relevanceai 3.2.14

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Relevance AI - The ML Platform for Unstructured Data Analysis

🌎 80% of data in the world is unstructured in the form of text, image, audio, videos, and more.

🔥 Use Relevance to unlock the value of your unstructured data:

• ⚡ Quickly analyze unstructured data with pre-trained machine learning models in a few lines of code.
• ✨ Visualize your unstructured data. Text highlights from Named entity recognition, Word cloud from keywords, Bounding box from images.
• 📊 Create charts for both structured and unstructured.
• 🔎 Drilldown with filters and similarity search to explore and find insights.
• 🚀 Share data apps with your team.

Sign up for a free account ->

Relevance AI also acts as a platform for:

• 🔑 Vectors, storing and querying vectors with flexible vector similarity search, that can be combined with multiple vectors, aggregates and filters.
• 🔮 ML Dataset Evaluation, for debugging dataset labels, model outputs and surfacing edge cases.

🧠 Documentation

Type	Link
Python API	Documentation
Python Reference	Documentation
Cloud Dashboard	Documentation

🛠️ Installation

Using pip:

pip install -U relevanceai

Using conda:

conda install -c relevance relevanceai

⏩ Quickstart

Login to relevanceai:

from relevanceai import Client

client = Client()

Prepare your documents for insertion by following the below format:

• Each document should be a dictionary
• Include a field _id as a primary key, otherwise it's automatically generated
• Suffix vector fields with _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"},
]

Insert data into a dataset

Create a dataset object with the name of the dataset you'd like to use. If it doesn't exist, it'll be created for you.

ds = client.Dataset("quickstart")
ds.insert_documents(docs)

Quick tip! Our Dataset object is compatible with common dataframes methods like .head(), .shape() and .info().

Perform vector search

query = [
    {"vector": [0.2, 0.2, 0.2], "field": "example_vector_"}
]
results = ds.search(
    vector_search_query=query,
    page_size=3,
)

Learn more about how to flexibly configure your vector search ->

Perform clustering

Generate clusters

clusterop = ds.cluster(vector_fields=["example_vector_"])
clusterop.list_closest()

Generate clusters with sklearn

from sklearn.cluster import AgglomerativeClustering

cluster_model = AgglomerativeClustering()
clusterop = ds.cluster(vector_fields=["example_vector_"], model=cluster_model, alias="agglomerative")
clusterop.list_closest()

Learn more about how to flexibly configure your clustering ->

🧰 Config

The config object 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"

🚧 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:

Don't forget 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