ad300c068f
- Update `jina.vsh` example to showcase the new classification API with support for both text and image inputs. This improves the flexibility and usability of the client. - Introduce new structs `TextDoc`, `ImageDoc`, `ClassificationInput`, `ClassificationOutput`, `ClassificationResult`, and `LabelScore` to represent data structures for classification requests and responses. This enhances code clarity and maintainability. - Implement the `classify` function in `jina_client.v` to handle classification requests with support for text and image inputs, model selection, and label specification. This adds a crucial feature to the Jina client. - Add comprehensive unit tests in `jina_client_test.v` to cover the new `classify` function's functionality. This ensures the correctness and robustness of the implemented feature. - Remove redundant code related to old classification API and data structures from `model_embed.v`, `model_rank.v`, and `jina_client.v`. This streamlines the codebase and removes obsolete elements.
81 lines
2.0 KiB
V
81 lines
2.0 KiB
V
module jina
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import time
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fn setup_client() !&Jina {
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mut client := get()!
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return client
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}
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fn test_create_embeddings() {
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time.sleep(1 * time.second)
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mut client := setup_client()!
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embeddings := client.create_embeddings(
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input: ['Hello', 'World']
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model: .jina_embeddings_v3
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task: 'separation'
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) or { panic('Error while creating embeddings: ${err}') }
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assert embeddings.data.len > 0
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assert embeddings.object == 'list' // Check the object type
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assert embeddings.model == 'jina-embeddings-v3'
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}
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fn test_rerank() {
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time.sleep(1 * time.second)
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mut client := setup_client()!
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rerank_result := client.rerank(
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model: .reranker_v2_base_multilingual
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query: 'skincare products'
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documents: ['Product A', 'Product B', 'Product C']
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top_n: 2
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) or { panic('Error while reranking: ${err}') }
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assert rerank_result.results.len == 2
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assert rerank_result.model == 'jina-reranker-v2-base-multilingual'
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}
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fn test_train() {
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time.sleep(1 * time.second)
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mut client := setup_client()!
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train_result := client.train(
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model: .jina_clip_v1
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input: [
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TrainingExample{
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text: 'A photo of a cat'
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label: 'cat'
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},
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TrainingExample{
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text: 'A photo of a dog'
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label: 'dog'
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},
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]
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) or { panic('Error while training: ${err}') }
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assert train_result.classifier_id.len > 0
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assert train_result.num_samples == 2
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}
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fn test_classify() {
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time.sleep(1 * time.second)
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mut client := setup_client()!
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classify_result := client.classify(
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model: .jina_clip_v1
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input: [
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ClassificationInput{
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text: 'A photo of a cat'
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},
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ClassificationInput{
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image: 'https://letsenhance.io/static/73136da51c245e80edc6ccfe44888a99/1015f/MainBefore.jpg'
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},
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]
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labels: ['cat', 'dog']
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) or { panic('Error while classifying: ${err}') }
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assert classify_result.data.len == 2
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assert classify_result.data[0].prediction in ['cat', 'dog']
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assert classify_result.data[1].prediction in ['cat', 'dog']
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assert classify_result.data[0].object == 'classification'
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assert classify_result.data[1].object == 'classification'
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}
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