PostgresVectorStore

import { Column, PostgresEngine, PostgresEngineArgs, PostgresVectorStore, VectorStoreTableArgs } from "@langchain/google-cloud-sql-pg";
// Or other embeddings
import { OpenAIEmbeddings } from '@langchain/openai';

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

const pgArgs: PostgresEngineArgs = {
    user: "db-user",
    password: "password"
}
// Create a shared connection pool
const engine: PostgresEngine = await PostgresEngine.fromInstance(
 "project-id",
 "region",
 "instance-name",
 "database-name",
 pgArgs
);
// (Optional) Specify metadata columns for filtering
// All other metadata will be added to JSON
const vectorStoreTableArgs: VectorStoreTableArgs = {
  metadataColumns: [new Column("baz", "TEXT")],
};
// Create a vector store table
await engine.initVectorstoreTable("my-table", 768, vectorStoreTableArgs);
// Customize the vector store
const pvectorArgs: PostgresVectorStoreArgs = {
    idColumn: "ID_COLUMN",
    contentColumn: "CONTENT_COLUMN",
    embeddingColumn: "EMBEDDING_COLUMN",
    metadataColumns: ["baz"]
}

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

import type { Document } from '@langchain/core/documents';

const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
const document3 = { pageContent: "i will be deleted :(", metadata: {} };

const documents: Document[] = [document1, document2, document3];
const ids = ["1", "2", "3"];
await vectorStore.addDocuments(documents, { ids });

await vectorStore.delete({ ids: ["3"] });

const results = await vectorStore.similaritySearch("thud", 1);
for (const doc of results) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output:thud [{"baz":"bar"}]

const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });

for (const doc of resultsWithFilter) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output:foo [{"baz":"bar"}]

const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
for (const [doc, score] of resultsWithScore) {
  console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output:[SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]

const retriever = vectorStore.asRetriever({
  searchType: "mmr", // Leave blank for standard similarity search
  k: 1,
});
const resultAsRetriever = await retriever.invoke("thud");
console.log(resultAsRetriever);

// Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]