lume-ai
Version:
A powerful yet simple library to build your own AI applications.
103 lines (102 loc) • 4.22 kB
JavaScript
;
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.Pinecone = void 0;
// ===============================
// SECTION | IMPORTS
// ===============================
const interfaces_1 = require("../interfaces");
const pinecone_1 = require("@pinecone-database/pinecone");
// ===============================
// ===============================
// SECTION | Pinecone
// ===============================
/**
* Pinecone is a VectorDB implementation using the Pinecone cloud vector database provider.
*/
class Pinecone extends interfaces_1.VectorDB {
/**
* Creates a new Pinecone instance with the given API key, index name, and namespace.
* @param opts - The configuration options for Pinecone.
* @param opts.apiKey - The Pinecone API key.
* @param opts.indexName - The name of the Pinecone index to use.
* @param opts.namespace - The namespace within the index to operate in.
*/
constructor(opts) {
super();
this.pc = new pinecone_1.Pinecone({ apiKey: opts.apiKey });
this.index = this.pc.Index(opts.indexName);
this.namespace = opts.namespace;
}
/**
* Adds a text and its vector representation to the Pinecone index, associating it with one or more tags.
* @param text - The text to store.
* @param vector - The vector representation of the text.
* @param tags - An array of tags to associate with the text/vector.
*/
add(text, vector, tags) {
return __awaiter(this, void 0, void 0, function* () {
for (const tag of tags) {
yield this.index.namespace(this.namespace).upsert([
{
id: `mem_${Date.now()}`,
values: vector,
metadata: {
text,
tag,
},
},
]);
}
});
}
/**
* Searches for items in the Pinecone index that match the given vector and tags.
* @param _text - The text to use for filtering or scoring (currently unused).
* @param vector - The query vector.
* @param tags - An array of tags to filter the search.
* @returns An array of matching texts as strings.
*/
search(_text, vector, tags, topK) {
return __awaiter(this, void 0, void 0, function* () {
const items = [];
for (const tag of tags) {
const item = yield this.index.namespace(this.namespace).query({
vector,
topK: topK || 3,
includeMetadata: true,
filter: {
tag,
},
});
items.push(...item.matches);
}
return items.map((item) => { var _a; return String((_a = item.metadata) === null || _a === void 0 ? void 0 : _a.text); });
});
}
/**
* Deletes all items from the Pinecone index that match the given tags.
* @param tags - An array of tags whose associated items should be deleted.
*/
delete(tags) {
return __awaiter(this, void 0, void 0, function* () {
for (const tag of tags) {
yield this.index.namespace(this.namespace).deleteMany({
filter: {
tag,
},
});
}
});
}
}
exports.Pinecone = Pinecone;
// ===============================