Langchain

Index allows you to search by vectors, by providing a LangChain plugin. This document will guide you through the steps to use this feature.

Search by Vector using IndexVectorStore

First install and import necessary libraries.

JavaScript

import IndexClient, {
  IndexVectorStore,
} from "@indexnetwork/sdk";
import { Wallet } from "ethers";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";

Python

message = "hello world"
print(message)

Then, create the IndexVectorStore instance with your configuration. The sources are the IDs of the Indexes that the search will operate on.

JavaScript

const wallet = new Wallet(process.env.PRIVATE_KEY);
const indexClient = new IndexClient({
  network: "dev", // or mainnet
  wallet, // or session
  domain: "index.network",
});

const embeddings = new OpenAIEmbeddings({
  apiKey: process.env.OPENAI_API_KEY,
  model: "text-embedding-ada-002",
});

const sourceIndexId =
  "kjzl6kcym7w8y7lvuklrt4mmon5h9u3wpkm9jd9rtdbghl9df2ujsyid8d0qxj4";

const vectorStore = new IndexVectorStore(embeddings, {
  client: indexClient,
  sources: [sourceIndexId],
});

Python

message = "hello world"
print(message)

Perform operations using the VectorStore

IndexVectorStore object provides the same Langchain methods. Here are a few examples:

Similarity Search

JavaScript

const question = "What is mesh.xyz?";
const response = await vectorStore.similaritySearch(question, 1);

Python

message = "hello world"
print(message)

It should print as below. Remember that pageContent is the stringified item data JSON.

[
    {
    "pageContent": "{...}",
    "metadata": {},
    "id": "kjzl6kcym7w8y5slja1yq8upkixhskarjd8292qsa9bmhh15xci1ndlzkhju9ie"
 },
]

Conversational Retrieval

JavaScript

const model = new ChatOpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  model: "gpt-3.5-turbo",
});

const chain = ConversationalRetrievalQAChain.fromLLM(
  model,
  vectorStore.asRetriever(),
);

/* Ask it a question */
const response = await chain.invoke({ question, chat_history: [] });const message = "hello world";

Python

message = "hello world"
print(message)

Response would be as following:

{
   "text": "Mesh.xyz is a platform that connects people, projects, and protocols building Web3. Founded in 2015 by Ethereum co-founder Joseph Lubin, Mesh has four core components: investment, incubation, research & development, and acceleration. They support a network of founders and builders, guiding projects through accelerators like Tachyon. Mesh aims to create a collaborative community of innovators to shape the future of Web3."
}