vercel_postgres.ts

import {
  type VercelPool,
  type VercelPoolClient,
  type VercelPostgresPoolConfig,
  createPool,
} from "@vercel/postgres";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
import { getEnvironmentVariable } from "@langchain/core/utils/env";

type Metadata = Record<string, string | number | Record<"in", string[]>>;

/**
 * Interface that defines the arguments required to create a
 * `VercelPostgres` instance. It includes Postgres connection options,
 * table name, filter, and verbosity level.
 */
export interface VercelPostgresFields {
  pool: VercelPool;
  client: VercelPoolClient;
  tableName?: string;
  columns?: {
    idColumnName?: string;
    vectorColumnName?: string;
    contentColumnName?: string;
    metadataColumnName?: string;
  };
  filter?: Metadata;
  verbose?: boolean;
}

/**
 * Class that provides an interface to a Vercel Postgres vector database. It
 * extends the `VectorStore` base class and implements methods for adding
 * documents and vectors and performing similarity searches.
 */
export class VercelPostgres extends VectorStore {
  declare FilterType: Metadata;

  tableName: string;

  idColumnName: string;

  vectorColumnName: string;

  contentColumnName: string;

  metadataColumnName: string;

  filter?: Metadata;

  _verbose?: boolean;

  pool: VercelPool;

  client: VercelPoolClient;

  _vectorstoreType(): string {
    return "vercel";
  }

  private constructor(
    embeddings: EmbeddingsInterface,
    config: VercelPostgresFields
  ) {
    super(embeddings, config);
    this.tableName = config.tableName ?? "langchain_vectors";
    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";

    this.pool = config.pool;
    this.client = config.client;

    this._verbose =
      getEnvironmentVariable("LANGCHAIN_VERBOSE") === "true" ??
      !!config.verbose;
  }

  /**
   * Static method to create a new `VercelPostgres` instance from a
   * connection. It creates a table if one does not exist, and calls
   * `connect` to return a new instance of `VercelPostgres`.
   *
   * @param embeddings - Embeddings instance.
   * @param fields - `VercelPostgres` configuration options.
   * @returns A new instance of `VercelPostgres`.
   */
  static async initialize(
    embeddings: EmbeddingsInterface,
    config?: Partial<VercelPostgresFields> & {
      postgresConnectionOptions?: VercelPostgresPoolConfig;
    }
  ): Promise<VercelPostgres> {
    // Default maxUses to 1 for edge environments:
    // https://github.com/vercel/storage/tree/main/packages/postgres#a-note-on-edge-environments
    const pool =
      config?.pool ??
      createPool({ maxUses: 1, ...config?.postgresConnectionOptions });
    const client = config?.client ?? (await pool.connect());
    const postgresqlVectorStore = new VercelPostgres(embeddings, {
      ...config,
      pool,
      client,
    });

    await postgresqlVectorStore.ensureTableInDatabase();

    return postgresqlVectorStore;
  }

  /**
   * 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.
   * @returns Promise that resolves when the documents have been added.
   */
  async addDocuments(
    documents: Document[],
    options?: { ids?: string[] }
  ): Promise<string[]> {
    const texts = documents.map(({ pageContent }) => pageContent);

    return this.addVectors(
      await this.embeddings.embedDocuments(texts),
      documents,
      options
    );
  }

  /**
   * 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.
   * @returns The SQL placeholders for the row values.
   */
  protected generatePlaceholderForRowAt(
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    row: (string | Record<string, any>)[],
    index: number
  ): string {
    const base = index * row.length;
    return `(${row.map((_, j) => `$${base + 1 + j}`)})`;
  }

  /**
   * 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.
   */
  protected async runInsertQuery(
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    rows: (string | Record<string, any>)[][],
    useIdColumn: boolean
  ) {
    const values = rows.map((row, j) =>
      this.generatePlaceholderForRowAt(row, j)
    );
    const flatValues = rows.flat();
    return this.client.query(
      `
    INSERT INTO ${this.tableName} (
      ${useIdColumn ? `${this.idColumnName},` : ""}
      ${this.contentColumnName}, 
      ${this.vectorColumnName}, 
      ${this.metadataColumnName}
    ) VALUES ${values.join(", ")}
    ON CONFLICT (${this.idColumnName}) 
    DO UPDATE 
    SET 
    ${this.contentColumnName} = EXCLUDED.${this.contentColumnName},
    ${this.vectorColumnName} = EXCLUDED.${this.vectorColumnName},
    ${this.metadataColumnName} = EXCLUDED.${this.metadataColumnName}
    RETURNING ${this.idColumnName}`,
      flatValues
    );
  }

  /**
   * 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.
   * @returns Promise that resolves when the vectors have been added.
   */
  async addVectors(
    vectors: number[][],
    documents: Document[],
    options?: { ids?: string[] }
  ): Promise<string[]> {
    if (options?.ids !== undefined && options?.ids.length !== vectors.length) {
      throw new Error(
        `If provided, the length of "ids" must be the same as the number of vectors.`
      );
    }
    const rows = vectors.map((embedding, idx) => {
      const embeddingString = `[${embedding.join(",")}]`;
      const row = [
        documents[idx].pageContent,
        embeddingString,
        documents[idx].metadata,
      ];
      if (options?.ids) {
        return [options.ids[idx], ...row];
      }
      return row;
    });

    const chunkSize = 500;

    const ids = [];

    for (let i = 0; i < rows.length; i += chunkSize) {
      const chunk = rows.slice(i, i + chunkSize);
      try {
        const result = await this.runInsertQuery(
          chunk,
          options?.ids !== undefined
        );
        ids.push(...result.rows.map((row) => row[this.idColumnName]));
      } catch (e) {
        console.error(e);
        throw new Error(`Error inserting: ${(e as Error).message}`);
      }
    }
    return ids;
  }

  /**
   * 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: this["FilterType"] = filter ?? {};
    const whereClauses = [];
    const values = [embeddingString, k];
    let paramCount = values.length;

    for (const [key, value] of Object.entries(_filter)) {
      if (typeof value === "object" && value !== null) {
        const currentParamCount = paramCount;
        const placeholders = value.in
          .map((_, index) => `$${currentParamCount + index + 1}`)
          .join(",");
        whereClauses.push(
          `${this.metadataColumnName}->>'${key}' IN (${placeholders})`
        );
        values.push(...value.in);
        paramCount += value.in.length;
      } else {
        paramCount += 1;
        whereClauses.push(
          `${this.metadataColumnName}->>'${key}' = $${paramCount}`
        );
        values.push(value);
      }
    }

    const whereClause = whereClauses.length
      ? `WHERE ${whereClauses.join(" AND ")}`
      : "";

    const queryString = `
            SELECT *, ${this.vectorColumnName} <=> $1 as "_distance"
            FROM ${this.tableName}
            ${whereClause}
            ORDER BY "_distance" ASC
            LIMIT $2;`;

    const documents = (await this.client.query(queryString, values)).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;
  }

  async delete(params: { ids?: string[]; deleteAll?: boolean }): Promise<void> {
    if (params.ids !== undefined) {
      await this.client.query(
        `DELETE FROM ${this.tableName} WHERE ${
          this.idColumnName
        } IN (${params.ids.map((_, idx) => `$${idx + 1}`)})`,
        params.ids
      );
    } else if (params.deleteAll) {
      await this.client.query(`TRUNCATE TABLE ${this.tableName}`);
    }
  }

  /**
   * 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.client.query(`CREATE EXTENSION IF NOT EXISTS vector;`);
    await this.client.query(`CREATE EXTENSION IF NOT EXISTS "uuid-ossp";`);
    await this.client.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
    );`);
  }

  /**
   * Static method to create a new `VercelPostgres` 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 fields - `VercelPostgres` configuration options.
   * @returns Promise that resolves with a new instance of `VercelPostgres`.
   */
  static async fromTexts(
    texts: string[],
    metadatas: object[] | object,
    embeddings: EmbeddingsInterface,
    dbConfig?: Partial<VercelPostgresFields> & {
      postgresConnectionOptions?: VercelPostgresPoolConfig;
    }
  ): Promise<VercelPostgres> {
    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 this.fromDocuments(docs, embeddings, dbConfig);
  }

  /**
   * Static method to create a new `VercelPostgres` 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 fields - `VercelPostgres` configuration options.
   * @returns Promise that resolves with a new instance of `VercelPostgres`.
   */
  static async fromDocuments(
    docs: Document[],
    embeddings: EmbeddingsInterface,
    dbConfig?: Partial<VercelPostgresFields> & {
      postgresConnectionOptions?: VercelPostgresPoolConfig;
    }
  ): Promise<VercelPostgres> {
    const instance = await this.initialize(embeddings, dbConfig);
    await instance.addDocuments(docs);

    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> {
    await this.client?.release();
    return this.pool.end();
  }
}