📄️ Indexing for Faster Queries
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🗃️ Ingestion
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📄️ Data Input & Output Formats
When sending data to or retrieving data from ApertureDB, encoding and decoding are common questions because ApertureDB deals with unstructured data.
📄️ Data Transformation Workflows
Checkout our Quick Start guide to set your ApertureDB server and client environment if not already set up.
📄️ VectorDB and RAG Resources
Curated videos, demos, notebooks, and reading for vector search and RAG with ApertureDB.
📄️ Building RAG Pipelines
Retrieval-Augmented Generation (RAG) retrieves the most relevant stored content at query time and passes it as context to an LLM. ApertureDB handles retrieval: embed the query, run KNN search against your stored Descriptors, return top-k results.
📄️ Parallel Ingestion Alternatives
Load large numbers of embeddings efficiently using ApertureDB's ParallelLoader. It ingests data concurrently using multiple threads, making it the right tool when you have thousands or millions of vectors to load.
📄️ Persistent Memory for AI Agents
Because embeddings, graph connections, and raw data all live in the same database, ApertureDB is a natural fit for agent memory. An agent can store observations as embeddings, retrieve the most relevant context at query time, and associate memories with graph entities like users, sessions, or documents.
📄️ Text Chunking
Most embedding models accept a maximum of 256–512 tokens. Longer documents must be split into overlapping passages before embedding so each chunk is semantically coherent and retrievable independently.
📄️ Structured Ingestion with DataModels
The ApertureDB Python SDK includes a Pydantic-based DataModels API that generates AQL queries from typed schemas. Use it when you want schema validation, readable ingestion code, or reusable models across multiple pipelines.