Vectorlake

The Vectorlake Library is a Python-based integration tool designed to simplify the implementation of vector-based operations for your product catalog. It provides a streamlined interface to interact with the Vectorlake endpoints, making it easier for developers to create, fetch, push, and search vectors. By integrating this library, businesses can harness the power of vector embeddings to enable AI-driven features like semantic search, personalized recommendations, and similarity matching.

By leveraging vector embeddings, Vectorlake enables precise and context-aware operations, making it ideal for use cases like personalized product recommendations, similarity searches, and metadata-driven catalog optimizations.

Key Features

1. Vector Creation: Generate vector embeddings for product catalogs using the vectorlake/vector/create endpoint. This allows for efficient representation of products based on their attributes.

2. Vector Fetching: Retrieve existing vectors using the vectorlake/vector/fetch endpoint, enabling seamless integration into downstream applications.

3. Vector Storage: Push custom vectors (text, audio, image, or multimodal) and associated metadata into the platform using the vectorlake/vector/push endpoint for future use.

4. Vector Search: Perform advanced searches with the vectorlake/vector/search endpoint by comparing vectors or applying metadata filters, making it perfect for similarity searches and intelligent catalog queries.

Why Choose Vectorlake?

• Scalable: Handles high-volume vector operations with ease.

• Versatile: Supports multiple data types, including text, audio, image, and multimodal vectors.

• AI-Ready: Designed for integration with AI/ML systems for enhanced catalog intelligence.

• Developer-Friendly: Simple, well-documented APIs for fast and efficient integration.