Introduction

Vectorize streamlines the development and deployment of Retrieval Augmented Generation (RAG) pipelines. With Vectorize you build, deploy, and manage RAG pipelines ensuring that your applications deliver accurate and contextually relevant responses to user queries.

Vectorize Features

Simple Pipeline Builder

Build complex RAG pipelines using our intuitive interface. No coding required.

Up-to-date Vector Databases

With Vectorize your vector databases are automatically kept in sync with your source data.

Flexible Data Integration

Seamlessly connect Vectorize pipelines to your data sources and knowledge bases to retrieve relevant information for generating responses. Choose from a marketplace of preconfigured connectors.

Scalable Infrastructure

Vectorize is built on a scalable infrastructure, allowing you to handle a large corpus of data with ease.

Multiple data sources

Create RAG pipelines that pull data from multiple sources, into a single vector store. Giving your applications the most accurate contextual information.

Vector Store Integrations

Store embedding vectors in your existing vector database. Choose from a marketplace of preconfigured connections.

Ready to use embedding models

Choose from a variety of pre-trained models to generate vectors.

Experiment to find the right model for your data

Use Vectorize Experiments to compare the accuracy of different embedding models. Find the most accurate combination of model and chunking to find semantically accurate data.

Your RAG solution

Retrieval Augmented Generation (RAG) pipelines are essential for natural language processing (NLP) applications. They provide more accurate and contextually relevant responses to user queries and improve overall user satisfaction and engagement.

Integrating your applications with Vectorize RAG pipelines will enable your system to understand user query context better. Leveraging both retrieval and generation stages with Vectorize can give your applications more accurate context while interacting with LLMs.

Vectorize RAG pipelines can scale to handle large volumes of data efficiently, making them suitable for frequently changing data sources.

Whether you're building a chatbot, a question-answering system, or a virtual assistant, RAG pipelines on Vectorize can significantly improve the performance and user experience of your application.