StarRocks Multimodal: Connecting Content Understanding, Hybrid Search & Analytics

Introduction: From Billion-Scale Embeddings to Unified Data Pipelines

A foundation model training team preparing the next training round faces a familiar challenge: from billions of embeddings and multimodal corpora, they must narrow data by task, language, source, quality, and version, then combine keywords, semantic similarity, deduplication rules, and sample distributions to form candidate sets. After selection, they still need to link back to raw content, annotation results, training versions, and model evaluations to judge fitness. This is far more than a simple "vector in, top-K out" similarity search.

A complete training data selection pipeline typically involves structured filtering, full-text search, vector recall, set operations, and large-scale result analysis. Post-retrieval, teams must further assess quality, coverage, and impact on model training. Similar patterns appear in autonomous driving, embodied AI, short-form video, and gaming — despite different raw data and business goals, the processing chain shares a common structure:

Raw objects & business data → Content understanding & featurization → Structured, full-text & vector retrieval → Association with business outcomes → Feedback into training, production, or operations.

Historically, this chain required object storage, data processing jobs, model services, search engines, vector databases, and analytical databases — each handling one segment. Teams maintained multiple data copies and sync links, then re-stitched retrieval and analysis results at the application layer. The problem: when retrieval results must join with data versions, device states, quality results, or operational metrics, "finding data" and "analyzing data" cannot be cleanly separated. Vector recall is just one step; full business judgment still relies on filtering, joins, aggregation, and metric computation.

What "Multimodal" Means Here: One Continuous Pipeline for All Data Shapes

"Multimodal" and "omnimodal" are often discussed together, but the focus is not on how many modalities a model understands. Instead, it's about how enterprises organize their real-world data — structured, semi-structured, and unstructured — into a single continuous processing, retrieval, and analysis pipeline.

Data falls into three broad categories:

Structured data: time, device, user, vehicle model, version, status, metrics — fields with stable schemas.

Semi-structured data: JSON, Variant, dynamic attributes, model outputs — structures that may change.

Unstructured data: images, video, audio, documents, and other files in object storage.

Merely storing these in the same storage system does not achieve omnimodal. Three further problems must be solved:

How to understand content: convert images, video, audio, documents into descriptions, tags, structured fields, or vectors.

How to find targets: combine deterministic conditions, keywords, and semantic similarity for retrieval.

How to use results: associate hit content with training results, device states, quality data, or operational metrics, then continue with joins, aggregation, and analysis.

Thus, omnimodal is not a new data type, nor does it require a single model or system to solve everything. It describes a usage pattern: let structured, semi-structured, and unstructured data pass through content understanding and featurization, then enter the same retrieval and analysis pipeline, ultimately participating in business computation and decision-making.

Why Vector Search Alone Is Not Enough in the AI Era

Traditionally, analytical systems received already cleaned and modeled data. Databases handled orders, device events, user behavior, and business metrics; images, video, and documents existed only as URLs, their actual content opaque to queries.

AI changes this. Models can batch-generate image descriptions, extract document fields, recognize video scenes, classify content, and map text, images, audio, and video into comparable vectors. Vast amounts of previously human-browsed unstructured content become searchable, computable data.

But turning content into vectors does not reduce business problems to a single vector top-K query. Consider autonomous driving long-tail scenario mining: teams may need "night, rain, intersection, vehicle avoiding sudden pedestrian." Visual and motion similarity can be retrieved via vectors, but queries also need to constrain vehicle model, software version, collection time, sensor status, match fault codes or event tags, and correlate hit clips with annotation results, model outputs, and regression test performance.

Similar needs exist across industries:

Foundation model training: filter corpora by source, language, quality, version; combine semantic similarity, deduplication, and model evaluation.

Embodied AI: temporally align camera, LiDAR, IMU, tactile, and action trajectories; find failure segments and similar successful experiences.

Short-form video & content production: search scripts, storyboards, video, audio, subtitles; check roles, copyright, versions, distribution effects.

Game & IP asset management: link characters, concept art, 3D models, animations, dialogue, design docs; maintain versions, localization, world consistency.

Despite different goals, these queries typically contain four condition types:

Deterministic conditions: permissions, time, device, version.

Exact text conditions: fault codes, role names, domain terminology.

Semantic similarity: visual, motion, or textual.

Association of retrieval results with training, quality, production, or operational metrics.

Vector search answers "is content semantically similar?" but cannot alone handle permission filtering, exact matching, temporal correlation, set operations, and result analysis. In real scenarios, vector recall must collaborate with full-text search, structured filtering, and SQL analytics.

Why StarRocks Builds Omnimodal Capabilities

StarRocks' core role has been real-time analysis of structured business data: joins, aggregation, multi-dimensional queries. When images, video, documents, and their descriptions, tags, and vectors enter business workflows, data shapes change, but many queries still ultimately rely on those analytical capabilities.

After training data selection, teams compare different data and model versions. After defect images are recalled, they link to devices, locations, and handling tickets. After content assets are found, they check copyright status, channel versions, and distribution effects. After knowledge fragments are retrieved, they layer tenant, permission, and time conditions.

The commonality: retrieval is not the query endpoint. Hit results must further join with business data, participate in filtering, joins, aggregation, and metric calculation to produce usable results for training, production, or operations.

If full-text and vector search live in separate systems, teams typically copy data to search engines or vector databases, return hit IDs to the analytical database for further computation. Data updates, deletes, permission changes, and schema evolution must then stay consistent across multiple systems.

Therefore, StarRocks builds omnimodal capabilities not to replace object storage, model platforms, or all specialized retrieval systems, but to progressively bring object processing, semi-structured analysis, full-text search, and vector search into the existing SQL analytics pipeline:

Let model-generated descriptions, tags, structured fields, and vectors continue as queryable, computable data.

Let full-text and vector recall results directly participate in filtering, joins, aggregation, and metric analysis.

StarRocks' core role in this chain is connecting content understanding, hybrid retrieval, and business analytics, reducing data duplication, result shuffling, and pipeline fragmentation caused by "find in one system, analyze in another."

How StarRocks Organizes Omnimodal Capabilities

StarRocks' omnimodal capabilities evolve along four directions: multimodal processing, internal tables, Paimon, and Lance. They address different needs: object content processing, real-time retrieval & analysis, lake-scale data management, and ingestion of existing multimodal/vector data.

These directions are not mutually exclusive; real projects combine them based on raw data location, update frequency, data scale, and query patterns.

1. Multimodal Processing: Turning Raw Objects into Computable Data

Images, video, audio, and documents usually sit in object storage as files; databases only store object locations and minimal metadata. To make content queryable, object discovery, understanding, and featurization must happen first.

Object Table manages file locations, versions, and basic metadata in object storage, ingesting raw objects into the data processing flow. AI Function calls models to perform content understanding, field extraction, classification, generation, and vectorization.

Raw files remain in object storage; generated descriptions, tags, structured fields, and vectors flow into downstream filtering, retrieval, and analysis pipelines.

2. StarRocks Internal Tables: For Faster-Updating Retrieval & Analysis

When data needs fast writes and immediate queryability, and retrieval results must frequently join with business fields for filtering, joins, aggregation, and online analysis, StarRocks internal tables can hold searchable features and hot data.

Internal tables organize structured fields, Flat JSON, text, and vectors, and support combined queries via full-text indexes, vector indexes, and regular SQL conditions. Suited for scenarios demanding high data freshness and interactive analysis.

3. Paimon: Managing Large-Scale, Long-Term Omnimodal Data

When image, video, trajectory, and document data volumes are large, and open formats, object storage cost efficiency, multi-engine sharing, and snapshot/version capabilities are needed, Paimon can organize and manage the data.

Structured fields, Variant semi-structured content, BLOB unstructured objects, text descriptions, and vectors can be organized around the same lake table. Data snapshots and versioning can fix training data boundaries, providing a data foundation for training reproducibility, historical rollback, and cross-engine usage.

4. Lance: Ingesting Existing Multimodal & Vector Data

Some teams already use Lance to manage multimodal and vector data. For these scenarios, StarRocks can connect via the Lance Connector, preserving existing data organization while providing a unified SQL query entry point, and continuing to execute structured filtering, full-text or vector search, and result analysis.

Production use requires checking the specific Lance Connector support scope and query boundaries for the corresponding version.

Choosing a Path: Start from Data Location and Query Needs

Selecting an omnimodal processing and query approach cannot rely solely on whether vector indexes are supported. One must comprehensively judge: where raw data resides, data scale and update frequency, whether a specific data format is already adopted, and whether retrieval results must further participate in joins, aggregation, and business analysis.

These capabilities are not mutually exclusive. Real projects often combine multiple paths across the data lifecycle: raw files stay in object storage, models generate descriptions, tags, and vectors; Paimon manages large-scale long-term data; StarRocks internal tables carry frequently updated or low-latency-query data; existing Lance data can be connected via Connector and queried uniformly with business details and metrics.

Omnimodal Queries Go Beyond Retrieval

In omnimodal scenarios, finding similar content is usually just the first step. To produce results usable for training, quality analysis, production, or operations, candidate data must be combined, associated, excluded, statistically analyzed, and version-managed.

A complete query pipeline may include:

Filter by tags, metadata, and JSON fields; run full-text search or vector top-K alone, or combine structured conditions, keywords, and vector recall for hybrid retrieval.

Perform intersections, unions, semi-joins, and anti-joins across multiple scenario trees or candidate sets — e.g., exclude already-annotated or already-in-training-set data.

Correlate video frames, sensor data, event segments, and model results via temporal relationships: point-in-interval, interval overlap.

While returning hit details, compute total hit counts, tag distributions, quality distributions, and bucketed statistics.

Generate traceable sample manifests by dataset version, data snapshot, and training recipe.

The value of omnimodal capabilities depends not only on "what was retrieved" but on whether those results can continue into training, quality, production, or operational workflows and be accurately reproduced in subsequent use.

Series Preview

This article introduced the overall direction of StarRocks omnimodal capabilities. Upcoming content will dive into:

Multimodal Processing: How AI Function and Object Table handle object discovery, content understanding, featurization, and incremental processing.

Internal Table Full-Text Search: Text indexing, keyword retrieval, and collaboration of full-text search with structured filtering, joins, and aggregation.

Internal Table Vector Search: Vector data organization, indexing, semantic top-K queries, and how recall results enter the SQL analytics pipeline.

Omnimodal Hybrid Retrieval: Combining structured conditions, full-text search, and vector recall for complex data selection.

Paimon Omnimodal Retrieval: Organizing and querying structured fields, Variant, BLOB, text, and vectors around a single lake table.

Lance Omnimodal Retrieval: How StarRocks connects to Lance multimodal/vector data and performs filtering, retrieval, and analysis.

Industry Practices: Application methods in foundation model training, autonomous driving, embodied AI, short-form video production, and game/IP asset management.

Related code has been merged into the StarRocks main branch and will ship in the next release. Future articles will further detail usage and applicable scenarios per capability as versions progress.