diff --git a/examples/clients/jina.vsh b/examples/clients/jina.vsh
new file mode 100755
index 00000000..7da391d8
--- /dev/null
+++ b/examples/clients/jina.vsh
@@ -0,0 +1,60 @@
+#!/usr/bin/env -S v -n -w -gc none  -cc tcc -d use_openssl -enable-globals run
+
+import freeflowuniverse.herolib.clients.jina
+
+mut jina_client := jina.get()!
+
+// Create embeddings
+embeddings := jina_client.create_embeddings(
+	input: ['Hello', 'World']
+	model: .jina_embeddings_v3
+	task:  'separation'
+) or { panic('Error while creating embeddings: ${err}') }
+
+println('Created embeddings: ${embeddings}')
+
+// Rerank
+rerank_result := jina_client.rerank(
+	model:     .reranker_v2_base_multilingual
+	query:     'skincare products'
+	documents: ['Product A', 'Product B', 'Product C']
+	top_n:     2
+) or { panic('Error while reranking: ${err}') }
+
+println('Rerank result: ${rerank_result}')
+
+// Train
+train_result := jina_client.train(
+	model: .jina_clip_v1
+	input: [
+		jina.TrainingExample{
+			text:  'Sample text'
+			label: 'positive'
+		},
+		jina.TrainingExample{
+			image: 'https://letsenhance.io/static/73136da51c245e80edc6ccfe44888a99/1015f/MainBefore.jpg'
+			label: 'negative'
+		},
+	]
+) or { panic('Error while training: ${err}') }
+
+println('Train result: ${train_result}')
+
+// Classify
+classify_result := jina_client.classify(
+	model:  .jina_clip_v1
+	input:  [
+		jina.ClassificationInput{
+			text: 'A photo of a cat'
+		},
+		jina.ClassificationInput{
+			image: 'https://letsenhance.io/static/73136da51c245e80edc6ccfe44888a99/1015f/MainBefore.jpg'
+		},
+	]
+	labels: ['cat', 'dog']
+) or { panic('Error while classifying: ${err}') }
+
+println('Classification result: ${classify_result}')
+
+classifiers := jina_client.list_classifiers() or { panic('Error fetching classifiers: ${err}') }
+println('Classifiers: ${classifiers}')
diff --git a/lib/clients/jina/classification_api.v b/lib/clients/jina/classification_api.v
new file mode 100644
index 00000000..820697d6
--- /dev/null
+++ b/lib/clients/jina/classification_api.v
@@ -0,0 +1,291 @@
+module jina
+
+import json
+import freeflowuniverse.herolib.core.httpconnection
+
+// ClassificationTrainAccess represents the accessibility of the classifier
+pub enum ClassificationTrainAccess {
+	public  // Classifier is publicly accessible
+	private // Classifier is private (default)
+}
+
+// TrainingExample represents a single training example (either text or image with a label)
+pub struct TrainingExample {
+pub mut:
+	text  ?string // Optional text content
+	image ?string // Optional image URL
+	label string  // Required label
+}
+
+// ClassificationTrainOutput represents the response from the training endpoint
+pub struct ClassificationTrainOutput {
+pub mut:
+	classifier_id string                   // Identifier of the trained classifier
+	num_samples   int                      // Number of samples used in training
+	usage         ClassificationTrainUsage // Token usage details
+}
+
+// ClassificationTrainUsage represents token usage for the training request
+pub struct ClassificationTrainUsage {
+pub mut:
+	total_tokens int // Total tokens consumed
+}
+
+// ClassificationTrain represents parameters for the training request
+@[params]
+pub struct ClassificationTrain {
+pub mut:
+	model         ?JinaModel // Optional model identifier (e.g., jina-clip-v1)
+	classifier_id ?string    // Optional existing classifier ID
+	access        ?ClassificationTrainAccess = .private // Accessibility, defaults to private
+	input         []TrainingExample // Array of training examples
+	num_iters     ?int = 10 // Number of training iterations, defaults to 10
+}
+
+// TrainRequest represents the JSON request body for the /v1/train endpoint
+struct TrainRequest {
+mut:
+	model         ?string
+	classifier_id ?string
+	access        ?string
+	input         []TrainingExample
+	num_iters     ?int
+}
+
+// Train a classifier by sending a POST request to /v1/train
+pub fn (mut j Jina) train(params ClassificationTrain) !ClassificationTrainOutput {
+	// Validate that only one of model or classifier_id is provided
+	mut model_provided := false
+	mut classifier_id_provided := false
+	if _ := params.model {
+		model_provided = true
+	}
+
+	if _ := params.classifier_id {
+		classifier_id_provided = true
+	}
+
+	if model_provided && classifier_id_provided {
+		return error('Provide either model or classifier_id, not both')
+	}
+
+	if model := params.model {
+		if model == .jina_embeddings_v3 {
+			return error('jina-embeddings-v3 is not a valid model for classification')
+		}
+	}
+
+	// Validate each training example has exactly one of text or image
+	for example in params.input {
+		mut text_provided := false
+		mut image_provided := false
+
+		if _ := example.text {
+			text_provided = true
+		}
+
+		if _ := example.image {
+			image_provided = true
+		}
+
+		if text_provided && image_provided {
+			return error('Each training example must have either text or image, not both')
+		}
+
+		if !text_provided && !image_provided {
+			return error('Each training example must have either text or image')
+		}
+	}
+
+	// Construct the request body
+	mut request := TrainRequest{
+		input: params.input
+	}
+	if v := params.model {
+		request.model = v.to_string() // Convert JinaModel enum to string
+	}
+	if v := params.classifier_id {
+		request.classifier_id = v
+	}
+	if v := params.access {
+		request.access = match v {
+			.public { 'public' }
+			.private { 'private' }
+		}
+	}
+	if v := params.num_iters {
+		request.num_iters = v
+	}
+
+	// Create and send the HTTP request
+	req := httpconnection.Request{
+		method:     .post
+		prefix:     'v1/train'
+		dataformat: .json
+		data:       json.encode(request)
+	}
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.post_json_str(req)!
+	result := json.decode(ClassificationTrainOutput, response)!
+	return result
+}
+
+// TextDoc represents a text document for classification
+pub struct TextDoc {
+pub mut:
+	text string // The text content
+}
+
+// ImageDoc represents an image document for classification
+pub struct ImageDoc {
+pub mut:
+	image string // The image URL or base64-encoded string
+}
+
+// ClassificationInput represents a single input for classification (text or image)
+pub struct ClassificationInput {
+pub mut:
+	text  ?string // Optional text content
+	image ?string // Optional image content
+}
+
+// ClassificationOutput represents the response from the classify endpoint
+pub struct ClassificationOutput {
+pub mut:
+	data  []ClassificationResult // List of classification results
+	usage ClassificationUsage    // Token usage details
+}
+
+// ClassificationResult represents a single classification result
+pub struct ClassificationResult {
+pub mut:
+	index       int          // Index of the input
+	prediction  string       // Predicted label
+	score       f64          // Confidence score
+	object      string       // Type of object (e.g., "classification")
+	predictions []LabelScore // List of label scores
+}
+
+// LabelScore represents a label and its corresponding score
+pub struct LabelScore {
+pub mut:
+	label string // Label name
+	score f64    // Confidence score
+}
+
+// ClassificationUsage represents token usage for the classification request
+pub struct ClassificationUsage {
+pub mut:
+	total_tokens int // Total tokens consumed
+}
+
+// ClassifyRequest represents the JSON request body for the /v1/classify endpoint
+struct ClassifyRequest {
+mut:
+	model         ?string
+	classifier_id ?string
+	input         []ClassificationInput
+	labels        []string
+}
+
+// ClassifyParams represents parameters for the classification request
+@[params]
+pub struct ClassifyParams {
+pub mut:
+	model         ?JinaModel            // Optional model identifier
+	classifier_id ?string               // Optional classifier ID
+	input         []ClassificationInput // Array of inputs (text or image)
+	labels        []string              // List of labels for classification
+}
+
+// Classify inputs by sending a POST request to /v1/classify
+pub fn (mut j Jina) classify(params ClassifyParams) !ClassificationOutput {
+	// Validate that only one of model or classifier_id is provided
+	mut model_provided := false
+	mut classifier_id_provided := false
+	if _ := params.model {
+		model_provided = true
+	}
+	if _ := params.classifier_id {
+		classifier_id_provided = true
+	}
+	if model_provided && classifier_id_provided {
+		return error('Provide either model or classifier_id, not both')
+	}
+	if !model_provided && !classifier_id_provided {
+		return error('Either model or classifier_id must be provided')
+	}
+
+	// Validate each input has exactly one of text or image
+	for input in params.input {
+		mut text_provided := false
+		mut image_provided := false
+		if _ := input.text {
+			text_provided = true
+		}
+		if _ := input.image {
+			image_provided = true
+		}
+		if text_provided && image_provided {
+			return error('Each input must have either text or image, not both')
+		}
+		if !text_provided && !image_provided {
+			return error('Each input must have either text or image')
+		}
+	}
+
+	// Construct the request body
+	mut request := ClassifyRequest{
+		input:  params.input
+		labels: params.labels
+	}
+	if v := params.model {
+		request.model = v.to_string() // Convert JinaModel enum to string
+	}
+	if v := params.classifier_id {
+		request.classifier_id = v
+	}
+
+	// Create and send the HTTP request
+	req := httpconnection.Request{
+		method:     .post
+		prefix:     'v1/classify'
+		dataformat: .json
+		data:       json.encode(request)
+	}
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.post_json_str(req)!
+	result := json.decode(ClassificationOutput, response)!
+	return result
+}
+
+// Define the Classifier struct
+pub struct Classifier {
+pub mut:
+	classifier_id  string
+	model_name     string
+	labels         []string
+	access         string
+	updated_number int
+	used_number    int
+	created_at     string
+	updated_at     string
+	used_at        ?string
+	metadata       map[string]string
+}
+
+// Implement the list_classifiers function
+pub fn (mut j Jina) list_classifiers() ![]Classifier {
+	req := httpconnection.Request{
+		method: .get
+		prefix: 'v1/classifiers'
+	}
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.get(req)!
+	println('response: ${response}')
+	classifiers := json.decode([]Classifier, response)!
+	return classifiers
+}
diff --git a/lib/clients/jina/jina_client.v b/lib/clients/jina/jina_client.v
index c33b47bd..5ef4c919 100644
--- a/lib/clients/jina/jina_client.v
+++ b/lib/clients/jina/jina_client.v
@@ -1,185 +1,245 @@
 module jina
 
 import freeflowuniverse.herolib.core.httpconnection
-import json
 import os
+import json
+
+@[params]
+pub struct CreateEmbeddingParams {
+pub mut:
+	input         []string  @[required] // Input texts
+	model         JinaModel @[required] // Model name
+	task          string    @[required] // Task type
+	type_         ?EmbeddingType // embedding type
+	truncate      ?TruncateType  // truncation type
+	late_chunking ?bool          // Flag to determine if late chunking is applied
+}
 
 // Create embeddings for input texts
-pub fn (mut j Jina) create_embeddings(input []string, model string, task string) !ModelEmbeddingOutput {
+pub fn (mut j Jina) create_embeddings(params CreateEmbeddingParams) !ModelEmbeddingOutput {
+	task := task_type_from_string(params.task)!
+
 	mut embedding_input := TextEmbeddingInput{
-		model: model
-		input: input
-		task: task
+		input: params.input
+		model: params.model.to_string()
+		task:  task
 	}
-	
+
+	if v := params.type_ {
+		embedding_input.type_ = v
+	}
+
+	if v := params.truncate {
+		embedding_input.truncate = v
+	}
+
+	embedding_input.late_chunking = if _ := params.late_chunking { true } else { false }
+
 	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/embeddings'
+		method:     .post
+		prefix:     'v1/embeddings'
 		dataformat: .json
-		data: embedding_input.to_json()
+		data:       embedding_input.to_json()
 	}
-	
-	response := j.http.get(req)!
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.post_json_str(req)!
 	return parse_model_embedding_output(response)!
 }
 
-// Create embeddings with a TextDoc input
-pub fn (mut j Jina) create_embeddings_with_docs(args TextEmbeddingInput) !ModelEmbeddingOutput {
-	
-	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/embeddings'
-		dataformat: .json
-		data: json.encode(args)
-	}
-	
-	response := j.http.get(req)!
-	return parse_model_embedding_output(response)!
+@[params]
+pub struct RerankParams {
+pub mut:
+	model            JinaRerankModel @[required]
+	query            string          @[required]
+	documents        []string        @[required]
+	top_n            ?int  // Optional: Number of top results to return
+	return_documents ?bool // Optional: Flag to determine if the documents should be returned
 }
 
 // Rerank documents based on a query
-pub fn (mut j Jina) rerank(query string, documents []string, model string, top_n int) !RankingOutput {
-	mut rank_input := RankAPIInput{
-		model: model
-		query: query
-		documents: documents
-		top_n: top_n
+pub fn (mut j Jina) rerank(params RerankParams) !RankingOutput {
+	mut rank_input := RerankInput{
+		model:     params.model.to_string()
+		query:     params.query
+		documents: params.documents
 	}
-	
+
+	if v := params.top_n {
+		rank_input.top_n = v
+	}
+
+	if v := params.return_documents {
+		rank_input.return_documents = v
+	}
+
 	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/rerank'
+		method:     .post
+		prefix:     'v1/rerank'
 		dataformat: .json
-		data: rank_input.to_json()
+		data:       json.encode(rank_input)
 	}
-	
-	response := j.http.get(req)!
-	return parse_ranking_output(response)!
+
+	mut httpclient := j.httpclient()!
+	response := httpclient.post_json_str(req)!
+	return json.decode(RankingOutput, response)!
 }
 
-// Simplified rerank function with default top_n
-pub fn (mut j Jina) rerank_simple(query string, documents []string, model string) !RankingOutput {
-	return j.rerank(query, documents, model, 0)!
-}
+// // Create embeddings with a TextDoc input
+// pub fn (mut j Jina) create_embeddings_with_docs(args TextEmbeddingInput) !ModelEmbeddingOutput {
 
-// Classify input texts
-pub fn (mut j Jina) classify(input []string, model string, labels []string) !ClassificationOutput {
-	mut classification_input := ClassificationAPIInput{
-		model: model
-		input: input
-		labels: labels
-	}
-	
-	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/classify'
-		dataformat: .json
-		data: classification_input.to_json()
-	}
-	
-	response := j.http.get(req)!
-	return parse_classification_output(response)!
-}
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/embeddings'
+// 		dataformat: .json
+// 		data: json.encode(args)
+// 	}
 
-// Train a classifier
-pub fn (mut j Jina) train(examples []TrainingExample, model string, access string) !TrainingOutput {
-	mut training_input := TrainingAPIInput{
-		model: model
-		input: examples
-		access: access
-	}
-	
-	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/train'
-		dataformat: .json
-		data: training_input.to_json()
-	}
-	
-	response := j.http.get(req)!
-	return parse_training_output(response)!
-}
+// 	response := j.http.get(req)!
+// 	return parse_model_embedding_output(response)!
+// }
 
-// List classifiers
-pub fn (mut j Jina) list_classifiers() !string {
-	req := httpconnection.Request{
-		method: .get
-		prefix: 'v1/classifiers'
-	}
-	
-	return j.http.get(req)!
-}
+// // Rerank documents based on a query
+// pub fn (mut j Jina) rerank(query string, documents []string, model string, top_n int) !RankingOutput {
+// 	mut rank_input := RankAPIInput{
+// 		model: model
+// 		query: query
+// 		documents: documents
+// 		top_n: top_n
+// 	}
 
-// Delete a classifier
-pub fn (mut j Jina) delete_classifier(classifier_id string) !bool {
-	req := httpconnection.Request{
-		method: .delete
-		prefix: 'v1/classifiers/${classifier_id}'
-	}
-	
-	j.http.get(req)!
-	return true
-}
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/rerank'
+// 		dataformat: .json
+// 		data: rank_input.to_json()
+// 	}
 
-// Create multi-vector embeddings
-pub fn (mut j Jina) create_multi_vector(input []string, model string) !ColbertModelEmbeddingsOutput {
-	mut data := map[string]json.Any{}
-	data['model'] = model
-	data['input'] = input
-	
-	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/multi-embeddings'
-		dataformat: .json
-		data: json.encode(data)
-	}
-	
-	response := j.http.get(req)!
-	return parse_colbert_model_embeddings_output(response)!
-}
+// 	response := j.http.get(req)!
+// 	return parse_ranking_output(response)!
+// }
 
-// Start a bulk embedding job
-pub fn (mut j Jina) start_bulk_embedding(file_path string, model string, email string) !BulkEmbeddingJobResponse {
-	// This endpoint requires multipart/form-data which is not directly supported by the current HTTPConnection
-	// We need to implement a custom solution for this
-	return error('Bulk embedding is not implemented yet')
-}
+// // Simplified rerank function with default top_n
+// pub fn (mut j Jina) rerank_simple(query string, documents []string, model string) !RankingOutput {
+// 	return j.rerank(query, documents, model, 0)!
+// }
 
-// Check the status of a bulk embedding job
-pub fn (mut j Jina) check_bulk_embedding_status(job_id string) !BulkEmbeddingJobResponse {
-	req := httpconnection.Request{
-		method: .get
-		prefix: 'v1/bulk-embeddings/${job_id}'
-	}
-	
-	response := j.http.get(req)!
-	return parse_bulk_embedding_job_response(response)!
-}
+// // Classify input texts
+// pub fn (mut j Jina) classify(input []string, model string, labels []string) !ClassificationOutput {
+// 	mut classification_input := ClassificationAPIInput{
+// 		model: model
+// 		input: input
+// 		labels: labels
+// 	}
 
-// Download the result of a bulk embedding job
-pub fn (mut j Jina) download_bulk_embedding_result(job_id string) !DownloadResultResponse {
-	req := httpconnection.Request{
-		method: .post
-		prefix: 'v1/bulk-embeddings/${job_id}/download-result'
-	}
-	
-	response := j.http.get(req)!
-	return parse_download_result_response(response)!
-}
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/classify'
+// 		dataformat: .json
+// 		data: classification_input.to_json()
+// 	}
 
-// Check if the API key is valid by making a simple request
-pub fn (mut j Jina) check_auth() !bool {
-	req := httpconnection.Request{
-		method: .get
-		prefix: '/'
-	}
-	
-	j.http.get(req) or {
-		return error('Failed to connect to Jina API: ${err}')
-	}
-	
-	// If we get a response, the API key is valid
-	return true
-}
+// 	response := j.http.get(req)!
+// 	return parse_classification_output(response)!
+// }
 
+// // Train a classifier
+// pub fn (mut j Jina) train(examples []TrainingExample, model string, access string) !TrainingOutput {
+// 	mut training_input := TrainingAPIInput{
+// 		model: model
+// 		input: examples
+// 		access: access
+// 	}
 
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/train'
+// 		dataformat: .json
+// 		data: training_input.to_json()
+// 	}
+
+// 	response := j.http.get(req)!
+// 	return parse_training_output(response)!
+// }
+
+// // List classifiers
+// pub fn (mut j Jina) list_classifiers() !string {
+// 	req := httpconnection.Request{
+// 		method: .get
+// 		prefix: 'v1/classifiers'
+// 	}
+
+// 	return j.http.get(req)!
+// }
+
+// // Delete a classifier
+// pub fn (mut j Jina) delete_classifier(classifier_id string) !bool {
+// 	req := httpconnection.Request{
+// 		method: .delete
+// 		prefix: 'v1/classifiers/${classifier_id}'
+// 	}
+
+// 	j.http.get(req)!
+// 	return true
+// }
+
+// // Create multi-vector embeddings
+// pub fn (mut j Jina) create_multi_vector(input []string, model string) !ColbertModelEmbeddingsOutput {
+// 	mut data := map[string]json.Any{}
+// 	data['model'] = model
+// 	data['input'] = input
+
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/multi-embeddings'
+// 		dataformat: .json
+// 		data: json.encode(data)
+// 	}
+
+// 	response := j.http.get(req)!
+// 	return parse_colbert_model_embeddings_output(response)!
+// }
+
+// // Start a bulk embedding job
+// pub fn (mut j Jina) start_bulk_embedding(file_path string, model string, email string) !BulkEmbeddingJobResponse {
+// 	// This endpoint requires multipart/form-data which is not directly supported by the current HTTPConnection
+// 	// We need to implement a custom solution for this
+// 	return error('Bulk embedding is not implemented yet')
+// }
+
+// // Check the status of a bulk embedding job
+// pub fn (mut j Jina) check_bulk_embedding_status(job_id string) !BulkEmbeddingJobResponse {
+// 	req := httpconnection.Request{
+// 		method: .get
+// 		prefix: 'v1/bulk-embeddings/${job_id}'
+// 	}
+
+// 	response := j.http.get(req)!
+// 	return parse_bulk_embedding_job_response(response)!
+// }
+
+// // Download the result of a bulk embedding job
+// pub fn (mut j Jina) download_bulk_embedding_result(job_id string) !DownloadResultResponse {
+// 	req := httpconnection.Request{
+// 		method: .post
+// 		prefix: 'v1/bulk-embeddings/${job_id}/download-result'
+// 	}
+
+// 	response := j.http.get(req)!
+// 	return parse_download_result_response(response)!
+// }
+
+// // Check if the API key is valid by making a simple request
+// pub fn (mut j Jina) check_auth() !bool {
+// 	req := httpconnection.Request{
+// 		method: .get
+// 		prefix: '/'
+// 	}
+
+// 	j.http.get(req) or {
+// 		return error('Failed to connect to Jina API: ${err}')
+// 	}
+
+// 	// If we get a response, the API key is valid
+// 	return true
+// }
diff --git a/lib/clients/jina/jina_client_test.v b/lib/clients/jina/jina_client_test.v
new file mode 100644
index 00000000..1b453f02
--- /dev/null
+++ b/lib/clients/jina/jina_client_test.v
@@ -0,0 +1,87 @@
+module jina
+
+import time
+
+fn setup_client() !&Jina {
+	mut client := get()!
+	return client
+}
+
+fn test_create_embeddings() {
+	time.sleep(1 * time.second)
+	mut client := setup_client()!
+	embeddings := client.create_embeddings(
+		input: ['Hello', 'World']
+		model: .jina_embeddings_v3
+		task:  'separation'
+	) or { panic('Error while creating embeddings: ${err}') }
+
+	assert embeddings.data.len > 0
+	assert embeddings.object == 'list' // Check the object type
+	assert embeddings.model == 'jina-embeddings-v3'
+}
+
+fn test_rerank() {
+	time.sleep(1 * time.second)
+	mut client := setup_client()!
+	rerank_result := client.rerank(
+		model:     .reranker_v2_base_multilingual
+		query:     'skincare products'
+		documents: ['Product A', 'Product B', 'Product C']
+		top_n:     2
+	) or { panic('Error while reranking: ${err}') }
+
+	assert rerank_result.results.len == 2
+	assert rerank_result.model == 'jina-reranker-v2-base-multilingual'
+}
+
+fn test_train() {
+	time.sleep(1 * time.second)
+	mut client := setup_client()!
+	train_result := client.train(
+		model: .jina_clip_v1
+		input: [
+			TrainingExample{
+				text:  'A photo of a cat'
+				label: 'cat'
+			},
+			TrainingExample{
+				text:  'A photo of a dog'
+				label: 'dog'
+			},
+		]
+	) or { panic('Error while training: ${err}') }
+
+	assert train_result.classifier_id.len > 0
+	assert train_result.num_samples == 2
+}
+
+fn test_classify() {
+	time.sleep(1 * time.second)
+	mut client := setup_client()!
+	classify_result := client.classify(
+		model:  .jina_clip_v1
+		input:  [
+			ClassificationInput{
+				text: 'A photo of a cat'
+			},
+			ClassificationInput{
+				image: 'https://letsenhance.io/static/73136da51c245e80edc6ccfe44888a99/1015f/MainBefore.jpg'
+			},
+		]
+		labels: ['cat', 'dog']
+	) or { panic('Error while classifying: ${err}') }
+
+	assert classify_result.data.len == 2
+	assert classify_result.data[0].prediction in ['cat', 'dog']
+	assert classify_result.data[1].prediction in ['cat', 'dog']
+	assert classify_result.data[0].object == 'classification'
+	assert classify_result.data[1].object == 'classification'
+}
+
+fn test_get_classifiers() {
+	time.sleep(1 * time.second)
+	mut client := setup_client()!
+	classifiers := client.list_classifiers() or { panic('Error fetching classifiers: ${err}') }
+	assert classifiers.len != 0
+}
diff --git a/lib/clients/jina/jina_model.v b/lib/clients/jina/jina_model.v
index c3df3f33..8057e768 100644
--- a/lib/clients/jina/jina_model.v
+++ b/lib/clients/jina/jina_model.v
@@ -1,6 +1,5 @@
 module jina
 
-import freeflowuniverse.herolib.data.paramsparser
 import freeflowuniverse.herolib.data.encoderhero
 import freeflowuniverse.herolib.core.httpconnection
 import net.http
@@ -17,16 +16,29 @@ const env_key = 'JINAKEY'
 @[heap]
 pub struct Jina {
 pub mut:
-	name          string = 'default'
-	secret        string
-	base_url      string = api_base_url
-	http          httpconnection.HTTPConnection @[str: skip]
+	name     string = 'default'
+	secret   string
+	base_url string = api_base_url
+	// http     httpconnection.HTTPConnection @[str: skip]
+}
+
+fn (mut self Jina) httpclient() !&httpconnection.HTTPConnection {
+	mut http_conn := httpconnection.new(
+		name: 'Jina_vclient'
+		url:  self.base_url
+	)!
+
+	// Add authentication header if API key is provided
+	if self.secret.len > 0 {
+		http_conn.default_header.add(.authorization, 'Bearer ${self.secret}')
+	}
+	return http_conn
 }
 
 // your checking & initialization code if needed
 fn obj_init(mycfg_ Jina) !Jina {
 	mut mycfg := mycfg_
-	
+
 	// Get API key from environment variable if not set
 	if mycfg.secret == '' {
 		if env_key in os.environ() {
@@ -35,16 +47,7 @@ fn obj_init(mycfg_ Jina) !Jina {
 			return error('Jina API key not provided and ${env_key} environment variable not set')
 		}
 	}
-	
-	// Initialize HTTP connection
-	mut header := http.new_header()
-	header.add_custom('Authorization', 'Bearer ${mycfg.secret}')
-	
-	mycfg.http = httpconnection.HTTPConnection{
-		base_url: mycfg.base_url
-		default_header: header
-	}
-	
+
 	return mycfg
 }
 
diff --git a/lib/clients/jina/model_embed.v b/lib/clients/jina/model_embed.v
index 69770e95..f7b564e4 100644
--- a/lib/clients/jina/model_embed.v
+++ b/lib/clients/jina/model_embed.v
@@ -2,53 +2,53 @@ module jina
 
 import json
 
-// JinaModelEnumerator represents the available models for Jina API
-pub enum JinaModelEnumerator {
-	clip_v1                  // jina-clip-v1, 223M, 768
-	clip_v2                  // jina-clip-v2, 865M, 1024
-	embeddings_v2_base_en    // jina-embeddings-v2-base-en, 137M, 768
-	embeddings_v2_base_es    // jina-embeddings-v2-base-es, 161M, 768
-	embeddings_v2_base_de    // jina-embeddings-v2-base-de, 161M, 768
-	embeddings_v2_base_zh    // jina-embeddings-v2-base-zh, 161M, 768
-	embeddings_v2_base_code  // jina-embeddings-v2-base-code, 137M, 768
-	embeddings_v3            // jina-embeddings-v3, 570M, 1024
+// JinaModel represents the available Jina models
+pub enum JinaModel {
+	jina_clip_v1
+	jina_clip_v2
+	jina_embeddings_v2_base_en
+	jina_embeddings_v2_base_es
+	jina_embeddings_v2_base_de
+	jina_embeddings_v2_base_zh
+	jina_embeddings_v2_base_code
+	jina_embeddings_v3
 }
 
-// to_string converts JinaModelEnumerator enum to its string representation
-pub fn (m JinaModelEnumerator) to_string() string {
+// to_string converts a JinaModel enum to its string representation as expected by the Jina API
+pub fn (m JinaModel) to_string() string {
 	return match m {
-		.clip_v1 { 'jina-clip-v1' }
-		.clip_v2 { 'jina-clip-v2' }
-		.embeddings_v2_base_en { 'jina-embeddings-v2-base-en' }
-		.embeddings_v2_base_es { 'jina-embeddings-v2-base-es' }
-		.embeddings_v2_base_de { 'jina-embeddings-v2-base-de' }
-		.embeddings_v2_base_zh { 'jina-embeddings-v2-base-zh' }
-		.embeddings_v2_base_code { 'jina-embeddings-v2-base-code' }
-		.embeddings_v3 { 'jina-embeddings-v3' }
+		.jina_clip_v1 { 'jina-clip-v1' }
+		.jina_clip_v2 { 'jina-clip-v2' }
+		.jina_embeddings_v2_base_en { 'jina-embeddings-v2-base-en' }
+		.jina_embeddings_v2_base_es { 'jina-embeddings-v2-base-es' }
+		.jina_embeddings_v2_base_de { 'jina-embeddings-v2-base-de' }
+		.jina_embeddings_v2_base_zh { 'jina-embeddings-v2-base-zh' }
+		.jina_embeddings_v2_base_code { 'jina-embeddings-v2-base-code' }
+		.jina_embeddings_v3 { 'jina-embeddings-v3' }
 	}
 }
 
-// from_string converts string to JinaModelEnumerator enum
-pub fn jina_model_from_string(s string) ?JinaModelEnumerator {
+// from_string converts a string to a JinaModel enum, returning an error if the string is invalid
+pub fn jina_model_from_string(s string) !JinaModel {
 	return match s {
-		'jina-clip-v1' { JinaModelEnumerator.clip_v1 }
-		'jina-clip-v2' { JinaModelEnumerator.clip_v2 }
-		'jina-embeddings-v2-base-en' { JinaModelEnumerator.embeddings_v2_base_en }
-		'jina-embeddings-v2-base-es' { JinaModelEnumerator.embeddings_v2_base_es }
-		'jina-embeddings-v2-base-de' { JinaModelEnumerator.embeddings_v2_base_de }
-		'jina-embeddings-v2-base-zh' { JinaModelEnumerator.embeddings_v2_base_zh }
-		'jina-embeddings-v2-base-code' { JinaModelEnumerator.embeddings_v2_base_code }
-		'jina-embeddings-v3' { JinaModelEnumerator.embeddings_v3 }
-		else { error('Invalid model string: $s') }
+		'jina-clip-v1' { JinaModel.jina_clip_v1 }
+		'jina-clip-v2' { JinaModel.jina_clip_v2 }
+		'jina-embeddings-v2-base-en' { JinaModel.jina_embeddings_v2_base_en }
+		'jina-embeddings-v2-base-es' { JinaModel.jina_embeddings_v2_base_es }
+		'jina-embeddings-v2-base-de' { JinaModel.jina_embeddings_v2_base_de }
+		'jina-embeddings-v2-base-zh' { JinaModel.jina_embeddings_v2_base_zh }
+		'jina-embeddings-v2-base-code' { JinaModel.jina_embeddings_v2_base_code }
+		'jina-embeddings-v3' { JinaModel.jina_embeddings_v3 }
+		else { error('Invalid Jina model string: ${s}') }
 	}
 }
 
 // EmbeddingType represents the available embedding types
 pub enum EmbeddingType {
-	float    // "float"
-	base64   // "base64"
-	binary   // "binary"
-	ubinary  // "ubinary"
+	float   // "float"
+	base64  // "base64"
+	binary  // "binary"
+	ubinary // "ubinary"
 }
 
 // to_string converts EmbeddingType enum to its string representation
@@ -68,17 +68,17 @@ pub fn embedding_type_from_string(s string) !EmbeddingType {
 		'base64' { EmbeddingType.base64 }
 		'binary' { EmbeddingType.binary }
 		'ubinary' { EmbeddingType.ubinary }
-		else { error('Invalid embedding type string: $s') }
+		else { error('Invalid embedding type string: ${s}') }
 	}
 }
 
 // TaskType represents the available task types for embeddings
 pub enum TaskType {
-	retrieval_query     // "retrieval.query"
-	retrieval_passage   // "retrieval.passage"
-	text_matching       // "text-matching"
-	classification      // "classification"
-	separation          // "separation"
+	retrieval_query   // "retrieval.query"
+	retrieval_passage // "retrieval.passage"
+	text_matching     // "text-matching"
+	classification    // "classification"
+	separation        // "separation"
 }
 
 // to_string converts TaskType enum to its string representation
@@ -100,13 +100,13 @@ pub fn task_type_from_string(s string) !TaskType {
 		'text-matching' { TaskType.text_matching }
 		'classification' { TaskType.classification }
 		'separation' { TaskType.separation }
-		else { error('Invalid task type string: $s') }
+		else { error('Invalid task type string: ${s}') }
 	}
 }
 
 // TruncateType represents the available truncation options
 pub enum TruncateType {
-	none  // "NONE"
+	none_ // "NONE"
 	start // "START"
 	end   // "END"
 }
@@ -114,7 +114,7 @@ pub enum TruncateType {
 // to_string converts TruncateType enum to its string representation
 pub fn (t TruncateType) to_string() string {
 	return match t {
-		.none { 'NONE' }
+		.none_ { 'NONE' }
 		.start { 'START' }
 		.end { 'END' }
 	}
@@ -123,90 +123,89 @@ pub fn (t TruncateType) to_string() string {
 // from_string converts string to TruncateType enum
 pub fn truncate_type_from_string(s string) !TruncateType {
 	return match s {
-		'NONE' { TruncateType.none }
+		'NONE' { TruncateType.none_ }
 		'START' { TruncateType.start }
 		'END' { TruncateType.end }
-		else { error('Invalid truncate type string: $s') }
+		else { error('Invalid truncate type string: ${s}') }
 	}
 }
 
 // TextEmbeddingInputRaw represents the raw input for text embedding requests as sent to the server
 struct TextEmbeddingInputRaw {
 mut:
-	model          string   = 'jina-embeddings-v2-base-en'
-	input          []string @[required]
-	task           string   // Optional: task type as string
-	type_          string   @[json: 'type'] // Optional: embedding type as string
-	truncate       string   // Optional: "NONE", "START", "END"
-	late_chunking  bool     // Optional: Flag to determine if late chunking is applied
+	model         string = 'jina-embeddings-v2-base-en'
+	input         []string @[required]
+	task          string // Optional: task type as string
+	type_         string @[json: 'type'] // Optional: embedding type as string
+	truncate      string // Optional: "NONE", "START", "END"
+	late_chunking bool   // Optional: Flag to determine if late chunking is applied
 }
 
 // TextEmbeddingInput represents the input for text embedding requests with enum types
 pub struct TextEmbeddingInput {
 pub mut:
-	model          JinaModelEnumerator      = JinaModelEnumerator.embeddings_v2_base_en
-	input          []string       @[required]
-	task           TaskType      // task type
-	type_          EmbeddingType // embedding type
-	truncate       TruncateType  // truncation type
-	late_chunking  bool           //Flag to determine if late chunking is applied
+	model         string = 'jina-embeddings-v2-base-en'
+	input         []string @[required]
+	task          TaskType       // task type
+	type_         ?EmbeddingType // embedding type
+	truncate      ?TruncateType  // truncation type
+	late_chunking ?bool          // Flag to determine if late chunking is applied
 }
 
 // dumps converts TextEmbeddingInput to JSON string
 pub fn (t TextEmbeddingInput) dumps() !string {
 	mut raw := TextEmbeddingInputRaw{
-		model: t.model.to_string()
-		input: t.input
-		late_chunking: t.late_chunking
+		model:         t.model
+		input:         t.input
+		late_chunking: if v := t.late_chunking { true } else { false }
 	}
-	
+
 	raw.task = t.task.to_string()
-	raw.type_ = t.type_.to_string()
-	raw.truncate = t.truncate.to_string()	
+	if v := t.type_ {
+		raw.type_ = v.to_string()
+	}
+
+	if v := t.truncate {
+		raw.truncate = v.to_string()
+	}
+
 	return json.encode(raw)
 }
 
 // from_raw converts TextEmbeddingInputRaw to TextEmbeddingInput
-pub fn loads_text_embedding_input(text string	) !TextEmbeddingInput {
-	// TODO: go from text to InputObject over json
-	mut input := TextEmbeddingInput{
-		model: jina_model_from_string(raw.model)?
-		input: raw.input
-		late_chunking: raw.late_chunking
-	}
-	
-	if raw.task != '' {
-		input.task = task_type_from_string(raw.task)!
-	}
-	
-	if raw.type_ != '' {
-		input.type_ = embedding_type_from_string(raw.type_)!
-	}
-	
-	if raw.truncate != '' {
-		input.truncate = truncate_type_from_string(raw.truncate)!
-	}
-	
-	return input
-}
+// pub fn loads_text_embedding_input(text string) !TextEmbeddingInput {
+// 	// TODO: go from text to InputObject over json
+// 	// mut input := TextEmbeddingInput{
+// 	// 	model:         jina_model_from_string(raw.model)?
+// 	// 	input:         raw.input
+// 	// 	late_chunking: raw.late_chunking
+// 	// }
+
+// 	// if raw.task != '' {
+// 	// 	input.task = task_type_from_string(raw.task)!
+// 	// }
+
+// 	// if raw.type_ != '' {
+// 	// 	input.type_ = embedding_type_from_string(raw.type_)!
+// 	// }
+
+// 	// if raw.truncate != '' {
+// 	// 	input.truncate = truncate_type_from_string(raw.truncate)!
+// 	// }
+
+// 	return TextEmbeddingInput{}
+// }
 
 // loads converts a JSON string to TextEmbeddingInput
-pub fn loads(text string) !TextEmbeddingInput {
-	// First decode the JSON string to the raw struct
-	raw := json.decode(TextEmbeddingInputRaw, text) or {
-		return error('Failed to decode JSON: $err')
-	}
-	
-	// Then convert the raw struct to the typed struct
-	return text_embedding_input_from_raw(raw)
-}
+// pub fn loads(text string) !TextEmbeddingInput {
+// 	// First decode the JSON string to the raw struct
+// 	raw := json.decode(TextEmbeddingInputRaw, text) or {
+// 		return error('Failed to decode JSON: ${err}')
+// 	}
 
-// TextDoc represents a document with ID and text for embedding
-pub struct TextDoc {
-pub mut:
-	id   string
-	text string
-}
+// 	// Then convert the raw struct to the typed struct
+// 	return text_embedding_input_from_raw(raw)
+// }
 
 // ModelEmbeddingOutput represents the response from embedding requests
 pub struct ModelEmbeddingOutput {
diff --git a/lib/clients/jina/model_rank.v b/lib/clients/jina/model_rank.v
index 3f7d1f1f..0dc0e43a 100644
--- a/lib/clients/jina/model_rank.v
+++ b/lib/clients/jina/model_rank.v
@@ -1,91 +1,6 @@
 module jina
 
-// RankAPIInput represents the input for reranking requests
-// model:
-// jina-reranker-v2-base-multilingual, 278M
-// jina-reranker-v1-base-en, 137M
-// jina-reranker-v1-tiny-en, 33M
-// jina-reranker-v1-turbo-en, 38M
-// jina-colbert-v1-en, 137M
-pub struct RankAPIInputRAW {
-pub mut:
-	model     string   @[required]
-	query     string   @[required]
-	documents []string @[required]
-	top_n     int      // Optional: Number of top results to return
-}
-
-// RankingOutput represents the response from reranking requests
-pub struct RankingOutput {
-pub mut:
-	model     string
-	results   []RankResult
-	usage     Usage
-	object    string
-}
-
-// RankResult represents a single reranking result
-pub struct RankResult {
-pub mut:
-	document        string
-	index           int
-	relevance_score f64
-}
-
-// ClassificationAPIInput represents the input for classification requests
-pub struct ClassificationAPIInput {
-pub mut:
-	model     string   @[required]
-	input     []string @[required]
-	labels    []string @[required]
-}
-
-// ClassificationOutput represents the response from classification requests
-pub struct ClassificationOutput {
-pub mut:
-	model     string
-	data      []ClassificationData
-	usage     Usage
-	object    string
-}
-
-// ClassificationData represents a single classification result
-pub struct ClassificationData {
-pub mut:
-	classifications []Classification
-	index           int
-}
-
-// Classification represents a single label classification with score
-pub struct Classification {
-pub mut:
-	label string
-	score f64
-}
-
-// TrainingExample represents a single training example for classifier training
-pub struct TrainingExample {
-pub mut:
-	text  string
-	label string
-}
-
-// TrainingAPIInput represents the input for training a classifier
-pub struct TrainingAPIInput {
-pub mut:
-	model     string            @[required]
-	input     []TrainingExample @[required]
-	access    string            // Optional: "public" or "private"
-}
-
-// TrainingOutput represents the response from training a classifier
-pub struct TrainingOutput {
-pub mut:
-	classifier_id string
-	model         string
-	status        string
-	object        string
-}
+import json
 
 // BulkEmbeddingJobResponse represents the response from bulk embedding operations
 pub struct BulkEmbeddingJobResponse {
@@ -136,9 +51,9 @@ pub mut:
 // ValidationError represents a single validation error
 pub struct ValidationError {
 pub mut:
-	loc     []string
-	msg     string
-	type_   string   @[json: 'type'] // 'type' is a keyword, so we need to specify the JSON name
+	loc   []string
+	msg   string
+	type_ string @[json: 'type'] // 'type' is a keyword, so we need to specify the JSON name
 }
 
 // Serialize and deserialize functions for the main request/response types
@@ -158,36 +73,16 @@ pub fn parse_model_embedding_output(json_str string) !ModelEmbeddingOutput {
 	return json.decode(ModelEmbeddingOutput, json_str)
 }
 
-// Serialize RankAPIInput to JSON
-pub fn (input RankAPIInput) to_json() string {
-	return json.encode(input)
-}
+// // Serialize RankAPIInput to JSON
+// pub fn (input RankAPIInput) to_json() string {
+// 	return json.encode(input)
+// }
 
 // Parse JSON to RankingOutput
 pub fn parse_ranking_output(json_str string) !RankingOutput {
 	return json.decode(RankingOutput, json_str)
 }
 
-// Serialize ClassificationAPIInput to JSON
-pub fn (input ClassificationAPIInput) to_json() string {
-	return json.encode(input)
-}
-
-// Parse JSON to ClassificationOutput
-pub fn parse_classification_output(json_str string) !ClassificationOutput {
-	return json.decode(ClassificationOutput, json_str)
-}
-
-// Serialize TrainingAPIInput to JSON
-pub fn (input TrainingAPIInput) to_json() string {
-	return json.encode(input)
-}
-
-// Parse JSON to TrainingOutput
-pub fn parse_training_output(json_str string) !TrainingOutput {
-	return json.decode(TrainingOutput, json_str)
-}
-
 // Parse JSON to BulkEmbeddingJobResponse
 pub fn parse_bulk_embedding_job_response(json_str string) !BulkEmbeddingJobResponse {
 	return json.decode(BulkEmbeddingJobResponse, json_str)
diff --git a/lib/clients/jina/rank_api.v b/lib/clients/jina/rank_api.v
new file mode 100644
index 00000000..e17efd50
--- /dev/null
+++ b/lib/clients/jina/rank_api.v
@@ -0,0 +1,67 @@
+module jina
+
+import json
+
+pub enum JinaRerankModel {
+	reranker_v2_base_multilingual // 278M
+	reranker_v1_base_en           // 137M
+	reranker_v1_tiny_en           // 33M
+	reranker_v1_turbo_en          // 38M
+	colbert_v1_en                 // 137M
+}
+
+// RankAPIInput represents the input for reranking requests
+pub struct RerankInput {
+pub mut:
+	model            string   @[required] // Model name
+	query            string   @[required] // Query text
+	documents        []string @[required] // Document texts
+	top_n            ?int  // Optional: Number of top results to return
+	return_documents ?bool // Optional: Flag to determine if the documents should be returned
+}
+
+// RankingOutput represents the response from reranking requests
+pub struct RankingOutput {
+pub mut:
+	model   string
+	results []RankResult
+	usage   Usage
+	object  string
+}
+
+// RankResult represents a single reranking result
+pub struct RankResult {
+pub mut:
+	document        RankDocument
+	index           int
+	relevance_score f64
+}
+
+// RankDocument represents a single document for reranking
+pub struct RankDocument {
+pub mut:
+	text string
+}
+
+// to_string converts a JinaRerankModel enum to its string representation as expected by the Jina API
+pub fn (m JinaRerankModel) to_string() string {
+	return match m {
+		.reranker_v2_base_multilingual { 'jina-reranker-v2-base-multilingual' }
+		.reranker_v1_base_en { 'jina-reranker-v1-base-en' }
+		.reranker_v1_tiny_en { 'jina-reranker-v1-tiny-en' }
+		.reranker_v1_turbo_en { 'jina-reranker-v1-turbo-en' }
+		.colbert_v1_en { 'jina-colbert-v1-en' }
+	}
+}
+
+// from_string converts a string to a JinaRerankModel enum, returning an error if the string is invalid
+pub fn jina_rerank_model_from_string(s string) !JinaRerankModel {
+	return match s {
+		'jina-reranker-v2-base-multilingual' { JinaRerankModel.reranker_v2_base_multilingual }
+		'jina-reranker-v1-base-en' { JinaRerankModel.reranker_v1_base_en }
+		'jina-reranker-v1-tiny-en' { JinaRerankModel.reranker_v1_tiny_en }
+		'jina-reranker-v1-turbo-en' { JinaRerankModel.reranker_v1_turbo_en }
+		'jina-colbert-v1-en' { JinaRerankModel.colbert_v1_en }
+		else { error('Invalid JinaRerankModel string: ${s}') }
+	}
+}