Introduction to Relevance AI

Python Library for Data Teams


Python Library - Deep Experimentation & Flexibility for Data Teams:

The Relevance AI Python SDK/Library is designed for Data Science and Machine Learning practitioners to customize and interact with Relevance's API deeply.

The only unstructured data platform you need.

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

Relevance AI unlocks the value of unstructured data by helping you:

  • ⚡ 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.

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.

Key features of the SDK:

  1. Interact with the API.
  2. Asynchronous data ingestion, transformation and retrieval.
  3. Insert data from your pandas dataframe, csv, numpy arrays, local file systems.
  4. Out-of-the-box integration with popular libraries like HuggingFace, Tensorflow Hub, etc.
  5. Dynamically deploy and edit data apps.

For SDK Reference, it is hosted at: