Vectorize Overview

Vectorize helps you build AI apps faster and with less hassle. It automates data extraction, finds the best vectorization strategy using RAG evaluation, and lets you quickly deploy real-time RAG pipelines for your unstructured data. Your vector search indexes stay up-to-date, and it integrates with your existing vector database, so you maintain full control of your data. Vectorize handles the heavy lifting, freeing you to focus on building robust AI solutions without getting bogged down by data management.

RAG Pipeline

Create an Account

Vectorize is forever free for individual developers who need simple RAG pipeline and RAG evaluation capabilities.

Create an account at https://platform.vectorize.io.

Vectorize Features

Vectorize equips you with tools to connect to data, manage RAG pipelines, and fine-tune embedding parameters.

RAG Evaluation Tools

• Automatically evaluates RAG strategies to find the best one for your unique data.

• Allows you to measure the performance of different embedding models and chunking strategies, usually in less than one minute.

RAG Evaluation

RAG Pipeline Builder

• Construct scalable RAG pipelines with our user-friendly interface (API coming soon)

• Populate vector search indexes with unstructured data from documents, SaaS platforms, knowledge bases and more.

• Automatically sync your vector databases with your source data so your LLM never has stale data.

RAG Pipeline Builder

Advanced Retrieval Capabilities

• Use the built-in retrieval endpoint to simplify your RAG application architecture to improve RAG performance.

• The retrieval endpoint:

  • Automatically vectorizes your input query and performs a k-ANN search on your vector search index

  • Provides built-in re-ranking of results

  • Enriches retrieved context from your vector search index with relevancy scores and cosine similarity.

  • Provides metadata

Retrieval Endpoint

Real Time Vector Updates

• Never worry about stale vector search indexes again

• Vectorize can be configured to immediately update changes in your unstructured data sources as soon as they occur

Real Time RAG Pipeline

Vector Database Integrations

• Store embedding vectors in your current vector database with preconfigured connectors.

• Select from a range of embedding models from OpenAI, Voyage AI, and more to generate vector representations.

• Built-in support for Pinecone, Couchbase, DataStax and others coming soon.

Optimize Pipelines with RAG Evaluation

• Use Vectorize to compare the accuracy of different embedding models dynamically.

• Materialize the RAG evaluation results as a pipeline with the confidence that you will always retrieve the most relevant context for your LLM.

What's Next?

• Sign up for Vectorize.