SDKs#

go get github.com/antflydb/antfly/go/pkg/sdk

npm install @antfly/sdk

import (
	"context"
	"net/http"

	"github.com/antflydb/antfly/go/pkg/sdk"
)

c, err := sdk.NewAntfly InferenceClient("http://localhost:8080", &http.Client{})
if err != nil {
	log.Fatal(err)
}

embeddings, err := c.Embed(
	context.Background(),
	"bge-small-en-v1.5",
	[]string{"hello world", "semantic search"},
)
if err != nil {
	log.Fatal(err)
}

// embeddings is [][]float32
fmt.Printf("dimensions: %d\n", len(embeddings[0]))

import { Antfly InferenceClient } from '@antfly/sdk';

const client = new Antfly InferenceClient({
  baseUrl: 'http://localhost:8080',
});

const result = await client.embed(
  'bge-small-en-v1.5',
  ['hello world', 'semantic search'],
);

console.log('dimensions:', result.embeddings[0].length);

// Use the Antfly Inference HTTP API directly for chunking
import "net/http"

req, _ := http.NewRequest("POST", "http://localhost:8080/ai/v1/chunk", strings.NewReader(`{
	"text": "Your long document text goes here...",
	"config": {
		"model": "fixed",
		"target_tokens": 500
	}
}`))
req.Header.Set("Content-Type", "application/json")

resp, err := http.DefaultClient.Do(req)

const chunks = await client.chunk(
  'Your long document text goes here...',
  { model: 'fixed', target_tokens: 500 },
);

for (const chunk of chunks.chunks) {
  console.log(chunk.text);
}

// Use the Antfly Inference HTTP API directly for reranking
import "net/http"

req, _ := http.NewRequest("POST", "http://localhost:8080/ai/v1/rerank", strings.NewReader(`{
	"model": "bge-reranker-v2-m3",
	"query": "what is semantic search?",
	"documents": [
		"Semantic search uses embeddings to find relevant results.",
		"Keyword search matches exact terms in documents.",
		"Vector databases store high-dimensional embeddings."
	]
}`))
req.Header.Set("Content-Type", "application/json")

resp, err := http.DefaultClient.Do(req)

const ranked = await client.rerank(
  'bge-reranker-v2-m3',
  'what is semantic search?',
  [
    'Semantic search uses embeddings to find relevant results.',
    'Keyword search matches exact terms in documents.',
    'Vector databases store high-dimensional embeddings.',
  ],
);

for (const result of ranked.results) {
  console.log(result.index, result.relevance_score);
}