Files
2024-01-18 17:42:26 -08:00

282 lines
8.0 KiB
JavaScript

const {
OrganizationWorkspace,
} = require('../../../backend/models/organizationWorkspace');
const { Queue } = require('../../../backend/models/queue');
const { InngestClient } = require('../../utils/inngest');
const { v4 } = require('uuid');
const path = require('path');
const {
WorkspaceDocument,
} = require('../../../backend/models/workspaceDocument');
const { DocumentVectors } = require('../../../backend/models/documentVectors');
const {
QDrant,
} = require('../../../backend/utils/vectordatabases/providers/qdrant');
const { vectorSpaceMetric } = require('../../utils/telemetryHelpers');
const { Notification } = require('../../../backend/models/notification');
const syncQDrantCluster = InngestClient.createFunction(
{ name: 'Sync Qdrant Instance' },
{ event: 'qdrant/sync' },
async ({ event, step: _step, logger }) => {
var result = {};
const { organization, connector, jobId } = event.data;
try {
const failedToSync = [];
const qdrantClient = new QDrant(connector);
const collections = await qdrantClient.collections();
if (collections.length === 0) {
result = { message: 'No collections found - nothing to do.' };
await Queue.updateJob(jobId, Queue.status.complete, result);
return { result };
}
logger.info(
`Deleting ALL existing workspaces for ${organization.name} and reseeding.`
);
await OrganizationWorkspace.delete({
organization_id: Number(organization.id),
});
for (const collection of collections) {
logger.info(
`Creating new workspace ${collection.name} for ${organization.name}`
);
const { workspace } = await OrganizationWorkspace.create(
collection.name,
organization.id
);
if (!workspace || collection.vectorCount === 0) continue;
logger.info(
`Working on ${collection.vectorCount} embeddings of ${collection.name}`
);
try {
await paginateAndStore(
qdrantClient,
collection,
workspace,
organization
);
} catch (e) {
logger.error(
`Failed to paginate records for ${collection.name} - workspace will remain in db but may be incomplete.`,
e
);
failedToSync.push({
namespace: collection.name,
reason: e.message,
});
}
}
result = {
message: `Qdrant instance vector data has been synced for ${
collections.length
} of ${collections.length - failedToSync.length} collections.`,
failedToSync,
};
await Notification.create(organization.id, {
textContent: 'Your QDrant cluster has been fully synced.',
symbol: Notification.symbols.qdrant,
link: `/dashboard/${organization.slug}/workspace/${workspace.fname}`,
target: '_blank',
});
await Queue.updateJob(jobId, Queue.status.complete, result);
await vectorSpaceMetric();
return { result };
} catch (e) {
const result = {
canRetry: true,
message: `Job failed with error`,
error: e.message,
details: e,
};
await Notification.create(organization.id, {
textContent: 'Your QDrant cluster failed to sync.',
symbol: Notification.symbols.qdrant,
link: `/dashboard/${organization.slug}/jobs`,
target: '_blank',
});
await Queue.updateJob(jobId, Queue.status.failed, result);
}
}
);
async function paginateAndStore(
qdrantClient,
collection,
workspace,
organization
) {
const PAGE_SIZE = 10;
var syncing = true;
var offset = 0;
const files = {};
const { client } = await qdrantClient.connect();
while (syncing) {
const { points, next_page_offset } = await client.scroll(collection.name, {
limit: PAGE_SIZE,
offset,
with_payload: true,
with_vector: true,
});
// If nothing to do - exit loop early
if (points.length === 0) {
syncing = false;
continue;
}
// No offset means we are on the last page - so don't loop again after this
// iteration.
if (next_page_offset === null) syncing = false;
// Normalize QDrant points into vectors with known keys.
const data = {
ids: [],
embeddings: [],
metadatas: [],
documents: [],
};
points.forEach((point) => {
const { id, vector = [], payload = {} } = point;
data.ids.push(id);
data.embeddings.push(vector);
data.metadatas.push(payload);
data.documents.push(payload?.text ?? '');
});
const { ids, metadatas, embeddings, documents } = data;
for (let i = 0; i < ids.length; i++) {
const documentName =
metadatas[i]?.title ||
metadatas[i]?.name ||
`imported-document-${v4()}.txt`;
if (!files.hasOwnProperty(documentName)) {
files[documentName] = {
currentLine: 0,
name: documentName,
documentId: v4(),
cacheFilename: `${WorkspaceDocument.vectorFilenameRaw(
documentName,
workspace.id
)}.json`,
ids: [],
embeddings: [],
metadatas: [],
fullText: '',
};
}
const text = documents[i];
const totalLines = (String(text).match(/\n/g) || '').length;
files[documentName].ids.push(ids[i]);
files[documentName].embeddings.push(embeddings[i]);
files[documentName].metadatas.push({
title: documentName,
'loc.lines.from': files[documentName].currentLine + 1,
'loc.lines.to': files[documentName].currentLine + 1 + totalLines,
...metadatas[i],
text,
});
files[documentName].fullText += text;
files[documentName].currentLine =
files[documentName].currentLine + 1 + totalLines;
}
offset = next_page_offset;
}
console.log('Creating Workspace Documents & Document Vectors');
await createDocuments(files, workspace, organization);
await createDocumentVectors(files);
for (const fileKey of Object.keys(files)) {
console.log('Creating vector cache for ', fileKey);
await saveVectorCache(files[fileKey]);
}
return;
}
async function createDocuments(files, workspace, organization) {
const documents = [];
Object.values(files).map((data) => {
documents.push({
documentId: data.documentId,
name: data.name,
workspaceId: workspace.id,
organizationId: organization.id,
});
});
await WorkspaceDocument.createMany(documents);
return;
}
async function createDocumentVectors(files) {
const docIds = Object.values(files).map((data) => data.documentId);
const existingDocuments = await WorkspaceDocument.where({
docId: { in: docIds },
});
const vectors = [];
Object.values(files).map((data) => {
const dbDocument = existingDocuments.find(
(doc) => doc.docId === data.documentId
);
if (!dbDocument) {
console.error(
'Could not find a database workspace document for ',
data.documentId
);
return;
}
data.ids.map((vectorId) => {
vectors.push({
docId: data.documentId,
vectorId,
documentId: dbDocument.id,
workspaceId: dbDocument.workspace_id,
organizationId: dbDocument.organization_id,
});
});
});
await DocumentVectors.createMany(vectors);
return;
}
async function saveVectorCache(data) {
const fs = require('fs');
const folder = path.resolve(
__dirname,
'../../../backend/storage/vector-cache'
);
if (!fs.existsSync(folder)) fs.mkdirSync(folder, { recursive: true });
const destination = path.resolve(
__dirname,
`../../../backend/storage/vector-cache/${data.cacheFilename}`
);
const toSave = [];
for (let i = 0; i < data.ids.length; i++) {
toSave.push({
vectorDbId: data.ids[i],
values: data.embeddings[i],
metadata: data.metadatas[i],
});
}
fs.writeFileSync(destination, JSON.stringify(toSave), 'utf8');
return;
}
module.exports = {
syncQDrantCluster,
};