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Turso is a SQLite-compatible database built on libSQL, the Open Contribution fork of SQLite. Vector Similiarity Search is built into Turso and libSQL as a native datatype, enabling you to store and query vectors directly in the database. LangChain.js supports using a local libSQL, or remote Turso database as a vector store, and provides a simple API to interact with it. This guide provides a quick overview for getting started with libSQL vector stores. For detailed documentation of all libSQL features and configurations head to the API reference.

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Overview

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Integration details

ClassPackagePY supportVersion
LibSQLVectorStore@langchain/community❌npm version

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Setup

To use libSQL vector stores, you’ll need to create a Turso account or set up a local SQLite database, and install the @langchain/community integration package. 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. You can use local SQLite when working with the libSQL vector store, or use a hosted Turso Database.
See this section for general instructions on installing LangChain packages.
npm
npm install @libsql/client @langchain/openai @langchain/community
Now it’s time to create a database. You can create one locally, or use a hosted Turso database.

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Local libSQL

Create a new local SQLite file and connect to the shell: 
sqlite3 file.db

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Hosted Turso

Visit sqlite.new to create a new database, give it a name, and create a database auth token. Make sure to copy the database auth token, and the database URL, it should look something like: 
libsql://[database-name]-[your-username].turso.io

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Setup the table and index

Execute the following SQL command to create a new table or add the embedding column to an existing table. Make sure to modify the following parts of the SQL:
  • TABLE_NAME is the name of the table you want to create.
  • content is used to store the Document.pageContent values.
  • metadata is used to store the Document.metadata object.
  • EMBEDDING_COLUMN is used to store the vector values, use the dimensions size used by the model you plan to use (1536 for OpenAI).
CREATE TABLE IF NOT EXISTS TABLE_NAME (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    content TEXT,
    metadata TEXT,
    EMBEDDING_COLUMN F32_BLOB(1536) -- 1536-dimensional f32 vector for OpenAI
);
Now create an index on the EMBEDDING_COLUMN column - the index name is important!: 
CREATE INDEX IF NOT EXISTS idx_TABLE_NAME_EMBEDDING_COLUMN ON TABLE_NAME(libsql_vector_idx(EMBEDDING_COLUMN));
Make sure to replace the TABLE_NAME and EMBEDDING_COLUMN with the values you used in the previous step.

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Instantiation

To initialize a new LibSQL vector store, you need to provide the database URL and Auth Token when working remotely, or by passing the filename for a local SQLite. 
import { LibSQLVectorStore } from "@langchain/community/vectorstores/libsql";
import { OpenAIEmbeddings } from "@langchain/openai";
import { createClient } from "@libsql/client";

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

const libsqlClient = createClient({
  url: "libsql://[database-name]-[your-username].turso.io",
  authToken: "...",
});

// Local instantiation
// const libsqlClient = createClient({
//  url: "file:./dev.db",
// });

const vectorStore = new LibSQLVectorStore(embeddings, {
  db: libsqlClient,
  table: "TABLE_NAME",
  column: "EMBEDDING_COLUMN",
});

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Manage vector store

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Add items to vector store

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

const documents: Document[] = [
  { pageContent: "Hello", metadata: { topic: "greeting" } },
  { pageContent: "Bye bye", metadata: { topic: "greeting" } },
];

await vectorStore.addDocuments(documents);

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Delete items from vector store

await vectorStore.deleteDocuments({ ids: [1, 2] });

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Query vector store

Once you have inserted the documents, you can query the vector store.

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Query directly

Performing a simple similarity search can be done as follows: 
const resultOne = await vectorStore.similaritySearch("hola", 1);

for (const doc of similaritySearchResults) {
  console.log(`${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
For similarity search with scores: 
const similaritySearchWithScoreResults =
  await vectorStore.similaritySearchWithScore("hola", 1);

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

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API reference

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