/firstbatch-sdk

Python SDK for FirstBatch: Real-time personalization using vectorDBs

Primary LanguagePythonMIT LicenseMIT

FirstBatch SDK

The FirstBatch SDK provides an interface for integrating vector databases and powering personalized AI experiences in your application.

Key Features

  • Seamlessly manage user sessions with persistent IDs or temporary sessions
  • Send signal actions like likes, clicks, etc. to update user embeddings in real-time
  • Fetch personalized batches of data tailored to each user's embeddings
  • Support for multiple vector database integrations: Pinecone, Weaviate, etc.
  • Built-in algorithms for common personalization use cases
  • Easy configuration with Python classes and environment variables

Getting Started

Prerequisites

  • Python 3.9+
  • API keys for FirstBatch and your chosen vector database

Installation

pip install firstbatch

Basic Usage

  1. Initialize VectorDB of your choice 
    api_key = os.environ["PINECONE_API_KEY"]
env = os.environ["PINECONE_ENV"]

pinecone.init(api_key=api_key, environment=env)
index = pinecone.Index("your_index_name")

# Init FirstBatch
config = Config(batch_size=20)
personalized = FirstBatch(api_key=os.environ["FIRSTBATCH_API_KEY"], config=config)

personalized.add_vdb("my_db", Pinecone(index, embedding_size=1536))

Personalization

  1. Create a session with an Algorithm suiting your needs

    session = personalized.session(algorithm=AlgorithmLabel.AI_AGENTS, vdbid="my_db")

  2. Make recommendations

    ids, batch = personalized.batch(session)

  3. Let users add signals to shape their embeddings

    user_pick = 0  # User liked the first content from the previous batch.
personalized.add_signal(session, UserAction(Signal.LIKE), ids[user_pick])

Support

For any issues or queries contact support@firstbatch.xyz.

Resources

Feel free to dive into the technicalities and leverage FirstBatch SDK for highly personalized user experiences.