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pgvector.ts
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pgvector.ts
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import pg, { type Pool, type PoolClient, type PoolConfig } from "pg";
import { VectorStore } from "@langchain/core/vectorstores";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { Document } from "@langchain/core/documents";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
type Metadata = Record<string, unknown>;
/**
* Interface that defines the arguments required to create a
* `PGVectorStore` instance. It includes Postgres connection options,
* table name, filter, and verbosity level.
*/
export interface PGVectorStoreArgs {
postgresConnectionOptions: PoolConfig;
tableName: string;
collectionTableName?: string;
collectionName?: string;
collectionMetadata?: Metadata | null;
columns?: {
idColumnName?: string;
vectorColumnName?: string;
contentColumnName?: string;
metadataColumnName?: string;
};
filter?: Metadata;
verbose?: boolean;
/**
* The amount of documents to chunk by when
* adding vectors.
* @default 500
*/
chunkSize?: number;
ids?: string[];
}
/**
* Class that provides an interface to a Postgres vector database. It
* extends the `VectorStore` base class and implements methods for adding
* documents and vectors, performing similarity searches, and ensuring the
* existence of a table in the database.
*/
export class PGVectorStore extends VectorStore {
declare FilterType: Metadata;
tableName: string;
collectionTableName?: string;
collectionName = "langchain";
collectionMetadata: Metadata | null;
idColumnName: string;
vectorColumnName: string;
contentColumnName: string;
metadataColumnName: string;
filter?: Metadata;
_verbose?: boolean;
pool: Pool;
client?: PoolClient;
chunkSize = 500;
_vectorstoreType(): string {
return "pgvector";
}
private constructor(
embeddings: EmbeddingsInterface,
config: PGVectorStoreArgs
) {
super(embeddings, config);
this.tableName = config.tableName;
this.collectionTableName = config.collectionTableName;
this.collectionName = config.collectionName ?? "langchain";
this.collectionMetadata = config.collectionMetadata ?? null;
this.filter = config.filter;
this.vectorColumnName = config.columns?.vectorColumnName ?? "embedding";
this.contentColumnName = config.columns?.contentColumnName ?? "text";
this.idColumnName = config.columns?.idColumnName ?? "id";
this.metadataColumnName = config.columns?.metadataColumnName ?? "metadata";
const pool = new pg.Pool(config.postgresConnectionOptions);
this.pool = pool;
this.chunkSize = config.chunkSize ?? 500;
this._verbose =
getEnvironmentVariable("LANGCHAIN_VERBOSE") === "true" ??
!!config.verbose;
}
/**
* Static method to create a new `PGVectorStore` instance from a
* connection. It creates a table if one does not exist, and calls
* `connect` to return a new instance of `PGVectorStore`.
*
* @param embeddings - Embeddings instance.
* @param fields - `PGVectorStoreArgs` instance.
* @returns A new instance of `PGVectorStore`.
*/
static async initialize(
embeddings: EmbeddingsInterface,
config: PGVectorStoreArgs
): Promise<PGVectorStore> {
const postgresqlVectorStore = new PGVectorStore(embeddings, config);
await postgresqlVectorStore._initializeClient();
await postgresqlVectorStore.ensureTableInDatabase();
if (postgresqlVectorStore.collectionTableName) {
await postgresqlVectorStore.ensureCollectionTableInDatabase();
}
return postgresqlVectorStore;
}
protected async _initializeClient() {
this.client = await this.pool.connect();
}
/**
* Method to add documents to the vector store. It converts the documents into
* vectors, and adds them to the store.
*
* @param documents - Array of `Document` instances.
* @param options - Optional arguments for adding documents
* @returns Promise that resolves when the documents have been added.
*/
async addDocuments(
documents: Document[],
options?: { ids?: string[] }
): Promise<void> {
const texts = documents.map(({ pageContent }) => pageContent);
return this.addVectors(
await this.embeddings.embedDocuments(texts),
documents,
options
);
}
/**
* Inserts a row for the collectionName provided at initialization if it does not
* exist and returns the collectionId.
*
* @returns The collectionId for the given collectionName.
*/
async getOrCreateCollection(): Promise<string> {
const queryString = `
SELECT uuid from ${this.collectionTableName}
WHERE name = $1;
`;
const queryResult = await this.pool.query(queryString, [
this.collectionName,
]);
let collectionId = queryResult.rows[0]?.uuid;
if (!collectionId) {
const insertString = `
INSERT INTO ${this.collectionTableName}(
uuid,
name,
cmetadata
)
VALUES (
uuid_generate_v4(),
$1,
$2
)
RETURNING uuid;
`;
const insertResult = await this.pool.query(insertString, [
this.collectionName,
this.collectionMetadata,
]);
collectionId = insertResult.rows[0]?.uuid;
}
return collectionId;
}
/**
* Generates the SQL placeholders for a specific row at the provided index.
*
* @param index - The index of the row for which placeholders need to be generated.
* @param numOfColumns - The number of columns we are inserting data into.
* @returns The SQL placeholders for the row values.
*/
private generatePlaceholderForRowAt(
index: number,
numOfColumns: number
): string {
const placeholders = [];
for (let i = 0; i < numOfColumns; i += 1) {
placeholders.push(`$${index * numOfColumns + i + 1}`);
}
return `(${placeholders.join(", ")})`;
}
/**
* Constructs the SQL query for inserting rows into the specified table.
*
* @param rows - The rows of data to be inserted, consisting of values and records.
* @param chunkIndex - The starting index for generating query placeholders based on chunk positioning.
* @returns The complete SQL INSERT INTO query string.
*/
private async buildInsertQuery(rows: (string | Record<string, unknown>)[][]) {
let collectionId;
if (this.collectionTableName) {
collectionId = await this.getOrCreateCollection();
}
const columns = [
this.contentColumnName,
this.vectorColumnName,
this.metadataColumnName,
];
if (collectionId) {
columns.push("collection_id");
}
// Check if we have added ids to the rows.
if (rows.length !== 0 && columns.length === rows[0].length - 1) {
columns.push(this.idColumnName);
}
const valuesPlaceholders = rows
.map((_, j) => this.generatePlaceholderForRowAt(j, columns.length))
.join(", ");
const text = `
INSERT INTO ${this.tableName}(
${columns.map((column) => `"${column}"`).join(", ")}
)
VALUES ${valuesPlaceholders}
`;
return text;
}
/**
* Method to add vectors to the vector store. It converts the vectors into
* rows and inserts them into the database.
*
* @param vectors - Array of vectors.
* @param documents - Array of `Document` instances.
* @param options - Optional arguments for adding documents
* @returns Promise that resolves when the vectors have been added.
*/
async addVectors(
vectors: number[][],
documents: Document[],
options?: { ids?: string[] }
): Promise<void> {
const ids = options?.ids;
// Either all documents have ids or none of them do to avoid confusion.
if (ids !== undefined && ids.length !== vectors.length) {
throw new Error(
"The number of ids must match the number of vectors provided."
);
}
const rows = [];
let collectionId;
if (this.collectionTableName) {
collectionId = await this.getOrCreateCollection();
}
for (let i = 0; i < vectors.length; i += 1) {
const values = [];
const embedding = vectors[i];
const embeddingString = `[${embedding.join(",")}]`;
values.push(
documents[i].pageContent.replace(/\0/g, ""),
embeddingString.replace(/\0/g, ""),
documents[i].metadata
);
if (collectionId) {
values.push(collectionId);
}
if (ids) {
values.push(ids[i]);
}
rows.push(values);
}
for (let i = 0; i < rows.length; i += this.chunkSize) {
const chunk = rows.slice(i, i + this.chunkSize);
const insertQuery = await this.buildInsertQuery(chunk);
const flatValues = chunk.flat();
try {
await this.pool.query(insertQuery, flatValues);
} catch (e) {
console.error(e);
throw new Error(`Error inserting: ${(e as Error).message}`);
}
}
}
/**
* Method to delete documents from the vector store. It deletes the
* documents that match the provided ids.
*
* @param ids - Array of document ids.
* @returns Promise that resolves when the documents have been deleted.
*/
private async deleteById(ids: string[]) {
let collectionId;
if (this.collectionTableName) {
collectionId = await this.getOrCreateCollection();
}
// Set parameters of dynamically generated query
const params = collectionId ? [ids, collectionId] : [ids];
const queryString = `
DELETE FROM ${this.tableName}
WHERE ${collectionId ? "collection_id = $2 AND " : ""}${
this.idColumnName
} = ANY($1::uuid[])
`;
await this.pool.query(queryString, params);
}
/**
* Method to delete documents from the vector store. It deletes the
* documents whose metadata contains the filter.
*
* @param filter - An object representing the Metadata filter.
* @returns Promise that resolves when the documents have been deleted.
*/
private async deleteByFilter(filter: Metadata) {
let collectionId;
if (this.collectionTableName) {
collectionId = await this.getOrCreateCollection();
}
// Set parameters of dynamically generated query
const params = collectionId ? [filter, collectionId] : [filter];
const queryString = `
DELETE FROM ${this.tableName}
WHERE ${collectionId ? "collection_id = $2 AND " : ""}${
this.metadataColumnName
}::jsonb @> $1
`;
return await this.pool.query(queryString, params);
}
/**
* Method to delete documents from the vector store. It deletes the
* documents that match the provided ids or metadata filter. Matches ids
* exactly and metadata filter according to postgres jsonb containment. Ids and filter
* are mutually exclusive.
*
* @param params - Object containing either an array of ids or a metadata filter object.
* @returns Promise that resolves when the documents have been deleted.
* @throws Error if neither ids nor filter are provided, or if both are provided.
* @example <caption>Delete by ids</caption>
* await vectorStore.delete({ ids: ["id1", "id2"] });
* @example <caption>Delete by filter</caption>
* await vectorStore.delete({ filter: { a: 1, b: 2 } });
*/
async delete(params: { ids?: string[]; filter?: Metadata }): Promise<void> {
const { ids, filter } = params;
if (!(ids || filter)) {
throw new Error(
"You must specify either ids or a filter when deleting documents."
);
}
if (ids && filter) {
throw new Error(
"You cannot specify both ids and a filter when deleting documents."
);
}
if (ids) {
await this.deleteById(ids);
} else if (filter) {
await this.deleteByFilter(filter);
}
}
/**
* Method to perform a similarity search in the vector store. It returns
* the `k` most similar documents to the query vector, along with their
* similarity scores.
*
* @param query - Query vector.
* @param k - Number of most similar documents to return.
* @param filter - Optional filter to apply to the search.
* @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
*/
async similaritySearchVectorWithScore(
query: number[],
k: number,
filter?: this["FilterType"]
): Promise<[Document, number][]> {
const embeddingString = `[${query.join(",")}]`;
const _filter = filter ?? "{}";
let collectionId;
if (this.collectionTableName) {
collectionId = await this.getOrCreateCollection();
}
const parameters = [embeddingString, _filter, k];
if (collectionId) {
parameters.push(collectionId);
}
const queryString = `
SELECT *, ${this.vectorColumnName} <=> $1 as "_distance"
FROM ${this.tableName}
WHERE ${this.metadataColumnName}::jsonb @> $2
${collectionId ? "AND collection_id = $4" : ""}
ORDER BY "_distance" ASC
LIMIT $3;
`;
const documents = (await this.pool.query(queryString, parameters)).rows;
const results = [] as [Document, number][];
for (const doc of documents) {
if (doc._distance != null && doc[this.contentColumnName] != null) {
const document = new Document({
pageContent: doc[this.contentColumnName],
metadata: doc[this.metadataColumnName],
});
results.push([document, doc._distance]);
}
}
return results;
}
/**
* Method to ensure the existence of the table in the database. It creates
* the table if it does not already exist.
*
* @returns Promise that resolves when the table has been ensured.
*/
async ensureTableInDatabase(): Promise<void> {
await this.pool.query("CREATE EXTENSION IF NOT EXISTS vector;");
await this.pool.query('CREATE EXTENSION IF NOT EXISTS "uuid-ossp";');
await this.pool.query(`
CREATE TABLE IF NOT EXISTS ${this.tableName} (
"${this.idColumnName}" uuid NOT NULL DEFAULT uuid_generate_v4() PRIMARY KEY,
"${this.contentColumnName}" text,
"${this.metadataColumnName}" jsonb,
"${this.vectorColumnName}" vector
);
`);
}
/**
* Method to ensure the existence of the collection table in the database.
* It creates the table if it does not already exist.
*
* @returns Promise that resolves when the collection table has been ensured.
*/
async ensureCollectionTableInDatabase(): Promise<void> {
try {
await this.pool.query(`
CREATE TABLE IF NOT EXISTS ${this.collectionTableName} (
uuid uuid NOT NULL DEFAULT uuid_generate_v4() PRIMARY KEY,
name character varying,
cmetadata jsonb
);
ALTER TABLE ${this.tableName}
ADD COLUMN collection_id uuid;
ALTER TABLE ${this.tableName}
ADD CONSTRAINT ${this.tableName}_collection_id_fkey
FOREIGN KEY (collection_id)
REFERENCES ${this.collectionTableName}(uuid)
ON DELETE CASCADE;
`);
} catch (e) {
if (!(e as Error).message.includes("already exists")) {
console.error(e);
throw new Error(`Error adding column: ${(e as Error).message}`);
}
}
}
/**
* Static method to create a new `PGVectorStore` instance from an
* array of texts and their metadata. It converts the texts into
* `Document` instances and adds them to the store.
*
* @param texts - Array of texts.
* @param metadatas - Array of metadata objects or a single metadata object.
* @param embeddings - Embeddings instance.
* @param dbConfig - `PGVectorStoreArgs` instance.
* @returns Promise that resolves with a new instance of `PGVectorStore`.
*/
static async fromTexts(
texts: string[],
metadatas: object[] | object,
embeddings: EmbeddingsInterface,
dbConfig: PGVectorStoreArgs
): Promise<PGVectorStore> {
const docs = [];
for (let i = 0; i < texts.length; i += 1) {
const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
const newDoc = new Document({
pageContent: texts[i],
metadata,
});
docs.push(newDoc);
}
return PGVectorStore.fromDocuments(docs, embeddings, dbConfig);
}
/**
* Static method to create a new `PGVectorStore` instance from an
* array of `Document` instances. It adds the documents to the store.
*
* @param docs - Array of `Document` instances.
* @param embeddings - Embeddings instance.
* @param dbConfig - `PGVectorStoreArgs` instance.
* @returns Promise that resolves with a new instance of `PGVectorStore`.
*/
static async fromDocuments(
docs: Document[],
embeddings: EmbeddingsInterface,
dbConfig: PGVectorStoreArgs
): Promise<PGVectorStore> {
const instance = await PGVectorStore.initialize(embeddings, dbConfig);
await instance.addDocuments(docs, { ids: dbConfig.ids });
return instance;
}
/**
* Closes all the clients in the pool and terminates the pool.
*
* @returns Promise that resolves when all clients are closed and the pool is terminated.
*/
async end(): Promise<void> {
this.client?.release();
return this.pool.end();
}
}