feat: Add multi-vector API support

- Added a new `create_multi_vector` function to the Jina client
  to support creating multi-vector embeddings.
- Added a new `multi_vector_api.v` file containing the
  implementation for the multi-vector API.
- Updated the `jina.vsh` example to demonstrate the usage of the
  new multi-vector API.

examples/clients/jina.vsh

@@ -65,3 +65,20 @@ delete_result := jina_client.delete_classifier(classifier_id: classifiers[0].cla
 	panic('Error deleting classifier: ${err}')
 }
 println('Delete result: ${delete_result}')
+
+// Create multi vector
+multi_vector := jina_client.create_multi_vector(
+	input:          [
+		jina.MultiVectorTextDoc{
+			text:       'Hello world'
+			input_type: .document
+		},
+		jina.MultiVectorTextDoc{
+			text:       "What's up?"
+			input_type: .query
+		},
+	]
+	embedding_type: ['float']
+	// dimensions:     96
+)!
+println('Multi vector: ${multi_vector}')

lib/clients/jina/jina_model.v

@@ -2,7 +2,6 @@ module jina
 
 import freeflowuniverse.herolib.data.encoderhero
 import freeflowuniverse.herolib.core.httpconnection
-import net.http
 import os
 
 pub const version = '0.0.0'

lib/clients/jina/multi_vector_api.v

@@ -0,0 +1,98 @@
+module jina
+
+import json
+import freeflowuniverse.herolib.core.httpconnection
+
+// Enum for available Jina multi-vector models
+pub enum MultiVectorModel {
+	jina_colbert_v1_en // jina-colbert-v1-en
+}
+
+// Convert the enum to a valid string
+pub fn (m MultiVectorModel) to_string() string {
+	return match m {
+		.jina_colbert_v1_en { 'jina-colbert-v1-en' }
+	}
+}
+
+// Enum for input types
+pub enum MultiVectorInputType {
+	document // document
+	query    // query
+}
+
+// MultiVectorTextDoc represents a text document for a multi-vector request
+pub struct MultiVectorTextDoc {
+pub mut:
+	id         ?string // Optional: ID of the document
+	text       string @[required] // Text of the document
+	input_type ?MultiVectorInputType // Optional: Type of the embedding to compute, query or document
+}
+
+// MultiVectorRequest represents the JSON request body for the /v1/multi-vector endpoint
+struct MultiVectorRequest {
+	model          string               // Model name
+	input          []MultiVectorTextDoc // Input documents
+	embedding_type ?[]string            // Optional: Embedding type
+	dimensions     ?int                 // Optional: Number of dimensions
+}
+
+// MultiVectorResponse represents the JSON response body for the /v1/multi-vector endpoint
+pub struct MultiVectorResponse {
+	data   []Embedding // List of embeddings
+	usage  Usage       // Usage information
+	model  string      // Model name
+	object string      // Object type as string
+}
+
+// EmbeddingObjType represents the embeddings object in the response
+pub struct EmbeddingObjType {
+pub mut:
+	float  ?[][]f64  // Optional 2D array of floats for multi-vector embeddings
+	base64 ?[]string // Optional array of base64 strings
+	binary ?[]u8     // Optional array of bytes
+}
+
+// SEmbeddingType is a sum type to handle different embedding formats
+pub type SEmbeddingType = EmbeddingObjType | []f64 | []string | []u8
+
+// Embedding represents an embedding vector
+pub struct Embedding {
+	index      int            // Index of the document
+	embeddings SEmbeddingType // Embedding vector as a sum type
+	object     string         // Object type as string
+}
+
+// MultiVectorParams represents the parameters for a multi-vector request
+@[params]
+pub struct MultiVectorParams {
+pub mut:
+	model          MultiVectorModel = .jina_colbert_v1_en // Model name
+	input          []MultiVectorTextDoc  // Input documents
+	input_type     ?MultiVectorInputType // Optional: Type of the embedding to compute, query or document
+	embedding_type ?[]string             // Optional: Embedding type
+	dimensions     ?int                  // Optional: Number of dimensions
+}
+
+// CreateMultiVector creates a multi-vector request and returns the response
+pub fn (mut j Jina) create_multi_vector(params MultiVectorParams) !MultiVectorResponse {
+	request := MultiVectorRequest{
+		model:          params.model.to_string()
+		input:          params.input
+		embedding_type: params.embedding_type
+		dimensions:     params.dimensions
+	}
+
+	req := httpconnection.Request{
+		method:     .post
+		prefix:     'v1/multi-vector'
+		dataformat: .json
+		data:       json.encode(request)
+	}
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.post_json_str(req)!
+	println('response: ${response}')
+	result := json.decode(MultiVectorResponse, response)!
+	return result
+}