Harmonize structured and multimodal data, scale to billions of vectors, and build faster with an open, developer‑first Enterprise Vector Store across on-premises, cloud, and hybrid environments.
Teradata Enterprise Vector Store unifies structured data and multimodal unstructured data—text, images, audio, and video—within a single, governed database. Hybrid and fusion search span vectors, metadata, and relational data to deliver more accurate RAG, context‑aware insights, and AI agents that reason across the full enterprise data landscape.
Built on Teradata’s proven MPP architecture, Enterprise Vector Store is designed to handle billions of vectors and high‑throughput workloads without performance degradation. Linear scalability, support for thousands of concurrent queries, and optimized cost structures enable organizations to operationalize vector workloads alongside analytics and transactions.
Designed to accelerate innovation, Enterprise Vector Store integrates natively with LangChain and supports familiar Python and SQL interfaces. Developers can easily ingest unstructured content, experiment with embeddings, and operationalize AI agents without re‑architecting pipelines. Open integrations, standardized ingestion, and end‑to‑end lifecycle support enable teams to move from prototype to production faster, while maintaining enterprise‑grade governance, security, and trust.
Teradata’s partnership with Unstructured brings enterprise‑grade unstructured and multimodal data ingestion directly into Enterprise Vector Store. Organizations can automatically parse, enrich, and transform documents, images, audio, and video into high‑quality vector embeddings stored natively in Teradata. This eliminates external pipelines and provides trusted context for RAG and agentic AI across cloud, on‑premises, and hybrid environments.
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Overiew
A multi-modal vector store is a specialized database that stores and indexes vector embeddings generated from multiple types of data—including text, images, audio, video, and structured records—within a single unified system. Unlike single-modal vector stores that handle only one data type, a multi-modal vector store enables multi-modal vector search across all your data modalities simultaneously. Each piece of content, regardless of its original format, is converted into a high-dimensional numerical vector that captures its semantic meaning. These vectors are then indexed so that multi-modal semantic search can retrieve the most relevant results across data types in milliseconds.
A traditional vector database is typically designed around a single data modality—most commonly text. It stores embeddings generated by a single embedding model and supports similarity search within that one modality. A multi-modal database, by contrast, is architected to ingest, store, and index embeddings from many different modalities using a shared vector space. This means a multi-modal search query can use an image to find related text documents or use a text query to retrieve matching audio clips. Traditional vector databases require you to maintain separate indexes per modality; multi-modal databases unify these into a single, coherent retrieval layer—reducing infrastructure complexity while dramatically expanding what your search can do.
A multi-modal vector store is designed to handle virtually any data type that can be represented as an embedding vector. Common modalities include: text (documents, articles, product descriptions, chat logs), images (photos, diagrams, product visuals, scanned documents), audio (speech recordings, music, sound clips), video (frame-level or clip-level embeddings), and structured data (tabular records, metadata, sensor readings). Because a multi-modal database normalizes all of these into a shared vector space, you can store heterogeneous data side by side and retrieve across boundaries — for example, finding the product image most semantically similar to a customer's written complaint.
Multi-modal embeddings are numerical representations that encode the semantic content of different data types into a shared vector space. Specialized embedding models—such as CLIP for image-text pairs, ImageBind for audio-visual content, or custom cross-modal transformers—are trained to map different modalities so that semantically related content ends up geometrically close in that space, regardless of its original format. For example, an embedding model trained on image-caption pairs will place a photo of a sunset and the phrase "golden hour over the ocean" near each other in the vector space. This is what makes multi-modal semantic search possible: a multi-modal vector search query computes the vector for your input, then finds the nearest neighbors across all stored embeddings—whether text, images, or audio—using approximate nearest neighbor (ANN) algorithms for speed at scale.
Multi-modal databases power a growing range of applications across industries. Common use cases include: e-commerce visual search, where shoppers upload a photo to find similar products described by text; media asset management, where broadcasters use multi-modal search to retrieve relevant video clips by typing a natural-language query; medical imaging and reports, where clinical teams run multi-modal semantic search across scan images and physician notes together; customer support AI, where agents retrieve relevant documentation, screenshots, and chat history in a single query; and content moderation, where platforms flag policy-violating content across text and image uploads simultaneously. In each case, the unifying capability is the ability to search across data types as if they were one—which is the core promise of a multi-modal vector store.
Enterprise Vector Store
Teradata Enterprise Vector Store is an integrated, enterprise‑grade capability within Teradata that manages, stores, and retrieves unstructured data using vector embeddings, enabling fast and intelligent search and retrieval for AI applications.
Now, Teradata expands Enterprise Vector Store to support agentic AI workflows and multimodal data. Users can generate and store embeddings for text, images, and support for additional modalities such as audio, enabling richer retrieval across multiple data types and powering autonomous workflows through Teradata‑LangChain integration.
Because vectors live alongside structured data in Teradata, Enterprise Vector Store helps teams unify structured and unstructured signals in a single governed environment across cloud, on‑premises, or hybrid deployments.
A vector database is primarily designed to store and search vector embeddings using similarity search. These systems are well suited for learning, experimentation, and smaller‑scale AI applications where embeddings are managed independently from enterprise data systems.
Teradata Enterprise Vector Store goes beyond basic vector storage by integrating vector capabilities directly into an enterprise data platform. Instead of operating as a standalone system, Enterprise Vector Store unifies vectors, structured data, metadata, and governance in a single, scalable environment.
This integrated approach enables:
Yes, there are various Teradata Enterprise Vector Store use cases, such as AI for CX, that include augmented call center, healthcare, Q&A, chat-based product recommendations, regulatory compliance, claims processing, customer complaint analyzer, and fraud detection.
Hybrid search combines semantic (vector) search with lexical (keyword) search to deliver more accurate, context‑aware results than either method alone, helping users find the right information across complex enterprise data sources.
Fusion search refers to retrieving insights across structured data (tables) and unstructured data (documents and other content) together, enabling richer context and better answers without requiring separate systems or complex stitching.
Enterprise Vector Store supports multimodal unstructured formats including text, PDFs, images, audio, and video. Through NVIDIA and Unstructured integrations, preprocessing and embedding generation are automated for unified ingestion.
Teradata partners with several leading technology providers to enhance the capabilities of the Enterprise Vector Store:
Teradata and NVIDIA have joined forces to deliver a solution that redefines the potential of unstructured data analysis. Combining Teradata’s Enterprise Vector Store and NVIDIA NIM™, this partnership unlocks unparalleled performance, scalability, and accuracy for AI/ML applications.
Whether accelerating PDF processing or enabling sophisticated RAG use cases like augmented call centers, this collaboration empowers organizations to drive operational efficiency, enhance customer experiences, and achieve breakthrough business outcomes.
Yes. Teradata’s partnership with Unstructured enables ingestion, preprocessing, enrichment, and embedding generation for unstructured and multimodal data so it can be loaded into Enterprise Vector Store. The integration provides auto‑preprocessing, chunking, metadata enrichment, and embedding generation for 50+ connectors and 70+ file formats.
The Unstructured integration supports enterprise ingestion pipelines by transforming diverse unstructured content into embeddings and enriched metadata, reducing the need for custom external pipelines. Unstructured brings a broad connector and file‑type support plus automated chunking and enrichment capabilities.
Yes. Enterprise Vector Store includes direct LangChain integration with SDK support for building enterprise‑scale RAG pipelines, enabling rapid prototyping to production and simplifying multi‑tool orchestration.
Enterprise Vector Store supports developer workflows through APIs and integrations that enable embedding generation, indexing, and retrieval, and it integrates with popular AI frameworks and providers for building RAG and agentic workflows. Teradata‑LangChain integration and SDK support are key enablers to move from rapid prototyping to enterprise deployment.
Deliver multimodal intelligence, hybrid search, and AI agents at enterprise scale with Teradata Enterprise Vector Store—while reducing cost and accelerating ROI.
