From e2a4d628e279658c18df25fa10a803bf8c1ca47d Mon Sep 17 00:00:00 2001 From: Kate Sv Date: Thu, 12 Sep 2024 09:07:26 -0700 Subject: [PATCH] (docs)Remove broken links (#118) --- cohere-openapi.yaml | 8 ++++---- fern/docs.yml | 13 ++++++++----- .../api-reference/connectors-1/delete-connector.mdx | 2 +- .../api-reference/connectors-1/get-connector.mdx | 2 +- .../2022-08-05-introducing-moderate-beta.mdx | 2 +- ...2023-09-27-release-notes-september-29th-2023.mdx | 2 +- .../2024-01-02-release-notes-january-x-2024.mdx | 2 +- .../2024-06-10-release-notes-for-june-10th-2024.mdx | 1 - fern/pages/changelog/release-notes.mdx | 2 +- .../getting-started-with-coral-toolkit.mdx | 2 +- .../chat-fine-tuning/chat-starting-the-training.mdx | 2 +- fern/pages/get-started/the-cohere-platform.mdx | 2 +- .../cohere-and-langchain/chat-on-langchain.mdx | 4 ++-- .../integrations/haystack-and-cohere.mdx | 2 +- .../clustering-using-embeddings.mdx | 2 +- .../introduction-to-semantic-search.mdx | 2 +- .../semantic-search-using-embeddings.mdx | 2 +- fern/pages/text-embeddings/embeddings.mdx | 2 +- .../retrieval-augmented-generation-rag.mdx | 2 +- fern/pages/tutorials/cookbooks.mdx | 2 +- 20 files changed, 30 insertions(+), 28 deletions(-) diff --git a/cohere-openapi.yaml b/cohere-openapi.yaml index 8f1d1bb3..3f23e0ba 100644 --- a/cohere-openapi.yaml +++ b/cohere-openapi.yaml @@ -5602,7 +5602,7 @@ paths: items: $ref: "#/components/schemas/ChatConnector" description: | - Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/connectors), if you've [created](https://docs.cohere.com/docs/creating-and-deploying-a-connector) one. + Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/overview-rag-connectors), if you've [created](https://docs.cohere.com/docs/creating-and-deploying-a-connector) one. When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG). @@ -7407,7 +7407,7 @@ paths: description: | The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations. - This parameter is off by default, and if it's not specified, the model will continue generating until it emits an EOS completion token. See [BPE Tokens](/bpe-tokens-wiki) for more details. + This parameter is off by default, and if it's not specified, the model will continue generating until it emits an EOS completion token. Can only be set to `0` if `return_likelihoods` is set to `ALL` to get the likelihood of the prompt. writeOnly: true @@ -13547,7 +13547,7 @@ paths: tags: - /connectors description: Retrieve a connector by ID. See - ['Connectors'](https://docs.cohere.com/docs/connectors) for more + ['Connectors'](https://docs.cohere.com/docs/overview-rag-connectors) for more information. operationId: get-connector x-fern-sdk-method-name: get @@ -13860,7 +13860,7 @@ paths: tags: - /connectors description: Delete a connector by ID. See - ['Connectors'](https://docs.cohere.com/docs/connectors) for more + ['Connectors'](https://docs.cohere.com/docs/overview-rag-connectors) for more information. operationId: delete-connector x-fern-sdk-method-name: delete diff --git a/fern/docs.yml b/fern/docs.yml index 1840307c..619c9bed 100644 --- a/fern/docs.yml +++ b/fern/docs.yml @@ -169,7 +169,7 @@ redirects: destination: /reference/about permanent: true - source: /bpe-tokens-wiki - destination: /docs/tokens + destination: /docs/tokens-and-tokenizers permanent: true - source: /classify-content-mod destination: /docs/content-moderation-with-classify @@ -304,7 +304,7 @@ redirects: destination: /docs/text-classification-with-classify permanent: true - source: /text-classification-embeddings - destination: /docs/text-classification-with-embed + destination: /page/text-classification-using-embeddings permanent: true - source: /text-summarization-example destination: /docs/text-summarization @@ -340,7 +340,7 @@ redirects: destination: /reference/about permanent: true - source: /bpe-tokens-wiki - destination: /docs/tokens + destination: /docs/tokens-and-tokenizers permanent: true - source: /classify-content-mod destination: /docs/content-moderation-with-classify @@ -490,10 +490,10 @@ redirects: destination: /docs/text-classification-with-classify permanent: true - source: /text-classification-embeddings - destination: /docs/text-classification-with-embed + destination: /page/text-classification-using-embeddings permanent: true - source: /text-classification-embeddings/ - destination: /docs/text-classification-with-embed + destination: /page/text-classification-using-embeddings permanent: true - source: /text-summarization-example destination: /docs/text-summarization @@ -546,6 +546,9 @@ redirects: - source: /docs/fine-tuning-with-the-web-ui destination: /docs/fine-tuning-with-the-cohere-dashboard permanent: true + - source: /docs/connectors + destination: /docs/overview-rag-connectors + permanent: true analytics: segment: diff --git a/fern/pages/api-reference/connectors-1/delete-connector.mdx b/fern/pages/api-reference/connectors-1/delete-connector.mdx index bb74ba6c..bb8ba657 100644 --- a/fern/pages/api-reference/connectors-1/delete-connector.mdx +++ b/fern/pages/api-reference/connectors-1/delete-connector.mdx @@ -1,7 +1,7 @@ --- title: "Delete a Connector" slug: "delete-connector" -subtitle: "Delete a connector by ID. See ['Connectors'](/docs/connectors) for more information." +subtitle: "Delete a connector by ID. See ['Connectors'](/docs/overview-rag-connectors) for more information." hidden: false createdAt: "Mon Jun 24 2024 13:21:35 GMT+0000 (Coordinated Universal Time)" updatedAt: "Mon Jun 24 2024 13:21:37 GMT+0000 (Coordinated Universal Time)" diff --git a/fern/pages/api-reference/connectors-1/get-connector.mdx b/fern/pages/api-reference/connectors-1/get-connector.mdx index 71b2e105..603d4f35 100644 --- a/fern/pages/api-reference/connectors-1/get-connector.mdx +++ b/fern/pages/api-reference/connectors-1/get-connector.mdx @@ -1,7 +1,7 @@ --- title: "Get a Connector" slug: "get-connector" -subtitle: "Retrieve a connector by ID. See ['Connectors'](/docs/connectors) for more information." +subtitle: "Retrieve a connector by ID. See ['Connectors'](/docs/overview-rag-connectors) for more information." hidden: false createdAt: "Mon Jun 24 2024 13:21:35 GMT+0000 (Coordinated Universal Time)" updatedAt: "Mon Jun 24 2024 13:21:36 GMT+0000 (Coordinated Universal Time)" diff --git a/fern/pages/changelog/2022-08-05-introducing-moderate-beta.mdx b/fern/pages/changelog/2022-08-05-introducing-moderate-beta.mdx index 3c05aedc..a7f2f430 100644 --- a/fern/pages/changelog/2022-08-05-introducing-moderate-beta.mdx +++ b/fern/pages/changelog/2022-08-05-introducing-moderate-beta.mdx @@ -6,4 +6,4 @@ createdAt: "Fri Aug 05 2022 17:46:00 GMT+0000 (Coordinated Universal Time)" hidden: false description: "Use the API to generate completions, distill text into semantically meaningful vectors, and more. Get state-of-the-art natural language processing without the need for expensive supercomputing infrastructure." --- -Use [Moderate (Beta)](/moderate-reference) to classify harmful text across the following categories: `profane`, `hate speech`, `violence`, `self-harm`, `sexual`, `sexual (non-consenual)`, `harassment`, `spam`, `information hazard (e.g., pii)`. Moderate returns an array containing each category and its associated confidence score. Over the coming weeks, expect performance to improve significantly as we optimize the underlying model. +Use Moderate (Beta) to classify harmful text across the following categories: `profane`, `hate speech`, `violence`, `self-harm`, `sexual`, `sexual (non-consenual)`, `harassment`, `spam`, `information hazard (e.g., pii)`. Moderate returns an array containing each category and its associated confidence score. Over the coming weeks, expect performance to improve significantly as we optimize the underlying model. diff --git a/fern/pages/changelog/2023-09-27-release-notes-september-29th-2023.mdx b/fern/pages/changelog/2023-09-27-release-notes-september-29th-2023.mdx index 5a3d8fdb..a2a9ffae 100644 --- a/fern/pages/changelog/2023-09-27-release-notes-september-29th-2023.mdx +++ b/fern/pages/changelog/2023-09-27-release-notes-september-29th-2023.mdx @@ -7,7 +7,7 @@ hidden: false --- **We're Releasing co.chat() and the Chat + RAG Playground** -We're pleased to announce that [we've released](/docs/cochat-beta) our `co.chat()` beta! Of particular importance is the fact that the `co.chat()` API is able to utilize [retrieval augmented generation](/docs/retrieval-augmented-generation-rag) (RAG), meaning developers can provide sources of context that inform and ground the model's output. +We're pleased to announce that we've released our `co.chat()` beta! Of particular importance is the fact that the `co.chat()` API is able to utilize [retrieval augmented generation](/docs/retrieval-augmented-generation-rag) (RAG), meaning developers can provide sources of context that inform and ground the model's output. This represents a leap forward in the accuracy, verifiability, and timeliness of our generative AI offering. For our public beta, developers can connect `co.chat()` to web search or plain text documents. diff --git a/fern/pages/changelog/2024-01-02-release-notes-january-x-2024.mdx b/fern/pages/changelog/2024-01-02-release-notes-january-x-2024.mdx index 8206f377..bb32707f 100644 --- a/fern/pages/changelog/2024-01-02-release-notes-january-x-2024.mdx +++ b/fern/pages/changelog/2024-01-02-release-notes-january-x-2024.mdx @@ -9,7 +9,7 @@ hidden: false One of the most exciting applications of generative AI is known as ["retrieval augmented generation"](/docs/retrieval-augmented-generation-rag) (RAG). This refers to the practice of _grounding_ the outputs of a large language model (LLM) by offering it resources -- like your internal technical documentation, chat logs, etc. -- from which to draw as it formulates its replies. -Cohere has made it much easier to utilize RAG in bespoke applications via [Connectors](/docs/connectors). As the name implies, Connectors allow you to _connect_ Cohere's generative AI platform up to whatever resources you'd like it to ground on, facilitating the creation of a wide variety of applications -- customer service chatbots, internal tutors, or whatever else you want to build. +Cohere has made it much easier to utilize RAG in bespoke applications via [Connectors](/docs/overview-rag-connectors). As the name implies, Connectors allow you to _connect_ Cohere's generative AI platform up to whatever resources you'd like it to ground on, facilitating the creation of a wide variety of applications -- customer service chatbots, internal tutors, or whatever else you want to build. Our docs cover how to [create and deploy connectors](/docs/creating-and-deploying-a-connector), [how to manage your connectors ](/docs/managing-your-connector), [how to handle authentication](/docs/connector-authentication), and [more](/docs/connector-faqs)! diff --git a/fern/pages/changelog/2024-06-10-release-notes-for-june-10th-2024.mdx b/fern/pages/changelog/2024-06-10-release-notes-for-june-10th-2024.mdx index ffc7a606..ff7d3d55 100644 --- a/fern/pages/changelog/2024-06-10-release-notes-for-june-10th-2024.mdx +++ b/fern/pages/changelog/2024-06-10-release-notes-for-june-10th-2024.mdx @@ -15,7 +15,6 @@ As of today, tool use will now be multi-step by default. Here are some resources - Check out our [multi-step tool use guide](/docs/multi-step-tool-use). - Experiment with multi-step tool use with [this notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/agents/Tool_Use.ipynb). -- To update from single-step to multi-step, follow our [migration guide](/page/changes-in-chat-api-and-tool-use). ## We've published additional docs! diff --git a/fern/pages/changelog/release-notes.mdx b/fern/pages/changelog/release-notes.mdx index 006c9735..7a9259a8 100644 --- a/fern/pages/changelog/release-notes.mdx +++ b/fern/pages/changelog/release-notes.mdx @@ -10,7 +10,7 @@ hidden: true ### August 5th, 2022 **Introducing Moderate (Beta)** -Use [Moderate (Beta)](/moderate-reference) to classify harmful text across the following categories: `profane`, `hate speech`, `violence`, `self-harm`, `sexual`, `sexual (non-consenual)`, `harassment`, `spam`, `information hazard (e.g., pii)`. Moderate returns an array containing each category and its associated confidence score. Over the coming weeks, expect performance to improve significantly as we optimize the underlying model. +Use Moderate (Beta) to classify harmful text across the following categories: `profane`, `hate speech`, `violence`, `self-harm`, `sexual`, `sexual (non-consenual)`, `harassment`, `spam`, `information hazard (e.g., pii)`. Moderate returns an array containing each category and its associated confidence score. Over the coming weeks, expect performance to improve significantly as we optimize the underlying model. ### July 20th, 2022 diff --git a/fern/pages/deployment-options/getting-started-with-coral-toolkit.mdx b/fern/pages/deployment-options/getting-started-with-coral-toolkit.mdx index 12698ab0..8c27acb8 100644 --- a/fern/pages/deployment-options/getting-started-with-coral-toolkit.mdx +++ b/fern/pages/deployment-options/getting-started-with-coral-toolkit.mdx @@ -68,7 +68,7 @@ The two basic parts of a RAG workflow are "retrieval" and "augmented generation. A developer can upload a PDF (to be parsed using LlamaIndex), or retrieve information from Wikipedia (using Langchain’s `WikipediaRetriever`). -You can also optionally configure your web app to utilize vector database retrieval or [Cohere Connectors](/docs/connectors). +You can also optionally configure your web app to utilize vector database retrieval or [Cohere Connectors](/docs/overview-rag-connectors). ### Model configurations diff --git a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx index afbc1072..0cc22bb8 100644 --- a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx +++ b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx @@ -250,7 +250,7 @@ create_response = co.finetuning.create_finetuned_model( ## Calling your Chat Model with co.chat() -Once your model completes training, you can call it via [co.chat()](/docs/cochat-beta) and pass your custom model's `model_id`. +Once your model completes training, you can call it via [co.chat()](/docs/chat-api) and pass your custom model's `model_id`. Please note, the `model_id` is the `id` returned by the fine-tuned model object with the `"-ft"` suffix. diff --git a/fern/pages/get-started/the-cohere-platform.mdx b/fern/pages/get-started/the-cohere-platform.mdx index 0c7cd166..a631164e 100644 --- a/fern/pages/get-started/the-cohere-platform.mdx +++ b/fern/pages/get-started/the-cohere-platform.mdx @@ -57,7 +57,7 @@ Embeddings enable you to search based on what a phrase _means_ rather than simpl How a query returns results. -Learn more about semantic search [here](/docs/intro-semantic-search). +Learn more about semantic search [here](https://cohere.com/llmu/what-is-semantic-search). ## Create Fine-Tuned Models with Ease diff --git a/fern/pages/integrations/cohere-and-langchain/chat-on-langchain.mdx b/fern/pages/integrations/cohere-and-langchain/chat-on-langchain.mdx index da43b791..3ebcdfbe 100644 --- a/fern/pages/integrations/cohere-and-langchain/chat-on-langchain.mdx +++ b/fern/pages/integrations/cohere-and-langchain/chat-on-langchain.mdx @@ -18,7 +18,7 @@ Running Cohere Chat with LangChain doesn't require many prerequisites, consult t ### Cohere Chat with LangChain -To use [Cohere chat](/docs/cochat-beta) with LangChain, simply create a [ChatCohere](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/chat_models/cohere.py) object and pass in the message or message history. In the example below, you will need to add your Cohere API key. +To use [Cohere chat](/docs/chat-api) with LangChain, simply create a [ChatCohere](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/chat_models/cohere.py) object and pass in the message or message history. In the example below, you will need to add your Cohere API key. ```python PYTHON from langchain_community.chat_models import ChatCohere @@ -149,7 +149,7 @@ print(citations) #### Using a Connector -In this example, we create a generation with a [connector](/docs/connectors) which allows us to get a generation with citations to results from the connector. We use the "web-search" connector, which is available to everyone. But if you have created your own connector in your org you can pass in its id, like so: `rag = CohereRagRetriever(llm=cohere_chat_model, connectors=[{"id": "example-connector-id"}])` +In this example, we create a generation with a [connector](/docs/overview-rag-connectors) which allows us to get a generation with citations to results from the connector. We use the "web-search" connector, which is available to everyone. But if you have created your own connector in your org you can pass in its id, like so: `rag = CohereRagRetriever(llm=cohere_chat_model, connectors=[{"id": "example-connector-id"}])` Here's a code sample illustrating how to use a connector: diff --git a/fern/pages/integrations/integrations/haystack-and-cohere.mdx b/fern/pages/integrations/integrations/haystack-and-cohere.mdx index 19fd386d..2ffe75b2 100644 --- a/fern/pages/integrations/integrations/haystack-and-cohere.mdx +++ b/fern/pages/integrations/integrations/haystack-and-cohere.mdx @@ -31,7 +31,7 @@ To use Cohere and Haystack you will need: ### Cohere Chat with Haystack -Haystack’s `CohereChatGenerator` component enables chat completion using Cohere's large language models (LLMs). For the latest information on Cohere Chat [see these docs](/docs/cochat-beta). +Haystack’s `CohereChatGenerator` component enables chat completion using Cohere's large language models (LLMs). For the latest information on Cohere Chat [see these docs](/docs/chat-api). In the example below, you will need to add your Cohere API key. We suggest using an environment variable, `COHERE_API_KEY`. Don’t commit API keys to source control! diff --git a/fern/pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx b/fern/pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx index 94787b63..0bd06dbf 100644 --- a/fern/pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx +++ b/fern/pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx @@ -50,7 +50,7 @@ The plot below shows the clusters that the algorithm returned. It looks to be sp ### Conclusion -In this chapter, you learned how to cluster a dataset of sentences, and you observed that each cluster corresponds to a particular topic. If you'd like to dive deeper into clustering, feel free to check this more elaborate example on Clustering Hacker News Posts! +In this chapter, you learned how to cluster a dataset of sentences, and you observed that each cluster corresponds to a particular topic. ### Original Source diff --git a/fern/pages/llm-university/intro-text-representation/introduction-to-semantic-search.mdx b/fern/pages/llm-university/intro-text-representation/introduction-to-semantic-search.mdx index 55149ba4..46f7b44a 100644 --- a/fern/pages/llm-university/intro-text-representation/introduction-to-semantic-search.mdx +++ b/fern/pages/llm-university/intro-text-representation/introduction-to-semantic-search.mdx @@ -8,7 +8,7 @@ updatedAt: "Fri Apr 19 2024 02:37:50 GMT+0000 (Coordinated Universal Time)" --- This chapter uses the same [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/llmu/Introduction_Text_Embeddings.ipynb) as the previous chapter. -_Note: This chapter covers the basics of semantic search. If you want to explore this topic further, we have a dedicated [LLMU module on semantic search](/docs/intro-semantic-search)._ +_Note: This chapter covers the basics of semantic search. If you want to explore this topic further, we have a dedicated [LLMU module on semantic search](https://cohere.com/llmu/what-is-semantic-search)._ We deal with unstructured text data on a regular basis, and one of the common needs is to search for information from a vast repository. A common approach is keyword-matching, but the problem with this is that the results are limited to the exact query entered. diff --git a/fern/pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx b/fern/pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx index 52bdccf0..2125a5e2 100644 --- a/fern/pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx +++ b/fern/pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx @@ -10,7 +10,7 @@ updatedAt: "Wed Apr 03 2024 19:03:06 GMT+0000 (Coordinated Universal Time)" --- ### Introduction -In this chapter you'll learn how to use embeddings for search. If you'd like to dive deeper into search, please check the [Search Module](/docs/intro-semantic-search) at LLMU. +In this chapter you'll learn how to use embeddings for search. If you'd like to dive deeper into search, please check the [Search Module](https://cohere.com/llmu/what-is-semantic-search) at LLMU. ### Colab notebook diff --git a/fern/pages/text-embeddings/embeddings.mdx b/fern/pages/text-embeddings/embeddings.mdx index 7c30f6bc..f9ed38b5 100644 --- a/fern/pages/text-embeddings/embeddings.mdx +++ b/fern/pages/text-embeddings/embeddings.mdx @@ -46,7 +46,7 @@ calculate_similarity(soup1, london) # 0.16 - not similar! ## The `input_type` parameter -Cohere embeddings are optimized for different types of inputs. For example, when using embeddings for semantic search, the search query should be embedded by setting `input_type="search_query"` whereas the text passages that are being searched over should be embedded with `input_type="search_document"`. You can find more details and a code snippet in the [Semantic Search guide](/docs/semantic-search). Similarly, the input type can be set to `classification` ([example](/docs/text-classification-with-embed)) and `clustering` to optimize the embeddings for those use cases. +Cohere embeddings are optimized for different types of inputs. For example, when using embeddings for semantic search, the search query should be embedded by setting `input_type="search_query"` whereas the text passages that are being searched over should be embedded with `input_type="search_document"`. You can find more details and a code snippet in the [Semantic Search guide](/docs/semantic-search). Similarly, the input type can be set to `classification` ([example](/page/text-classification-using-embeddings)) and `clustering` to optimize the embeddings for those use cases. ## Multilingual Support diff --git a/fern/pages/text-generation/retrieval-augmented-generation-rag.mdx b/fern/pages/text-generation/retrieval-augmented-generation-rag.mdx index f26195a0..0f49d7ea 100644 --- a/fern/pages/text-generation/retrieval-augmented-generation-rag.mdx +++ b/fern/pages/text-generation/retrieval-augmented-generation-rag.mdx @@ -162,7 +162,7 @@ Not only will we discover that the Backstreet Boys were the more popular band, b ### Connectors -As an alternative to manually implementing the 3 step workflow, the Chat API offers a 1-line solution for RAG using [Connectors](/docs/connectors). In the example below, specifying the `web-search` connector will generate search queries, use them to conduct an internet search and use the results to inform the model and produce an answer. +As an alternative to manually implementing the 3 step workflow, the Chat API offers a 1-line solution for RAG using [Connectors](/docs/overview-rag-connectors). In the example below, specifying the `web-search` connector will generate search queries, use them to conduct an internet search and use the results to inform the model and produce an answer. **Request** diff --git a/fern/pages/tutorials/cookbooks.mdx b/fern/pages/tutorials/cookbooks.mdx index 0daf3a93..3be64771 100644 --- a/fern/pages/tutorials/cookbooks.mdx +++ b/fern/pages/tutorials/cookbooks.mdx @@ -26,6 +26,6 @@ Here are some of the ones we think are most exciting! - [A Data Analyst Agent Built with Cohere and Langchain](/page/data-analyst-agent) - Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain. - [Creating a QA Bot From Technical Documentation](/page/creating-a-qa-bot) - Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex. - [Multilingual Search with Cohere and Langchain](/page/multilingual-search) - Perform searches across a corpus of mixed-language documents with Cohere and Langchain. -- [Using Redis with Cohere](/page/redis-guide) - Learn how to use Cohere's text vectorizer with Redis to create a semantic search index. +- [Using Redis with Cohere](/docs/redis-and-cohere#building-a-retrieval-pipeline-with-cohere-and-redis) - Learn how to use Cohere's text vectorizer with Redis to create a semantic search index. - [Wikipedia Semantic Search with Cohere + Weaviate](/page/wikipedia-search-with-weaviate) - Search 10 million Wikipedia vectors with Cohere's multilingual model and Weaviate's public dataset. - [Long Form General Strategies](/page/long-form-general-strategies) - Techniques to address lengthy documents exceeding the context window of LLMs.