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Milvus

Milvus is a vector database built for embeddings similarity search and AI applications.

Compatibility

Only available on Node.js.

Setup

  1. Run Milvus instance with Docker on your computer docs

  2. Install the Milvus Node.js SDK.

    npm install -S @zilliz/milvus2-sdk-node
  3. Setup Env variables for Milvus before running the code

    3.1 OpenAI

    export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
    export MILVUS_URL=YOUR_MILVUS_URL_HERE # for example http://localhost:19530

    3.2 Azure OpenAI

    export AZURE_OPENAI_API_KEY=YOUR_AZURE_OPENAI_API_KEY_HERE
    export AZURE_OPENAI_API_INSTANCE_NAME=YOUR_AZURE_OPENAI_INSTANCE_NAME_HERE
    export AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPENAI_API_VERSION_HERE
    export AZURE_OPENAI_BASE_PATH=YOUR_AZURE_OPENAI_BASE_PATH_HERE
    export MILVUS_URL=YOUR_MILVUS_URL_HERE # for example http://localhost:19530

Index and query docs

npm install @langchain/openai @langchain/core
import { Milvus } from "langchain/vectorstores/milvus";
import { OpenAIEmbeddings } from "@langchain/openai";

// text sample from Godel, Escher, Bach
const vectorStore = await Milvus.fromTexts(
[
"Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little\
Harmonic Labyrinth of the dreaded Majotaur?",
"Achilles: Yiikes! What is that?",
"Tortoise: They say-although I person never believed it myself-that an I\
Majotaur has created a tiny labyrinth sits in a pit in the middle of\
it, waiting innocent victims to get lost in its fears complexity.\
Then, when they wander and dazed into the center, he laughs and\
laughs at them-so hard, that he laughs them to death!",
"Achilles: Oh, no!",
"Tortoise: But it's only a myth. Courage, Achilles.",
],
[{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }],
new OpenAIEmbeddings(),
{
collectionName: "goldel_escher_bach",
}
);

// or alternatively from docs
const vectorStore = await Milvus.fromDocuments(docs, new OpenAIEmbeddings(), {
collectionName: "goldel_escher_bach",
});

const response = await vectorStore.similaritySearch("scared", 2);

Query docs from existing collection

import { Milvus } from "langchain/vectorstores/milvus";
import { OpenAIEmbeddings } from "@langchain/openai";

const vectorStore = await Milvus.fromExistingCollection(
new OpenAIEmbeddings(),
{
collectionName: "goldel_escher_bach",
}
);

const response = await vectorStore.similaritySearch("scared", 2);

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