[site] fix unnecessary internal redirects (#713)

* [site] remove redirects for local builds
* [site] fix unnecessary internal redirects

_includes/javascript-maintenance-warning.mdx

@@ -1,3 +1,3 @@
 :::warning JS → TS
-Please note that the [JavaScript](/developers/weaviate/client-libraries/javascript) client library is no longer maintained. Please migrate to the [TypeScript](/developers/weaviate/client-libraries/typescript) library.
+Please note that the [JavaScript](https://www.npmjs.com/package/weaviate-client) client library is no longer maintained. Please migrate to the [TypeScript](/developers/weaviate/client-libraries/typescript) library.
 :::

blog/2022-07-04-weaviate-1-14-release/index.mdx

@@ -177,7 +177,7 @@ The distance values provided are raw numbers, which allow you to interpret the r
 For example, you can normalize and convert **distance values** to **certainty values** that fit the machine learning model you use and the kind of results you expect.
 
 ### Learn more
-For more info, check out [the documentation](/developers/weaviate/configuration/distances).
+For more info, check out [the documentation](/developers/weaviate/config-refs/distances).
 
 ### Contribute
 Adding other distances is surprisingly easy, which could be a great way to contribute to the Weaviate project.

blog/2022-09-07-weaviate-1-15-release/index.mdx

@@ -309,7 +309,7 @@ But don't take our word for it, this was from a test run by one of our community
 
 Thanks to the community contributions from [Aakash Thatte](https://github.com/sky-2002), Weaviate `v1.15` adds two new distance metrics: **Hamming** distance and **Manhattan** distance. In total, you can now choose between five various distance metrics to support your datasets.
 
-Check out the [metrics documentation page](/developers/weaviate/configuration/distances#distance-implementations-and-optimizations) for a complete overview of all available metrics in Weaviate.
+Check out the [metrics documentation page](/developers/weaviate/config-refs/distances#distance-implementations-and-optimizations) for a complete overview of all available metrics in Weaviate.
 
 ### Hamming distance
 The Hamming distance is a metric for comparing two numerical vectors. <br/>

blog/2022-09-21-distance-metrics-in-vector-search/index.mdx

@@ -199,12 +199,12 @@ To summarize, there is no ‘one size fits all' distance metric. It depends on y
 
 ## Implementation in Weaviate
 
-In total, Weaviate users can choose between five various distance metrics to support their dataset. [Here](/developers/weaviate/configuration/distances#distance-implementations-and-optimizations) you can find each metric in detail. Weaviate makes it easy to choose a metric depending on your application. With one edit to your schema, you can use any of the metrics implemented on Weaviate (`cosine`, `dot`, `l2-squared`, `hamming`, and `manhattan`), or you have the flexibility to create your own!
+In total, Weaviate users can choose between five various distance metrics to support their dataset. [Here](/developers/weaviate/config-refs/distances#distance-implementations-and-optimizations) you can find each metric in detail. Weaviate makes it easy to choose a metric depending on your application. With one edit to your schema, you can use any of the metrics implemented on Weaviate (`cosine`, `dot`, `l2-squared`, `hamming`, and `manhattan`), or you have the flexibility to create your own!
 
 ### Optimizations
 Even with ANN-indexes, which reduce the number of distance calculations necessary, a vector database still spends a large portion of its compute time calculating vector distances. As a result, it is very important that the engine can do this not just correctly, but also efficiently.
 
-The distance metrics in Weaviate have been optimized to be highly efficient using "Single Instruction, Multiple Data" ("SIMD") instruction sets. Using these instructions, a CPU can do multiple calculations in a single CPU cycle. To achieve this, we had to write some of our distance metrics in [pure Assembly code](https://github.com/weaviate/weaviate/blob/master/adapters/repos/db/vector/hnsw/distancer/asm/dot_amd64.s). [Here is an overview](/developers/weaviate/configuration/distances#distance-implementations-and-optimizations) of the current state of optimizations; including which distance metrics have SIMD optimizations for which architecture.
+The distance metrics in Weaviate have been optimized to be highly efficient using "Single Instruction, Multiple Data" ("SIMD") instruction sets. Using these instructions, a CPU can do multiple calculations in a single CPU cycle. To achieve this, we had to write some of our distance metrics in [pure Assembly code](https://github.com/weaviate/weaviate/blob/master/adapters/repos/db/vector/hnsw/distancer/asm/dot_amd64.s). [Here is an overview](/developers/weaviate/config-refs/distances#distance-implementations-and-optimizations) of the current state of optimizations; including which distance metrics have SIMD optimizations for which architecture.
 
 ### Open source contributions
 Weaviate is open-source and values feedback and input from the community. A community member contributed to the Weaviate project by adding two new metrics to the 1.15 release. How cool is that! If this is something you're interested in, [here](https://github.com/weaviate/weaviate/tree/master/adapters/repos/db/vector/hnsw/distancer) is the repository with the implementation of the current metrics.

blog/2023-04-27-how-to-chatgpt-plugin/index.mdx

@@ -431,7 +431,7 @@ This is the last step and allows you to deploy your plugin to Fly.io, the detail
 
 And that, ladies and gentlemen, is how we created our Weavaite retrieval plugin that augments ChatGPT with longterm memory. The process to create different plugins is quite similar and we believe most of these steps can be executed in a similar manner to create a wide variety of plugins with the most variation being found in Part 2 where you define endpoint specific to you plugin.
 
-Let's end off by visualizing the [flow]((https://platform.openai.com/docs/plugins/introduction/plugin-flow)) of how ChatGPT can now use the plugin we just created. The figure below details how the `/query` endpoint can be used. Depending on the prompt it can also call the `/delete` and `/upsert` endpoints as well.
+Let's end off by visualizing the [flow](https://platform.openai.com/docs/plugins/introduction/plugin-flow) of how ChatGPT can now use the plugin we just created. The figure below details how the `/query` endpoint can be used. Depending on the prompt it can also call the `/delete` and `/upsert` endpoints as well.
 
 ![diagram](./img/diagram-light.png#gh-light-mode-only)
 ![diagram](./img/diagram-dark.png#gh-dark-mode-only)

developers/wcs/quickstart.mdx

@@ -72,7 +72,7 @@ In the meantime, let's start installing a client library.
 
 ## Install a client library
 
-You can communicate with Weaviate using the available [client libraries](../weaviate/client-libraries/index.md) (currently for `Python`, `JavaScript`, `Java` and `Go`), the [GraphQL API](../weaviate/api/graphql/index.md), or the [RESTful API](../weaviate/api/rest/index.md).
+You can communicate with Weaviate using the available [client libraries](../weaviate/client-libraries/index.md) (currently for `Python`, `JavaScript`, `TypeScript`, `Java` and `Go`), the [GraphQL API](../weaviate/api/graphql/index.md), or the [RESTful API](../weaviate/api/rest/index.md).
 
 import ClientCapabilitiesOverview from '/_includes/client.capabilities.mdx'
 

developers/weaviate/quickstart/_installation.md

@@ -62,7 +62,7 @@ If you do enable OIDC in WCS for added security, the easiest way to authenticate
 
 Consult the `WCS authentication` section for your preferred client library and use the "Resource Owner Password Flow" method, by passing on the `username` and `password` you used to sign in to WCS. See the docs below:
 - [Python](../client-libraries/python.md#wcs-authentication)
-- [JavaScript](../client-libraries/javascript.md#wcs-authentication)
+- [JavaScript/TypeScript](../client-libraries/typescript.mdx.md#wcs-authentication)
 - [Go](../client-libraries/go.md#wcs-authentication)
 - [Java](../client-libraries/java.md#wcs-authentication)
 :::

docusaurus.dev.config.js

@@ -1,5 +1,16 @@
 let config = require('./docusaurus.config')
 
+const isRedirectPlugin = (plugin) => {
+    // the redirect plugin is an array
+    // with '@docusaurus/plugin-client-redirects' as the first item
+    return Array.isArray(plugin) && plugin[0] == '@docusaurus/plugin-client-redirects';
+}
+
+// remove the redirect plugin, to esure that
+// we don't use redirects for links within our docs
+config.plugins = config.plugins.filter(plugin => !isRedirectPlugin(plugin));
+
+// use trailing slash, to be able to use link checker on a local build folder
 config.trailingSlash = true;
 
 module.exports = config;

src/components/Home/Ready/index.jsx

@@ -9,9 +9,9 @@ export default function HomepageReady() {
       <h2 className={styles.title}>Ready to get started?</h2>
       <div className={styles.links}>
         <p>
-          Install{' '}
-          <Link to="/developers/weaviate/quickstart/installation">
-            Weaviate open source {'>'}{' '}
+          Follow the {' '}
+          <Link to="/developers/weaviate/quickstart">
+            Quickstart tutorial {'>'}{' '}
           </Link>
         </p>
         <p>

src/pages/google-summer/gsoc-22.md

@@ -128,7 +128,7 @@ If you're new to ML and Weaviate, but are already a bit familiar with Python, th
 
 **Documentation**
 
-* [JavaScript · Weaviate Documentation](/developers/weaviate/client-libraries/javascript)
+* [JavaScript/TypeScript · Weaviate Documentation](/developers/weaviate/client-libraries/typescript)
 * [Weaviate CLI · Weaviate Documentation](/developers/weaviate/client-libraries/cli)
 
 ---
@@ -158,7 +158,7 @@ You'll be guided and supported by Etienne and Marcin who are Go-gurus, so you ca
 
 ### 5. Make Weaviate support a new datatype (e.g. videos, or 3D models, etc)
 
-**Project Description:** At the moment, Weaviate is able to store [datatypes](/developers/weaviate/configuration/datatypes) like text and images. The goal of this project is to create a new datatype for Weaviate, like speech, videos or 3D mesh. You'll work in Go.
+**Project Description:** At the moment, Weaviate is able to store [datatypes](/developers/weaviate/config-refs/datatypes) like text and images. The goal of this project is to create a new datatype for Weaviate, like speech, videos or 3D mesh. You'll work in Go.
 You'll be guided and supported by Etienne and Marcin who are Go-gurus, so you can learn along the way.
 
 **Required Skills:** Check the Go Documentation and Modules Documentation below to get an understanding of how to create new/custom modules. The knowledge of Machine Learning & NLP Based Models like text2vec, word vectors, etc. is important. Medium-advanced knowledge of golang is desired.