Multimodal RAG
Build multimodal retrieval-augmented generation pipelines that search and synthesize answers from text, images, video, and audio. Go beyond text-only RAG with Mixpeek multimodal embeddings and retrievers.
AI engineering teams, product builders, and enterprise developers building RAG applications that need to reason over documents, images, video, and audio rather than text alone
Traditional RAG pipelines only understand text. When your knowledge base includes product images, instructional videos, audio recordings, diagrams, and scanned documents, text-only retrieval misses the majority of your information. Answers are incomplete, hallucination rates increase, and users lose trust in the system.
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Before & After Mixpeek
Before
Knowledge coverage
Text documents only, ~40% of total knowledge base
Answer completeness
Missing visual context, diagrams, and multimedia references
Retrieval pipeline
Separate systems per modality, no cross-modal search
After
Knowledge coverage
All modalities indexed, 100% of knowledge base searchable
Answer completeness
LLM receives text, image, video, and audio context
Retrieval pipeline
Single unified retriever across all content types
Answer accuracy (factual grounding)
+26%
Knowledge base coverage
2.5x
Retrieval pipeline complexity
75% reduction
Why Mixpeek
Purpose-built for multimodal retrieval rather than bolted-on image search. Mixpeek produces unified embeddings across modalities so text queries find relevant images, video queries surface related documents, and cross-modal reasoning happens naturally. Feature extractors, collections, and retrievers are designed as composable primitives that integrate with any LLM framework.
Overview
Multimodal RAG extends retrieval-augmented generation beyond text to include images, video, audio, and complex documents. Standard RAG pipelines chunk text, embed it, and retrieve relevant passages for LLM generation. But real-world knowledge bases are multimodal: product catalogs contain images and specifications, training libraries include video lectures, support systems reference diagrams and screenshots, and compliance archives hold scanned documents with stamps and signatures. A multimodal retrieval layer makes all of this content available to your RAG pipeline. Instead of building separate retrieval systems for each modality, a unified multimodal index brings them together. A user question about a product feature retrieves the relevant documentation paragraph, the product image showing that feature, and the video tutorial demonstrating it. Your LLM receives rich, multimodal context and produces more complete, more accurate, and more trustworthy answers. The architecture is straightforward: ingest content through collections with feature extractors configured per content type, organize into namespaces for data isolation, and query through retrievers that combine semantic search with metadata filters. The retriever output feeds directly into your LLM context window. The hard parts of multimodal retrieval include cross-modal embedding alignment, efficient vector search at scale, chunking strategies for video and long documents, and relevance ranking across heterogeneous content types. A well-designed retrieval infrastructure handles these complexities so you can focus on your application logic and user experience.
Challenges This Solves
Text-Only Retrieval Gaps
Standard RAG pipelines only index text, missing information encoded in images, video, audio, and document layouts
Impact: 40-60% of organizational knowledge is non-textual. Text-only RAG produces incomplete answers and higher hallucination rates when relevant information exists in other modalities.
Cross-Modal Alignment
Building separate retrieval systems per modality creates silos where a text query cannot find relevant images and a visual query cannot surface related documents
Impact: Users must know which modality contains the answer and search each system separately, defeating the purpose of unified knowledge access.
Chunking and Embedding Heterogeneity
Video, images, and complex documents require fundamentally different chunking and embedding strategies than plain text
Impact: Naive approaches (e.g., only indexing transcripts from video) lose visual and structural information that carries critical context.
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
embed text
embed image
transcription
ocr text extraction
scene classification
Retriever Stages Used
semantic search
filter aggregate
Expected Outcomes
+26% over text-only RAG
Answer accuracy with multimodal context
100% of content indexed and retrievable
Knowledge base coverage
Hours instead of months
Time to build multimodal retrieval
35% fewer unsupported claims
Hallucination rate reduction
Build this in the docs
The exact stages and extractors this use case runs on, with API reference and worked examples.
Build Multimodal RAG in Under an Hour
Clone the multimodal RAG pipeline, connect your content sources, and start retrieving across text, images, video, and audio.
Frequently Asked Questions
How does multimodal RAG differ from standard text-based RAG?
What LLM frameworks does Mixpeek integrate with for RAG?
How are video and audio content chunked for retrieval?
Can a single query retrieve results from multiple modalities simultaneously?
How does Mixpeek handle large document ingestion for RAG?
What is the retrieval latency for multimodal queries?
How do I evaluate multimodal RAG quality?
Can I use multimodal RAG for internal enterprise knowledge bases?
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Ready to Implement This Use Case?
Our team can help you get started with Multimodal RAG in your organization.