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
The system is optimized for production use with consistent performance and scalability.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
The system is optimized for production use with consistent performance and scalability.
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 nucliadb is live and ready to receive multi-modal data from configured sources. Then, you pass the output to NucliaDB to all multi-modal files have been processed into raw text, captions, and metadata. Then, you pass the output to NucliaDB to every content chunk now has a vector embedding and enriched metadata for semantic search. Then, you pass the output to NucliaDB to a high-performance vector index is ready for multi-modal similarity search. Then, you pass the output to NucliaDB to users can search across all data types with a single query and get unified results. Then, you pass the output to NucliaDB to users receive a synthesized, cited answer derived from multi-modal data. Finally, NucliaDB is used to the system is optimized for production use with consistent performance and scalability.
Configure NucliaDB and Multi-Modal Data Sources
NucliaDB is live and ready to receive multi-modal data from configured sources.
Ingest and Extract Raw Content from All Modalities
All multi-modal files have been processed into raw text, captions, and metadata.
Enrich Content with AI-Generated Embeddings and Labels
Every content chunk now has a vector embedding and enriched metadata for semantic search.
Index Vectors and Metadata in NucliaDB
A high-performance vector index is ready for multi-modal similarity search.
Implement Multi-Modal Query Interface
Users can search across all data types with a single query and get unified results.
Generate Context-Aware Answers with RAG
Users receive a synthesized, cited answer derived from multi-modal data.
Monitor, Tune, and Scale the System
The system is optimized for production use with consistent performance and scalability.
Set up a NucliaDB instance (cloud or self-hosted) and connect your data sources: document storage (S3, local), image repositories, audio/video files. Define ingestion pipelines for each modality, ensuring file formats (PDF, MP4, WAV, JPEG) are supported. This step establishes the foundation for all subsequent processing.
Why NucliaDB: NucliaDB is the core requirement for this step, providing semantic search over multi-modal documents and automated ingestion/indexing, directly matching the need to configure NucliaDB and multi-modal data sources.
Upload or stream documents, images, audio, and video into NucliaDB. The system automatically extracts text from PDFs/Word files, performs OCR on images, transcribes audio (speech-to-text), and extracts key frames with captions from video. This raw content becomes the basis for vectorization.
Why NucliaDB: NucliaDB's automated document ingestion and indexing directly handles ingesting and extracting raw content from multiple modalities, aligning with the step's need for the NucliaDB ingestion API.
Apply NucliaDB’s built-in AI models to generate high-dimensional vector embeddings for each extracted text segment, image, audio clip, and video frame. Additionally, run classification and entity recognition to enrich metadata. This step turns raw content into searchable vectors and structured tags.
Why NucliaDB: NucliaDB provides AI models for embedding generation (e.g., Sentence-BERT, CLIP) and NER, directly matching the need to enrich content with embeddings and labels.
Configure the vector index (HNSW or IVF) and metadata index in NucliaDB. Set similarity metrics (cosine, dot product) and index parameters (M, efConstruction). The system automatically indexes all embeddings and metadata, enabling fast approximate nearest neighbor search across modalities.
Why NucliaDB: NucliaDB's indexing engine is the primary tool for indexing vectors and metadata, directly fulfilling the step's requirement for NucliaDB indexing and HNSW library integration.
Build or use NucliaDB’s built-in search API to accept queries in text, image, or audio form. Convert user queries into the same embedding space (e.g., text-to-vector, image-to-vector) and perform hybrid search combining vector similarity with metadata filters. Return ranked results from all modalities.
Why NucliaDB: NucliaDB SDK enables building a multi-modal query interface directly, matching the step's need for NucliaDB SDK and embedding model integration.
Pass the top-k retrieved chunks (from any modality) to a large language model (LLM) via NucliaDB’s generative AI integration. The LLM synthesizes a natural language answer using the retrieved context, citing sources. This step provides the 'RAG' in RAG-powered search.
Why NucliaDB: NucliaDB includes a generative AI module for RAG, directly supporting context-aware answer generation with its RAG pipeline evaluation and optimization.
Set up logging and metrics for query latency, recall, and answer quality. Fine-tune embedding models, index parameters, and LLM prompts based on usage patterns. Scale NucliaDB horizontally for larger datasets. This step ensures long-term reliability and performance.
Why NucliaDB: NucliaDB provides admin tools and RAG pipeline evaluation/optimization, directly addressing the need to monitor, tune, and scale the system.
§ 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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