Use Case

Learn how to connect multiple RavenDB AI Agents into a multi-agent system, where specialized sub-agents handle focused tasks while a coordinator routes work between them.

Learn how to attach binary files such as PDFs and images to RavenDB AI Agent conversations, reducing token usage while keeping files accessible across the conversation lifecycle.

Learn how to implement spatial search in RavenDB with Python. Covers radius queries, custom polygon shapes, WKT syntax, and reverse point-in-polygon lookup using the Flat Finder demo.

Learn to implement the transactional outbox pattern in C# using RavenDB data subscriptions or Queue ETL, with RabbitMQ and Kafka code examples.

Configure JSON schema validation in RavenDB to enforce data consistency across teams. Learn how to audit documents against a schema or block invalid writes at the database level.

Store RavenDB attachments in Amazon S3 or Azure Blob Storage to cut costs. Studio and C# setup guide with bulk migration of existing attachments.

Move operational data out of RavenDB and into an OLAP engine using OLAP ETL, shaping documents into Parquet and shipping them to local storage or Amazon S3 on a schedule you control.

Use RavenDB Data Archival to exclude historical documents from indexes and queries, keeping performance high without deleting data you need to retain.

Push RavenDB document changes onto Azure Queue Storage with an ETL task, then process them serverlessly with an Azure Function.

Build text-to-image and image-to-image product search on top of RavenDB vector search, using CLIP embeddings stored in documents and the original images kept as attachments.

Build a persistent Akka.NET console app that stores its actor state in RavenDB through event sourcing and snapshots, so it can recover exactly where it left off after a crash or restart.

External

Read about GenAI: How We Make You Work Less on the RavenDB.net news section

Build a semantic search API with RavenDB, Python, and FastAPI. Learn manual and automatic embedding generation with OpenAI, and run vector similarity search in under 50 lines of application code.

Learn how to use RavenDB's GenAI feature to automatically summarize, prioritize, and enrich documents. Step-by-step walkthrough with code examples and screenshots.

Match accented terms like Kraków against both Kraków and Krakow. Compares manual diacritics normalization with a custom ASCIIFoldingFilter analyzer for Lucene and Corax indexes.

Add AI-driven vector search to a Next.js app using RavenDB's embeddings generation AI Task and a single vector.search RQL query, almost like using a regular search API.

Connect RavenDB to Power BI through the PostgreSQL protocol integration, then build charts and filter your data with queries during import to visualize your documents.

Set up an ongoing Snowflake ETL task in RavenDB to continuously sync documents to your Snowflake data warehouse, using a worked traffic-sensor IoT example.

Set up an ongoing Amazon SQS ETL task in RavenDB to stream documents into an SQS queue, then process them with an AWS Lambda function that emails speeding-ticket alerts through SES.

Replicate production-quality data into staging on demand with RavenDB using External Replication, ETL with anonymization and filtering, or Export/Import.

See how RavenDB Data Subscriptions let you offload invoice processing to a background worker in C#, with PDF generation and attachment storage included.

Extend RavenDB's indexing process with machine learning in C#: train an ML.NET model to flag toxic comments during indexing, and use a pre-trained Catalyst model to detect each document's language, all through AdditionalAssemblies and AdditionalSources.