DOC: <Issue related to /v0.2/docs/integrations/vectorstores/typesense>

[madforce]

Checklist

• I added a very descriptive title to this issue.
• I included a link to the documentation page I am referring to (if applicable).

Issue with current documentation:

in 0.2 typsense has been moved from langchain/vectorstores/typesense to @lanchain/community/vectorstores/typesense

https://js.langchain.com/v0.2/docs/integrations/vectorstores/typesense

Idea or request for content:

No response

[dosubot]

Hey @madforce! I'm here to help you with any bugs, questions, or contributions you might have. Let's work together to resolve this!

The documentation for integrating Typesense with a vector store in version 0.2 has been updated to reflect the new import path for Typesense. The correct import path is now @langchain/community/vectorstores/typesense. Here is the updated code snippet:

import {
  Typesense,
  TypesenseConfig,
} from "@langchain/community/vectorstores/typesense";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Client } from "typesense";
import { Document } from "@langchain/core/documents";

const vectorTypesenseClient = new Client({
  nodes: [
    {
      // Ideally should come from your .env file
      host: "...",
      port: 123,
      protocol: "https",
    },
  ],
  // Ideally should come from your .env file
  apiKey: "...",
  numRetries: 3,
  connectionTimeoutSeconds: 60,
});

const typesenseVectorStoreConfig = {
  // Typesense client
  typesenseClient: vectorTypesenseClient,
  // Name of the collection to store the vectors in
  schemaName: "your_schema_name",
  // Optional column names to be used in Typesense
  columnNames: {
    vector: "vec", // Default name for the vector column
    pageContent: "text", // Default name for the text column
    metadataColumnNames: ["foo", "bar", "baz"], // Metadata columns
  },
  // Optional search parameters to be passed to Typesense when searching
  searchParams: {
    q: "*",
    filter_by: "foo:[fooo]",
    query_by: "",
  },
  // Custom import function
  import: async (data, collectionName) => {
    await vectorTypesenseClient
      .collections(collectionName)
      .documents()
      .import(data, { action: "emplace", dirty_values: "drop" });
  },
} satisfies TypesenseConfig;

/**
 * Creates a Typesense vector store from a list of documents.
 * Will update documents if there is a document with the same id, at least with the default import function.
 * @param documents list of documents to create the vector store from
 * @returns Typesense vector store
 */
const createVectorStoreWithTypesense = async (documents: Document[] = []) =>
  Typesense.fromDocuments(
    documents,
    new OpenAIEmbeddings(),
    typesenseVectorStoreConfig
  );

/**
 * Returns a Typesense vector store from an existing index.
 * @returns Typesense vector store
 */
const getVectorStoreWithTypesense = async () =>
  new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);

// Do a similarity search
const vectorStore = await getVectorStoreWithTypesense();
const documents = await vectorStore.similaritySearch("hello world");

// Add filters based on metadata with the search parameters of Typesense
vectorStore.similaritySearch("Rowling", undefined, {
  filter_by: "author:!=JK Rowling",
});

// Delete a document
vectorStore.deleteDocuments(["document_id_1", "document_id_2"]);

For more details, you can refer to the updated documentation here ^[1][2].

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