Merge branch 'development_action007_mahmoud' into development_actions007
This commit is contained in:
60
examples/clients/jina.vsh
Executable file
60
examples/clients/jina.vsh
Executable file
@@ -0,0 +1,60 @@
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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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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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classifiers := jina_client.list_classifiers() or { panic('Error fetching classifiers: ${err}') }
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println('Classifiers: ${classifiers}')
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291
lib/clients/jina/classification_api.v
Normal file
291
lib/clients/jina/classification_api.v
Normal file
@@ -0,0 +1,291 @@
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module jina
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import json
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import freeflowuniverse.herolib.core.httpconnection
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// ClassificationTrainAccess represents the accessibility of the classifier
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pub enum ClassificationTrainAccess {
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public // Classifier is publicly accessible
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private // Classifier is private (default)
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}
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// TrainingExample represents a single training example (either text or image with a label)
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pub struct TrainingExample {
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pub mut:
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text ?string // Optional text content
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image ?string // Optional image URL
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label string // Required label
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}
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// ClassificationTrainOutput represents the response from the training endpoint
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pub struct ClassificationTrainOutput {
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pub mut:
|
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classifier_id string // Identifier of the trained classifier
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num_samples int // Number of samples used in training
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usage ClassificationTrainUsage // Token usage details
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}
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// ClassificationTrainUsage represents token usage for the training request
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pub struct ClassificationTrainUsage {
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pub mut:
|
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total_tokens int // Total tokens consumed
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}
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// ClassificationTrain represents parameters for the training request
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@[params]
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pub struct ClassificationTrain {
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pub mut:
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model ?JinaModel // Optional model identifier (e.g., jina-clip-v1)
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||||
classifier_id ?string // Optional existing classifier ID
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access ?ClassificationTrainAccess = .private // Accessibility, defaults to private
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input []TrainingExample // Array of training examples
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num_iters ?int = 10 // Number of training iterations, defaults to 10
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||||
}
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||||
|
||||
// TrainRequest represents the JSON request body for the /v1/train endpoint
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struct TrainRequest {
|
||||
mut:
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||||
model ?string
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||||
classifier_id ?string
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||||
access ?string
|
||||
input []TrainingExample
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||||
num_iters ?int
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||||
}
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||||
|
||||
// Train a classifier by sending a POST request to /v1/train
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||||
pub fn (mut j Jina) train(params ClassificationTrain) !ClassificationTrainOutput {
|
||||
// Validate that only one of model or classifier_id is provided
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||||
mut model_provided := false
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||||
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')
|
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}
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||||
|
||||
if model := params.model {
|
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if model == .jina_embeddings_v3 {
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return error('jina-embeddings-v3 is not a valid model for classification')
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}
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||||
}
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// Validate each training example has exactly one of text or image
|
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for example in params.input {
|
||||
mut text_provided := false
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mut image_provided := false
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|
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if _ := example.text {
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text_provided = true
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}
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||||
|
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if _ := example.image {
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image_provided = true
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||||
}
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||||
|
||||
if text_provided && image_provided {
|
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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')
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||||
}
|
||||
}
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// Construct the request body
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mut request := TrainRequest{
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input: params.input
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}
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if v := params.model {
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request.model = v.to_string() // Convert JinaModel enum to string
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}
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if v := params.classifier_id {
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request.classifier_id = v
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||||
}
|
||||
if v := params.access {
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||||
request.access = match v {
|
||||
.public { 'public' }
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||||
.private { 'private' }
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||||
}
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||||
}
|
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if v := params.num_iters {
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||||
request.num_iters = v
|
||||
}
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||||
|
||||
// Create and send the HTTP request
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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
|
||||
data: json.encode(request)
|
||||
}
|
||||
|
||||
mut httpclient := j.httpclient()!
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||||
response := httpclient.post_json_str(req)!
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||||
result := json.decode(ClassificationTrainOutput, response)!
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||||
return result
|
||||
}
|
||||
|
||||
// TextDoc represents a text document for classification
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||||
pub struct TextDoc {
|
||||
pub mut:
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||||
text string // The text content
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}
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||||
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// ImageDoc represents an image document for classification
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pub struct ImageDoc {
|
||||
pub mut:
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||||
image string // The image URL or base64-encoded string
|
||||
}
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||||
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||||
// ClassificationInput represents a single input for classification (text or image)
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||||
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
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||||
usage ClassificationUsage // Token usage details
|
||||
}
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||||
|
||||
// ClassificationResult represents a single classification result
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||||
pub struct ClassificationResult {
|
||||
pub mut:
|
||||
index int // Index of the input
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||||
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
|
||||
}
|
||||
@@ -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
|
||||
// }
|
||||
|
||||
87
lib/clients/jina/jina_client_test.v
Normal file
87
lib/clients/jina/jina_client_test.v
Normal file
@@ -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
|
||||
}
|
||||
@@ -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
|
||||
}
|
||||
|
||||
|
||||
@@ -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 {
|
||||
|
||||
@@ -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)
|
||||
|
||||
67
lib/clients/jina/rank_api.v
Normal file
67
lib/clients/jina/rank_api.v
Normal file
@@ -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}') }
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user