@langchain/google-cloud-sql-pg

@langchain/google-cloud-sql-pg

The LangChain package for CloudSQL for Postgres provides a way to connect to Cloud SQL instances from the LangChain ecosystem.

Main features:

• The package creates a shared connection pool to connect to Google Cloud Postgres databases utilizing different ways for authentication such as IAM, user and password authorization.
• Store metadata in columns instead of JSON, resulting in significant performance improvements.

Before you begin

In order to use this package, you first need to go through the following steps:

• Select or create a Cloud Platform project.
• Enable billing for your project.
• Enable the Cloud SQL Admin API.
• Setup Authentication.

Installation

$ pnpm install @langchain/google-cloud-sql-pg

Example usage

PostgresEngine usage

Before you use the PostgresVectorStore you will need to create a postgres connection through the PostgresEngine interface.

import { Column, PostgresEngine, PostgresEngineArgs, PostgresVectorStore, VectorStoreTableArgs } from "@langchain/google-cloud-sql-pg";
import { SyntheticEmbeddings } from "@langchain/core/utils/testing";

const pgArgs: PostgresEngineArgs = {
    user: "db-user",
    password: "password"
}

const engine: PostgresEngine = await PostgresEngine.fromInstance(
 "project-id",
 "region",
 "instance-name",
 "database-name",
 pgArgs
);

const vectorStoreTableArgs: VectorStoreTableArgs = {
  metadataColumns: [new Column("page", "TEXT"), new Column("source", "TEXT")],
};

await engine.initVectorstoreTable("my-table", 768, vectorStoreTableArgs);
const embeddingService = new SyntheticEmbeddings({ vectorSize: 768 });

• You can pass the ipType, user, password and iamAccountEmail through the PostgresEngineArgs interface to the PostgresEngine creation.
• You can pass the schemaName, contentColumn, embeddingColum, metadataColumns and others through the VectorStoreTableArgs interface to the init_vectorstore_table method.
• Passing an empty object to these methods allows you to use the default values.

Vector Store usage

Use a PostgresVectorStore to store embedded data and perform vector similarity search for Postgres.

const pvectorArgs: PostgresVectorStoreArgs = {
    idColumn: "ID_COLUMN",
    contentColumn: "CONTENT_COLUMN",
    embeddingColumn: "EMBEDDING_COLUMN",
    metadataColumns: ["page", "source"]
}

const vectorStoreInstance = await PostgresVectorStore.initialize(engine, embeddingService, "my-table", pvectorArgs)

• You can pass the schemaName, contentColumn, embeddingColumn, distanceStrategy and others through the PostgresVectorStoreArgs interface to the PostgresVectorStore creation.
• Passing an empty object to these methods allows you to use the default values.

PostgresVectorStore interface methods available:

• addDocuments
• addVectors
• similaritySearch
• and others.

See the full Vector Store tutorial.

Chat Message History usage

Use PostgresChatMessageHistory to store messages and provide conversation history in Postgres.

First, initialize the Chat History Table and then create the ChatMessageHistory instance.

// ChatHistory table initialization
await engine.initChatHistoryTable("chat_message_table");

const historyInstance = await PostgresChatMessageHistory.initialize(
  engine,
  "test",
  "chat_message_table"
);

The create method of the PostgresChatMessageHistory receives the engine, the session Id and the table name.

PostgresChatMessageHistory interface methods available:

• addMessage
• addMessages
• getMessages
• clear

See the full Chat Message History tutorial.

Document Loader usage

Use a document loader to load data as LangChain Documents.

import { PostgresEngine, PostgresLoader } from "@langchain/google-cloud-sql-pg";

const documentLoaderArgs: PostgresLoaderOptions = {
  tableName: "test_table_custom",
  contentColumns: ["fruit_name", "variety"],
  metadataColumns: [
    "fruit_id",
    "quantity_in_stock",
    "price_per_unit",
    "organic",
  ],
  format: "text",
};

const documentLoaderInstance = await PostgresLoader.initialize(
  PEInstance,
  documentLoaderArgs
);

const documents = await documentLoaderInstance.load();

See the full Loader tutorial.