The FirstBatch SDK provides an interface for integrating vector databases and powering personalized AI experiences in your application.
- 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
- Python 3.9+
- API keys for FirstBatch and your chosen vector database
pip install firstbatch
- 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))
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Create a session with an Algorithm suiting your needs
session = personalized.session(algorithm=AlgorithmLabel.AI_AGENTS, vdbid="my_db")
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Make recommendations
ids, batch = personalized.batch(session)
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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])
For any issues or queries contact [email protected].
Feel free to dive into the technicalities and leverage FirstBatch SDK for highly personalized user experiences.