@genkit-ai/vertexai
Version:
Genkit AI framework plugin for Google Cloud Vertex AI APIs including Gemini APIs, Imagen, and more.
93 lines • 3.28 kB
JavaScript
;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
for (var name in all)
__defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var bigquery_exports = {};
__export(bigquery_exports, {
getBigQueryDocumentIndexer: () => getBigQueryDocumentIndexer,
getBigQueryDocumentRetriever: () => getBigQueryDocumentRetriever
});
module.exports = __toCommonJS(bigquery_exports);
var import_genkit = require("genkit");
var import_logging = require("genkit/logging");
var import_retriever = require("genkit/retriever");
const getBigQueryDocumentRetriever = (bq, tableId, datasetId) => {
const bigQueryRetriever = async (neighbors) => {
const ids = neighbors.map((neighbor) => neighbor.datapoint?.datapointId).filter(Boolean);
const query = `
SELECT * FROM \`${datasetId}.${tableId}\`
WHERE id IN UNNEST(@ids)
`;
const options = {
query,
params: { ids }
};
let rows;
try {
[rows] = await bq.query(options);
} catch (queryError) {
import_logging.logger.error("Failed to execute BigQuery query:", queryError);
return [];
}
const documents = [];
for (const row of rows) {
try {
const docData = {
content: JSON.parse(row.content)
};
if (row.metadata) {
docData.metadata = JSON.parse(row.metadata);
}
const parsedDocData = import_retriever.DocumentDataSchema.parse(docData);
documents.push(new import_retriever.Document(parsedDocData));
} catch (error) {
const id = row.id;
const errorPrefix = `Failed to parse document data for document with ID ${id}:`;
if (error instanceof import_genkit.z.ZodError || error instanceof Error) {
import_logging.logger.warn(`${errorPrefix} ${error.message}`);
} else {
import_logging.logger.warn(errorPrefix);
}
}
}
return documents;
};
return bigQueryRetriever;
};
const getBigQueryDocumentIndexer = (bq, tableId, datasetId) => {
const bigQueryIndexer = async (docs) => {
const ids = [];
const rows = docs.map((doc) => {
const id = Math.random().toString(36).substring(7);
ids.push(id);
return {
id,
content: JSON.stringify(doc.content),
metadata: JSON.stringify(doc.metadata)
};
});
await bq.dataset(datasetId).table(tableId).insert(rows);
return ids;
};
return bigQueryIndexer;
};
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
getBigQueryDocumentIndexer,
getBigQueryDocumentRetriever
});
//# sourceMappingURL=bigquery.js.map