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Add local vectors storage

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This commit is contained in:
Cohee 2023-09-07 21:53:47 +03:00
parent 89705391d1
commit 8c00f38a1f
5 changed files with 879 additions and 3 deletions
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1
.gitignore vendored
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@ -35,3 +35,4 @@ content.log
cloudflared.exe
public/assets/
access.log
/vectors/

683
package-lock.json generated
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@ -12,6 +12,8 @@
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},
@ -3372,6 +4023,14 @@
"node": ">= 0.4.0"
}
},
"node_modules/uuid": {
"version": "9.0.0",
"resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz",
"integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==",
"bin": {
"uuid": "dist/bin/uuid"
}
},
"node_modules/vary": {
"version": "1.1.2",
"resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
@ -3380,6 +4039,24 @@
"node": ">= 0.8"
}
},
"node_modules/vectra": {
"version": "0.2.2",
"resolved": "https://registry.npmjs.org/vectra/-/vectra-0.2.2.tgz",
"integrity": "sha512-Z1d29Zil+dlA9/Qz4VJB3zcWjIARY8QH5xQEnHmGgmTUP58cPRV8NK7P0QH91IRs0RvAWrYiQf9Y5JqQo0vJlQ==",
"dependencies": {
"axios": "^1.3.4",
"cheerio": "^1.0.0-rc.12",
"dotenv": "^8.2.0",
"gpt-3-encoder": "1.1.4",
"json-colorizer": "^2.2.2",
"openai": "^3.2.1",
"uuid": "^9.0.0",
"yargs": "^17.7.2"
},
"bin": {
"vectra": "bin/vectra.js"
}
},
"node_modules/webidl-conversions": {
"version": "3.0.1",
"resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",

3
package.json
View File

@ -3,6 +3,8 @@
"@agnai/sentencepiece-js": "^1.1.1",
"@agnai/web-tokenizers": "^0.1.3",
"@dqbd/tiktoken": "^1.0.2",
"@tensorflow-models/universal-sentence-encoder": "^1.3.3",
"@tensorflow/tfjs": "^4.10.0",
"command-exists": "^1.2.9",
"compression": "^1",
"cookie-parser": "^1.4.6",
@ -31,6 +33,7 @@
"sanitize-filename": "^1.6.3",
"simple-git": "^3.19.1",
"uniqolor": "^1.1.0",
"vectra": "^0.2.2",
"webp-converter": "2.3.2",
"write-file-atomic": "^5.0.1",
"ws": "^8.13.0",

3
server.js
View File

@ -5825,3 +5825,6 @@ app.post('/get_character_assets_list', jsonParser, async (request, response) =>
return response.sendStatus(500);
}
});
// Vector storage DB
require('./src/vectors').registerEndpoints(app, jsonParser);

192
src/vectors.js Normal file
View File

@ -0,0 +1,192 @@
const express = require('express');
const vectra = require('vectra');
const path = require('path');
const sanitize = require('sanitize-filename');
require('@tensorflow/tfjs');
const encoder = require('@tensorflow-models/universal-sentence-encoder');
/**
* Lazy loading class for the embedding model.
*/
class EmbeddingModel {
/**
* @type {encoder.UniversalSentenceEncoder} - The embedding model
*/
#model;
async get() {
if (!this.#model) {
this.model = await encoder.load();
}
return this.#model;
}
}
/**
* Hard limit on the number of results to return from the vector search.
*/
const TOP_K = 100;
const model = new EmbeddingModel();
/**
* Gets the index for the vector collection
* @param {string} collectionId - The collection ID
* @returns {Promise<vectra.LocalIndex>} - The index for the collection
*/
async function getIndex(collectionId) {
const index = new vectra.LocalIndex(path.join(process.cwd(), 'vectors', sanitize(collectionId)));
if (!await index.isIndexCreated()) {
await index.createIndex();
}
return index;
}
/**
* Inserts items into the vector collection
* @param {string} collectionId - The collection ID
* @param {{ hash: number; text: string; }[]} items - The items to insert
*/
async function insertVectorItems(collectionId, items) {
const index = await getIndex(collectionId);
const use = await model.get();
await index.beginUpdate();
for (const item of items) {
const text = item.text;
const hash = item.hash;
const tensor = await use.embed(text);
const vector = Array.from(await tensor.data());
await index.upsertItem({ vector: vector, metadata: { hash, text } });
}
await index.endUpdate();
}
/**
* Gets the hashes of the items in the vector collection
* @param {string} collectionId - The collection ID
* @returns {Promise<number[]>} - The hashes of the items in the collection
*/
async function getSavedHashes(collectionId) {
const index = await getIndex(collectionId);
const items = await index.listItems();
const hashes = items.map(x => Number(x.metadata.hash));
return hashes;
}
/**
* Deletes items from the vector collection by hash
* @param {string} collectionId - The collection ID
* @param {number[]} hashes - The hashes of the items to delete
*/
async function deleteVectorItems(collectionId, hashes) {
const index = await getIndex(collectionId);
const items = await index.listItemsByMetadata({ hash: { '$in': hashes } });
await index.beginUpdate();
for (const item of items) {
await index.deleteItem(item.id);
}
await index.endUpdate();
}
/**
* Gets the hashes of the items in the vector collection that match the search text
* @param {string} collectionId
* @param {string} searchText
* @returns {Promise<number[]>} - The hashes of the items that match the search text
*/
async function queryCollection(collectionId, searchText) {
const index = await getIndex(collectionId);
const use = await model.get();
const tensor = await use.embed(searchText);
const vector = Array.from(await tensor.data());
const result = await index.queryItems(vector, TOP_K);
const hashes = result.map(x => Number(x.item.metadata.hash));
return hashes;
}
/**
* Registers the endpoints for the vector API
* @param {express.Express} app - Express app
* @param {any} jsonParser - Express JSON parser
*/
async function registerEndpoints(app, jsonParser) {
app.post('/api/vector/query', jsonParser, async (req, res) => {
try {
if (!req.body.collectionId || !req.body.searchText) {
return res.sendStatus(400);
}
const collectionId = String(req.body.collectionId);
const searchText = String(req.body.searchText);
const results = await queryCollection(collectionId, searchText);
return res.json(results);
} catch (error) {
console.error(error);
return res.sendStatus(500);
}
});
app.post('/api/vector/insert', jsonParser, async (req, res) => {
try {
if (!Array.isArray(req.body.items) || !req.body.collectionId) {
return res.sendStatus(400);
}
const collectionId = String(req.body.collectionId);
const items = req.body.items.map(x => ({ hash: x.hash, text: x.text }));
await insertVectorItems(collectionId, items);
return res.sendStatus(200);
} catch (error) {
console.error(error);
return res.sendStatus(500);
}
});
app.post('/api/vector/list', jsonParser, async (req, res) => {
try {
if (!req.body.collectionId) {
return res.sendStatus(400);
}
const collectionId = String(req.body.collectionId);
const hashes = await getSavedHashes(collectionId);
return res.json(hashes);
} catch (error) {
console.error(error);
return res.sendStatus(500);
}
});
app.post('/api/vector/delete', jsonParser, async (req, res) => {
try {
if (!Array.isArray(req.body.hashes) || !req.body.collectionId) {
return res.sendStatus(400);
}
const collectionId = String(req.body.collectionId);
const hashes = req.body.hashes.map(x => Number(x));
await deleteVectorItems(collectionId, hashes);
return res.sendStatus(200);
} catch (error) {
console.error(error);
return res.sendStatus(500);
}
});
}
module.exports = { registerEndpoints };