implement basic lancedb and dataset

Signed-off-by: cbh778899 <cbh778899@outlook.com>

database/index.js

@@ -0,0 +1,42 @@
+import * as lancedb from "@lancedb/lancedb";
+import { get, post } from "../tools/request.js"
+import { Schema, Field, FixedSizeList, Int16, Float16, Utf8 } from "apache-arrow";
+
+const uri = "/database/lancedb/";
+const db = await lancedb.connect(uri);
+
+const table = await db.createEmptyTable("rag_data", new Schema([
+    new Field("id", new Int16()),
+    new Field("vector", new FixedSizeList(384, new Field("item", new Float16(), true)), false),
+    new Field("question", new Utf8()),
+    new Field("answer", new Utf8())
+]), {
+    // mode: "overwrite",
+    existOk: true
+})
+
+export async function loadDataset(dataset_link) {
+    const {rows, http_error} = await get('', {}, { URL: dataset_link })
+    if(http_error) {
+        return false;
+    }
+    await table.add(rows.map(({ row_id, row })=>{
+        const { question, answer, question_embedding } = row;
+        return { id: row_id, question, answer, vector: question_embedding }
+    }))
+    return true;
+}
+
+export async function searchByEmbedding(vector) {
+    const record = await table.search(vector).limit(1).toArray();
+    if(!record.length) return null;
+    const { question, answer } = record[0];
+    return { question, answer };
+}
+
+export async function searchByMessage(msg) {
+    const { embedding } = await post('embedding', {body: {
+        content: msg
+    }}, { eng: "rag" });
+    return await searchByEmbedding(embedding);
+}