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RAG and Vector Database

The vMind RAG and Vector Database service enables language models to work with your enterprise documents. Your contracts, procedures and technical documents are indexed in a vector database; when answering a question, the model refers to these sources and shows which document it used. This way, the model does not make up answers; it uses enterprise knowledge. All data is stored on vMind's Istanbul-based, ISO 27001-certified infrastructure and does not leave it.

What Is RAG?

RAG, short for Retrieval-Augmented Generation, is the architecture in which the language model retrieves and reads the relevant documents before generating an answer. The model answers based not only on the general knowledge it was trained on, but on up-to-date content provided by the organization.

This approach solves two problems at once: the model making up answers and the training data going out of date. Because the document an answer is based on is shown, the information provided remains verifiable. 

How Does RAG Work?

Your enterprise documents are split into chunks and indexed in a vector database. When a user asks a question, the system first finds the semantically closest document chunks in this index, then gives these chunks to the model as context.

The model generates the answer based only on these chunks given to it and lists in its output which document it used. When new or updated documents are added to the index, the model keeps working with up-to-date information without being retrained. 

Why vMind RAG and Vector Database: Advantages

"Don't Let AI Make Things Up, Let It Use Enterprise Knowledge"


A general-purpose language model does not know your organization's procedures. The RAG architecture combines the power of the model with your enterprise knowledge. 

Answers That Cite Sources Answers That Cite Sources
Document-Level Authorization Document-Level Authorization
Stays Up to Date Stays Up to Date
Data Stays Within the Organization Data Stays Within the Organization
Multi-Format Support Multi-Format Support

What Do We Do Under RAG and Vector Database?

  • 1- Vector database setup...
    Setting up a scalable index structure on an open-source vector database.
  • 2- Document processing pipeline...
    Splitting PDF, Word, Excel and web content into chunks and indexing it.
  • 3- Access authorization...
    Defining document access control by user and role.
  • 4- Source citation...
    Listing the documents each answer is based on and linking to them.
  • 5- Automatic updates...
    Adding new and changed documents to the index at regular intervals.
  • 6- Quality monitoring...
    Monitoring answer accuracy and reporting on index quality.

The right architecture is needed to open your enterprise knowledge to artificial intelligence. Share your document archive and use case, and let's build the right structure together.

We offer customized solutions to our customers by bringing together the necessary technology and services.

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