Multimodal Search Infrastructure
Multimodal search is one retrieval stage in the multimodal data warehouse. Search across text, images, video, audio, and documents, then compose filter, rerank, and enrich stages on top to build precise, production-grade retrieval pipelines.
What is Multimodal Search?
Traditional search only understands text. Multimodal search uses ML models to understand and retrieve content across every data type -- text, images, video, and audio -- in a unified system.
Text Search
Semantic search across documents, transcripts, and metadata using natural language queries.
Image Search
Search visual content by description, similarity, or embedded text with vision models.
Video Search
Search within video content at the frame and scene level, including spoken words and visual elements.
Audio Search
Search audio files by transcribed speech, speaker identity, or acoustic characteristics.
How It Works
A four-stage pipeline takes your raw files and makes them searchable across every modality.
Ingest
Upload any file type -- documents, images, video, audio -- through a single API endpoint or bucket trigger.
Extract
ML models automatically extract features: embeddings, transcripts, OCR text, scene descriptions, and metadata.
Index
Vector embeddings and structured metadata are indexed for fast retrieval across all modalities.
Retrieve
Compose retrieval pipelines with filter, search, and rerank stages to get precisely the results you need.
Capabilities
Everything you need to build production multimodal search applications.
Cross-Modal Search
Query across modalities -- search video with text, find images with audio descriptions, match documents to visual content.
- Text-to-video search at frame level
- Image-to-image similarity matching
- Audio-to-text cross-referencing
- Any-to-any modality queries
Semantic Understanding
Go beyond keyword matching with deep semantic understanding of content meaning and context.
- Contextual meaning extraction
- Intent-aware query parsing
- Concept-level matching
- Multilingual understanding
Hybrid Search
Combine vector similarity with keyword matching and metadata filters for precision and recall.
- Vector + BM25 keyword fusion
- Metadata filtering at query time
- Weighted scoring across methods
- Tunable relevance parameters
Real-Time Processing
Ingest and index content in real time so new files are searchable within seconds of upload.
- Sub-second indexing pipeline
- Streaming ingestion support
- Live webhook notifications
- Batch and real-time modes
Custom Extractors
Bring your own models or configure extraction pipelines tailored to your domain and data.
- Plug in custom embedding models
- Domain-specific feature extraction
- Configurable chunking strategies
- Model versioning and A/B testing
Composable Retrieval Pipelines
Chain filter, search, and rerank stages into retrieval pipelines that match your exact use case.
- Multi-stage retriever composition
- Filter, search, and rerank stages
- Conditional branching logic
- Shared retrievers across teams
Use Cases
See how teams use multimodal search to power their applications.
E-commerce Visual Search
Let customers search your product catalog with images, text, or both. Power visual discovery and recommendation.
Media Intelligence
Search and analyze video libraries, broadcast archives, and multimedia content at scale.
Content Moderation
Detect and flag unsafe content across images, video, and text with multimodal understanding.
Multimodal Search vs Traditional Search
See what changes when you move beyond text-only retrieval.
| Feature | Traditional Search | Multimodal Search |
|---|---|---|
| Data Types Supported | Text only | Text, images, video, audio, documents |
| Query Types | Keyword strings | Natural language, images, audio, cross-modal |
| Understanding Level | Lexical matching | Semantic meaning across modalities |
| Infrastructure | Inverted index (Elasticsearch) | Vector database + ML pipeline |
| Retrieval Method | BM25 / TF-IDF | Vector similarity + hybrid fusion |
| Scalability | Scales with text volume | Scales across all content types |
Simple API Integration
Get multimodal search running in minutes with our Python SDK.
from mixpeek import Mixpeek
client = Mixpeek(api_key="YOUR_API_KEY")
# Search across all modalities with a text query
results = client.retrievers.search(
retriever_id="my-multimodal-retriever",
queries=[
{
"type": "text",
"value": "product demo showing the dashboard",
"modalities": ["text", "image", "video"]
}
],
filters={
"AND": [
{"key": "status", "value": "published", "operator": "eq"}
]
},
limit=20
)
for result in results:
print(f"{result.modality}: {result.score:.3f} - {result.source}")Frequently Asked Questions
What is multimodal search?
Multimodal search is a retrieval approach that understands and searches across multiple data types -- text, images, video, audio, and documents -- using a unified system. Unlike traditional text-only search, multimodal search uses ML models to extract meaning from every modality, enabling queries like searching a video library with a text description or finding similar images using natural language.
How does multimodal search differ from text search?
Traditional text search relies on keyword matching (BM25, TF-IDF) against text documents. Multimodal search uses neural embedding models to represent content from any modality as vectors in a shared semantic space. This enables semantic understanding, cross-modal queries (e.g., text-to-image), and retrieval based on meaning rather than exact keyword overlap.
What file types does Mixpeek's multimodal search support?
Mixpeek supports a wide range of file types including images (JPEG, PNG, WebP, TIFF), video (MP4, MOV, AVI, MKV), audio (MP3, WAV, FLAC), documents (PDF, DOCX, PPTX), and plain text. Files are automatically processed through the appropriate extraction pipeline based on their type.
Can I search video content with text queries?
Yes. Mixpeek extracts features from video at the frame and scene level -- including visual embeddings, transcribed speech, OCR text, and scene descriptions. You can then search this content with natural language queries and get results pinpointed to specific timestamps within the video.
What is cross-modal retrieval?
Cross-modal retrieval is the ability to query in one modality and retrieve results in another. For example, you can submit a text query and retrieve matching video frames, or provide an image and find related audio clips. This works by mapping all content into a shared embedding space where similarity can be measured across modalities.
How does multimodal search work with RAG?
Multimodal search serves as the retrieval layer in Retrieval-Augmented Generation (RAG) pipelines. Instead of limiting RAG to text chunks, Mixpeek enables retrieval across images, video frames, audio segments, and documents. The retrieved multimodal context can then be passed to LLMs for generation, grounding responses in rich, diverse source material.
Is multimodal search available as a self-hosted solution?
Yes. Mixpeek offers BYO Cloud deployment where the entire multimodal search infrastructure runs in your own VPC. This gives you complete data sovereignty while leveraging the full feature set. We also offer managed cloud and dedicated cloud options depending on your requirements.
What embedding models does Mixpeek support?
Mixpeek supports a range of embedding models for different modalities including vision transformers for images and video, speech models for audio, and text embedding models for documents. You can also bring your own custom models and plug them into the extraction pipeline for domain-specific use cases.