You’ve been duct-taping search across PDFs, screenshots, videos, and call audio. It works, until it doesn’t. One query hits text, another hits images. The video archive might as well be a black box. Users ask normal questions. Your stack answers like it learned English yesterday.
Here’s the unlock. A single embedding model that understands text, documents, images, video, and audio. It maps them into one vector space. That’s Amazon Nova Multimodal Embeddings in Amazon Bedrock. One model. One space. Cross-modal search at industry-leading cost.
If multimodal RAG is your next roadmap checkbox, this changes the physics. Take a voice memo, a product clip, a slide deck with charts, and a long FAQ. You can make all of it retrievable, and consistent. Less plumbing. Fewer edge cases. More, wow, that actually worked.
The kicker: you don’t touch infra. Bedrock handles provisioning, endpoints, and scaling. You focus on ingestion, indexing, and prompts. With 8K-token context and 30-second media segments, you’re set. Use smart chunking for longer files. You can tune for speed, accuracy, and storage.
You probably stitched separate models: a text embedder for docs, a vision model for images. Then something bespoke for audio or video transcripts. That splits your index and complicates ranking. It makes “What’s the clip where the engineer says ‘latency dropped’ while the graph spikes?” nearly impossible.
Nova flips that. As Amazon describes it, it’s “the industry’s first embedding model that supports text, documents, images, video, and audio through a single unified model.” It enables cross-modal retrieval with leading accuracy and cost efficiency. Translation: fewer models, fewer heuristics, better recall.
A common result: higher user trust. When search returns the chart, the timestamp, and the sentence that explains it, users believe your system gets it. Wire that into RAG and your model cites stronger evidence. That usually reduces hallucinations and speeds task completion.
Behind the scenes, this works through a shared representation across formats. Think of a multilingual friend who speaks content, not English versus pixels or audio waves. If your query mentions “the chart that spikes after Q2,” the space places that phrase near the exact image region. It also sits near the paragraph explaining the spike. You’re not translating between separate systems. You’re asking one brain to recall one memory.
If you’ve tried cross-modal models like CLIP, the intuition will feel familiar. Align text and visuals so similar concepts cluster. The difference is Nova’s scope. Not just images and captions, but documents, audio, and video moments in one space. That unlocks “find the moment,” not just “find the file.”
Move from simple search to real workflows. Think triaging customer tickets or auditing marketing claims. The reliability from consistent retrieval keeps projects alive past the demo. Teams stop babysitting edge cases, and start building features.
Nova accepts text and documents up to 8K tokens. It handles video and audio segments up to 30 seconds. Larger assets? Segment them. For video, chunk by scene or slide transitions. For audio, chunk by silence or speaker turns. For long PDFs, chunk by headings or logical sections. For example, per chart with its caption. You then embed each chunk. Retrieval reassembles the answer.
“As a single, unified model that supports text, documents, images, video, and audio,” Nova maps them into a unified embedding space. That breaks data silos without you maintaining modality-specific pipelines.
A few practical hints for clean segmentation:
In Amazon Bedrock, you choose:
Because Bedrock is fully managed, you skip instance sizing and endpoints. You also skip networking glue. You focus on payloads and retrieval logic. Doing an internal “amazon nova multimodal understanding workshop”? This simplicity helps teams ship a proof-of-concept in hours, not weeks.
Operationally, treat synchronous calls like a hot path. Set clear timeouts. Use small chunk sizes for on-the-fly embeds. Cache aggressively to cut repeat work. For async, design for throughput. Batch payloads and parallelize across collections. Track progress so you can resume if a job fails midway. Build idempotency into ingestion. You don’t want duplicate vectors when a job retries.
A clean workflow looks like this. A streaming sync path embeds ad hoc user inputs, like queries and uploads. A steady async pipeline keeps your corpus fresh, like new docs, product media, and edited transcripts. Both feed the same vector store.
Nova offers multiple embedding dimensionalities. Higher dimensions can improve retrieval fidelity. But they produce larger vectors, more storage, and slightly higher latency. Lower dimensions cut costs and can still be strong for many workloads. Pilot both. Measure nDCG and recall@K on a labeled set. Choose what moves your metrics at the best price per query.
Expert tip: normalize vectors and test cosine versus dot product in your vector database. Ranking can shift meaningfully with the right metric.
Napkin math to keep you honest:
If your search quality is already good enough at a lower dimension, take the win. Spending 2× on storage to chase a 1–2% recall bump may not move the business.
“Asynchronous API for efficient processing of larger files” lets you pipeline ingestion. Detect, extract, chunk, embed, and index. For PDFs, capture images and nearby text together. For videos, align visuals, ASR text, and optional captions per 20–30 second chunk.
Build a minimal, stable schema:
This lets you filter stale or restricted chunks at query time. It keeps answers relevant and compliant.
Also consider confidence weighting. If your transcript includes ASR confidence scores, store them. At rerank time, slightly downweight segments with poor ASR confidence. Users notice when quotes are crisp and accurate.
When in doubt, sample your chunks. Manually label 50–100 random segments. If a human can’t tell what the chunk is about fast, your chunking is too coarse or messy.
“As Amazon Bedrock is fully managed,” your LLM and retrieval can share one platform. That cuts latency and ops. Need on-the-fly embeddings during a chat? The synchronous API handles it. The rest lives in batch pipelines.
Add a thin relevance guardrail. If top results fall below a similarity threshold, ask a clarifying question. It’s better to ask “Do you mean the 2022 or 2023 report?” than to fake confidence.
This blueprint is simple on purpose. Plug in your favorite vector DB like OpenSearch, pgvector, or a managed service. The flow stays the same.
This isn’t busywork. Clean governance speeds legal reviews and keeps your rollout unblocked.
A shopper types, “sleek black running shoes with reflective strip like the Vaporfly, under $120.” Nova embeds the query, then matches product images, lifestyle photos, and spec sheets in one hop. You show the right images and specs. An LLM summarizes fit and care from reviews. This is the “amazon nova multimodal embeddings RAG semantic search example” you can demo to any VP in five minutes.
Bonus: handle user-uploaded images for “find similar.” Handle natural-language follow-ups like “show men’s sizes with wide toe box.” One vector space makes image-to-text and text-to-image retrieval symmetric and predictable.
You’ve got a decade of shows, podcast episodes, and promo clips. With Nova, producers search, “the episode where the guest explains ‘fallacy of sunk cost’ while gesturing at a whiteboard.” Retrieve the exact 25-second clip and the transcript snippet. Editors stitch faster. Fans get better search inside your app.
A content lead put it best. “Our creators don’t remember filenames; they remember moments.” Cross-modal vectors find moments.
Financial and legal teams live in PDFs packed with charts, footnotes, and tables. Nova maps the chart image and the paragraph that explains it into the same space. A compliance analyst can ask, “Show the stress-test chart where Tier 1 capital dips below 10% and the note that explains why.” The system returns both.
Working in SAP landscapes? You can embed policy PDFs, export table screenshots, and training videos. Then wire Nova’s vectors into your existing governance portal. That satisfies “amazon nova sap” style needs without a platform overhaul.
Agents and bots pull answers from screenshots, how-to videos, and long KB articles. With a single embedding space, a user can ask, “Why does login keep failing with code 8127?” They get a screenshot of the exact dialog. They get the paragraph that explains the fix. They also get a 15-second clip showing the steps. Less bouncing between tools. More first-contact resolution.
It’s an embedding model in Amazon Bedrock. It converts text, documents, images, video, and audio into vectors in one unified space. You use those vectors for semantic search, cross-modal retrieval, and RAG pipelines.
Per AWS, it’s available in the US East (N. Virginia) region via Amazon Bedrock. You invoke it through Bedrock’s managed runtime without provisioning infrastructure.
Nova supports up to 8K tokens for text and document inputs. It supports video or audio segments up to 30 seconds. For longer content, segment into chunks and embed each chunk. Retrieval stitches relevant chunks at query time.
Use the synchronous API for near real-time scenarios like chat, agent loops, or interactive search. Use the asynchronous API for high-volume or large-file processing. Think “nova batch inference” overnight jobs that index a media library or a compliance archive.
Choose the embedding dimensionality that fits your goals. Higher dimensions can improve recall and ranking, but increase storage and latency. Benchmark on your own corpus. Measure recall@K, MRR, and nDCG. Pick the minimal dimension that meets your quality bar.
Yes. Nova outputs standard vectors you can store in your preferred vector database. Associate them with records from systems like SAP. You’re not changing SAP. You’re augmenting search and RAG over exported documents, images, and media.
Not necessarily. Many teams start with what they already run. OpenSearch k-NN, PostgreSQL with pgvector, or a managed vector service. The key is support for your similarity metric, metadata filters, and hybrid search. Start with the simplest option your team can operate.
Treat your index like a source of truth with versioning. When a document changes, write a new version and mark old chunks inactive. Run a background purge. For deletions, remove vectors and invalidate caches right away. Keep a lightweight audit trail for compliance and debugging.
If your corpus spans multiple languages, store language as metadata. Evaluate retrieval quality per language. You can route queries through translation for consistency, or keep parallel indexes. Measure with a multilingual test set before you scale.
Keep chunk sizes tight. Retrieve fewer but higher-precision candidates, like top-20. Push heavy rerankers off the critical path. Precompute and cache embeddings for common queries. Use streaming responses so users see partial results fast, then refine.
1) Ingest: collect PDFs, slide decks, images, and short video or audio clips. Extract text and captions.
2) Chunk: 1–2 paragraph text chunks. 20–30 second media chunks aligned to scenes. Pair charts with captions.
3) Embed: call Nova via Bedrock. Pick a dimension that fits your budget.
4) Index: store vectors and metadata like modality, source, and timestamps in your vector DB. Enable hybrid search.
5) Retrieve: top-K by vector similarity. Then rerank with metadata rules.
6) Generate: feed multimodal evidence into your LLM. Cite timestamps and pages.
7) Evaluate: track recall@K, MRR, latency, and cost per query. Iterate.
You don’t need a forklift upgrade. Start with one journey, like e-commerce search or policy Q&A, then scale.
Here’s the takeaway. The gap between “we have all this content” and “users can find what matters” is no longer a moat of glue code. With Nova, you collapse modalities into one language, vectors. Then let search and RAG do the rest. If you’ve been waiting for a simple, accurate, affordable way to unify retrieval across formats, this is the green light.
If you want help pressure-testing queries and tightening retrieval quality, take a look at Requery.
For real-world outcomes and patterns you can borrow, explore our Case Studies.