[Vectorize] Update Vectorize Dev Docs according to V2 Spec

src/content/docs/vectorize/best-practices/create-indexes.mdx

@@ -23,11 +23,7 @@ The configuration of an index cannot be changed after creation.
 
 <Render file="vectorize-wrangler-version" />
 
-:::note[Using Vectorize v1?]
-
-Please use the 'wrangler vectorize --deprecated-v1' flag to create, get, list, delete and insert vectors into legacy Vectorize v1 indexes.
-
-:::
+<Render file="vectorize-legacy" />
 
 To create an index with `wrangler`:
 
@@ -41,6 +37,38 @@ To create an index that can accept vector embeddings from Worker's AI's [`@cf/ba
 npx wrangler vectorize create your-index-name --dimensions=768 --metric=cosine
 ```
 
+### HTTP API
+
+Vectorize also supports creating indexes via [REST API](/api/operations/vectorize-create-vectorize-index).
+
+For example, to create an index directly from a Python script:
+
+```py
+import requests
+
+url = "https://api.cloudflare.com/client/v4/accounts/{}/vectorize/v2/indexes".format("your-account-id")
+
+headers = {
+    "Authorization": "Bearer <your-api-token>"
+}
+
+body = {
+	"name": "demo-index"
+	"description": "some index description",
+  "config": {
+    "dimensions": 1024,
+    "metric": "euclidean"
+  },
+}
+
+resp = requests.post(url, headers=headers, json=body)
+
+print('Status Code:', resp.status_code)
+print('Response JSON:', resp.json())
+```
+
+This script should print the response with a status code 201, along with a JSON response body indicating the creation of an index with the provided configuration.
+
 ## Dimensions
 
 Dimensions are determined from the output size of the machine learning (ML) model used to generate them, and are a function of how the model encodes and describes features into a vector embedding.

src/content/docs/vectorize/best-practices/insert-vectors.mdx

@@ -9,6 +9,16 @@ import { Render } from "~/components";
 
 Vectorize indexes allow you to insert vectors at any point: Vectorize will optimize the index behind the scenes to ensure that vector search remains efficient, even as new vectors are added or existing vectors updated.
 
+:::note[Insert vs Upsert]
+
+If the same vector id is _inserted_ twice in a Vectorize index, the index would reflect the vector that was added first.
+
+If the same vector id is _upserted_ twice in a Vectorize index, the index would reflect the vector that was added second.
+
+Use the upsert operation if you want to overwrite the vector value for a vector id that already exists in an index.
+
+:::
+
 ## Supported vector formats
 
 Vectorize supports vectors in three formats:
@@ -34,7 +44,7 @@ Metadata can be used to:
 For example, a vector embedding representing an image could include the path to the [R2 object](/r2/) it was generated from, the format, and a category lookup:
 
 ```ts
-{ id: '1', values: [32.4, 74.1, 3.2], metadata: { path: 'r2://bucket-name/path/to/image.png', format: 'png', category: 'profile_image' } }
+{ id: '1', values: [32.4, 74.1, 3.2, ...], metadata: { path: 'r2://bucket-name/path/to/image.png', format: 'png', category: 'profile_image' } }
 ```
 
 ## Namespaces
@@ -43,9 +53,9 @@ Namespaces provide a way to segment the vectors within your index. For example,
 
 To associate vectors with a namespace, you can optionally provide a `namespace: string` value when performing an insert or upsert operation. When querying, you can pass the namespace to search within as an optional parameter to your query.
 
-A namespace can be up to 63 characters (bytes) in length and you can have up to 1,000 namespaces per index. Refer to the [Limits](/vectorize/platform/limits/) documentation for more details.
+A namespace can be up to 64 characters (bytes) in length and you can have up to 1,000 namespaces per index. Refer to the [Limits](/vectorize/platform/limits/) documentation for more details.
 
-When a namespace is provided, only vectors within that namespace are used for the search. Namespace filtering is applied before vector search, not after.
+When a namespace is specified in a query operation, only vectors within that namespace are used for the search. Namespace filtering is applied before vector search, not after.
 
 To insert vectors with a namespace:
 
@@ -56,17 +66,17 @@ To insert vectors with a namespace:
 const sampleVectors: Array<VectorizeVector> = [
 	{
 		id: "1",
-		values: [32.4, 74.1, 3.2],
+		values: [32.4, 74.1, 3.2, ...],
 		namespace: "text",
 	},
 	{
 		id: "2",
-		values: [15.1, 19.2, 15.8],
+		values: [15.1, 19.2, 15.8, ...],
 		namespace: "images",
 	},
 	{
 		id: "3",
-		values: [0.16, 1.2, 3.8],
+		values: [0.16, 1.2, 3.8, ...],
 		namespace: "pdfs",
 	},
 ];
@@ -97,17 +107,17 @@ Use the `insert()` and `upsert()` methods available on an index from within a Cl
 const sampleVectors: Array<VectorizeVector> = [
 	{
 		id: "1",
-		values: [32.4, 74.1, 3.2],
+		values: [32.4, 74.1, 3.2, ...],
 		metadata: { url: "/products/sku/13913913" },
 	},
 	{
 		id: "2",
-		values: [15.1, 19.2, 15.8],
+		values: [15.1, 19.2, 15.8, ...],
 		metadata: { url: "/products/sku/10148191" },
 	},
 	{
 		id: "3",
-		values: [0.16, 1.2, 3.8],
+		values: [0.16, 1.2, 3.8, ...],
 		metadata: { url: "/products/sku/97913813" },
 	},
 ];
@@ -154,7 +164,7 @@ For example, to insert embeddings in [NDJSON format](#workers-api) directly from
 ```py
 import requests
 
-url = "https://api.cloudflare.com/client/v4/accounts/{}/vectorize/indexes/{}/insert".format("your-account-id", "index-name")
+url = "https://api.cloudflare.com/client/v4/accounts/{}/vectorize/v2/indexes/{}/insert".format("your-account-id", "index-name")
 
 headers = {
     "Authorization": "Bearer <your-api-token>"

src/content/docs/vectorize/best-practices/query-vectors.mdx

@@ -3,7 +3,6 @@ title: Query vectors
 pcx_content_type: concept
 sidebar:
   order: 5
-
 ---
 
 Querying an index, or vector search, enables you to search an index by providing an input vector and returning the nearest vectors based on the [configured distance metric](/vectorize/best-practices/create-indexes/#distance-metrics).
@@ -17,52 +16,56 @@ To pass a vector as a query to an index, use the `query()` method on the index i
 A query vector is either an array of JavaScript numbers, 32-bit floating point or 64-bit floating point numbers: `number[]`, `Float32Array`, or `Float64Array`. Unlike when [inserting vectors](/vectorize/best-practices/insert-vectors/), a query vector does not need an ID or metadata.
 
 ```ts
-let queryVector = [54.8, 5.5, 3.1];
+// query vector dimensions should match the Vectorize index dimension being queried
+let queryVector = [54.8, 5.5, 3.1, ...]; 
 let matches = await env.YOUR_INDEX.query(queryVector);
 ```
 
-This would return a set of matches resembling the following, based on a `cosine` distance metric:
+This would return a set of matches resembling the following, based on the distance metric configured for the Vectorize index. Example response with `cosine` distance metric:
 
 ```json
 {
-  "matches": {
-    "count": 3,
-    "matches": [
-      { "score": 0.999909486, "id": "5" },
-      { "score": 0.789848214, "id": "4" },
-      { "score": 0.720476967, "id": "4444" }
-    ]
-  }
+	"matches": {
+		"count": 5,
+		"matches": [
+			{ "score": 0.999909486, "id": "5" },
+			{ "score": 0.789848214, "id": "4" },
+			{ "score": 0.720476967, "id": "4444" },
+			{ "score": 0.463884663, "id": "6" },
+			{ "score": 0.378282232, "id": "1" }
+		]
+	}
 }
 ```
 
 You can optionally change the number of results returned and/or whether results should include metadata and values:
 
 ```ts
-let queryVector = [54.8, 5.5, 3.1];
-// topK defaults to 3; returnValues defaults to false; returnMetadata defaults to false
+// query vector dimensions should match the Vectorize index dimension being queried
+let queryVector = [54.8, 5.5, 3.1, ...]; 
+// topK defaults to 5; returnValues defaults to false; returnMetadata defaults to "none"
 let matches = await env.YOUR_INDEX.query(queryVector, {
-  topK: 1,
-  returnValues: true,
-  returnMetadata: true,
+	topK: 1,
+	returnValues: true,
+	returnMetadata: "all",
 });
 ```
 
-This would return a set of matches resembling the following, based on a `cosine` distance metric:
+This would return a set of matches resembling the following, based on the distance metric configured for the Vectorize index. Example response with `cosine` distance metric:
 
 ```json
 {
-  "matches": {
-    "count": 1,
-    "matches": [
-      {
-        "score": 0.999909486,
-        "id": "5",
-        "values": [58.79999923706055, 6.699999809265137, 3.4000000953674316],
-        "metadata": { "url": "/products/sku/55519183" }
-      }
-    ]
-  }
+	"matches": {
+		"count": 1,
+		"matches": [
+			{
+				"score": 0.999909486,
+				"id": "5",
+				"values": [58.79999923706055, 6.699999809265137, 3.4000000953674316, ...],
+				"metadata": { "url": "/products/sku/55519183" }
+			}
+		]
+	}
 }
 ```
 
@@ -74,16 +77,16 @@ If you are generating embeddings from a [Workers AI](/workers-ai/models/#text-em
 
 ```ts
 interface EmbeddingResponse {
-  shape: number[];
-  data: number[][];
+	shape: number[];
+	data: number[][];
 }
 
 let userQuery = "a query from a user or service";
 const queryVector: EmbeddingResponse = await env.AI.run(
-  "@cf/baai/bge-base-en-v1.5",
-  {
-    text: [userQuery],
-  }
+	"@cf/baai/bge-base-en-v1.5",
+	{
+		text: [userQuery],
+	},
 );
 ```
 
@@ -107,15 +110,15 @@ const openai = new OpenAI({ apiKey: env.YOUR_OPENAPI_KEY });
 let userQuery = "a query from a user or service";
 
 let embeddingResponse = await openai.embeddings.create({
-  input: userQuery,
-  model: "text-embedding-ada-002",
+	input: userQuery,
+	model: "text-embedding-ada-002",
 });
 ```
 
 Similar to Workers AI, you will need to provide the vector embedding itself (`.embedding[0]`) and not the `EmbeddingResponse` wrapper when querying a Vectorize index:
 
 ```ts
 let matches = await env.TEXT_EMBEDDINGS.query(embeddingResponse.embedding[0], {
-  topK: 1,
+	topK: 1,
 });
 ```

src/content/docs/vectorize/get-started/embeddings.mdx

@@ -77,11 +77,7 @@ To create your first Vectorize index, change into the directory you just created
 cd embeddings-tutorial
 ```
 
-:::note[Using Vectorize v1?]
-
-Please use the 'wrangler vectorize --deprecated-v1' flag to create, get, list, delete and insert vectors into legacy Vectorize v1 indexes.
-
-:::
+<Render file="vectorize-legacy" />
 
 To create an index, use the `wrangler vectorize create` command and provide a name for the index. A good index name is:
 
@@ -103,7 +99,7 @@ npx wrangler vectorize create embeddings-index --dimensions=768 --metric=cosine
 ✅ Successfully created index 'embeddings-index'
 
 [[vectorize]]
-binding = "VECTORIZE_INDEX" # available in your Worker on env.VECTORIZE_INDEX
+binding = "VECTORIZE" # available in your Worker on env.VECTORIZE
 index_name = "embeddings-index"
 ```
 
@@ -117,13 +113,13 @@ To bind your index to your Worker, add the following to the end of your `wrangle
 
 ```toml
 [[vectorize]]
-binding = "VECTORIZE_INDEX" # available in your Worker on env.VECTORIZE_INDEX
+binding = "VECTORIZE" # available in your Worker on env.VECTORIZE
 index_name = "embeddings-index"
 ```
 
 Specifically:
 
-- The value (string) you set for `<BINDING_NAME>` will be used to reference this database in your Worker. In this tutorial, name your binding `VECTORIZE_INDEX`.
+- The value (string) you set for `<BINDING_NAME>` will be used to reference this database in your Worker. In this tutorial, name your binding `VECTORIZE`.
 - The binding must be [a valid JavaScript variable name](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Grammar_and_types#variables). For example, `binding = "MY_INDEX"` or `binding = "PROD_SEARCH_INDEX"` would both be valid names for the binding.
 - Your binding is available in your Worker at `env.<BINDING_NAME>` and the Vectorize [client API](/vectorize/reference/client-api/) is exposed on this binding for use within your Workers application.
 
@@ -135,7 +131,7 @@ From within the `embeddings-tutorial` directory, open your `wrangler.toml` file
 
 ```toml
 [[vectorize]]
-binding = "VECTORIZE_INDEX" # available in your Worker on env.VECTORIZE_INDEX
+binding = "VECTORIZE" # available in your Worker on env.VECTORIZE
 index_name = "embeddings-index"
 
 [ai]
@@ -148,11 +144,11 @@ With Workers AI ready, you can write code in your Worker.
 
 To write code in your Worker, go to your `embeddings-tutorial` Worker and open the `src/index.ts` file. The `index.ts` file is where you configure your Worker's interactions with your Vectorize index.
 
-Clear the content of `index.ts`. Paste the following code snippet into your `index.ts` file. On the `env` parameter, replace `<BINDING_NAME>` with `VECTORIZE_INDEX`:
+Clear the content of `index.ts`. Paste the following code snippet into your `index.ts` file. On the `env` parameter, replace `<BINDING_NAME>` with `VECTORIZE`:
 
 ```typescript
 export interface Env {
-	VECTORIZE_INDEX: VectorizeIndex;
+	VECTORIZE: Vectorize;
 	AI: Ai;
 }
 interface EmbeddingResponse {
@@ -195,7 +191,7 @@ export default {
 				id++;
 			});
 
-			let inserted = await env.VECTORIZE_INDEX.upsert(vectors);
+			let inserted = await env.VECTORIZE.upsert(vectors);
 			return Response.json(inserted);
 		}
 
@@ -208,12 +204,12 @@ export default {
 			},
 		);
 
-		let matches = await env.VECTORIZE_INDEX.query(queryVector.data[0], {
+		let matches = await env.VECTORIZE.query(queryVector.data[0], {
 			topK: 1,
 		});
 		return Response.json({
 			// Expect a vector ID. 1 to be your top match with a score of
-			// ~0.896888444
+			// ~0.89693683
 			// This tutorial uses a cosine distance metric, where the closer to one,
 			// the more similar.
 			matches: matches,
@@ -252,7 +248,17 @@ With the URL for your deployed Worker (for example,`https://embeddings-tutorial.
 This should return the following JSON:
 
 ```json
-{ "matches": { "count": 1, "matches": [{ "score": 0.896888444, "id": "1" }] } }
+{
+	"matches": {
+		"count": 1,
+		"matches": [
+			{
+				"id": "1",
+				"score": 0.89693683
+			}
+		]
+	}
+}
 ```
 
 Extend this example by: