SupabaseVectorStore

import { SupabaseVectorStore } from "@langchain/community/vectorstores/supabase";
import { OpenAIEmbeddings } from "@langchain/openai";

import { createClient } from "@supabase/supabase-js";

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

const supabaseClient = createClient(
  process.env.SUPABASE_URL,
  process.env.SUPABASE_PRIVATE_KEY
);

const vectorStore = new SupabaseVectorStore(embeddings, {
  client: supabaseClient,
  tableName: "documents",
  queryName: "match_documents",
});

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" })]