Vectara FAQ

About Vectara

What is the Vectara Platform?

Vectara is an enterprise AI agent platform that helps organizations build and operate AI agents grounded in their business data. It combines document processing, information retrieval, knowledge re-ranking, grounded generation, and agent capabilities in a unified platform.

Organizations use Vectara to build enterprise search, Retrieval-Augmented Generation (RAG) applications, and AI agents that can access relevant information, generate contextually informed responses, and support business workflows. Vectara is designed for enterprise requirements, including security, scalability, observability, and governance.

What are Vectara’s Managed Agents?

Vectara's Managed Agents help organizations build and operate AI agents without having to manage every aspect of the underlying agent infrastructure themselves. They provide a managed approach to deploying AI agents for enterprise workflows, helping teams simplify implementation and ongoing operations while focusing on business outcomes.

Managed Agents are particularly relevant for organizations that want to bring AI-powered workflows into production while addressing enterprise requirements for reliability, security, governance, and observability. Contact Vectara to learn more about available capabilities and how Managed Agents can support your use case.

What makes Vectara different from other agentic AI platforms?

Vectara focuses on enterprise AI applications that require accurate information retrieval, grounded responses, governance, and observability. Its platform combines document processing, hybrid retrieval, re-ranking, generation, and agent capabilities rather than requiring organizations to assemble every component independently.

Vectara also provides capabilities for evaluating factual consistency, inspecting agent behavior, and supporting governed deployments. These capabilities help organizations evaluate and manage AI applications as they move from prototypes into production.

What is the relationship between Vectara, RAG, and AI agents?

RAG provides a way to ground AI-generated responses in relevant information. AI agents can use retrieval and RAG as part of broader workflows that may involve reasoning, tools, and actions.

Vectara brings these capabilities together in an enterprise platform. Organizations can use its retrieval foundation for search and RAG applications and extend that foundation to build AI agents.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant information from a knowledge source and supplies it to a language model when generating a response. RAG helps applications answer questions using specific information rather than relying exclusively on the model's pre-existing knowledge.

Vectara provides retrieval and generation capabilities that developers can use to build RAG applications grounded in their organization's data.

You can learn more about RAG here.

Is Vectara a vector database?

No. Vectara is an enterprise AI agent platform that includes an internal vector database as part of its retrieval infrastructure. The platform also provides document processing, chunking, embedding, retrieval, re-ranking, and generation capabilities used to build RAG applications and AI agents.

Organizations looking for a complete retrieval and AI application foundation can use Vectara without having to assemble these components separately.

Is Vectara an embedding service?

No. Vectara is an enterprise AI agent platform, not simply an embedding service. It includes embedding models as part of a broader architecture for processing documents, indexing information, retrieving relevant content, and grounding AI-generated responses.

How enterprises use Vectara

Who should use Vectara?

Vectara is designed for organizations that want to build AI applications grounded in their own information and that need enterprise-grade governance, security, and observability.

Potential users include enterprise technology teams, AI and machine learning engineers, application developers, IT leaders, and business teams seeking to improve knowledge access or automate information-intensive workflows.

What are the main use cases for Vectara?

Vectara supports enterprise search, RAG applications, and AI agents across a range of business workflows. Examples include:

Engineering knowledge search and technical troubleshooting
Failure analysis and root-cause investigation
IT service management
Customer support and knowledge assistance
Legal document analysis
Enterprise knowledge discovery
Document analysis and generation
AI-powered information delivery and workflow automation

The best use case depends on the organization's data, business objectives, security requirements, and workflow needs.

Can Vectara support multimodal enterprise data?

Vectara supports document content that can include text, tables, images, diagrams, charts, and other graphical information. These capabilities help organizations make information contained in complex business documents available to retrieval and AI applications.

Supported formats and processing capabilities depend on the input type and current platform configuration.

How does Vectara help reduce AI hallucinations?

Vectara helps reduce the risk of unsupported AI responses by retrieving relevant information to ground generation and provide its agents with accurate context. It also provides capabilities to evaluate factual consistency and detect hallucinations, and guardrails to prevent inaccurate responses from reaching the end user.

Actual results depend on the quality and coverage of source data, retrieval performance, model behavior, application design, and evaluation methodology. No retrieval or generation system can guarantee that every response will be correct.

How can Vectara improve enterprise knowledge search?

Vectara combines semantic and lexical search with knowledge re-ranking to help applications retrieve relevant information from enterprise data. This allows users to search using natural-language questions as well as specific terms, depending on the query and retrieval configuration.

Organizations can use these capabilities to make information distributed across documents and knowledge sources more accessible to employees and AI applications.

You can learn more about enterprise knowledge search here.

Can Vectara help move AI applications from prototypes into production?

Vectara provides integrated retrieval, generation, agent tooling, and enterprise deployment capabilities to help organizations develop production AI applications. Its governance and observability capabilities also help teams evaluate system behavior and investigate issues.

Production readiness depends on the use case, integration requirements, testing, access controls, operational procedures, and acceptance criteria.

Vectara’s capabilities

Which AI models does Vectara support?

Vectara provides built-in model capabilities and supports integrating external models through supported interfaces and providers. Supported options include compatible OpenAI APIs, Anthropic models through supported providers, and Google Gemini through Vertex AI.

Model availability can vary by task, provider, deployment, and configuration. Consult Vectara's current documentation for the supported models and integration requirements.

Can I use my own large language model with Vectara?

Vectara supports a bring-your-own-model approach for supported configurations. This allows organizations to select models that align with their requirements for quality, cost, deployment, and governance.

The specific models and connection methods available depend on the model provider and the Vectara deployment.

Can Vectara support multiple AI agents?

Yes. Vectara provides a shared platform that organizations can use to build multiple agents for different business workflows. Reusing a common data and retrieval foundation can reduce the need to assemble separate infrastructure for every application.

Specific agent configurations depend on the required tools, workflows, models, and deployment environment.

Can Vectara index data from different sources?

Yes. Organizations can ingest supported files and raw text through Vectara's APIs and Console. Integrations and other ingestion methods can also help bring information from different business systems into the platform.

The appropriate approach depends on the source system, data format, update frequency, metadata, and access-control requirements.

Can Vectara search across multiple indexes?

Yes. Vectara supports querying one or multiple indexes, including issuing queries in parallel. This can help applications retrieve information across multiple collections of data.

The appropriate indexing and query strategy depends on the application's information architecture, access requirements, and retrieval objectives.

Which file formats does Vectara support?

Vectara supports a range of document and data formats, including PDF, Microsoft Word, PowerPoint, OpenDocument formats, HTML, JSON, XML, email, plain text, RTF, EPUB, CommonMark, and image data. Tables and diagrams are also supported.

Audio processing through speech-to-text capabilities may be available by request. Consult the current documentation for supported formats, ingestion methods, and processing requirements.

Which languages does Vectara support?

Vectara supports more than 100 languages and dialects across relevant platform capabilities, including ingestion, embedding, retrieval, and generation. Exact performance can vary by language, model, content type, and task.

For multilingual applications, organizations should evaluate retrieval and answer quality using representative data from the languages they intend to support.

Can Vectara scale to millions of documents?

Vectara is designed to scale across growing document collections and increasing query workloads. Its SaaS offering includes autoscaling capabilities to accommodate changing demand.

Actual capacity, throughput, latency, and resource requirements depend on the workload, data characteristics, configuration, and service plan. Organizations with demanding workloads should validate performance against their requirements.

How Vectara is implemented

What deployment options does Vectara offer?

Vectara offers three principal deployment models:

SaaS: A fully managed cloud service operated by Vectara.
Virtual Private Cloud (VPC): A deployment within a customer's cloud environment.
On-premises: A deployment within the customer's own infrastructure.

These options help organizations choose an environment aligned with their security, infrastructure, operational, and governance requirements.

Which cloud providers does Vectara support?

Vectara's SaaS service runs on AWS or Google Cloud infrastructure.

Where is Vectara available?

Vectara's availability depends on the deployment model, cloud provider, region, and infrastructure requirements. SaaS and VPC deployments may be available across supported cloud regions, while on-premises availability can be subject to additional regional constraints.

You can begin by exploring Vectara's documentation, reviewing the available deployment options, or starting a free trial. If you are evaluating Vectara for an enterprise use case, contact us to discuss your requirements, deployment preferences, and implementation goals.

How do I apply for a job at Vectara?

Visit our Careers page to review current openings and application instructions.

Where can I learn more about Vectara?

Explore Vectara's Platform, pricing, documentation, customer stories, and Trust Center for additional information.

If your question is not answered here, contact us.