How to Build a Multimodal Search Engine in 2025

Traditional search engines work with one data type at a time — text searches text, image searches images. But modern applications need to search across modalities: find a video clip by describing it in words, locate similar product images from a text query, or match audio to visual content.

This guide walks through building a multimodal search engine from scratch, covering the architecture, embedding models, indexing strategies, and retrieval pipelines you need.

What Makes Search "Multimodal"?

A multimodal search engine maps different data types — text, images, video frames, and audio — into a shared vector space. In this space, content with similar meaning clusters together regardless of its original format. A text description like "golden retriever playing fetch" lands near images and videos of dogs playing, even though the inputs are completely different data types.

The key technology enabling this is contrastive learning. Models like CLIP are trained on millions of image-text pairs, learning to align visual and textual representations. When you encode both a sentence and an image through their respective encoders, semantically matching pairs produce vectors that are close together in the shared space.

Architecture Overview

A production multimodal search system has four layers:

1. Ingestion Layer — Accepts files (video, images, PDFs, audio) and routes them to the appropriate processing pipeline
2. Extraction Layer — Runs AI models to generate embeddings, metadata, and structured features from raw content
3. Indexing Layer — Stores vectors in an approximate nearest neighbor (ANN) index alongside metadata in a document store
4. Retrieval Layer — Handles query encoding, vector search, filtering, ranking, and result assembly

Step 1: Choose Your Embedding Models

Your choice of embedding model determines what cross-modal queries are possible:

• CLIP (OpenAI) — Text ↔ Image, 512/768 dimensions. The standard for vision-language alignment.
• Vertex Multimodal Embeddings — Text ↔ Image ↔ Video, 1408 dimensions. Supports video natively.
• ImageBind (Meta) — Six modalities including audio, depth, and thermal. Research-stage but powerful.
• E5-Large — Text-only, 1024 dimensions. Best-in-class for text-to-text retrieval.

For most applications, you will want at least two models: a multimodal model for cross-modal queries and a text-specific model for high-quality text search.

Step 2: Build the Ingestion Pipeline

Raw files need preprocessing before embedding. Videos must be split into scenes or sampled at regular intervals. PDFs need layout analysis and text extraction. Audio needs transcription.

from mixpeek import Mixpeek

client = Mixpeek(api_key="your-api-key")

# Create a collection with multiple extractors
collection = client.collections.create(
    name="multimodal_search",
    feature_extractors=[
        "multimodal-embedding",   # CLIP-based cross-modal vectors
        "text-embedding",         # E5 for text-specific search
        "transcription",          # Whisper for audio/video speech
        "scene-splitting",        # Temporal segmentation for video
    ]
)

# Ingest from cloud storage
client.ingest(
    collection_id=collection.id,
    source="s3://my-content-library/",
    process_async=True
)

Step 3: Configure Retrieval

Raw vector search is only the starting point. Production systems need multi-stage retrieval pipelines that combine vector similarity with metadata filtering, re-ranking, and result deduplication.

# Create a retriever with multi-stage pipeline
retriever = client.retrievers.create(
    name="multimodal_retriever",
    collection_id=collection.id,
    stages=[
        {
            "type": "filter",
            "conditions": {"modality": {"$in": ["video", "image"]}}
        },
        {
            "type": "vector_search",
            "model": "multimodal-embedding",
            "top_k": 50
        },
        {
            "type": "sort",
            "field": "score",
            "order": "desc"
        },
        {
            "type": "reduce",
            "method": "deduplicate",
            "field": "source_id",
            "keep": "highest_score"
        }
    ]
)

# Search with natural language
results = client.retrievers.execute(
    retriever_id=retriever.id,
    query="person presenting at a conference",
    top_k=10
)

Step 4: Optimize for Production

Several considerations matter at scale:

• Hybrid search — Combine vector similarity with keyword matching (BM25) using weighted fusion. This catches exact matches that pure semantic search misses.
• Metadata filtering — Apply filters before vector search to reduce the search space. Date ranges, content types, and access controls should narrow candidates before expensive vector operations.
• Caching — Cache embedding computations for frequent queries. A query embedding cache can reduce latency by 10x for repeated searches.
• Async processing — Ingest and embed content asynchronously. Use webhooks for completion notifications rather than polling.

Measuring Quality

Use standard information retrieval metrics adapted for multimodal scenarios:

• Recall@K — What fraction of relevant items appear in the top K results?
• Mean Reciprocal Rank — How high does the first relevant result appear?
• Cross-modal accuracy — When you search with text, how often is the correct image/video in the top results?

Build evaluation datasets with known relevant pairs across modalities. Run evaluations after any model change, index configuration update, or pipeline modification.

Next Steps

Multimodal search is the foundation for more advanced applications: agentic retrieval (where AI agents autonomously search your data), multimodal RAG (retrieval-augmented generation with images and video), and real-time content monitoring. Start with the basics — ingest, embed, retrieve — then layer on complexity as your use case demands.

Explore the Mixpeek documentation to get started, or check out our glossary entry on multimodal search for more background on the core concepts.