Skip to main content

Github Banner

Documentation Status License

For guides, tutorials on how to use this package, visit https://docs.relevance.ai/docs.

🔥 Features

Features of the library include:

  • Quick vector search with free dashboard to preview results
  • Vector clustering with support with built-in easy customisation
  • Multi-vector search with filtering, facets, weighting
  • Hybrid search (weighting exact text matching and vector search together) ... and more!

🧠 Documentation

There are two main ways of documentations to take a look at:

API type Link
Guides Documentation
Python Reference Documentation

🛠️ Installation

pip install -U relevanceai

Or you can install it via conda to:

conda install pip
pip install -c relevanceai

You can also install on conda (only available on Linux environments at the moment): conda install -c relevance relevanceai.

⏩ Quickstart

Login into your project space

from relevanceai import Client

client = Client(<project_name>, <api_key>)

This is a data example in the right format to be uploaded to relevanceai. Every document you upload should:

  • Be a list of dictionaries
  • Every dictionary has a field called _id
  • Vector fields end in vector
docs = [
    {"_id": "1", "example_vector_": [0.1, 0.1, 0.1], "data": "Documentation"},
    {"_id": "2", "example_vector_": [0.2, 0.2, 0.2], "data": "Best document!"},
    {"_id": "3", "example_vector_": [0.3, 0.3, 0.3], "data": "document example"},
    {"_id": "4", "example_vector_": [0.4, 0.4, 0.4], "data": "this is another doc"},
    {"_id": "5", "example_vector_": [0.5, 0.5, 0.5], "data": "this is a doc"},
]

Upload data into a new dataset

The documents will be uploaded into a new dataset that you can name in whichever way you want. If the dataset name does not exist yet, it will be created automatically. If the dataset already exist, the uploaded _id will be replacing the old data.

client.insert_documents(dataset_id="quickstart", docs=docs)
client.services.search.vector(
    dataset_id="quickstart",
    multivector_query=[
        {"vector": [0.2, 0.2, 0.2], "fields": ["example_vector_"]},
    ],
    page_size=3,
    query="sample search" # Stored on the dashboard but not required

🚧 Development

Getting Started

To get started with development, ensure you have pytest and mypy installed. These will help ensure typechecking and testing.

python -m pip install pytest mypy

Then run testing using:

Make sure to set your test credentials!

export TEST_PROJECT = xxx
export TEST_API_KEY = xxx

python -m pytest
mypy relevanceai

Set up precommit

pip install precommit
pre-commit install

🧰 Config

The config contains the adjustable global settings for the SDK. For a description of all the settings, see here.

To view setting options, run the following:

client.config.options

The syntax for selecting an option is section.key. For example, to disable logging, run the following to modify logging.enable_logging:

client.config.set_option('logging.enable_logging', False)

To restore all options to their default, run the following:

Changing the base URL

You can change the base URL as such:

client.base_url = "https://.../latest"

You can also update the ingest base URL:

client.ingest_base_url = "https://.../latest

Release files for RelevanceAI-dev 0.33.0.2022.2.3.13.39.59.245239

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for RelevanceAI-dev 0.33.0.2022.2.3.13.39.59.245239
File Size Uploaded
RelevanceAI-dev-0.33.0.2022.2.3.13.39.59.245239.tar.gz 125.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for RelevanceAI-dev 0.33.0.2022.2.3.13.39.59.245239
File Interpreter ABI Platform
RelevanceAI_dev-0.33.0.2022.2.3.13.39.59.245239-py3-none-any.whl Python 3 none any Details

Total release size: 297.3 kB

Release files / RelevanceAI-dev-0.33.0.2022.2.3.13.39.59.245239.tar.gz

Download URL RelevanceAI-dev-0.33.0.2022.2.3.13.39.59.245239.tar.gz
Size 125.9 kB
Tags Source
SHA-256 checksum
How to use checksums
641a816afdd6624c3542f69de499ab6d6f98e3b987ec2a21100356c03c0356cf
BLAKE2b-256 checksum
How to use checksums
6c37492e1a9c40ea8df139bba8bf6a254447e26d3e4a9503bb838bee4f7fbe32
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.10.2

Release files / RelevanceAI_dev-0.33.0.2022.2.3.13.39.59.245239-py3-none-any.whl

Download URL RelevanceAI_dev-0.33.0.2022.2.3.13.39.59.245239-py3-none-any.whl
Size 171.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b2d3fe9475bc4e0ccef70d1f0f51009e974778ca70d47136098df7dd182a8c2b
BLAKE2b-256 checksum
How to use checksums
00bcd041f1f411279f232aa54768a42fc1b971247f2be0090232493027ecbfc9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.10.2

Release history Release notifications | RSS feed

This release

0.17.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page