From 736ba478b98d0572c9c8114b6bf2d22382b8f7e8 Mon Sep 17 00:00:00 2001 From: Lauren Hirata Singh Date: Tue, 14 May 2024 13:30:03 -0400 Subject: [PATCH] Update README.md Update doc links --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 76d5198c..3df323c1 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ await pc.createIndex({ #### Create a pod-based index with optional configurations -To create a pod-based index, you define `pod` in the `spec` object which contains the `environment` where the index should be hosted, and the `podType` and `pods` size to use. Many optional configuration fields allow greater control over hardware resources and availability. To learn more about the purpose of these fields, see [Understanding indexes](https://docs.pinecone.io/docs/indexes) and [Scaling indexes](https://docs.pinecone.io/docs/scaling-indexes). +To create a pod-based index, you define `pod` in the `spec` object which contains the `environment` where the index should be hosted, and the `podType` and `pods` size to use. Many optional configuration fields allow greater control over hardware resources and availability. To learn more about the purpose of these fields, see [Understanding indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes) and [Scale pod-based indexes](https://docs.pinecone.io/guides/indexes/scale-pod-based-indexes). ```typescript import { Pinecone } from '@pinecone-database/pinecone'; @@ -191,7 +191,7 @@ await pc.createIndex({ > > Serverless and starter indexes do not support collections. -As you use Pinecone for more things, you may wish to explore different index configurations with the same vector data. [Collections](https://docs.pinecone.io/docs/collections) provide an easy way to do this. See other client methods for working with collections [here](https://github.com/pinecone-io/pinecone-ts-client#collections). +As you use Pinecone for more things, you may wish to explore different index configurations with the same vector data. [Collections](https://docs.pinecone.io/guides/indexes/understanding-collections) provide an easy way to do this. See other client methods for working with collections [here](https://github.com/pinecone-io/pinecone-ts-client#collections). Given that you have an existing collection: @@ -277,9 +277,9 @@ await pc.describeIndex('serverless-index'); > ℹ️ **Note** > -> This section applies to [pod-based indexes](https://docs.pinecone.io/docs/indexes#pod-based-indexes) only. With serverless indexes, you don't configure any compute or storage resources. Instead, serverless indexes scale automatically based on usage. +> This section applies to [pod-based indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes#pod-based-indexes) only. With serverless indexes, you don't configure any compute or storage resources. Instead, serverless indexes scale automatically based on usage. -You can adjust the number of replicas or scale to a larger pod size (specified with `podType`). See [Scale pod-based indexes](https://docs.pinecone.io/docs/scaling-indexes). You cannot downgrade pod size or change the base pod type. +You can adjust the number of replicas or scale to a larger pod size (specified with `podType`). See [Scale pod-based indexes](https://docs.pinecone.io/guides/indexes/scale-pod-based-indexes). You cannot downgrade pod size or change the base pod type. ```typescript import { Pinecone } from '@pinecone-database/pinecone'; @@ -375,7 +375,7 @@ await pc.listIndexes(); > > Serverless and starter indexes do not support collections. -A collection is a static copy of a pod-based index that may be used to create backups, to create copies of indexes, or to perform experiments with different index configurations. To learn more about Pinecone collections, see [Understanding collections](https://docs.pinecone.io/docs/collections). +A collection is a static copy of a pod-based index that may be used to create backups, to create copies of indexes, or to perform experiments with different index configurations. To learn more about Pinecone collections, see [Understanding collections](https://docs.pinecone.io/guides/indexes/understanding-collections). ### Create Collection @@ -549,7 +549,7 @@ const index = pc.index('test-index').namespace('ns1'); console.log(index.target); // { index: 'test-index', namespace: 'ns1', indexHostUrl: undefined } ``` -See [Using namespaces](https://docs.pinecone.io/docs/namespaces) for more information. +See [Use namespaces](https://docs.pinecone.io/guides/indexes/use-namespaces) for more information. ### Upsert records @@ -701,7 +701,7 @@ const results = await index.query({ topK: 10, id: '1' }); #### Hybrid search with sparseVector -If you are working with [sparse-dense vectors](https://docs.pinecone.io/v2/docs/hybrid-search#sparse-dense-workflow), you can add sparse vector values to perform a hybrid search. +If you are working with [sparse-dense vectors](https://docs.pinecone.io/guides/data/understanding-hybrid-search#sparse-dense-workflow), you can add sparse vector values to perform a hybrid search. ```typescript import { Pinecone } from '@pinecone-database/pinecone'; @@ -836,7 +836,7 @@ await index.deleteMany({ genre: 'rock' }); > ℹ️ **NOTE** > -> Indexes in the [gcp-starter environment](https://docs.pinecone.io/docs/starter-environment) do not support namespaces. +> Indexes in the [gcp-starter environment](https://docs.pinecone.io/guides/indexes/convert-a-gcp-starter-index-to-serverless) do not support namespaces. To nuke everything in the targeted namespace, use the `deleteAll` method.