Enterprise Vector Store FAQ

Teradata Enterprise Vector Store is an integrated, enterprise‑grade capability within Teradata Vantage® 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 Vantage®, 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:

• Production‑grade scale and reliability, designed for large‑volume, high‑concurrency enterprise workloads 
• Hybrid and fusion search, combining semantic vector search with lexical and structured data queries 
• Enterprise governance, security, and deployment flexibility across cloud, on‑premises, and hybrid environments
• Agentic and RAG workflows, where AI systems retrieve trusted enterprise context and act on it through governed processes

In short, while vector databases focus on storing and searching embeddings, Enterprise Vector Store is built to operationalize vectors as part of end‑to‑end enterprise AI workflows, connecting AI models and agents directly to trusted enterprise data.

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:

• NVIDIA: We integrate with NVIDIA NIM™ and NVAIE to accelerate data ingestion, embedding generation, intelligent search, and AI‑powered retrieval. NVIDIA’s in‑database reranker and guardrails further improve search accuracy, safety, and compliance for enterprise‑grade workloads.
• Unstructured: The Unstructured connector processes unstructured content (including text, PDFs, images, audio, and video) and converts it into clean, high‑quality embeddings for seamless integration with the Enterprise Vector Store. It automates preprocessing, chunking, and enrichment, and supports flexible integration with a wide range of embedding models and AI pipelines.
• LangChain: Native integration with LangChain APIs enables seamless, enterprise‑scale RAG workflows and accelerates prototyping‑to‑production. LangChain simplifies multi‑tool integration by unifying vector databases, LLM frameworks, and data pipelines into one governed environment for AI and data.
• Cloud service providers (CSPs): Teradata VantageCloud users continue to benefit from Teradata’s partnerships with AWS Bedrock and Azure OpenAI for embedding and LLM model access.

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.

• Accelerated unstructured data processing: NVIDIA NIM provides GPU‑accelerated processing that helps transform unstructured content into embeddings and actionable insights at enterprise scale
• Optimized embedding and retrieval: NVIDIA technologies combined with Teradata Vantage® enable fast, accurate embedding generation and intelligent retrieval for AI‑driven workloads
• Natural language processing: Integration with NVIDIA‑supported language models enables advanced RAG use cases, with results grounded in enterprise data from Teradata Vantage® for greater trust and explainability. 

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.