Pseudocode for a multi-modal overview pipeline

A multi-modal AI overview is a dynamic, machine-generated synthesis that draws from all available content modalities for a given topic. Unlike traditional search results that return a ranked list of links, an overview assembles relevant text passages, images, video clips, and audio snippets into a unified answer. Google's AI Overviews (2024) and Perplexity's multi-modal answer mode (2025) are consumer-facing examples, but the same architecture applies to enterprise knowledge bases.

1. Unified Embedding Index

All content across modalities must be embedded into a shared vector space. Use a multi-modal embedding model like CLIP, ImageBind, or Gemini Embeddings (2025). Each content chunk receives a vector regardless of its original modality. The embedding captures semantic meaning, not surface format.

For retrieval, a single vector index (powered by FAISS, Qdrant, or Pinecone) stores all modality embeddings together. Query vectors are compared against this unified index, returning results from any modality.

2. Cross-modal Re-ranker

Initial retrieval often returns a mix of relevant and irrelevant results across modalities. A cross-modal re-ranker model (e.g., ColPALI, 2025) scores each retrieved chunk against the query using joint text-image representations. Re-ranking boosts precision by 25-40 percent over naive cosine similarity (Weaviate, 2025).

3. Synthesis Engine with Modality Routing

The synthesis engine takes the top-K retrieved chunks and generates an overview. The key innovation is modality routing: the engine determines which modality best answers each sub-part of the query. For example:

• A question about "API latency trends" routes to a line chart image
• A question about "configuration steps" routes to a video clip with transcript
• A question about "error code meanings" routes to a text table

The routing decision is made dynamically based on the content type tags stored during ingestion. The engine then assembles chunks into a coherent answer, preserving source modality labels so users know the origin of each piece of information.

def generate_overview(query, top_k=10):
    # Step 1: Retrieve from unified embedding index
    results = unified_index.search(embed(query), k=top_k)
    
    # Step 2: Cross-modal re-ranking
    reranked = cross_modal_reranker.rerank(query, results)
    
    # Step 3: Modality routing and synthesis
    overview_parts = []
    for chunk in reranked:
        if chunk.modality == "image":
            route = "visual_element"
        elif chunk.modality == "video":
            route = "video_clip_with_transcript"
        else:
            route = "text_passage"
        overview_parts.append((route, chunk))
    
    # Step 4: Generate final overview
    return synthesis_engine.generate(overview_parts)

Prepare your content by standardizing metadata across modalities with a unified schema (id, title, modality, timestamp, source_url, summary, embedding). For time-sensitive topics, include timestamps or version numbers so the overview engine prioritizes the most recent information.

Measure multi-modal overview quality with three metrics: modality coverage (target: 80 percent of overviews contain at least two modalities), answer completeness (use ROUGE-L or BERTScore against a gold answer), and source grounding (every claim must trace to a specific content chunk with citations).

• [ ] Unified embedding index contains all content modalities
• [ ] Cross-modal re-ranker deployed and benchmarked
• [ ] Modality routing logic validated with test queries
• [ ] Metadata schema standardized across text, image, video, and audio
• [ ] Overview quality metrics tracked monthly (coverage, completeness, grounding)

Multi-modal AI overviews transform disconnected content assets into a unified knowledge layer. By investing in unified indexing, cross-modal re-ranking, and dynamic modality routing, organizations can make their entire content library accessible and synthesizable by AI systems.

References

1. Google Research. (2024; updated 2025). "AI Overviews: Multi-modal Search and Synthesis." Google Search Blog.
2. Weaviate. (2025). "Benchmarking Cross-modal Re-rankers for Enterprise RAG." Weaviate Research.
3. Meta AI. (2024). "ImageBind: Holistic AI Learning Across Six Modalities." Meta Research. https://imagebind.metademolab.com/
4. Perplexity AI. (2025). "Multi-modal Answer Mode: Architecture and Performance." Perplexity Engineering Blog.