superlinked/superlinkedArchived

Features

• Semantic Vector Search - Executes semantic searches combined with metadata filters and dynamic parameters to retrieve relevant results from vector databases.
• Vector Search Frameworks - Provides a unified development framework for building semantic search and retrieval pipelines.
• Data Modeling and Schemas - Constructs structured data models that map raw content and metadata into unified representations for consistent organization.
• Data Processing Pipelines - Orchestrates multi-stage workflows that transform and embed unstructured data through a unified execution graph for search applications.
• Schema-Driven Data Modeling - Defines structured data models and processing pipelines to unify the ingestion, evaluation, and serving of information.
• Semantic Retrieval Engines - Executes high-performance semantic searches with granular scoring and explainability for retrieved results.
• Metadata-Aware Querying - Enables combining vector similarity searches with logical boolean filtering to retrieve relevant results based on both content and structured attributes.
• Structured Data Schemas - Defines structured data models and processing pipelines within a single codebase to unify information ingestion and serving.
• Declarative Index Schemas - Defines structured data models declaratively to translate raw inputs into unified vector representations for efficient search and retrieval.
• Embedding Generation - Generates vector representations from text, images, and numerical data using pre-trained or custom models.
• Machine Learning Pipelines - Orchestrates the end-to-end flow of multi-modal data processing and model execution for search applications.
• Embedding Models - Handles the lifecycle, warm-up routines, and persistence of embedding models used for generating vector representations.
• Machine Learning Model Lifecycle Managers - Manages the lifecycle, persistence, and execution of embedding models within the data pipeline.
• Model Orchestrators - Manages the lifecycle and execution of various machine learning models to convert multi-modal data into high-dimensional vector spaces.
• Vector Database Integrations - Connects to external vector storage systems to perform read and write operations while managing connection settings and search algorithms.
• Multi-Modal RAG Pipelines - Provides end-to-end pipelines for encoding and processing multi-modal data into unified vector representations.
• Search Result Filtering - Filters query results by applying logical predicates and boolean constraints based on metadata fields or schema attributes.
• Storage Data Encodings - Encodes unstructured data and metadata into unified vector embeddings to improve search and recommendation accuracy.
• Vector Database Abstractions - Provides a standardized interface for interacting with diverse vector storage backends while managing connection lifecycles and search parameters.
• Vector Database Integrations - Integrates with diverse vector storage backends to manage read, write, and search operations.
• Vector Database Orchestrators - Acts as an orchestration layer for managing vector database connections, search logic, and metadata filtering.
• Self-Hosted Search Services - Deploys a self-hosted server to manage data ingestion, model inference, and connections to external database providers.

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