firstbatch 0.1.73

FirstBatch SDK for integrating user embeddings to your project. Add real-time personalization to your AI application without user data.

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 [e-mail].

Resources

• User Embedding Guide
• SDK Documentation

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