nnext 0.0.35

Python client library for the NNext. A ⚡ blazingly fast, 🔍 nearest-neighbors vector search engine for building delightful ML apps

About

NNext is a

• ⚡ blazingly fast

• 📖 source-available [Elastic License 2.0]

• 🔍 nearest-neighbors vector search engine

Quick Start

Here’s a quick example showcasing how you can create an index, insert vectors/documents and search it on NNext.

Let’s begin by starting the NNext server via Docker:

docker run -p 6040:6040 -v/tmp/data:/data nnext/nnext:latest --data-dir /data --api-key=Hu52dwsas2AdxdE

We have a API Client in python only, but let’s use it for this example.

Install the Python client for NNext:

pip install nnext

We can now initialize the client, create a test index

import numpy as np
import nnext
from nnext import _and, _eq, _gte, _in

# Create and initialize the vector client
nnclient = nnext.Client(
    nodes=[
    {'host': 'localhost', 'port': '6040'}
  ])

Broadly speaking, you can create two types of indices

1. Simple indices

n_dim = 768

# Create an vector index.
nnindex = nnclient.index.create(
    d=n_dim,
    name='test_ANN')

n_vecs = 1000
k = 5
n_queries = 10
vectors = np.random.rand(n_vecs, n_dim)

# Insert vectors into the index.
nnindex.add(vectors)

# Create a query vector set.
q_vectors = np.random.rand(n_queries, n_dim)

# Now search the vectors.
_idx, _res = nnindex.search(q_vectors, k)  # search

# The search operation returns a tuple of vectors and optionally the data
# associated with the vectors.

2. Compound indices

🚧 WIP 🚧. Not implemented.

NNext is capable of storing additional metadata related to your vectors in a rich format. In this example we will use the movie plots dataset from Kaggle.

nnindex = client.index.create({
  "name": "movies",
  "schema": {
      "id" : "string", #⬅ inferred primary key
      "title" : "string",
      "released_year" : "int32",
      "genre" :  "float",
      "wikipage" : "string",
      "plot" : "string",
      "rating" :  "float"
  },
  "index_type": "approximated", #⬅ indexes assumed to be approximated by default.
  "dims": n_dim
})

Now, let’s add a vector to the collection we just created:

vector = {
 "id": "124",
 "company_name": "Stark Industries",
 "num_employees": 5215,
 "country": "USA",
}

nnindex.documents.create(document)

Finally, let’s search for the document we just indexed:

q_filter = {
    _and: [
        { "Release Year": { _gte: 2015 } },
        { "Genre": { _eq: "comedy" } },
        { "actors": { _in: ["Russell Crowe"] } }
    ]
}

client.collections['companies'].documents.search(search_parameters)

Contributing

Introduction

First off, 🙏🏾 thank you for considering contributing to nnext. We value community contributions!

How can you help?

You may already know what you want to contribute – a fix for a bug you encountered, or a new feature your team wants to use.

If you don’t know what to contribute, keep an open mind! Here’s some examples of helpful contributions that mean less work for you

• Improving documentation

• bug triaging

• writing tutorials

Checkout guide to contributing to learn more.

Documentation

All NNext Server and Client documentation, including pynext integration articles and helpful recipes, can be found at:

https://nnext.ai/docs/