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.
58 lines
1.4 KiB
GLSL
Executable File
58 lines
1.4 KiB
GLSL
Executable File
#!/usr/bin/env -S v -n -w -gc none -cc tcc -d use_openssl -enable-globals run
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import freeflowuniverse.herolib.clients.jina
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mut jina_client := jina.get()!
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// Create embeddings
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embeddings := jina_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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println('Created embeddings: ${embeddings}')
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// Rerank
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rerank_result := jina_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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println('Rerank result: ${rerank_result}')
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// Train
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train_result := jina_client.train(
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model: .jina_clip_v1
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input: [
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jina.TrainingExample{
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text: 'Sample text'
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label: 'positive'
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},
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jina.TrainingExample{
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image: 'https://letsenhance.io/static/73136da51c245e80edc6ccfe44888a99/1015f/MainBefore.jpg'
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label: 'negative'
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},
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]
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) or { panic('Error while training: ${err}') }
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println('Train result: ${train_result}')
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// Classify
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classify_result := jina_client.classify(
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model: .jina_clip_v1
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input: [
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jina.ClassificationInput{
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text: 'A photo of a cat'
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},
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jina.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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println('Classification result: ${classify_result}')
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