📄️ Labeling
Labeling is a common task in supervised machine learning involving multiple
📄️ Training and Inference
A key purpose of collecting labeled data is to use it for training and validation of ML models
📄️ AI/ML Models
As a database, ApertureDB is agnostic to which machine learning models our
📄️ LangChain
LangChain is a development framework that makes it easy to construct LLM-powered applications using a variety of different providers for different services. ApertureDB's capabilities fit within the LangChain ecosystem in two main areas: vector store and graph database.
📄️ LlamaIndex
LlamaIndex is a simple, flexible framework for building knowledge
📄️ Text Embedding Models
ApertureDB stores text embeddings as Descriptors in a DescriptorSet. Any model that produces a fixed-size float vector works — open-source local models and API-based models both follow the same pattern.
📄️ Image Embedding Models
ApertureDB stores images and their embeddings together, linked by a graph edge. A KNN query can traverse from matching descriptors directly to image blobs — no separate fetch step.
🗃️ Query Languages
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📄️ Video Embedding Models
ApertureDB stores video frame embeddings linked to the source video via graph edges. A single query retrieves matched frame metadata and the parent clip — no join required.