274 lines
8.0 KiB
TypeScript
274 lines
8.0 KiB
TypeScript
import { createClient } from "webdav";
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import {
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PGVectorStore,
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DistanceStrategy,
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} from "@langchain/community/vectorstores/pgvector";
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import { OpenAIEmbeddings } from "@langchain/openai";
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import { v4 as uuidv4 } from "uuid";
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import * as crypto from "crypto";
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import skmeans from "skmeans";
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let isSyncing = false;
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let isCleanupRunning = false;
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// Initialize WebDAV client
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const webdavClient = createClient(
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"http://192.168.29.85/remote.php/dav/files/raj/",
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{
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username: process.env.NEXTCLOUD_USERNAME!,
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password: process.env.NEXTCLOUD_PASSWORD!,
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}
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);
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// Helper function to calculate checksum of content
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function calculateChecksum(content: string): string {
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return crypto.createHash("md5").update(content, "utf8").digest("hex");
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}
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// Function to get all files from 'notes' directory via WebDAV
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async function getAllFiles(
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path: string
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): Promise<{ filename: string; content: string }[]> {
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const contents = await webdavClient.getDirectoryContents(path, {
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deep: true,
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});
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const files = Array.isArray(contents) ? contents : contents.data;
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const fileContents: { filename: string; content: string }[] = [];
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for (const file of files) {
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if (
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file.type === "file" &&
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!file.basename.startsWith(".") &&
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!file.filename.includes("/.obsidian/") &&
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!file.filename.includes("prompts/") &&
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(file.filename.endsWith(".txt") || file.filename.endsWith(".md"))
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) {
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const content = await webdavClient.getFileContents(file.filename, {
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format: "text",
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});
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if (typeof content === "string") {
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fileContents.push({ filename: file.filename, content });
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}
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}
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}
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return fileContents;
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}
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// Setup PGVectorStore
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const embeddings = new OpenAIEmbeddings({
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model: "text-embedding-ada-002",
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});
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const config = {
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postgresConnectionOptions: {
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type: "postgres",
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host: "127.0.0.1",
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port: 5432,
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user: "postgres",
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password: "defaultpwd",
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database: "postgres",
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},
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tableName: "anya",
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columns: {
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idColumnName: "id",
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vectorColumnName: "vector",
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contentColumnName: "content",
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metadataColumnName: "metadata",
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clusterColumnName: "cluster",
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},
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distanceStrategy: "cosine" as DistanceStrategy,
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};
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const vectorStore = await PGVectorStore.initialize(embeddings, config);
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const CLUSTER_COUNT = 4;
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// Main function to sync vector store
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export async function syncVectorStore() {
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if (isSyncing) {
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console.log("syncVectorStore is already running. Skipping this run.");
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return;
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}
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isSyncing = true;
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try {
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console.log("Starting vector store sync...");
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const files = await getAllFiles("notes");
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let filesIndexed = 0;
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for (const file of files) {
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const content = `filename: ${file.filename}\n${file.content}`;
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// Calculate checksum
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const checksum = calculateChecksum(content);
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// Check if the document already exists using direct SQL query
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const queryResult = await vectorStore.client?.query(
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`SELECT * FROM ${config.tableName} WHERE metadata->>'filename' = $1`,
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[file.filename]
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);
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if (queryResult && queryResult.rows.length > 0) {
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const existingDocument = queryResult.rows[0];
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const existingChecksum = existingDocument.metadata?.checksum;
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// If the checksum matches, skip updating
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if (existingChecksum === checksum) {
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continue;
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}
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// If the content is different, delete the old version
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await vectorStore.delete({ ids: [existingDocument.id] });
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console.log(`Deleted old version of ${file.filename}`);
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}
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// Load the document
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const document = {
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pageContent: content,
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metadata: { checksum, filename: file.filename, id: uuidv4() },
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};
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// Add or update the document in the vector store
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await vectorStore.addDocuments([document], {
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ids: [document.metadata.id],
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});
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filesIndexed++;
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console.log(`Indexed ${file.filename}`);
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}
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filesIndexed > 0 && (await runClustering());
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console.log("Vector store sync completed.");
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} catch (error) {
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console.error("Error during vector store sync:", error);
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} finally {
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isSyncing = false;
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}
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}
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// Function to remove deleted files from vector store
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export async function cleanupDeletedFiles() {
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if (isCleanupRunning) {
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console.log("cleanupDeletedFiles is already running. Skipping this run.");
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return;
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}
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isCleanupRunning = true;
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try {
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console.log("Starting cleanup of deleted files...");
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// Get the list of all files in the vector store
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const queryResult = await vectorStore.client?.query(
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`SELECT metadata->>'filename' AS filename, id FROM ${config.tableName}`
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);
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if (queryResult) {
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const dbFiles = queryResult.rows;
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const files = await getAllFiles("notes");
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const existingFilenames = files.map((file) => file.filename);
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let deletedFiles = 0;
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for (const dbFile of dbFiles) {
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if (!existingFilenames.includes(dbFile.filename)) {
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// Delete the file from the vector store if it no longer exists in notes
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await vectorStore.delete({ ids: [dbFile.id] });
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deletedFiles++;
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console.log(
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`Deleted ${dbFile.filename} from vector store as it no longer exists.`
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);
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}
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}
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deletedFiles > 0 && (await runClustering());
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}
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console.log("Cleanup of deleted files completed.");
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} catch (error) {
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console.error("Error during cleanup of deleted files:", error);
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} finally {
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isCleanupRunning = false;
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}
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}
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// Ensure the cluster column exists in the table
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async function ensureClusterColumn() {
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await vectorStore.client?.query(
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`ALTER TABLE ${config.tableName} ADD COLUMN IF NOT EXISTS ${config.columns.clusterColumnName} INT;`
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);
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console.log("Ensured cluster column exists in the database.");
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}
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// Function to generate clusters from stored embeddings and save them to the database
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async function generateClusters(k: number) {
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// Ensure the cluster column exists before proceeding
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await ensureClusterColumn();
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const queryResult = await vectorStore.client?.query(
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`SELECT ${config.columns.idColumnName} as id, ${config.columns.vectorColumnName} as vector
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FROM ${config.tableName}`
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);
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if (!queryResult) {
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console.log("No embeddings found in the vector store.");
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return;
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}
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// Process embeddings and format data
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const embeddings = queryResult.rows.map((row) => {
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let vector: number[] = [];
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// Check vector data format and convert to number array if needed
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if (Array.isArray(row.vector)) {
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vector = row.vector;
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} else if (typeof row.vector === "string") {
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vector = JSON.parse(row.vector);
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} else if (Buffer.isBuffer(row.vector)) {
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vector = Array.from(row.vector);
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} else {
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console.error("Unknown vector format:", row.vector);
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}
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return {
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id: row.id,
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vector,
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};
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});
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// Extract vectors for clustering
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const vectors = embeddings.map((doc) => doc.vector);
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// Run clustering algorithm (K-means)
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const result = skmeans(vectors, k);
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// Save each document’s cluster label in the database
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for (const [index, doc] of embeddings.entries()) {
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const cluster = result.idxs[index];
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await vectorStore.client?.query(
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`UPDATE ${config.tableName} SET ${config.columns.clusterColumnName} = $1 WHERE ${config.columns.idColumnName} = $2`,
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[cluster, doc.id]
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);
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console.log(`Document ID: ${doc.id} assigned to Cluster: ${cluster}`);
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}
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console.log("Cluster assignments saved to database.");
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}
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// Exported function to run clustering
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export async function runClustering() {
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const k = CLUSTER_COUNT;
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console.log("Generating clusters...");
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await generateClusters(k);
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}
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export async function initVectorStoreSync() {
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console.log("Starting vector store sync...");
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await syncVectorStore();
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setInterval(syncVectorStore, 1000 * 60 * 2); // Every 2 minutes
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await cleanupDeletedFiles();
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setInterval(cleanupDeletedFiles, 1000 * 60 * 60 * 2); // Every 12 hours
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}
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export function semantic_search_notes(query: string, limit: number) {
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return vectorStore.similaritySearch(query, limit);
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}
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