fix: Ensure the code compiles and add a test example
- Fixed compilation issues and ensured the code builds successfully - Created an example to test the client functionality - Started implementing additional endpoints
This commit is contained in:
Mahmoud Emad
6
examples/clients/jina.vsh
Executable file
6
examples/clients/jina.vsh
Executable file
@@ -0,0 +1,6 @@
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#!/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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jina_client := jina.get()!
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println('jina: ${jina_client}')
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@@ -3,183 +3,218 @@ module jina
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import freeflowuniverse.herolib.core.httpconnection
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import json
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import os
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import net.http
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// Create embeddings for input texts
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pub fn (mut j Jina) create_embeddings(input []string, model string, task string) !ModelEmbeddingOutput {
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model_ := jina_model_from_string(model)!
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task_ := task_type_from_string(task)!
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mut embedding_input := TextEmbeddingInput{
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model: model
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input: input
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task: task
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input: input
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model: model_
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task: task_
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late_chunking: false
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}
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/embeddings'
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method: .post
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prefix: 'v1/embeddings'
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dataformat: .json
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data: embedding_input.to_json()
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data: embedding_input.to_json()
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}
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response := j.http.get(req)!
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mut httpclient := j.httpclient()!
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response := httpclient.post_json_str(req)!
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return parse_model_embedding_output(response)!
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}
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// Create embeddings with a TextDoc input
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pub fn (mut j Jina) create_embeddings_with_docs(args TextEmbeddingInput) !ModelEmbeddingOutput {
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/embeddings'
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dataformat: .json
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data: json.encode(args)
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}
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response := j.http.get(req)!
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return parse_model_embedding_output(response)!
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}
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// pub fn (mut j Jina) start_bulk_embedding(file_path string, model string, email string) !BulkEmbeddingJobResponse {
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// // Read the file content
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// file_content := os.read_file(file_path) or {
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// return error('Failed to read file: ${err}')
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// }
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// Rerank documents based on a query
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pub fn (mut j Jina) rerank(query string, documents []string, model string, top_n int) !RankingOutput {
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mut rank_input := RankAPIInput{
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model: model
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query: query
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documents: documents
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top_n: top_n
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}
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/rerank'
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dataformat: .json
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data: rank_input.to_json()
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}
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response := j.http.get(req)!
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return parse_ranking_output(response)!
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}
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// // Create a multipart form
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// mut form := http.FormData{}
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// form.add_field('file', file_content, 'input.csv', 'text/csv')
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// form.add_field('model', model)
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// form.add_field('email', email)
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// Simplified rerank function with default top_n
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pub fn (mut j Jina) rerank_simple(query string, documents []string, model string) !RankingOutput {
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return j.rerank(query, documents, model, 0)!
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}
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// // Create a custom HTTP request
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// mut req := http.new_request(.post, '${j.base_url}/v1/bulk-embeddings', '')!
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// req.header = j.http.default_header // Add Authorization header
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// req.set_form_data(form) // Set multipart form data
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// Classify input texts
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pub fn (mut j Jina) classify(input []string, model string, labels []string) !ClassificationOutput {
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mut classification_input := ClassificationAPIInput{
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model: model
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input: input
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labels: labels
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}
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/classify'
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dataformat: .json
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data: classification_input.to_json()
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}
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response := j.http.get(req)!
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return parse_classification_output(response)!
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}
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// // Send the request
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// response := req.do() or {
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// return error('Failed to send bulk embedding request: ${err}')
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// }
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// Train a classifier
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pub fn (mut j Jina) train(examples []TrainingExample, model string, access string) !TrainingOutput {
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mut training_input := TrainingAPIInput{
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model: model
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input: examples
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access: access
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}
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/train'
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dataformat: .json
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data: training_input.to_json()
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}
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response := j.http.get(req)!
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return parse_training_output(response)!
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}
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// // Check for errors
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// if response.status_code != 200 {
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// return error('Bulk embedding request failed with status ${response.status_code}: ${response.body}')
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// }
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// List classifiers
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pub fn (mut j Jina) list_classifiers() !string {
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req := httpconnection.Request{
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method: .get
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prefix: 'v1/classifiers'
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}
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return j.http.get(req)!
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}
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// // Parse the JSON response
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// return json.decode(BulkEmbeddingJobResponse, response.body)!
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// }
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// Delete a classifier
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pub fn (mut j Jina) delete_classifier(classifier_id string) !bool {
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req := httpconnection.Request{
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method: .delete
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prefix: 'v1/classifiers/${classifier_id}'
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}
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j.http.get(req)!
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return true
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}
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// // Create embeddings with a TextDoc input
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// pub fn (mut j Jina) create_embeddings_with_docs(args TextEmbeddingInput) !ModelEmbeddingOutput {
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// Create multi-vector embeddings
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pub fn (mut j Jina) create_multi_vector(input []string, model string) !ColbertModelEmbeddingsOutput {
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mut data := map[string]json.Any{}
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data['model'] = model
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data['input'] = input
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/multi-embeddings'
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dataformat: .json
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data: json.encode(data)
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}
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response := j.http.get(req)!
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return parse_colbert_model_embeddings_output(response)!
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}
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/embeddings'
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// dataformat: .json
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// data: json.encode(args)
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// }
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// Start a bulk embedding job
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pub fn (mut j Jina) start_bulk_embedding(file_path string, model string, email string) !BulkEmbeddingJobResponse {
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// This endpoint requires multipart/form-data which is not directly supported by the current HTTPConnection
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// We need to implement a custom solution for this
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return error('Bulk embedding is not implemented yet')
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}
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// response := j.http.get(req)!
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// return parse_model_embedding_output(response)!
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// }
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// Check the status of a bulk embedding job
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pub fn (mut j Jina) check_bulk_embedding_status(job_id string) !BulkEmbeddingJobResponse {
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req := httpconnection.Request{
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method: .get
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prefix: 'v1/bulk-embeddings/${job_id}'
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}
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response := j.http.get(req)!
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return parse_bulk_embedding_job_response(response)!
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}
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// // Rerank documents based on a query
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// pub fn (mut j Jina) rerank(query string, documents []string, model string, top_n int) !RankingOutput {
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// mut rank_input := RankAPIInput{
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// model: model
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// query: query
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// documents: documents
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// top_n: top_n
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// }
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// Download the result of a bulk embedding job
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pub fn (mut j Jina) download_bulk_embedding_result(job_id string) !DownloadResultResponse {
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req := httpconnection.Request{
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method: .post
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prefix: 'v1/bulk-embeddings/${job_id}/download-result'
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}
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response := j.http.get(req)!
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return parse_download_result_response(response)!
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}
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/rerank'
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// dataformat: .json
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// data: rank_input.to_json()
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// }
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// Check if the API key is valid by making a simple request
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pub fn (mut j Jina) check_auth() !bool {
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req := httpconnection.Request{
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method: .get
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prefix: '/'
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}
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j.http.get(req) or {
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return error('Failed to connect to Jina API: ${err}')
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}
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// If we get a response, the API key is valid
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return true
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}
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// response := j.http.get(req)!
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// return parse_ranking_output(response)!
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// }
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// // Simplified rerank function with default top_n
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// pub fn (mut j Jina) rerank_simple(query string, documents []string, model string) !RankingOutput {
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// return j.rerank(query, documents, model, 0)!
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// }
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// // Classify input texts
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// pub fn (mut j Jina) classify(input []string, model string, labels []string) !ClassificationOutput {
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// mut classification_input := ClassificationAPIInput{
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// model: model
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// input: input
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// labels: labels
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// }
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/classify'
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// dataformat: .json
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// data: classification_input.to_json()
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// }
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// response := j.http.get(req)!
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// return parse_classification_output(response)!
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// }
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// // Train a classifier
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// pub fn (mut j Jina) train(examples []TrainingExample, model string, access string) !TrainingOutput {
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// mut training_input := TrainingAPIInput{
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// model: model
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// input: examples
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// access: access
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// }
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/train'
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// dataformat: .json
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// data: training_input.to_json()
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// }
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// response := j.http.get(req)!
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// return parse_training_output(response)!
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// }
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// // List classifiers
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// pub fn (mut j Jina) list_classifiers() !string {
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// req := httpconnection.Request{
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// method: .get
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// prefix: 'v1/classifiers'
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// }
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// return j.http.get(req)!
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// }
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// // Delete a classifier
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// pub fn (mut j Jina) delete_classifier(classifier_id string) !bool {
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// req := httpconnection.Request{
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// method: .delete
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// prefix: 'v1/classifiers/${classifier_id}'
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// }
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// j.http.get(req)!
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// return true
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// }
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// // Create multi-vector embeddings
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// pub fn (mut j Jina) create_multi_vector(input []string, model string) !ColbertModelEmbeddingsOutput {
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// mut data := map[string]json.Any{}
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// data['model'] = model
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// data['input'] = input
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/multi-embeddings'
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// dataformat: .json
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// data: json.encode(data)
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// }
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// response := j.http.get(req)!
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// return parse_colbert_model_embeddings_output(response)!
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// }
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// // Start a bulk embedding job
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// pub fn (mut j Jina) start_bulk_embedding(file_path string, model string, email string) !BulkEmbeddingJobResponse {
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// // This endpoint requires multipart/form-data which is not directly supported by the current HTTPConnection
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// // We need to implement a custom solution for this
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// return error('Bulk embedding is not implemented yet')
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// }
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// // Check the status of a bulk embedding job
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// pub fn (mut j Jina) check_bulk_embedding_status(job_id string) !BulkEmbeddingJobResponse {
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// req := httpconnection.Request{
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// method: .get
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// prefix: 'v1/bulk-embeddings/${job_id}'
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// }
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// response := j.http.get(req)!
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// return parse_bulk_embedding_job_response(response)!
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// }
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// // Download the result of a bulk embedding job
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// pub fn (mut j Jina) download_bulk_embedding_result(job_id string) !DownloadResultResponse {
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// req := httpconnection.Request{
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// method: .post
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// prefix: 'v1/bulk-embeddings/${job_id}/download-result'
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// }
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// response := j.http.get(req)!
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// return parse_download_result_response(response)!
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// }
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// // Check if the API key is valid by making a simple request
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// pub fn (mut j Jina) check_auth() !bool {
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// req := httpconnection.Request{
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// method: .get
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// prefix: '/'
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// }
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// j.http.get(req) or {
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// return error('Failed to connect to Jina API: ${err}')
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// }
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// // If we get a response, the API key is valid
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// return true
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// }
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@@ -1,6 +1,5 @@
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module jina
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import freeflowuniverse.herolib.data.paramsparser
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import freeflowuniverse.herolib.data.encoderhero
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import freeflowuniverse.herolib.core.httpconnection
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import net.http
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@@ -17,16 +16,29 @@ const env_key = 'JINAKEY'
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@[heap]
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pub struct Jina {
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pub mut:
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name string = 'default'
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secret string
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base_url string = api_base_url
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http httpconnection.HTTPConnection @[str: skip]
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name string = 'default'
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secret string
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base_url string = api_base_url
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// http httpconnection.HTTPConnection @[str: skip]
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}
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fn (mut self Jina) httpclient() !&httpconnection.HTTPConnection {
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mut http_conn := httpconnection.new(
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name: 'Jina_vclient'
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url: self.base_url
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)!
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// Add authentication header if API key is provided
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if self.secret.len > 0 {
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http_conn.default_header.add(.authorization, 'Bearer ${self.secret}')
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}
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return http_conn
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}
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// your checking & initialization code if needed
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fn obj_init(mycfg_ Jina) !Jina {
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mut mycfg := mycfg_
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// Get API key from environment variable if not set
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if mycfg.secret == '' {
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if env_key in os.environ() {
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@@ -35,16 +47,7 @@ fn obj_init(mycfg_ Jina) !Jina {
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return error('Jina API key not provided and ${env_key} environment variable not set')
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}
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}
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// Initialize HTTP connection
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mut header := http.new_header()
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header.add_custom('Authorization', 'Bearer ${mycfg.secret}')
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mycfg.http = httpconnection.HTTPConnection{
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base_url: mycfg.base_url
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default_header: header
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}
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return mycfg
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}
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@@ -4,14 +4,14 @@ import json
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// JinaModelEnumerator represents the available models for Jina API
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pub enum JinaModelEnumerator {
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clip_v1 // jina-clip-v1, 223M, 768
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clip_v2 // jina-clip-v2, 865M, 1024
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embeddings_v2_base_en // jina-embeddings-v2-base-en, 137M, 768
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embeddings_v2_base_es // jina-embeddings-v2-base-es, 161M, 768
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embeddings_v2_base_de // jina-embeddings-v2-base-de, 161M, 768
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embeddings_v2_base_zh // jina-embeddings-v2-base-zh, 161M, 768
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embeddings_v2_base_code // jina-embeddings-v2-base-code, 137M, 768
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embeddings_v3 // jina-embeddings-v3, 570M, 1024
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clip_v1 // jina-clip-v1, 223M, 768
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clip_v2 // jina-clip-v2, 865M, 1024
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embeddings_v2_base_en // jina-embeddings-v2-base-en, 137M, 768
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embeddings_v2_base_es // jina-embeddings-v2-base-es, 161M, 768
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embeddings_v2_base_de // jina-embeddings-v2-base-de, 161M, 768
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embeddings_v2_base_zh // jina-embeddings-v2-base-zh, 161M, 768
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embeddings_v2_base_code // jina-embeddings-v2-base-code, 137M, 768
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embeddings_v3 // jina-embeddings-v3, 570M, 1024
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}
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// to_string converts JinaModelEnumerator enum to its string representation
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@@ -29,7 +29,7 @@ pub fn (m JinaModelEnumerator) to_string() string {
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||||
}
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||||
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||||
// from_string converts string to JinaModelEnumerator enum
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pub fn jina_model_from_string(s string) ?JinaModelEnumerator {
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pub fn jina_model_from_string(s string) !JinaModelEnumerator {
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return match s {
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'jina-clip-v1' { JinaModelEnumerator.clip_v1 }
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'jina-clip-v2' { JinaModelEnumerator.clip_v2 }
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@@ -39,16 +39,16 @@ pub fn jina_model_from_string(s string) ?JinaModelEnumerator {
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'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') }
|
||||
else { error('Invalid 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' }
|
||||
}
|
||||
@@ -123,83 +123,83 @@ 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 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
|
||||
}
|
||||
|
||||
// dumps converts TextEmbeddingInput to JSON string
|
||||
pub fn (t TextEmbeddingInput) dumps() !string {
|
||||
mut raw := TextEmbeddingInputRaw{
|
||||
model: t.model.to_string()
|
||||
input: t.input
|
||||
model: t.model.to_string()
|
||||
input: t.input
|
||||
late_chunking: t.late_chunking
|
||||
}
|
||||
|
||||
|
||||
raw.task = t.task.to_string()
|
||||
raw.type_ = t.type_.to_string()
|
||||
raw.truncate = t.truncate.to_string()
|
||||
raw.truncate = t.truncate.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}')
|
||||
// }
|
||||
|
||||
// // Then convert the raw struct to the typed struct
|
||||
// return text_embedding_input_from_raw(raw)
|
||||
// }
|
||||
|
||||
// TextDoc represents a document with ID and text for embedding
|
||||
pub struct TextDoc {
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
module jina
|
||||
|
||||
import json
|
||||
|
||||
// RankAPIInput represents the input for reranking requests
|
||||
// model:
|
||||
// jina-reranker-v2-base-multilingual, 278M
|
||||
@@ -12,16 +14,16 @@ pub mut:
|
||||
model string @[required]
|
||||
query string @[required]
|
||||
documents []string @[required]
|
||||
top_n int // Optional: Number of top results to return
|
||||
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
|
||||
model string
|
||||
results []RankResult
|
||||
usage Usage
|
||||
object string
|
||||
}
|
||||
|
||||
// RankResult represents a single reranking result
|
||||
@@ -35,18 +37,18 @@ pub mut:
|
||||
// ClassificationAPIInput represents the input for classification requests
|
||||
pub struct ClassificationAPIInput {
|
||||
pub mut:
|
||||
model string @[required]
|
||||
input []string @[required]
|
||||
labels []string @[required]
|
||||
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
|
||||
model string
|
||||
data []ClassificationData
|
||||
usage Usage
|
||||
object string
|
||||
}
|
||||
|
||||
// ClassificationData represents a single classification result
|
||||
@@ -73,9 +75,9 @@ pub mut:
|
||||
// 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"
|
||||
model string @[required]
|
||||
input []TrainingExample @[required]
|
||||
access string // Optional: "public" or "private"
|
||||
}
|
||||
|
||||
// TrainingOutput represents the response from training a classifier
|
||||
@@ -136,9 +138,9 @@ 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
|
||||
@@ -158,10 +160,10 @@ 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 {
|
||||
|
||||
Reference in New Issue
Block a user