genkitx-cloud-sql-pg/lib/index.mjs.map

Genkit AI framework plugin for Cloud SQL for PostgreSQL.

{"version":3,"sources":["../src/index.ts"],"sourcesContent":["/**\n * Copyright 2025 Google LLC\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nimport { z, type Genkit } from 'genkit';\nimport { type EmbedderArgument } from 'genkit/embedder';\nimport { genkitPlugin, type GenkitPlugin } from 'genkit/plugin';\nimport {\n  CommonRetrieverOptionsSchema,\n  Document,\n  indexerRef,\n  retrieverRef,\n} from 'genkit/retriever';\nimport { v4 as uuidv4 } from 'uuid';\nimport { PostgresEngine } from './engine.js';\nimport { DistanceStrategy, type QueryOptions } from './indexes.js';\n\nexport { Column, PostgresEngine } from './engine.js';\nexport {\n  DistanceStrategy,\n  ExactNearestNeighbor,\n  HNSWIndex,\n  HNSWQueryOptions,\n  IVFFlatIndex,\n  IVFFlatQueryOptions,\n} from './indexes.js';\n\nconst PostgresRetrieverOptionsSchema = CommonRetrieverOptionsSchema.extend({\n  k: z.number().max(1000),\n  filter: z.string().optional(),\n});\n\nconst PostgresIndexerOptionsSchema = z.object({\n  batchSize: z.number().default(100),\n});\n\n/**\n * postgresRetrieverRef function creates a retriever for Postgres.\n * @param params The params for the new Postgres retriever\n * @param params.tableName The table name for the postgres retriever\nIf not specified, the default label will be `Postgres - <tableName>`\n * @returns A reference to a Postgres retriever.\n */\nexport const postgresRetrieverRef = (params: { tableName: string }) => {\n  return retrieverRef({\n    name: `postgres/${params.tableName}`,\n    info: {\n      label: params.tableName ?? `Postgres - ${params.tableName}`,\n    },\n    configSchema: PostgresRetrieverOptionsSchema,\n  });\n};\n\n/**\n * postgresIndexerRef function creates an indexer for Postgres.\n * @param params The params for the new Postgres indexer.\n * @param params.tableName The table name for the Postgres indexer.\nIf not specified, the default label will be `Postgres - <tableName>`\n * @returns A reference to a Postgres indexer.\n */\nexport const postgresIndexerRef = (params: { tableName: string }) => {\n  return indexerRef({\n    name: `postgres/${params.tableName}`,\n    info: {\n      label: params.tableName ?? `Postgres - ${params.tableName}`,\n    },\n    configSchema: PostgresIndexerOptionsSchema.optional(),\n  });\n};\n\n/**\n * Postgres plugin that provides a Postgres retriever and indexer\n * @param params An array of params to set up Postgres retrievers and indexers\n * @param params.tableName The name of the table\n * @param params.embedder The embedder to use for the indexer and retriever\n * @param params.embedderOptions Options to customize the embedder\n * @returns The Postgres Genkit plugin\n */\nexport function postgres<EmbedderCustomOptions extends z.ZodTypeAny>(\n  params: {\n    tableName: string;\n    embedder: EmbedderArgument<EmbedderCustomOptions>;\n    embedderOptions?: z.infer<EmbedderCustomOptions>;\n    engine: PostgresEngine;\n    schemaName?: string;\n    contentColumn?: string;\n    embeddingColumn?: string;\n    metadataColumns?: string[];\n    ignoreMetadataColumns?: string[];\n    idColumn?: string;\n    metadataJsonColumn?: string;\n    distanceStrategy?: DistanceStrategy;\n    indexQueryOptions?: QueryOptions;\n  }[]\n): GenkitPlugin {\n  return genkitPlugin('postgres', async (ai: Genkit) => {\n    params.map((i) => configurePostgresRetriever(ai, i));\n    params.map((i) => configurePostgresIndexer(ai, i));\n  });\n}\n\nexport default postgres;\n\n/**\n * Configures a Postgres retriever.\n * @param ai A Genkit instance\n * @param params The params for the retriever\n * @param params.tableName The name of the table\n * @param params.embedder The embedder to use for the retriever\n * @param params.embedderOptions  Options to customize the embedder\n * @returns A Postgres retriever\n */\nexport async function configurePostgresRetriever<\n  EmbedderCustomOptions extends z.ZodTypeAny,\n>(\n  ai: Genkit,\n  params: {\n    tableName: string;\n    embedder: EmbedderArgument<EmbedderCustomOptions>;\n    embedderOptions?: z.infer<EmbedderCustomOptions>;\n    engine: PostgresEngine;\n    schemaName?: string;\n    contentColumn?: string;\n    embeddingColumn?: string;\n    metadataColumns?: string[];\n    ignoreMetadataColumns?: string[];\n    idColumn?: string;\n    metadataJsonColumn?: string;\n    distanceStrategy?: DistanceStrategy;\n    indexQueryOptions?: QueryOptions;\n  }\n) {\n  const schemaName = params.schemaName ?? 'public';\n  const contentColumn = params.contentColumn ?? 'content';\n  const embeddingColumn = params.embeddingColumn ?? 'embedding';\n  const distanceStrategy =\n    params.distanceStrategy ?? DistanceStrategy.COSINE_DISTANCE;\n  if (!params.engine) {\n    throw new Error('Engine is required');\n  }\n\n  async function checkColumns() {\n    if (\n      params.metadataColumns !== undefined &&\n      params.ignoreMetadataColumns !== undefined\n    ) {\n      throw 'Can not use both metadata_columns and ignore_metadata_columns.';\n    }\n\n    const { rows } = await params.engine.pool.raw(\n      `SELECT column_name, data_type FROM information_schema.columns WHERE table_name = '${params.tableName}' AND table_schema = '${schemaName}'`\n    );\n    const columns: { [key: string]: any } = {};\n\n    for (const index in rows) {\n      const row = rows[index];\n      columns[row['column_name']] = row['data_type'];\n    }\n\n    if (params.idColumn && !columns.hasOwnProperty(params.idColumn)) {\n      throw `Id column: ${params.idColumn}, does not exist.`;\n    }\n\n    if (contentColumn && !columns.hasOwnProperty(contentColumn)) {\n      throw `Content column: ${params.contentColumn}, does not exist.`;\n    }\n\n    const contentType = contentColumn ? columns[contentColumn] : '';\n\n    if (contentType !== 'text' && !contentType.includes('char')) {\n      throw `Content column: ${params.contentColumn}, is type: ${contentType}. It must be a type of character string.`;\n    }\n\n    if (embeddingColumn && !columns.hasOwnProperty(embeddingColumn)) {\n      throw `Embedding column: ${embeddingColumn}, does not exist.`;\n    }\n\n    if (embeddingColumn && columns[embeddingColumn] !== 'USER-DEFINED') {\n      throw `Embedding column: ${embeddingColumn} is not of type Vector.`;\n    }\n\n    const metadataJsonColumnToCheck = params.metadataJsonColumn ?? '';\n    params.metadataJsonColumn = columns.hasOwnProperty(\n      metadataJsonColumnToCheck\n    )\n      ? params.metadataJsonColumn\n      : '';\n\n    if (params.metadataColumns) {\n      for (const column of params.metadataColumns) {\n        if (column && !columns.hasOwnProperty(column)) {\n          throw `Metadata column: ${column}, does not exist.`;\n        }\n      }\n    }\n\n    const allColumns = columns;\n    if (\n      params.ignoreMetadataColumns !== undefined &&\n      params.ignoreMetadataColumns.length > 0\n    ) {\n      for (const column of params.ignoreMetadataColumns) {\n        delete allColumns[column];\n      }\n\n      if (params.idColumn) {\n        delete allColumns[params.idColumn];\n      }\n      if (contentColumn) {\n        delete allColumns[contentColumn];\n      }\n      if (embeddingColumn) {\n        delete allColumns[embeddingColumn];\n      }\n      params.metadataColumns = Object.keys(allColumns);\n    }\n  }\n\n  async function queryCollection(\n    embedding: number[],\n    k?: number | undefined,\n    filter?: string | undefined\n  ) {\n    k = k ?? 4;\n    const operator = distanceStrategy.operator;\n    const searchFunction = distanceStrategy.searchFunction;\n    const _filter = filter !== undefined ? `WHERE ${filter}` : '';\n    const metadataColNames =\n      params.metadataColumns && params.metadataColumns.length > 0\n        ? `\"${params.metadataColumns.join('\",\"')}\"`\n        : '';\n    const metadataJsonColName = params.metadataJsonColumn\n      ? `, \"${params.metadataJsonColumn}\"`\n      : '';\n\n    const query = `SELECT \"${params.idColumn}\", \"${contentColumn}\", \"${embeddingColumn}\", ${metadataColNames} ${metadataJsonColName}, ${searchFunction}(\"${embeddingColumn}\", '[${embedding}]') as distance FROM \"${schemaName}\".\"${params.tableName}\" ${_filter} ORDER BY \"${embeddingColumn}\" ${operator} '[${embedding}]' LIMIT ${k};`;\n\n    if (params.indexQueryOptions) {\n      await params.engine.pool.raw(\n        `SET LOCAL ${params.indexQueryOptions.to_string()}`\n      );\n    }\n\n    const { rows } = await params.engine.pool.raw(query);\n\n    return rows;\n  }\n\n  return ai.defineRetriever(\n    {\n      name: `postgres/${params.tableName}`,\n      configSchema: PostgresRetrieverOptionsSchema,\n    },\n    async (content, options) => {\n      console.log(`Retrieving data for table: ${params.tableName}`);\n      checkColumns();\n      const queryEmbeddings = await ai.embed({\n        embedder: params.embedder,\n        content,\n        options: params.embedderOptions,\n      });\n      const embedding = queryEmbeddings[0].embedding;\n      const results = await queryCollection(\n        embedding,\n        options.k,\n        options.filter\n      );\n      const documents: Document[] = [];\n      for (const row of results) {\n        const metadata =\n          params.metadataJsonColumn && row[params.metadataJsonColumn]\n            ? row[params.metadataJsonColumn]\n            : {};\n        if (params.metadataColumns) {\n          for (const col of params.metadataColumns) {\n            metadata[col] = row[col];\n          }\n        }\n        documents.push(\n          new Document({\n            content: row[contentColumn],\n            metadata: metadata,\n          })\n        );\n      }\n\n      return { documents };\n    }\n  );\n}\n\n/**\n * Configures a Postgres indexer.\n * @param ai A Genkit instance\n * @param params The params for the indexer\n * @param params.tableName The name of the indexer\n * @param params.engine The engine to use for the indexer\n * @param params.embedder The embedder to use for the retriever\n * @param params.embedderOptions  Options to customize the embedder\n * @param params.metadataColumns The metadata columns to use for the indexer\n * @param params.idColumn The id column to use for the indexer\n * @param params.metadataJsonColumn The metadata json column to use for the indexer\n * @param params.contentColumn The content column to use for the indexer\n * @param params.embeddingColumn The embedding column to use for the indexer\n * @param params.schemaName The schema name to use for the indexer\n * @param params.chunkSize The chunk size to use for the indexer\n * @returns Add documents to vector store\n */\nexport function configurePostgresIndexer<\n  EmbedderCustomOptions extends z.ZodTypeAny,\n>(\n  ai: Genkit,\n  params: {\n    tableName: string;\n    engine: PostgresEngine;\n    schemaName?: string;\n    contentColumn?: string;\n    embeddingColumn?: string;\n    metadataColumns?: string[];\n    ignoreMetadataColumns?: string[];\n    idColumn?: string;\n    metadataJsonColumn?: string;\n    embedder: EmbedderArgument<EmbedderCustomOptions>;\n    embedderOptions?: z.infer<EmbedderCustomOptions>;\n  }\n) {\n  const schemaName = params.schemaName ?? 'public';\n  const contentColumn = params.contentColumn ?? 'content';\n  const embeddingColumn = params.embeddingColumn ?? 'embedding';\n  const idColumn = params.idColumn ?? 'id';\n  const metadataJsonColumn = params.metadataJsonColumn ?? 'metadata';\n\n  if (!params.engine) {\n    throw new Error('Engine is required');\n  }\n\n  if (params.metadataColumns && params.ignoreMetadataColumns) {\n    throw new Error(\n      'Cannot use both metadataColumns and ignoreMetadataColumns'\n    );\n  }\n\n  async function checkColumns() {\n    const { rows } = await params.engine.pool.raw(\n      `SELECT column_name, data_type FROM information_schema.columns WHERE table_name = '${params.tableName}' AND table_schema = '${schemaName}'`\n    );\n\n    const columns: { [key: string]: any } = {};\n\n    for (const index in rows) {\n      const row = rows[index];\n      columns[row['column_name']] = row['data_type'];\n    }\n\n    if (!columns.hasOwnProperty(idColumn)) {\n      throw new Error(`Id column: ${idColumn}, does not exist.`);\n    }\n\n    if (!columns.hasOwnProperty(contentColumn)) {\n      throw new Error(`Content column: ${contentColumn}, does not exist.`);\n    }\n\n    if (!columns.hasOwnProperty(embeddingColumn)) {\n      throw new Error(`Embedding column: ${embeddingColumn}, does not exist.`);\n    }\n\n    if (columns[embeddingColumn] !== 'USER-DEFINED') {\n      throw new Error(\n        `Embedding column: ${embeddingColumn} is not of type Vector.`\n      );\n    }\n\n    if (params.metadataColumns) {\n      for (const column of params.metadataColumns) {\n        if (column && !columns.hasOwnProperty(column)) {\n          throw new Error(`Metadata column: ${column}, does not exist.`);\n        }\n      }\n    }\n  }\n\n  return ai.defineIndexer(\n    {\n      name: `postgres/${params.tableName}`,\n      configSchema: PostgresIndexerOptionsSchema.optional(),\n    },\n    async (\n      docs: Document[] | { documents: Document[]; options?: any },\n      options?: { batchSize?: number }\n    ) => {\n      try {\n        await checkColumns();\n\n        // Normalize input to always have documents array and merged options\n        const documents = Array.isArray(docs) ? docs : docs.documents || [];\n     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