From aea3b163c9d23b567cf98561cf5afcc19169803a Mon Sep 17 00:00:00 2001 From: Trent Fowler Date: Mon, 7 Oct 2024 13:16:19 -0600 Subject: [PATCH 1/3] Removing unhelpful 'learn more' buttons. --- .../integrations/integrations/chroma-and-cohere.mdx | 8 +------- .../integrations/integrations/milvus-and-cohere.mdx | 8 -------- .../integrations/integrations/mongodb-and-cohere.mdx | 10 +--------- .../integrations/integrations/pinecone-and-cohere.mdx | 8 +------- .../integrations/integrations/qdrant-and-cohere.mdx | 8 +------- .../integrations/integrations/redis-and-cohere.mdx | 10 +--------- .../integrations/integrations/vespa-and-cohere.mdx | 8 +------- .../integrations/integrations/weaviate-and-cohere.mdx | 10 +--------- .../integrations/integrations/zilliz-and-cohere.mdx | 10 +--------- 9 files changed, 8 insertions(+), 72 deletions(-) diff --git a/fern/pages/integrations/integrations/chroma-and-cohere.mdx b/fern/pages/integrations/integrations/chroma-and-cohere.mdx index 1fe04055..1288e5c0 100644 --- a/fern/pages/integrations/integrations/chroma-and-cohere.mdx +++ b/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. - - Learn More - +You can get started with [Chroma here](https://trychroma.com). diff --git a/fern/pages/integrations/integrations/milvus-and-cohere.mdx b/fern/pages/integrations/integrations/milvus-and-cohere.mdx index e47f3f70..21bc4414 100644 --- a/fern/pages/integrations/integrations/milvus-and-cohere.mdx +++ b/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. - - - Learn More - diff --git a/fern/pages/integrations/integrations/mongodb-and-cohere.mdx b/fern/pages/integrations/integrations/mongodb-and-cohere.mdx index 287c059c..64c7de76 100644 --- a/fern/pages/integrations/integrations/mongodb-and-cohere.mdx +++ b/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. - - - Learn More - +[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. diff --git a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx b/fern/pages/integrations/integrations/pinecone-and-cohere.mdx index e0762e2a..ecebefc6 100644 --- a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx +++ b/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. - - Learn More - +You can learn more by following this [step-by-step guide](https://docs.pinecone.io/integrations/cohere) \ No newline at end of file diff --git a/fern/pages/integrations/integrations/qdrant-and-cohere.mdx b/fern/pages/integrations/integrations/qdrant-and-cohere.mdx index cc06eb9e..fae082e2 100644 --- a/fern/pages/integrations/integrations/qdrant-and-cohere.mdx +++ b/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. - - Learn More - +To learn more about how to work with Cohere's embeddings on Qdrant, [read this guide](https://qdrant.tech/documentation/embeddings/cohere/) \ No newline at end of file diff --git a/fern/pages/integrations/integrations/redis-and-cohere.mdx b/fern/pages/integrations/integrations/redis-and-cohere.mdx index 323be159..e99b44f0 100644 --- a/fern/pages/integrations/integrations/redis-and-cohere.mdx +++ b/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). - - - Learn More - +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: diff --git a/fern/pages/integrations/integrations/vespa-and-cohere.mdx b/fern/pages/integrations/integrations/vespa-and-cohere.mdx index 9bbaddab..d0af031f 100644 --- a/fern/pages/integrations/integrations/vespa-and-cohere.mdx +++ b/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. - - Learn More - +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. diff --git a/fern/pages/integrations/integrations/weaviate-and-cohere.mdx b/fern/pages/integrations/integrations/weaviate-and-cohere.mdx index b81782bc..966d397d 100644 --- a/fern/pages/integrations/integrations/weaviate-and-cohere.mdx +++ b/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. - - - Learn More - +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). diff --git a/fern/pages/integrations/integrations/zilliz-and-cohere.mdx b/fern/pages/integrations/integrations/zilliz-and-cohere.mdx index d0fbcd7d..a53d19ec 100644 --- a/fern/pages/integrations/integrations/zilliz-and-cohere.mdx +++ b/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. - - - Learn More - +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. From 9b3191ac4fd24840c55c8b1b893e21ca1f9a9f71 Mon Sep 17 00:00:00 2001 From: Trent Fowler Date: Mon, 7 Oct 2024 13:46:43 -0600 Subject: [PATCH 2/3] A few typos. --- .../integrations/elasticsearch-and-cohere.mdx | 10 +--------- .../integrations/opensearch-and-cohere.mdx | 2 +- .../integrations/integrations/pinecone-and-cohere.mdx | 2 +- 3 files changed, 3 insertions(+), 11 deletions(-) diff --git a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx b/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx index d442fe50..6113c8c6 100644 --- a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx +++ b/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). - - - Learn More - +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 diff --git a/fern/pages/integrations/integrations/opensearch-and-cohere.mdx b/fern/pages/integrations/integrations/opensearch-and-cohere.mdx index af4ed230..4229e68f 100644 --- a/fern/pages/integrations/integrations/opensearch-and-cohere.mdx +++ b/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)' -[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. diff --git a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx b/fern/pages/integrations/integrations/pinecone-and-cohere.mdx index ecebefc6..c5bccfa6 100644 --- a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx +++ b/fern/pages/integrations/integrations/pinecone-and-cohere.mdx @@ -16,4 +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. -You can learn more by following this [step-by-step guide](https://docs.pinecone.io/integrations/cohere) \ No newline at end of file +You can learn more by following this [step-by-step guide](https://docs.pinecone.io/integrations/cohere). \ No newline at end of file From 80735d02ead3ce929f9451314a8479145d116331 Mon Sep 17 00:00:00 2001 From: Trent Fowler Date: Mon, 7 Oct 2024 13:58:00 -0600 Subject: [PATCH 3/3] Adding a missing period. --- .../integrations/integrations/elasticsearch-and-cohere.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx b/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx index 6113c8c6..dac5f8d6 100644 --- a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx +++ b/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx @@ -31,7 +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). You can also find an integration guide [here](https://www.elastic.co/search-labs/integrations/cohere) +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