data_entry_ops · workflow
ApertureDB builds JSON-first AQL for multimodal AI with RAG, GraphRAG, and agentic MCP interfaces
SQL and graph query languages could not handle multimodal AI queries—involving vector search, cross-modal joins, and media preprocessing—without compounding JOIN complexity, performance penalties, and language-extension sprawl that slowed development and broke compatibility.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Natural language query submitted
A user submits a natural language query requesting multimodal content such as highlights, pictures, video snippets, and performer lists.
Tools used
ApertureDBAQLRAGGraphRAGMCPOpenCVFFMPEGLabelStudioGrafana · partnerMetaBase · partnerPixeltablePandasSQLAlchemySPARQL
Outcome
ApertureDB built AQL, a JSON-first query language natively supporting multimodal data types, vector search, graph traversal, and preprocessing, with layered interfaces including RAG/GraphRAG chatbots and an MCP server for agentic systems.
What failed first
SQL required ever-growing JOIN chains and syntax extensions for each new multimodal data type, hurting development speed while breaking SQL compatibility; graph query languages lacked native vector search and data preprocessing support.
Grounding & classification
Source type: technical build writeup
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agentic workflowchatbotknowledge searchragfailure mode describedtools describedworkflow describedsoftwaretechnical build writeupdata entry opsagentic task executionrag answering