Removing unhelpful 'learn more' buttons. (#185)

* Removing unhelpful 'learn more' buttons.

* A few typos.

* Adding a missing period.

---------

Co-authored-by: Trent Fowler <[email protected]>

fern/pages/integrations/integrations/chroma-and-cohere.mdx

@@ -17,10 +17,4 @@ updatedAt: "Thu May 23 2024 16:53:54 GMT+0000 (Coordinated Universal Time)"
 
 Chroma is an open-source vector search engine that's quick to install and start building with using Python or Javascript.
 
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://trychroma.com"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+You can get started with [Chroma here](https://trychroma.com).

fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx

@@ -31,15 +31,7 @@ This guide uses a dataset of Wikipedia articles to set up a pipeline for semanti
 - Performing hybrid search on the Elasticsearch index and reranking results
 - Performing basic RAG
 
-To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Elastic_Guide.ipynb).
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://www.elastic.co/search-labs/integrations/cohere"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Elastic_Guide.ipynb). You can also find an integration guide [here](https://www.elastic.co/search-labs/integrations/cohere).
 
 ## Prerequisites
 

fern/pages/integrations/integrations/milvus-and-cohere.mdx

@@ -17,11 +17,3 @@ updatedAt: "Thu May 23 2024 16:59:13 GMT+0000 (Coordinated Universal Time)"
 [Milvus](https://milvus.io/) is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus is a graduated-stage project of the LF AI & Data Foundation.
 
 The following [guide](https://milvus.io/docs/integrate_with_cohere.md) walks through how to integrate [Cohere embeddings](/docs/embeddings) with Milvus.
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://milvus.io/docs/integrate_with_cohere.md"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>

fern/pages/integrations/integrations/mongodb-and-cohere.mdx

@@ -15,12 +15,4 @@ updatedAt: 'Thu May 23 2024 17:06:29 GMT+0000 (Coordinated Universal Time)'
 
 MongoDB Atlas Vector Search is a fully managed vector search platform from MongoDB. It can be used with Cohere's Embed and Rerank models to easily build semantic search or retrieval-augmented generation (RAG) systems with your data from MongoDB.
 
-The following guide walks through how to integrate Cohere models with MongoDB Atlas Vector Search.
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://www.mongodb.com/developer/products/atlas/how-use-cohere-embeddings-rerank-modules-mongodb-atlas/"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+[This guide](https://www.mongodb.com/developer/products/atlas/how-use-cohere-embeddings-rerank-modules-mongodb-atlas/) walks through how to integrate Cohere models with MongoDB Atlas Vector Search.

fern/pages/integrations/integrations/opensearch-and-cohere.mdx

@@ -13,4 +13,4 @@ updatedAt: 'Thu May 23 2024 17:09:12 GMT+0000 (Coordinated Universal Time)'
 <img src='../../../assets/images/d6bc303-open-search-logo.svg' width="200px" height="auto" class="light-bg" />
 
 
-[OpenSearch](https://opensearch.org/platform/search/vector-database.html) is an open-source, distributed search and analytics engine platform that allows users to search, analyze, and visualize large volumes of data in real time. When it comes to text search, OpenSearch is well-known for powering keyword-based (also called lexical) search methods. OpenSearch supports Vector Search and integrates with Cohere through [3rd-Party ML Connectors](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/) as well as Amazon Bedrock
+[OpenSearch](https://opensearch.org/platform/search/vector-database.html) is an open-source, distributed search and analytics engine platform that allows users to search, analyze, and visualize large volumes of data in real time. When it comes to text search, OpenSearch is well-known for powering keyword-based (also called lexical) search methods. OpenSearch supports Vector Search and integrates with Cohere through [3rd-Party ML Connectors](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/) as well as Amazon Bedrock.

fern/pages/integrations/integrations/pinecone-and-cohere.mdx

@@ -16,10 +16,4 @@ updatedAt: "Thu May 23 2024 16:57:06 GMT+0000 (Coordinated Universal Time)"
 
 The [Pinecone](https://www.pinecone.io/) vector database makes it easy to build high-performance vector search applications. Use Cohere to generate language embeddings, then store them in Pinecone and use them for Semantic Search.
 
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://docs.pinecone.io/docs/cohere"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+You can learn more by following this [step-by-step guide](https://docs.pinecone.io/integrations/cohere).

fern/pages/integrations/integrations/qdrant-and-cohere.mdx

@@ -18,10 +18,4 @@ updatedAt: "Thu May 23 2024 16:55:09 GMT+0000 (Coordinated Universal Time)"
 
 Qdrant is written in Rust, which makes it fast and reliable even under high load.
 
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://qdrant.tech/documentation/embeddings/cohere/"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+To learn more about how to work with Cohere's embeddings on Qdrant, [read this guide](https://qdrant.tech/documentation/embeddings/cohere/)

fern/pages/integrations/integrations/redis-and-cohere.mdx

@@ -20,15 +20,7 @@ updatedAt: 'Thu May 23 2024 17:06:49 GMT+0000 (Coordinated Universal Time)'
 - Embedding the user’s search query and searching against your Redis index
 - Exploring different filtering options for your query
 
-To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Redis_Guide.ipynb).
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://www.redisvl.com/user_guide/vectorizers_04.html#cohere"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Redis_Guide.ipynb). You can also consult [this guide](https://www.redisvl.com/user_guide/vectorizers_04.html#cohere) for more information on using Cohere with Redis.
 
 ## Prerequisites:
 

fern/pages/integrations/integrations/vespa-and-cohere.mdx

@@ -16,10 +16,4 @@ updatedAt: "Thu May 23 2024 16:52:39 GMT+0000 (Coordinated Universal Time)"
 
 [Vespa](https://vespa.ai/) is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.
 
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://blog.vespa.ai/scaling-large-vector-datasets-with-cohere-binary-embeddings-and-vespa/"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+Check out [this post](https://blog.vespa.ai/scaling-large-vector-datasets-with-cohere-binary-embeddings-and-vespa/) to find more information about working with Cohere's embeddings on Vespa.

fern/pages/integrations/integrations/weaviate-and-cohere.mdx

@@ -16,12 +16,4 @@ updatedAt: "Thu May 23 2024 16:56:09 GMT+0000 (Coordinated Universal Time)"
 
 [Weaviate](https://weaviate.io/) is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering.
 
-The `text2vec-cohere` module allows you to use [Cohere embeddings](/docs/embeddings) directly in the Weaviate vector search engine as a vectorization module.
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-cohere"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+The `text2vec-cohere` module allows you to use [Cohere embeddings](/docs/embeddings) directly in the Weaviate vector search engine as a vectorization module. You can find more information on using Cohere's functionality on Weaviate [here](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-cohere).

fern/pages/integrations/integrations/zilliz-and-cohere.mdx

@@ -16,12 +16,4 @@ updatedAt: "Thu May 23 2024 20:28:12 GMT+0000 (Coordinated Universal Time)"
 
 [Zilliz Cloud](https://zilliz.com/cloud) is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud provides a fully-managed Milvus service, made by the creators of Milvus that allows for easy integration with vectorizers from Cohere and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.
 
-The following [guide](https://docs.zilliz.com/docs/question-answering-using-zilliz-cloud-and-cohere) walks through how to integrate [Cohere embeddings](/docs/embeddings) with Zilliz.
-
-<a
-  className="rounded-4xl group flex h-fit items-center justify-center whitespace-nowrap transition ease-in-out py-3 px-6 disabled:cursor-not-allowed bg-primary-neutral border border-primary-neutral hover:bg-primary-light hover:text-primary-dark hover:border-primary-light active:bg-primary-neutral active:text-white active:border-primary-neutral disabled:bg-gray-80 disabled:text-gray-30 disabled:border-gray-80 focus:outline-none focus:ring-offset-2 focus:ring focus:ring-black w-100-to-75-to-50 mt-6"
-  href="https://docs.zilliz.com/docs/quick-start"
-  target="_self"
->
-  <span className="text-sm lg:text-base">Learn More</span>
-</a>
+The following [guide](https://docs.zilliz.com/docs/question-answering-using-zilliz-cloud-and-cohere) walks through how to integrate [Cohere embeddings](/docs/embeddings) with Zilliz. You might also find this [quickstart guide](https://docs.zilliz.com/docs/quick-start) helpful.