart.mdx
@@ -18,7 +18,7 @@ import CodeBlock from "@theme/CodeBlock";
 </Tabs>
 
 
-For more details, see our [Installation guide](/docs/get_started/installation.html).
+For more details, see our [Installation guide](/docs/get_started/installation).
 
 ## Environment setup
 

diff --git a/docs/docs/guides/deployments/index.mdx b/docs/docs/guides/deployments/index.mdx
@@ -20,11 +20,11 @@ This guide aims to provide a comprehensive overview of the requirements for depl
 
 Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include:
 
-- [Ray Serve](/docs/ecosystem/integrations/ray_serve.html)
+- [Ray Serve](/docs/ecosystem/integrations/ray_serve)
 - [BentoML](https://github.com/bentoml/BentoML)
-- [OpenLLM](/docs/ecosystem/integrations/openllm.html)
-- [Modal](/docs/ecosystem/integrations/modal.html)
-- [Jina](/docs/ecosystem/integrations/jina.html#deployment)
+- [OpenLLM](/docs/ecosystem/integrations/openllm)
+- [Modal](/docs/ecosystem/integrations/modal)
+- [Jina](/docs/ecosystem/integrations/jina#deployment)
 
 These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
 

diff --git a/docs/docs/integrations/callbacks/argilla.ipynb b/docs/docs/integrations/callbacks/argilla.ipynb
@@ -14,7 +14,7 @@
     "> using both human and machine feedback. We provide support for each step in the MLOps cycle, \n",
     "> from data labeling to model monitoring.\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/integrations/callbacks/argilla.html\">\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/integrations/callbacks/argilla\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
     "</a>"
    ]

diff --git a/docs/docs/integrations/document_loaders/apify_dataset.ipynb b/docs/docs/integrations/document_loaders/apify_dataset.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify.html) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
+    "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
    ]
   },
   {

diff --git a/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb b/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Pandas DataFrame\n",
     "\n",
-    "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html) DataFrame."
+    "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index) DataFrame."
    ]
   },
   {

diff --git a/docs/docs/integrations/document_loaders/psychic.ipynb b/docs/docs/integrations/document_loaders/psychic.ipynb
@@ -5,10 +5,10 @@
    "metadata": {},
    "source": [
     "# Psychic\n",
-    "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic.html) for more details.\n",
+    "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic) for more details.\n",
     "\n",
     "## Prerequisites\n",
-    "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic.html)\n",
+    "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic)\n",
     "2. Log into the [Psychic dashboard](https://dashboard.psychic.dev/) and get your secret key\n",
     "3. Install the frontend react library into your web app and have a user authenticate a connection. The connection will be created using the connection id that you specify."
    ]

diff --git a/docs/docs/integrations/llms/llm_caching.ipynb b/docs/docs/integrations/llms/llm_caching.ipynb
@@ -319,7 +319,7 @@
    "metadata": {},
    "source": [
     "### Standard Cache\n",
-    "Use [Redis](/docs/ecosystem/integrations/redis.html) to cache prompts and responses."
+    "Use [Redis](/docs/ecosystem/integrations/redis) to cache prompts and responses."
    ]
   },
   {
@@ -405,7 +405,7 @@
    "metadata": {},
    "source": [
     "### Semantic Cache\n",
-    "Use [Redis](/docs/ecosystem/integrations/redis.html) to cache prompts and responses and evaluate hits based on semantic similarity."
+    "Use [Redis](/docs/ecosystem/integrations/redis) to cache prompts and responses and evaluate hits based on semantic similarity."
    ]
   },
   {
@@ -730,7 +730,7 @@
    },
    "source": [
     "## `Momento` Cache\n",
-    "Use [Momento](/docs/ecosystem/integrations/momento.html) to cache prompts and responses.\n",
+    "Use [Momento](/docs/ecosystem/integrations/momento) to cache prompts and responses.\n",
     "\n",
     "Requires momento to use, uncomment below to install:"
    ]

diff --git a/docs/docs/integrations/platforms/aws.mdx b/docs/docs/integrations/platforms/aws.mdx
@@ -75,9 +75,9 @@ from langchain.llms.sagemaker_endpoint import ContentHandlerBase
 >[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
 >[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
 
-See a [usage example for S3DirectoryLoader](/docs/integrations/document_loaders/aws_s3_directory.html).
+See a [usage example for S3DirectoryLoader](/docs/integrations/document_loaders/aws_s3_directory).
 
-See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s3_file.html).
+See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s3_file).
 
 ```python
 from langchain.document_loaders import S3DirectoryLoader, S3FileLoader

diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx
@@ -83,7 +83,7 @@ First, we need to install several python packages.
 pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
 ```
 
-See a [usage example and authorizing instructions](/docs/integrations/document_loaders/google_drive.html).
+See a [usage example and authorizing instructions](/docs/integrations/document_loaders/google_drive).
 
 ```python
 from langchain.document_loaders import GoogleDriveLoader
@@ -182,7 +182,7 @@ There exists a `GoogleSearchAPIWrapper` utility which wraps this API. To import
 from langchain.utilities import GoogleSearchAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search.html).
+For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search).
 
 We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
 

diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/platforms/microsoft.mdx
@@ -70,13 +70,13 @@ from langchain.chat_models import AzureChatOpenAI
 pip install azure-storage-blob
 ```
 
-See a [usage example for the Azure Blob Storage](/docs/integrations/document_loaders/azure_blob_storage_container.html).
+See a [usage example for the Azure Blob Storage](/docs/integrations/document_loaders/azure_blob_storage_container).
 
 ```python
 from langchain.document_loaders import AzureBlobStorageContainerLoader
 ```
 
-See a [usage example for the Azure Files](/docs/integrations/document_loaders/azure_blob_storage_file.html).
+See a [usage example for the Azure Files](/docs/integrations/document_loaders/azure_blob_storage_file).
 
 ```python
 from langchain.document_loaders import AzureBlobStorageFileLoader

diff --git a/docs/docs/integrations/providers/airtable.md b/docs/docs/integrations/providers/airtable.md
@@ -25,4 +25,4 @@ pip install pyairtable
 from langchain.document_loaders import AirtableLoader
 ```
 
-See an [example](/docs/integrations/document_loaders/airtable.html).
+See an [example](/docs/integrations/document_loaders/airtable).
diff --git a/docs/docs/integrations/providers/analyticdb.mdx b/docs/docs/integrations/providers/analyticdb.mdx
@@ -12,4 +12,4 @@ To import this vectorstore:
 from langchain.vectorstores import AnalyticDB
 ```
 
-For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](/docs/integrations/vectorstores/analyticdb.html)
+For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](/docs/integrations/vectorstores/analyticdb)
diff --git a/docs/docs/integrations/providers/apify.mdx b/docs/docs/integrations/providers/apify.mdx
@@ -32,7 +32,7 @@ You can use the `ApifyWrapper` to run Actors on the Apify platform.
 from langchain.utilities import ApifyWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/apify.html).
+For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/apify).
 
 
 ### Loader
@@ -43,4 +43,4 @@ You can also use our `ApifyDatasetLoader` to get data from Apify dataset.
 from langchain.document_loaders import ApifyDatasetLoader
 ```
 
-For a more detailed walkthrough of this loader, see [this notebook](/docs/integrations/document_loaders/apify_dataset.html).
+For a more detailed walkthrough of this loader, see [this notebook](/docs/integrations/document_loaders/apify_dataset).
diff --git a/docs/docs/integrations/providers/arangodb.mdx b/docs/docs/integrations/providers/arangodb.mdx
@@ -13,7 +13,7 @@ pip install python-arango
 
 Connect your ArangoDB Database with a chat model to get insights on your data. 
 
-See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa.html).
+See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa).
 
 ```python
 from arango import ArangoClient

diff --git a/docs/docs/integrations/providers/argilla.mdx b/docs/docs/integrations/providers/argilla.mdx
@@ -22,7 +22,7 @@ If you don't you can refer to [Argilla - 🚀 Quickstart](https://docs.argilla.i
 
 ## Tracking
 
-See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla.html).
+See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla).
 
 ```python
 from langchain.callbacks import ArgillaCallbackHandler

diff --git a/docs/docs/integrations/providers/chroma.mdx b/docs/docs/integrations/providers/chroma.mdx
@@ -18,7 +18,7 @@ whether for semantic search or example selection.
 from langchain.vectorstores import Chroma
 ```
 
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma.html)
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma)
 
 ## Retriever
 

diff --git a/docs/docs/integrations/providers/clarifai.mdx b/docs/docs/integrations/providers/clarifai.mdx
@@ -25,7 +25,7 @@ from langchain.llms import Clarifai
 llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
 ```
 
-For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/integrations/llms/clarifai.html).
+For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/integrations/llms/clarifai).
 
 
 ### Text Embedding Models
@@ -37,7 +37,7 @@ There is a Clarifai Embedding model in LangChain, which you can access with:
 from langchain.embeddings import ClarifaiEmbeddings
 embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
 ```
-For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walkthrough](/docs/integrations/text_embedding/clarifai.html).
+For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walkthrough](/docs/integrations/text_embedding/clarifai).
 
 ## Vectorstore
 

diff --git a/docs/docs/integrations/providers/cohere.mdx b/docs/docs/integrations/providers/cohere.mdx
@@ -27,7 +27,7 @@ There exists an Cohere Embedding model, which you can access with
 ```python
 from langchain.embeddings import CohereEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/cohere.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/cohere)
 
 ## Retriever
 

diff --git a/docs/docs/integrations/providers/comet_tracking.ipynb b/docs/docs/integrations/providers/comet_tracking.ipynb
@@ -20,7 +20,7 @@
    "source": [
     "In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook).  \n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/comet_tracking.html\">\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/comet_tracking\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
     "</a>\n",
     "\n",

diff --git a/docs/docs/integrations/providers/ctransformers.mdx b/docs/docs/integrations/providers/ctransformers.mdx
@@ -54,4 +54,4 @@ llm = CTransformers(model='marella/gpt-2-ggml', config=config)
 
 See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters.
 
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers.html).
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers).
diff --git a/docs/docs/integrations/providers/dashvector.mdx b/docs/docs/integrations/providers/dashvector.mdx
@@ -21,4 +21,4 @@ You may import the vectorstore by:
 from langchain.vectorstores import DashVector
 ```
 
-For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector.html)
+For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector)
diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
@@ -33,11 +33,11 @@ See [MLflow AI Gateway](/docs/integrations/providers/mlflow_ai_gateway).
 Databricks as an LLM provider
 -----------------------------
 
-The notebook [Wrap Databricks endpoints as LLMs](/docs/integrations/llms/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
+The notebook [Wrap Databricks endpoints as LLMs](/docs/integrations/llms/databricks) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
 
 Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the Hugging Face ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises.
 
 Databricks Dolly
 ----------------
 
-Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](/docs/integrations/llms/huggingface_hub.html) for instructions to access it through the Hugging Face Hub integration with LangChain. 
+Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](/docs/integrations/llms/huggingface_hub) for instructions to access it through the Hugging Face Hub integration with LangChain. 
diff --git a/docs/docs/integrations/providers/dingo.mdx b/docs/docs/integrations/providers/dingo.mdx
@@ -16,4 +16,4 @@ To import this vectorstore:
 from langchain.vectorstores import Dingo
 ```
 
-For a more detailed walkthrough of the DingoDB wrapper, see [this notebook](/docs/integrations/vectorstores/dingo.html)
+For a more detailed walkthrough of the DingoDB wrapper, see [this notebook](/docs/integrations/vectorstores/dingo)
diff --git a/docs/docs/integrations/providers/epsilla.mdx b/docs/docs/integrations/providers/epsilla.mdx
@@ -20,4 +20,4 @@ To import this vectorstore:
 from langchain.vectorstores import Epsilla
 ```
 
-For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla.html)
+For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla)
diff --git a/docs/docs/integrations/providers/golden.mdx b/docs/docs/integrations/providers/golden.mdx
@@ -20,7 +20,7 @@ There exists a GoldenQueryAPIWrapper utility which wraps this API. To import thi
 from langchain.utilities.golden_query import GoldenQueryAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/golden_query.html).
+For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/golden_query).
 
 ### Tool
 

diff --git a/docs/docs/integrations/providers/google_serper.mdx b/docs/docs/integrations/providers/google_serper.mdx
@@ -59,7 +59,7 @@ So the final answer is: El Palmar, Spain
 'El Palmar, Spain'
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_serper.html).
+For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_serper).
 
 ### Tool
 

diff --git a/docs/docs/integrations/providers/gpt4all.mdx b/docs/docs/integrations/providers/gpt4all.mdx
@@ -45,4 +45,4 @@ model("Once upon a time, ", callbacks=callbacks)
 
 You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
 
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/gpt4all.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/gpt4all)
diff --git a/docs/docs/integrations/providers/gradient.mdx b/docs/docs/integrations/providers/gradient.mdx
@@ -24,4 +24,4 @@ There exists an Gradient Embedding model, which you can access with
 ```python
 from langchain.embeddings import GradientEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/gradient.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/gradient)
diff --git a/docs/docs/integrations/providers/huggingface.mdx b/docs/docs/integrations/providers/huggingface.mdx
@@ -30,7 +30,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.llms import HuggingFaceHub
 ```
-For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/integrations/llms/huggingface_hub.html)
+For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/integrations/llms/huggingface_hub)
 
 
 ### Embeddings

diff --git a/docs/docs/integrations/providers/infino.mdx b/docs/docs/integrations/providers/infino.mdx
@@ -28,7 +28,7 @@ you don't, follow the next steps to start it:
 
 ## Using Infino
 
-See a [usage example of `InfinoCallbackHandler`](/docs/integrations/callbacks/infino.html).
+See a [usage example of `InfinoCallbackHandler`](/docs/integrations/callbacks/infino).
 
 ```python
 from langchain.callbacks import InfinoCallbackHandler

diff --git a/docs/docs/integrations/providers/jina.mdx b/docs/docs/integrations/providers/jina.mdx
@@ -15,7 +15,7 @@ There exists a Jina Embeddings wrapper, which you can access with
 ```python
 from langchain.embeddings import JinaEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina)
 
 ## Deployment
 

diff --git a/docs/docs/integrations/providers/lancedb.mdx b/docs/docs/integrations/providers/lancedb.mdx
@@ -20,4 +20,4 @@ To import this vectorstore:
 from langchain.vectorstores import LanceDB
 ```
 
-For a more detailed walkthrough of the LanceDB wrapper, see [this notebook](/docs/integrations/vectorstores/lancedb.html)
+For a more detailed walkthrough of the LanceDB wrapper, see [this notebook](/docs/integrations/vectorstores/lancedb)
diff --git a/docs/docs/integrations/providers/llamacpp.mdx b/docs/docs/integrations/providers/llamacpp.mdx
@@ -15,12 +15,12 @@ There exists a LlamaCpp LLM wrapper, which you can access with
 ```python
 from langchain.llms import LlamaCpp
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp)
 
 ### Embeddings
 
 There exists a LlamaCpp Embeddings wrapper, which you can access with 
 ```python
 from langchain.embeddings import LlamaCppEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp.html)
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp)
diff --git a/docs/docs/integrations/providers/marqo.md b/docs/docs/integrations/providers/marqo.md
@@ -28,4 +28,4 @@ To import this vectorstore:
 from langchain.vectorstores import Marqo
 ```
 
-For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](/docs/integrations/vectorstores/marqo.html)
+For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](/docs/integrations/vectorstores/marqo)
diff --git a/docs/docs/integrations/providers/milvus.mdx b/docs/docs/integrations/providers/milvus.mdx
@@ -22,4 +22,4 @@ To import this vectorstore:
 from langchain.vectorstores import Milvus
 ```
 
-For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus.html)
+For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus)
diff --git a/docs/docs/integrations/providers/minimax.mdx b/docs/docs/integrations/providers/minimax.mdx
@@ -11,15 +11,15 @@ Get a [Minimax group id](https://api.minimax.chat/user-center/basic-information)
 ## LLM
 
 There exists a Minimax LLM wrapper, which you can access with
-See a [usage example](/docs/modules/model_io/models/llms/integrations/minimax.html).
+See a [usage example](/docs/modules/model_io/models/llms/integrations/minimax).
 
 ```python
 from langchain.llms import Minimax
 ```
 
 ## Chat Models
 
-See a [usage example](/docs/modules/model_io/models/chat/integrations/minimax.html)
+See a [usage example](/docs/modules/model_io/models/chat/integrations/minimax)
 
 ```python
 from langchain.chat_models import MiniMaxChat