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

2. Create a session with an Algorithm suiting your needs

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

3. Make recommendations

   ids, batch = personalized.batch(session)
   

4. 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

• User Embedding Guide
• SDK Documentation

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