AI

Learn how to use withContextPrefix() in RavenDB 7.2.3 to inject document-level context into every embedding chunk, improving vector search precision for large documents.

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.

Set up Context7 MCP with OpenAI Codex in VS Code to give your AI direct access to RavenDB docs — faster, context-rich vibe coding with fewer interruptions.

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Read about A Practical Look at AI Agents with RavenDB on the RavenDB.net news section

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Read about New in 7.0: RavenDB Vector Search on the RavenDB.net news section

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Read about Unlock RavenDB GenAI Potential with Attachments on the RavenDB.net news section

Read about AI Image Search with RavenDB on the RavenDB.net news section

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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.

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Read about Enable AI Search in Your Web App in 5 Minutes on the RavenDB.net news section

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Read about Embeddings Generation with RavenDB on the RavenDB.net news section

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Read about Machine Learning Inside RavenDB Indexing on the RavenDB.net news section