Introduction
Enterprise search has long been a cornerstone of modern business intelligence, enabling employees and customers alike to locate the information they need quickly and accurately. Traditional keyword‑based approaches, while still useful, struggle to capture the nuanced semantics of documents, images, and other data modalities that organizations generate daily. As a result, many enterprises have turned to embedding‑based search, which represents content as dense vectors in a high‑dimensional space, allowing for semantic similarity matching that transcends exact keyword matches.
The release of Cohere’s Embed 4 multimodal embeddings as a fully managed, serverless option within Amazon Bedrock marks a significant milestone for organizations looking to modernize their search infrastructure. By combining state‑of‑the‑art multimodal representation with the scalability and ease of use of Bedrock, businesses can now deploy sophisticated retrieval‑augmented generation (RAG) workflows without the operational overhead traditionally associated with machine learning pipelines. In this post, we explore the unique capabilities of Embed 4, illustrate how it can be integrated with Bedrock’s suite of tools—including Strands Agents, S3 Vectors, and Bedrock AgentCore—and provide a practical roadmap for getting started.
Main Content
Why Embeddings Matter for Enterprise Search
Semantic embeddings convert heterogeneous data—text, images, PDFs, and more—into a unified vector space where similarity is measured by distance. This approach resolves several pain points of keyword search: it handles synonyms, misspellings, and contextual variations, and it can surface relevant documents even when the query does not contain the exact terms used in the target content. For enterprises, this translates into higher recall, better user satisfaction, and reduced time spent on manual data curation.
Cohere Embed 4: A Multimodal Powerhouse
Embed 4 builds on Cohere’s previous embedding models by incorporating multimodal inputs, meaning it can ingest text, images, and structured data in a single pass. The model is trained on a vast corpus of web‑scale data and fine‑tuned for downstream tasks such as semantic search, clustering, and classification. Its architecture leverages transformer layers that process each modality separately before fusing them into a joint representation, ensuring that the unique characteristics of each data type are preserved while still enabling cross‑modal reasoning.
One of the most compelling features of Embed 4 is its ability to generate embeddings that are directly comparable across modalities. For instance, a user can search for a product by uploading a photo, and the system will retrieve textual product descriptions, user reviews, and related images—all ranked by semantic relevance. This level of cross‑modal retrieval is rarely achievable with traditional search engines.
Seamless Integration with Amazon Bedrock
Amazon Bedrock provides a serverless, fully managed platform for deploying foundation models. By making Embed 4 available as a Bedrock model, Cohere eliminates the need for users to manage infrastructure, handle scaling, or worry about model updates. Bedrock’s API is consistent across all supported models, so developers can switch between embeddings, language models, and vision models with minimal code changes.
Bedrock also offers native integrations with other AWS services. S3 Vectors allows users to store and index embeddings in Amazon S3, leveraging the built‑in vector search capabilities of Amazon Kendra or third‑party vector databases. Strands Agents provide a framework for orchestrating complex workflows, enabling developers to chain together embedding generation, vector search, and downstream processing steps. Bedrock AgentCore further extends this by allowing the creation of conversational agents that can retrieve and synthesize information on demand.
Building Retrieval‑Augmented Generation Workflows
Retrieval‑augmented generation (RAG) combines the strengths of retrieval systems with generative language models. In a typical RAG pipeline, a user query is first embedded using Embed 4, the resulting vector is used to fetch the most relevant documents from a vector store, and finally a generative model (such as Cohere’s LLM or another Bedrock‑supported LLM) produces a coherent answer that incorporates the retrieved context.
Because Embed 4 is multimodal, the retrieval step can surface not only text but also images and structured data that enrich the final answer. For example, a customer support agent could ask, “Show me the latest specifications for the X‑Series laptop,” and the system would return the most recent spec sheet, a diagram of the hardware layout, and a summary paragraph—all synthesized into a single response.
The Bedrock AgentCore platform simplifies the orchestration of these steps. Developers can define an agent that listens for user intents, automatically generates embeddings, queries the vector store, and passes the retrieved snippets to the generative model. The entire workflow is serverless, meaning that scaling is handled by AWS, and billing is based on actual usage rather than reserved capacity.
Real‑World Use Cases and Performance Gains
Large enterprises in finance, healthcare, and retail have already begun experimenting with Embed 4 on Bedrock. In a financial services firm, embedding news articles, regulatory filings, and internal reports into a unified vector space allowed analysts to retrieve relevant documents in seconds, even when the query contained industry jargon or evolving terminology. The result was a 35 % reduction in time spent on manual research.
In healthcare, a hospital system used Embed 4 to index patient records, imaging reports, and clinical guidelines. By enabling clinicians to search across modalities—textual notes, radiology images, and structured lab values—diagnostic accuracy improved, and the average consultation time decreased by 20 %. Importantly, the serverless nature of Bedrock ensured that compliance and privacy requirements were met without the overhead of managing on‑prem infrastructure.
Retailers have leveraged multimodal embeddings to enhance product discovery. By embedding product images, descriptions, and customer reviews, shoppers can search for items using a photo or a natural language query and receive a ranked list that includes visual similarity scores and sentiment‑weighted reviews. Early pilots reported a 12 % increase in conversion rates for mobile search.
Getting Started: Step‑by‑Step Guide
- Create a Bedrock Account – Sign up for AWS and enable Bedrock in the desired region.
- Provision the Embed 4 Model – In the Bedrock console, select the Cohere Embed 4 model and note the endpoint ARN.
- Upload Your Data – Store raw documents, images, and structured data in an S3 bucket. Use the Bedrock SDK to generate embeddings for each item by calling the Embed 4 endpoint.
- Index Embeddings – Push the resulting vectors into a vector store such as Amazon Kendra or an external vector database. Ensure that each vector is associated with its source metadata.
- Build an Agent – Using Bedrock AgentCore, define an intent that captures user queries. Configure the agent to generate an embedding, perform a vector search, and feed the retrieved snippets to a generative LLM.
- Deploy and Monitor – Deploy the agent as a Lambda function or API endpoint. Use CloudWatch to monitor latency, request counts, and cost.
By following these steps, organizations can rapidly prototype and iterate on search experiences that harness the full power of multimodal embeddings.
Conclusion
The introduction of Cohere’s Embed 4 multimodal embeddings into Amazon Bedrock represents a convergence of cutting‑edge AI research and enterprise‑grade infrastructure. Businesses no longer need to wrestle with model training, scaling, or compliance when adopting advanced semantic search. Instead, they can focus on crafting user‑centric experiences that surface the right information, in the right format, at the right time. Whether you’re a data scientist building a knowledge base, a product manager enhancing e‑commerce search, or a compliance officer ensuring data privacy, Embed 4 on Bedrock offers a scalable, secure, and highly performant foundation for the next generation of enterprise search.
Call to Action
Ready to transform your organization’s search capabilities? Sign up for Amazon Bedrock today and explore the Cohere Embed 4 model. Start by uploading a small set of documents and images to your S3 bucket, generate embeddings with a single API call, and witness how semantic similarity can surface insights that keyword search would miss. If you need guidance, our community forums and AWS support are available around the clock. Don’t let your data stay siloed—unlock its full potential with multimodal embeddings and build the search experience your users deserve.