SingleStoreDB is a robust, high-performance distributed SQL database solution designed to excel in both cloud and on-premises environments. Boasting a versatile feature set, it offers seamless deployment options while delivering unparalleled performance. A standout feature of SingleStoreDB is its advanced support for vector storage and operations, making it an ideal choice for applications requiring intricate AI capabilities such as text similarity matching. With built-in vector functions like dot_product and euclidean_distance, SingleStoreDB empowers developers to implement sophisticated algorithms efficiently. For developers keen on leveraging vector data within SingleStoreDB, a comprehensive tutorial is available, guiding them through the intricacies of working with vector data. This tutorial delves into the Vector Store within SingleStoreDB, showcasing its ability to facilitate searches based on vector similarity. Leveraging vector indexes, queries can be executed with remarkable speed, enabling swift retrieval of relevant data. Moreover, SingleStoreDB’s Vector Store seamlessly integrates with full-text indexing based on Lucene, enabling powerful text similarity searches. Users can filter search results based on selected fields of document metadata objects, enhancing query precision. What sets SingleStoreDB apart is its ability to combine vector and full-text searches in various ways, offering flexibility and versatility. Whether prefiltering by text or vector similarity and selecting the most relevant data, or employing a weighted sum approach to compute a final similarity score, developers have multiple options at their disposal. In essence, SingleStoreDB provides a comprehensive solution for managing and querying vector data, offering unparalleled performance and flexibility for AI-driven applications.
CompatibilityOnly available on Node.js.
LangChain.js requires the mysql2 library to create a connection to a SingleStoreDB instance.

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Setup

  1. Establish a SingleStoreDB environment. You have the flexibility to choose between Cloud-based or On-Premise editions.
  2. Install the mysql2 JS client
npm
npm install -S mysql2

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Usage

SingleStoreVectorStore manages a connection pool. It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.

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Standard usage

See this section for general instructions on installing LangChain packages.
npm
npm install @langchain/openai @langchain/community @langchain/core
Below is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the SingleStoreVectorStore: 
import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await SingleStoreVectorStore.fromTexts(
    ["Hello world", "Bye bye", "hello nice world"],
    [{ id: 2 }, { id: 1 }, { id: 3 }],
    new OpenAIEmbeddings(),
    {
      connectionOptions: {
        host: process.env.SINGLESTORE_HOST,
        port: Number(process.env.SINGLESTORE_PORT),
        user: process.env.SINGLESTORE_USERNAME,
        password: process.env.SINGLESTORE_PASSWORD,
        database: process.env.SINGLESTORE_DATABASE,
      },
    }
  );

  const resultOne = await vectorStore.similaritySearch("hello world", 1);
  console.log(resultOne);
  await vectorStore.end();
};

​
Metadata Filtering

If it is needed to filter results based on specific metadata fields, you can pass a filter parameter to narrow down your search to the documents that match all specified fields in the filter object: 
import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await SingleStoreVectorStore.fromTexts(
    ["Good afternoon", "Bye bye", "Boa tarde!", "Até logo!"],
    [
      { id: 1, language: "English" },
      { id: 2, language: "English" },
      { id: 3, language: "Portugese" },
      { id: 4, language: "Portugese" },
    ],
    new OpenAIEmbeddings(),
    {
      connectionOptions: {
        host: process.env.SINGLESTORE_HOST,
        port: Number(process.env.SINGLESTORE_PORT),
        user: process.env.SINGLESTORE_USERNAME,
        password: process.env.SINGLESTORE_PASSWORD,
        database: process.env.SINGLESTORE_DATABASE,
      },
      distanceMetric: "EUCLIDEAN_DISTANCE",
    }
  );

  const resultOne = await vectorStore.similaritySearch("greetings", 1, {
    language: "Portugese",
  });
  console.log(resultOne);
  await vectorStore.end();
};

​
Vector indexes

Enhance your search efficiency with SingleStore DB version 8.5 or above by leveraging ANN vector indexes. By setting useVectorIndex: true during vector store object creation, you can activate this feature. Additionally, if your vectors differ in dimensionality from the default OpenAI embedding size of 1536, ensure to specify the vectorSize parameter accordingly. SingleStoreDB presents a diverse range of search strategies, each meticulously crafted to cater to specific use cases and user preferences. The default VECTOR_ONLY strategy utilizes vector operations such as DOT_PRODUCT or EUCLIDEAN_DISTANCE to calculate similarity scores directly between vectors, while TEXT_ONLY employs Lucene-based full-text search, particularly advantageous for text-centric applications. For users seeking a balanced approach, FILTER_BY_TEXT first refines results based on text similarity before conducting vector comparisons, whereas FILTER_BY_VECTOR prioritizes vector similarity, filtering results before assessing text similarity for optimal matches. Notably, both FILTER_BY_TEXT and FILTER_BY_VECTOR necessitate a full-text index for operation. Additionally, WEIGHTED_SUM emerges as a sophisticated strategy, calculating the final similarity score by weighing vector and text similarities, albeit exclusively utilizing dot_product distance calculations and also requiring a full-text index. These versatile strategies empower users to fine-tune searches according to their unique needs, facilitating efficient and precise data retrieval and analysis. Moreover, SingleStoreDB’s hybrid approaches, exemplified by FILTER_BY_TEXT, FILTER_BY_VECTOR, and WEIGHTED_SUM strategies, seamlessly blend vector and text-based searches to maximize efficiency and accuracy, ensuring users can fully leverage the platform’s capabilities for a wide range of applications. 
import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await SingleStoreVectorStore.fromTexts(
    [
      "In the parched desert, a sudden rainstorm brought relief, as the droplets danced upon the thirsty earth, rejuvenating the landscape with the sweet scent of petrichor.",
      "Amidst the bustling cityscape, the rain fell relentlessly, creating a symphony of pitter-patter on the pavement, while umbrellas bloomed like colorful flowers in a sea of gray.",
      "High in the mountains, the rain transformed into a delicate mist, enveloping the peaks in a mystical veil, where each droplet seemed to whisper secrets to the ancient rocks below.",
      "Blanketing the countryside in a soft, pristine layer, the snowfall painted a serene tableau, muffling the world in a tranquil hush as delicate flakes settled upon the branches of trees like nature's own lacework.",
      "In the urban landscape, snow descended, transforming bustling streets into a winter wonderland, where the laughter of children echoed amidst the flurry of snowballs and the twinkle of holiday lights.",
      "Atop the rugged peaks, snow fell with an unyielding intensity, sculpting the landscape into a pristine alpine paradise, where the frozen crystals shimmered under the moonlight, casting a spell of enchantment over the wilderness below.",
    ],
    [
      { category: "rain" },
      { category: "rain" },
      { category: "rain" },
      { category: "snow" },
      { category: "snow" },
      { category: "snow" },
    ],
    new OpenAIEmbeddings(),
    {
      connectionOptions: {
        host: process.env.SINGLESTORE_HOST,
        port: Number(process.env.SINGLESTORE_PORT),
        user: process.env.SINGLESTORE_USERNAME,
        password: process.env.SINGLESTORE_PASSWORD,
        database: process.env.SINGLESTORE_DATABASE,
      },
      distanceMetric: "DOT_PRODUCT",
      useVectorIndex: true,
      useFullTextIndex: true,
    }
  );

  const resultOne = await vectorStore.similaritySearch(
    "rainstorm in parched desert, rain",
    1,
    { category: "rain" }
  );
  console.log(resultOne[0].pageContent);

  await vectorStore.setSearchConfig({
    searchStrategy: "TEXT_ONLY",
  });
  const resultTwo = await vectorStore.similaritySearch(
    "rainstorm in parched desert, rain",
    1
  );
  console.log(resultTwo[0].pageContent);

  await vectorStore.setSearchConfig({
    searchStrategy: "FILTER_BY_TEXT",
    filterThreshold: 0.1,
  });
  const resultThree = await vectorStore.similaritySearch(
    "rainstorm in parched desert, rain",
    1
  );
  console.log(resultThree[0].pageContent);

  await vectorStore.setSearchConfig({
    searchStrategy: "FILTER_BY_VECTOR",
    filterThreshold: 0.1,
  });
  const resultFour = await vectorStore.similaritySearch(
    "rainstorm in parched desert, rain",
    1
  );
  console.log(resultFour[0].pageContent);

  await vectorStore.setSearchConfig({
    searchStrategy: "WEIGHTED_SUM",
    textWeight: 0.2,
    vectorWeight: 0.8,
    vectorselectCountMultiplier: 10,
  });
  const resultFive = await vectorStore.similaritySearch(
    "rainstorm in parched desert, rain",
    1
  );
  console.log(resultFive[0].pageContent);

  await vectorStore.end();
};