Who should use the RAG-Powered Multi-Modal Search workflow?
Teams or solo builders working on data & ai search tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data & AI Search
Leverage NucliaDB to ingest, index, and search across documents, images, audio, and video with generative AI answers.
Deliverable outcome
Final deliverable is packaged and ready to publish or integrate.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Final deliverable is packaged and ready to publish or integrate.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use NucliaDB to inputs and setup are ready for the core execution step. Then, you pass the output to NucliaDB to supporting assets are prepared and connected to the main pipeline. Finally, NucliaDB is used to final deliverable is packaged and ready to publish or integrate.
Ingest and Enrich Multi-Modal Data
Inputs and setup are ready for the core execution step.
Index and Store High-Dimensional Vectors
Supporting assets are prepared and connected to the main pipeline.
Query and Generate Context-Aware Answers
Final deliverable is packaged and ready to publish or integrate.
Use NucliaDB's auto-embedding pipelines and Ingestion AI Agents to ingest documents, images, audio, and video, automatically extracting vectors and rich metadata.
Ingest and Enrich Multi-Modal Data sets up the inputs needed for stable execution.
Inputs and setup are ready for the core execution step.
Leverage NucliaDB's distributed indexing engine to store and manage high-dimensional vectors and metadata for fast retrieval.
Supporting inputs from this step improve quality and reduce rework later in the workflow.
Supporting assets are prepared and connected to the main pipeline.
Use NucliaDB's generative search and MCP to allow AI agents to query the knowledge base and synthesize answers from indexed content.
Delivery turns intermediate output into a usable result for real users or channels.
Final deliverable is packaged and ready to publish or integrate.
§ Before you start
Teams or solo builders working on data & ai search tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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