Vectorize and Firecrawl: Real-Time Data Integration for Smarter RAG Pipelines

When building AI applications, it’s tough to keep up with data that changes by the minute—especially when you need reliable, real-time context from multiple sources. Firecrawl solves this by continuously gathering data from websites so it’s ready for your retrieval-augmented generation (RAG) pipelines. With Vectorize’s new integration, you can now bring Firecrawl’s live web data directly into your RAG pipelines, ensuring your models always work with the latest and most relevant information.

Vectorize’s Firecrawl integration provides the scalable, real-time data retrieval that complex RAG pipelines demand. With Firecrawl’s real-time indexing and optimized data organization, Vectorize accesses data as soon as it’s available. This is especially useful in large-scale AI applications that require accurate, current information to perform well. By combining Firecrawl’s search efficiency with Vectorize’s RAG optimization, you can build responsive, high-performing pipelines that reliably deliver relevant results, freeing you up to focus on your AI applications.

Setting up your Firecrawl data source in Vectorize is easy. Simply configure the JSON settings for Firecrawl’s /crawl endpoint in your RAG pipeline.

To build your pipeline, select your vector database, AI platform, embedding model, and chunking strategy, then add your Firecrawl data source. This example shows a RAG pipeline that uses Firecrawl to ingest data from Vectorize’s documentation.

Once deployed, your pipeline will prompt Firecrawl to crawl the specified sites. Vectorize ingests and chunks the data, generates search indexes, and writes the indexes to your vector database. After the initial load, your pipeline automatically updates with new data as the websites refresh.

Get Started with Firecrawl and Vectorize

Ready to automate your RAG pipelines with live web data? Create a Firecrawl account, sign up for the Vectorize platform, and start building your pipelines!