@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:
1. Select or create a Cloud Platform project.
2. Enable billing for your project.
3. Enable the Cloud SQL Admin API.
4. Setup Authentication.

$3

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

Example usage

$3

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

`javascript
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.

$3

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

`javascript
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.

$3

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

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

`javascript
// 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.

$3

Use a document loader to load data as LangChain Documents.

`typescript
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.