Vectorize

Vectorize lets developers convert unstructured data into optimized vector search indexes tuned for retrieval augmented generation (RAG). The service lets developers create RAG pipelines that are fast and accurate by tapping into the collective knowledge of various sources, including content management systems, file systems, collaboration tools and more.

Some of the key features of Vectorize include:

• Import: Upload documents or connect to external knowledge management systems to pull in natural language data.
• Experiment: Run different combinations of chunking and embedding in parallel to experiment and find the best approach.
• Deploy: Turn selected vector configurations into real-time vector pipelines that update automatically when changes are made.

Vectorize comes with out-of-the-box connectors to popular services like Hugging Face, Google Vertex, LangChain and others, so you can use a variety of embedding models and chunking methods. You can also store your data in one of several vector databases.

Vectorize pricing is simple:

• Free: Good for solo developers, with one basic RAG pipeline, daily vector index updates, up to 5 experiments per month and community support on Discord.
• Starter: Good for small teams, $89 per month, with up to 3 RAG pipelines, daily updates, 3 users and up to 15 experiments per month.
• Professional: Good for teams building production pipelines, $399 per month, with up to 10 RAG pipelines, hourly updates, 10 users and 50 experiments per month, and 24x5 support.
• Enterprise: Custom plans for enterprise customers, with more features and support.

Vectorize is designed to make it easier to build RAG applications that work well by helping you find the best embedding models and vectorization techniques for your particular data. The service supports a variety of use cases, including better chatbots, content generation engines and AI assistants, and can be used to power a variety of generative AI applications.

Vectorize is particularly useful for companies that want to build productivity-boosting copilots and innovative customer experiences using large language models. Its RAG pipeline approach helps companies overcome the difficulties of building LLM-based applications, ensuring that data is accurate and up to date for mission-critical use.

Published on June 14, 2024