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Milvus.io PHP API Client

Milvus is an open-source vector database that is highly flexible, reliable, and blazing fast. It supports adding, deleting, updating, and near real-time search of vectors on a trillion-byte scale.

This package is an API Client for the Milvus v2.3.3 Restful API, and is built on the Saloon package.

Documentation about the Restful API is available on the Milvus website, and an OpenAPI spec is available here.

Versions

(*) But is mostly compatible, the only difference (that I can see) between them is the new Vector Upsert endpoint, and new parameters (params.range_filter and params.radius) in the Vector Search endpoint.

Installation

You can install the package via composer:

You can publish the config file with:

This is the contents of the published config/milvus.php file:

Usage

With Laravel

For Laravel users, you can use the Milvus facade to interact with the Milvus API:

Without Laravel

If you are not using laravel, you will have to create a new instance of the Milvus class and provide a token or user/pass, the host and the port.

Using with Zilliz Cloud

If you are using the hosted version of Milvus, you will need to specify the following host and port along with your API token:

Example: Semantic Search with Milvus and OpenAI Embeddings

This example demonstrates how to perform a semantic search in Milvus using embeddings generated from OpenAI.

Prepare Your Data

First, create an array of data you wish to index. In this example, we'll use blog posts with titles, summaries, and tags.

Generate Embeddings

Use OpenAI's embeddings API to convert the summaries of your blog posts into vector embeddings.

Create Milvus collection

Create a collection in Milvus to store your blog post embeddings, note that the dimension of the embeddings must match the dimension of the embeddings generated by OpenAI (1536 if you are using the text-embedding-ada-002 model).

Insert into Milvus

Insert these embeddings, along with other blog post data, into your Milvus collection.

Creating a Search Vector with OpenAI

Generate a search vector for your query, akin to how you processed the blog posts.

Searching using the Embedding in Milvus

Use the Milvus client to perform a search with the generated embedding.

Running Milvus in Docker

To quickly get started with Milvus, you can run it in Docker, by using the following command

A healthcheck endpoint will now be available on http://localhost:9091/healthz, and the Milvus API will be available on http://localhost:19530.

To stop Milvus, run docker compose down, to wipe all the data, run docker compose down -v.

For more details Installing Milvus Standalone with Docker Compose

For production workloads, consider checking out Zilliz.com, which are the developers behind Milvus and provides a hosted version of Milvus in the Cloud ☁️.

Testing

License

The MIT License (MIT). Please see License File for more information.

Disclaimer

"Milvus®" and the Milvus logo are registered trademarks of the Linux Foundation (LF Projects, LLC). This package is not affiliated with, endorsed by, or sponsored by the Linux Foundation. It's developed independently and uses the "Milvus" name under fair use, solely for identification. All trademarks and registered trademarks, including "Milvus®", are the property of their respective owners. "Milvus®" is a registered trademark of the Linux Foundation.