On-Device Vector Database
for the Edge

val db = EdgeVDB.open(context)
val embedding: FloatArray = yourProvider.embed("Neural networks classify images")
val chunkId = db.insertChunk(
    embedding, "Neural networks classify images", docId = 1, pageNumber = 0
)

// QueryResults is AutoCloseable — use .use{} to avoid handle leaks
db.queryVector(embedding, "image classification", topK = 5).use { results ->
    results.toList().forEach { println("${it.score}: ${it.text}") }
}
db.close()

let db = try EdgeVDB(storageDir: storageDir)
let embedding: [Float] = yourProvider.embed("Neural networks classify images")
let chunkId = try db.insertChunk(
    text: "Neural networks classify images",
    embedding: embedding, docId: 1, page: 0
)

let results = try db.queryVector(
    embedding: embedding, queryText: "image classification", topK: 5
)
defer { results.close() }
for r in results.toArray() {
    print("\\(r.score): \\(r.text)")
}

#include "edgevdb/vectordb.h"

EvdbConfig config;
evdb_default_config(&config);
config.storage_dir = "./data";

EvdbHandle* db = evdb_open(&config);

float embedding[384] = { /* from your provider */ };
uint64_t chunk_id;
evdb_insert_chunk(db, "Hello world", embedding, 1, 0, &chunk_id);

EvdbQueryHandle* q = evdb_query_vector(db, embedding, "hello", 5);
printf("Top: %s (%.3f)\\n", evdb_result_text(q, 0), evdb_result_score(q, 0));
evdb_query_free(q);
evdb_close(db);