Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. LangChain.js supports using a Supabase Postgres database as a vector store, using the pgvector extension. Refer to the Supabase blog post for more information. This guide provides a quick overview for getting started with Supabase vector stores. For detailed documentation of all SupabaseVectorStore features and configurations head to the API reference.

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Overview

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Integration details

ClassPackagePY supportVersion
SupabaseVectorStore@langchain/community✅NPM - Version

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Setup

To use Supabase vector stores, you’ll need to set up a Supabase database and install the @langchain/community integration package. You’ll also need to install the official @supabase/supabase-js SDK as a peer dependency. This guide will also use OpenAI embeddings, which require you to install the @langchain/openai integration package. You can also use other supported embeddings models if you wish. 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/community @langchain/core @supabase/supabase-js @langchain/openai
</Npm2Yarn>
Once you’ve created a database, run the following SQL to set up pgvector and create the necessary table and functions: 
-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create table documents (
  id bigserial primary key,
  content text, -- corresponds to Document.pageContent
  metadata jsonb, -- corresponds to Document.metadata
  embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);

-- Create a function to search for documents
create function match_documents (
  query_embedding vector(1536),
  match_count int DEFAULT null,
  filter jsonb DEFAULT '{}'
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  embedding jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    (embedding::text)::jsonb as embedding,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

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Credentials

Once you’ve done this set the SUPABASE_PRIVATE_KEY and SUPABASE_URL environment variables: 
process.env.SUPABASE_PRIVATE_KEY = "your-api-key";
process.env.SUPABASE_URL = "your-supabase-db-url";
If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well: 
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"

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Instantiation

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",
});

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Manage vector store

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Add items to vector store

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

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { source: "https://example.com" }
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { source: "https://example.com" }
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { source: "https://example.com" }
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { source: "https://example.com" }
}

const documents = [document1, document2, document3, document4];

await vectorStore.addDocuments(documents, { ids: ["1", "2", "3", "4"] });

[ 1, 2, 3, 4 ]

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Delete items from vector store

await vectorStore.delete({ ids: ["4"] });

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Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

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Query directly

Performing a simple similarity search can be done as follows: 
const filter = { source: "https://example.com" };

const similaritySearchResults = await vectorStore.similaritySearch("biology", 2, filter);

for (const doc of similaritySearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}

* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]
If you want to execute a similarity search and receive the corresponding scores you can run: 
const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore("biology", 2, filter)

for (const [doc, score] of similaritySearchWithScoreResults) {
  console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);
}

* [SIM=0.165] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.148] Mitochondria are made out of lipids [{"source":"https://example.com"}]

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Metadata Query Builder Filtering

You can also use query builder-style filtering similar to how the Supabase JavaScript library works instead of passing an object. Note that since most of the filter properties are in the metadata column, you need to use arrow operators (-> for integer or ->> for text) as defined in Postgrest API documentation and specify the data type of the property (e.g. the column should look something like metadata->some_int_prop_name::int). 
import { SupabaseFilterRPCCall } from "@langchain/community/vectorstores/supabase";

const funcFilter: SupabaseFilterRPCCall = (rpc) =>
  rpc.filter("metadata->>source", "eq", "https://example.com");

const funcFilterSearchResults = await vectorStore.similaritySearch("biology", 2, funcFilter);

for (const doc of funcFilterSearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}

* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]

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Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains. 
const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");

[
  Document {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' },
    id: undefined
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' },
    id: undefined
  }
]

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Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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API reference

For detailed documentation of all SupabaseVectorStore features and configurations head to the API reference.