What is Vectara?

Vectara is an end-to-end platform that empowers product builders to embed powerful Generative AI features into their applications with extraordinary results. Built on a solid hybrid search core, Vectara delivers the shortest path to an answer or action through a safe, secure, and trusted entry point. Vectara is built for product managers and developers with an easily leveraged API that gives full access to the platform’s powerful features. Vectara’s Retrieval Augmented Generation (RAG) allows businesses to quickly, safely, and affordably integrate best-in-class conversational AI and question-answering into their application. Vectara never trains its models on customer data, allowing businesses to embed generative AI capabilities without the risk of data or privacy violations.

How does Vectara work?

Vectara is RAG-as-a-service, encapsulating the various components required for a scalable and high-performance RAG pipeline (document processing, an embedding model, a retrieval engine, a reranker, and an LLM) behind an easy-to-use developer API. Developers use Vectara to build RAG and semantic search applications by using the API to index their documents and respond to user queries with the full power of RAG, while Vectara works behind the scenes to execute the RAG ingest and query flows in a secure and scalable way while maintaining low latency and low TCO.

How can I benefit from Vectara?

You can benefit from Vectara if:

1. You are looking to build a chatbot based on your documents and data
2. You are looking to build a question-answer system to boost productivity and automate information delivery.
3. You need AI-based summarization with world-class retrieval performance

What are the use cases for Vectara?

1. Conversational AI (Chatbot)
2. Retrieval Augmented Generation (RAG)
3. Question and Answering Systems
4. Semantic App Search
5. User Generated Content Systems

How is Vectara different from other GenAI solutions?

Vectara gives all types of builders an end-to-end platform for embedding powerful generative AI capabilities into your app or site without the need for data science and machine learning experience..

Some of Vectara’s unique differentiators include:

1. 1. End-to-End RAG Platform: Vectara is an end-to-end platform for serverless RAG, expertly tuned and always available.
   2. Quick Time to Value: Vectara is a trusted partner for companies that brings powerful generative AI tooling to all types of developers and business users. Vectara helps companies turn deployments from years to weeks.
   3. Ultimate Trust and Control: Vectara also provides a factual consistency score (FCS) with every response and many ways to configure your queries and responses.

What is Retrieval Augmented Generation or RAG?

Retrieval Augmented Generation, otherwise known as RAG, is an approach to building GenAI applications that builds on semantic search (or retrieval) to provide answers to user questions by retrieving the most relevant facts and providing them to a generative LLM for summarization.  This approach has several advantages:

1. It reduces GenAI costs by telling the LLM to only use relevant information when providing answers instead of providing much larger amounts of data.
2. It drastically reduces hallucinations and copyright issues and keeps answers focused on the types of answers you’d want to provide in your business because it only uses the LLM for its knowledge of language, not for knowledge of how to answer end-user questions.  The answers to the questions come from the data you provide.
3. It increases GenAI security since ACLs can be used for filtering data that the user does not have access to before it ever gets to the LLM.
4. It provides explainability of GenAI responses by citing references to where it found the answers.
5. It keeps information up-to-date and eliminates costly, time-consuming, and privacy-concerning fine-tuning based on your enterprise data.  Information can be added and removed in seconds, just as it would with a traditional keyword search application.

Is Vectara a Vector Database?

No, Vectara is not a vector database. It is a RAG-as-a-service platform that includes multiple components required for RAG, such as a document processing engine, chunking, a state-of-the-art embedding model, and its own internal vector database, which is used by its high retrieval engine.

Is Vectara an Embedding Service?

No, Vectara is not an embedding service, although it does have a state-of-the-art embedding model it uses within the platform. It is a RAG-as-a-service platform that includes multiple components required for RAG, such as a document processing engine, chunking, a state-of-the-art embedding model, and its own internal vector database, which is used by its high retrieval engine.

What countries is Vectara available in?

Vectara is a cloud-based GenAI platform running on AWS infrastructure. See the AWS Regional Services page for more details.

How do I apply for a job at Vectara?

You can check all our available openings on our Careers page.

How do I get up and running with Vectara?

All you need to do is sign up with a company email address. You will then get access to the Vectara console to get started with ingesting documents and testing the platfomr.

For more information on setting up Vectara, you can check out our Getting Started and Docs.

How long does it take to implement Vectara?

Setting up and implementing Vectara in production can be done on the same day. Index your first document and issue your first batch of queries in under 5 minutes.

What file types does Vectara support?

Vectara’s file upload API supports PDF, Microsoft Word, Microsoft Powerpoint, Open Office, HTML, JSON, XML, email in RFC822, text, RTF, ePUB, and Common Mark.

What languages does Vectara support?

Vectara supports over 100 languages in dialects. This support is integrated across the platform, including data ingest, the embedding model, retrieval, and generation with the LLM.

Can I index from any data source?

You can index data from any supported file format, as well as raw text from data source systems, via Vectara APIs or the file upload feature within the Vectara console.

Can I search across multiple indexes?

Yes, you can issue a single query or multiple queries in parallel to one or multiple indexes.

What are the deployment options for Vectara?

Vectara is a fully managed cloud platform, maintained by the Vectara team and deployed on AWS. Like other multi-tenant SaaS services, it employs a release process designed to ensure features ship faster, the product can scale seamlessly when any client load increases and it leverages enterprise-grade security provided, in part, by AWS. This eliminates the responsibility for server maintenance, upgrades and capacity provisioning.

What resources do I need to deploy Vectara?

No additional or specialized engineering, hardware, or infrastructure resources are needed to deploy Vectara successfully. Vectara was developed to make it easy for web and application developers to integrate generative AI in sites and applications without the need for additional training or resources.

For more information on setting up Vectara, you can check out our Getting Started and Docs.

Does Vectara have any scalability limits?

Vectara can autoscale from small text volumes to millions of documents without the need to manage the provisioning of additional computing infrastructure. It automatically adds capacity as needed to handle higher query volumes.

I am having trouble setting up Vectara. What should I do

If you are having any trouble setting up Vectara or need any help with implementation, you can send a message on our Vectara Discourse Community or contact Vectara support directly at support@vectara.com

You can also visit https://support.vectara.com.

What are Vectara’s SLAs?

Vectara offers SLAs in support, availability, and performance.

For a full list of available Vectara SLAs, please review our Pricing page and order form documents.

What are the different options for buying Vectara?

There are 2 Vectara subscription plans to choose from: Growth and Scale.

You can visit Vectara’s pricing page to view the different features for each plan and determine the best one for your needs.

What is Vectara’s pricing model?

Vectara’s pricing model is usage-based and based on the number of search queries processed and account size. Visit our pricing page for more details.

How do you count queries in your pricing model?

Any queries that are issued to indexed content – via the Vectara console or the Vectara API – are counted towards the query count.

What is the definition of account size in the pricing model?

Account size is the sum of text and metadata size (measured in MB) within all corpora in the customer account before any replication factor is applied.

How does billing work?

For new customers, please contact sales for billing and pricing details.

For existing customers, the Billing tab within the Vectara Console contains invoice and payment history.

Which payment methods and currencies are accepted by Vectara?

Vectara accepts payments made through a credit card. Vectara supports billing in United States Dollar (USD).

Is there any commitment once I start paying for Vectara?

For Vectara’s Growth plan, there is no commitment requirement.

What happens if I exceed my committed plan usage?

Vectara users will receive a notification when they have consumed their monthly query allocation.

Users will then get an option to buy additional capacity or wait until their account resets for the next month.

How do I switch from one plan to another?

Some upgrade and downgrade functions can be performed directly within the Vectara Console For any other requests, please reach out directly to sales@vectara.com

How can I change my account details and billing information?

Account details and billing information can be accessed and changed from the Billing tab within the Vectara Console

How does Vectara handle and process personal and customer sensitive information?

Vectara supports full client control over how data is preserved, including the support of a full deletion of customer data via API. Clients decide what data they transmit to the service and what data remains within the service, with the exception of billing and billing contact data.

Does Vectara implement any encryption standards for information processing?

Vectara ensures sensitive data is encrypted and the keys are managed both logically and physically by the appropriate teams.

Encryption at rest is AES 256-bit symmetric key encryption. Separate keys are managed per corpus, and access to keys is through an account master key managed on FIPS 140-2 compliant hardware. Vectara also provides the option of a customer managed account master key. Encryption in transit is TLS 1.3.

Does Vectara have a security program?

Vectara has a documented security program that is audited periodically for major security program objectives, status of security program non conformities, and risk logs. The system architecture was designed to enable ready compliance with SOC 2 and ISO 27001 standards.

Does Vectara have a privacy policy?

Vectara has a documented privacy policy that is reviewed periodically. Vectara’s privacy policy covers responsibilities under GDPR regulations. CCPA regulations are not applicable to Vectara. Vectara’s privacy policy can be found here.

Does Vectara have a disaster recovery plan?

Vectara has policies and procedures for disaster recovery and backup. Vectara has established, documented, implemented, and maintained processes, procedures, and controls to ensure the required level of continuity for information security during an adverse situation.

Information processing facilities, infrastructure, and application architecture are implemented with redundancy sufficient to meet and support high availability requirements.

I have a question that is not listed here. How can I get an answer

You can contact Vectara Support to get an answer to your question at support@vectara.com or https://support.vectara.com.