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‎docs/ai-integration/ai-agents/ai-agents_start.mdx‎

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import aiAgentsStartApiImage from "./assets/ai-agents_start_apiImage.png";
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import aiAgentsStartStudioImage from "./assets/ai-agents_start_studioImage.png";
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# AI Agents: Start
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<Admonition type="note" title="">
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# AI Agents
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### Create conversational AI proxies for your applications.
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AI agents are server-side components that act as secure proxies between RavenDB clients and AI models. They can be easily customized to handle specific client needs, tasks or workflows, such as answering questions, performing data analysis, or automating processes.
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- Using AI agents frees developers from the need to manage the communication with the AI model in their code, and enables rapid integration of AI capabilities into their applications.
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- An agent receives requests from clients and maintains continuous conversations with AI models to fulfill them. During the conversation, the agent can enable the model to securely query a RavenDB database and request the client to perform actions.
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- An agent receives requests from clients and maintains continuous conversations with AI models to fulfill them. During the conversation, the agent can enable the model to securely query a RavenDB database (e.g., fetch recent orders or run vector searches on products) and request the client to perform actions (like sending emails or creating new orders).
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- You can use AI agents to quickly create an intelligent, actionable, conversational interface for your applications, in a way that abstracts much of the complexity of AI integration.
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- [See common AI agents use cases](../../ai-integration/ai-agents/ai-agents_start#use-cases)
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</Admonition>
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## Technical documentation
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Our technical documentation explains in detail what AI agents are and how to define and use them.
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## Videos
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Watch our webinars to see AI agents in action and learn practical implementation techniques.
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<CardWithImageHorizontal title="How to run AI agents natively in your database" description="Webinar with Oren Eini" url="https://www.youtube.com/watch?v=A17GSLGN-cQ" imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc" imgAlt="imgAlt" ctaLabel="Watch" />
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<CardWithImageHorizontal title="How to create powerful and secure AI agents with RavenDB" description="Webinar with Oren Eini" url="https://www.youtube.com/watch?v=jzUxL9P17G4" imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc" imgAlt="imgAlt" ctaLabel="Watch" />
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</ColGrid>

‎docs/ai-integration/ai-integration_start.mdx‎

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import aiAgentsStartImage from "./assets/ai-start_ai-agents_light.png";
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# AI Integration: Start
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# AI Integration
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### Use native AI features to create intelligent database applications.
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RavenDB is equipped with a set of powerful native AI features that can
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be used independently or in conjunction with each other, allowing you to easily integrate advanced AI capabilities into your applications.
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These features include [AI agents](../ai-integration/ai-integration_start#ai-agents), [GenAI tasks](../ai-integration/ai-integration_start#genai-tasks), [Embeddings generation](../ai-integration/ai-integration_start#embeddings-generation), and [Vector search](../ai-integration/ai-integration_start#vector-search).
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<hr />
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<br />
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### AI agents
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AI agents are server-side components that act as secure proxies between RavenDB clients and AI models. They can be easily customized to handle specific client needs, tasks or workflows, such as answering questions, performing data analysis, or automating processes.
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- Using AI agents frees developers from the need to manage the communication with the AI model in their code, and enables rapid integration of AI capabilities into their applications.
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- An agent receives requests from clients and maintains continuous conversations with AI models to fulfill them. During the conversation, the agent can enable the model to securely query a RavenDB database and request the client to perform actions.
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- An agent receives requests from clients and maintains continuous conversations with AI models to fulfill them. During the conversation, the agent can enable the model to securely query a RavenDB database (e.g., fetch recent orders or run vector searches on products) and request the client to perform actions (like sending emails or creating new orders).
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- You can use AI agents to quickly create an intelligent, actionable, conversational interface for your applications, in a way that abstracts much of the complexity of AI integration.
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<Admonition type="note" title="">
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See common AI agents use cases:
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<Admonition type="note" title="">
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<CardWithImage title="Start page" description="Continue to the AI agents Start page" url="../ai-integration/ai-agents/ai-agents_start" imgSrc={aiAgentsStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Overview" description="Enter the AI agents technical documentation" url="../ai-integration/ai-agents/ai-agents_overview" imgSrc={aiAgentsStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Practical Look at AI Agents with RavenDB" description="Read an in-depth article about AI agents" url="https://ravendb.net/articles/practical-look-at-ai-agents-with-ravendb" imgSrc={aiAgentsStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Technical documentation" description="Enter the AI agents technical documentation" url="../ai-integration/ai-agents/ai-agents_start#technical-documentation" imgSrc={aiAgentsStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="In-depth articles" description="Read in-depth article about AI agents" url="../ai-integration/ai-agents/ai-agents_start#in-depth-articles" imgSrc={aiAgentsStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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</Admonition>
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</Admonition>
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<br />
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### GenAI tasks
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GenAI tasks are [ongoing operations](../studio/database/tasks/ongoing-tasks/general-info) that continuously monitor specified collections and process documents as they are added or modified.
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<CardWithImage title="Start page" description="Continue to the GenAI tasks Start page" url="../ai-integration/gen-ai-integration/gen-ai_start" imgSrc={genAiStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Overview" description="Enter the GenAI tasks technical documentation" url="../ai-integration/gen-ai-integration/gen-ai-overview" imgSrc={genAiStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="RavenDB GenAI Deep Dive" description="Read an in-depth article about GenAI" url="https://ravendb.net/articles/ravendb-genai-deep-dive" imgSrc={genAiStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Technical documentation" description="Enter the GenAI tasks technical documentation" url="../ai-integration/gen-ai-integration/gen-ai_start#technical-documentation" imgSrc={genAiStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="In-depth articles" description="Read in-depth articles about GenAI" url="../ai-integration/gen-ai-integration/gen-ai_start#in-depth-articles" imgSrc={genAiStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<br />
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### Embeddings generation
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[Embeddings](https://en.wikipedia.org/wiki/Embedding_(machine_learning)) are numeric vectors that you can create for data (like a text or an image) to capture meanings, contexts, or relationships related to the data. You can then search the data by running intelligent queries over its embeddings using [vector search](../ai-integration/vector-search/vector-search_start) to find content by semantic similarity rather than exact matches.
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- RavenDB allows you to create embeddings using native [ongoing embeddings-generation tasks](../ai-integration/generating-embeddings/embeddings-generation-task) that systematically process document collections and convert document fields (like texts or arrays) into embeddings. To create the embeddings, the tasks can use either an external AI model (such as OpenAI) or RavenDB's default embedding model.
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- You can also create embeddings using external embeddings providers and store them in your database (e.g., to handle other content types like images), or avoid pre-generating embeddings and let vector search operations generate embeddings on-the-fly, while searching.
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- Pre-generated embeddings and vector search operations can be used by other RavenDB AI features. For example, [GenAI tasks](../ai-integration/gen-ai-integration/gen-ai_start) can use vector search to find documents and update them with LLM-generated content, or [AI agents](../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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[Embeddings](https://en.wikipedia.org/wiki/Embedding_(machine_learning)) are numeric vectors that you can create for data (like a text or an image) to capture meanings, contexts, or relationships related to the data. You can then search the data by running intelligent queries over its embeddings using [vector search](../../ai-integration/vector-search/vector-search_start) to find content by semantic similarity rather than exact matches.
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- RavenDB allows you to create embeddings using native [ongoing embeddings-generation tasks](../../ai-integration/generating-embeddings/embeddings-generation-task) that systematically process document collections and convert document fields (like texts or arrays) into embeddings. To create the embeddings, the tasks can use either an external AI model (such as OpenAI) or RavenDB's default embedding model.
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- You can also create embeddings using external embeddings providers and store them in your database (e.g., to handle other content types such as images).
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- You can avoid pre-generating embeddings, and let vector search operations generate embeddings on-the-fly, while searching.
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- Embeddings can be used by other RavenDB AI features, e.g., [AI agents](../../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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[See common embeddings-generation use cases](../ai-integration/generating-embeddings/embeddings-generation_start#use-cases)
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<CardWithImage title="Start page" description="Continue to the embeddings generation Start page" url="../ai-integration/generating-embeddings/embeddings-generation_start" imgSrc={embedGenStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Overview" description="Enter the embeddings generation technical documentation" url="../ai-integration/generating-embeddings/overview" imgSrc={embedGenStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Generate Embeddings for AI Search with RavenDB and External Models" description="Read an in-depth article about embeddings generation" url="https://ravendb.net/articles/embeddings-generation-with-ravendb" imgSrc={embedGenStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Technical documentation" description="Enter the embeddings generation technical documentation" url="../ai-integration/generating-embeddings/embeddings-generation_start#technical-documentation" imgSrc={embedGenStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="In-depth articles" description="Read in-depth articles about embeddings generation" url="../ai-integration/generating-embeddings/embeddings-generation_start#in-depth-articles" imgSrc={embedGenStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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[Vector search](../ai-integration/vector-search/vector-search_start) operations let you find related content by comparing embeddings, e.g. to find texts by meaning or images by context.
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<CardWithImage title="Start page" description="Continue to the vector search Start page" url="../ai-integration/vector-search/vector-search_start" imgSrc={vectorSearchStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="RavenDB as a Vector Database" description="Enter the vector search technical documentation" url="../ai-integration/vector-search/ravendb-as-vector-database" imgSrc={vectorSearchStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Using vector search with AI agents" description="Read an in-depth article about vector search" url="https://ayende.com/blog/203142-A/building-an-ai-agent-using-ravendb" imgSrc={vectorSearchStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="Technical documentation" description="Enter the vector search technical documentation" url="../ai-integration/vector-search/vector-search_start#technical-documentation" imgSrc={vectorSearchStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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<CardWithImage title="In-depth articles" description="Read in-depth articles about vector search" url="../ai-integration/vector-search/vector-search_start#in-depth-articles" imgSrc={vectorSearchStartImage} imgAlt="imgAlt" ctaLabel="Read" />
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‎docs/ai-integration/gen-ai-integration/gen-ai_start.mdx‎

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import genAiStartStudioImage from "./assets/gen-ai_start_studio-image.png";
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# GenAI tasks: Start
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# GenAI tasks
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### Build intelligent workflows with GenAI tasks.
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### GenAI tasks
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GenAI tasks are [ongoing operations](../../studio/database/tasks/ongoing-tasks/general-info) that continuously monitor specified collections and process documents as they are added or modified.
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- Similar to [ETL tasks](../../studio/database/tasks/ongoing-tasks/ravendb-etl-task), a GenAI task extracts content from documents. But instead of sending the content to another database, the task sends it to an AI model (like OpenAI) along with a guiding **prompt** and a **JSON schema** that defines the layout for the model's response.
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- You can easily create GenAI tasks using Studio or the client API.
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## Technical documentation
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Use our technical documentation to learn more about GenAI tasks and how to create and manage them.
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## Videos
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Learn how GenAI tasks help create reliable and effective AI-powered workflows.
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<CardWithImageHorizontal title="Why are GenAI tasks so effective" description="Webinar with Oren Eini" url="https://www.youtube.com/watch?v=NgvyeHwwVjM" imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc" imgAlt="imgAlt" ctaLabel="Watch" />
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‎docs/ai-integration/generating-embeddings/embeddings-generation_start.mdx‎

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# Generating embeddings: Start
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# Generating embeddings
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### Create embeddings to enable AI-powered similarity search.
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[Embeddings](https://en.wikipedia.org/wiki/Embedding_(machine_learning)) are numeric vectors that you can create for data (like a text or an image) to capture meanings, contexts, or relationships related to the data. You can then search the data by running intelligent queries over its embeddings using [vector search](../../ai-integration/vector-search/vector-search_start) to find content by semantic similarity rather than exact matches.
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- RavenDB allows you to create embeddings using native [ongoing embeddings-generation tasks](../../ai-integration/generating-embeddings/embeddings-generation-task) that systematically process document collections and convert document fields (like texts or arrays) into embeddings. To create the embeddings, the tasks can use either an external AI model (such as OpenAI) or RavenDB's default embedding model.
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- You can also create embeddings using external embeddings providers and store them in your database (e.g., to handle other content types like images), or avoid pre-generating embeddings and let vector search operations generate embeddings on-the-fly, while searching.
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- Pre-generated embeddings and vector search operations can be used by other RavenDB AI features. For example, [GenAI tasks](../../ai-integration/gen-ai-integration/gen-ai_start) can use vector search to find documents and update them with LLM-generated content, or [AI agents](../../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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- You can also create embeddings using external embeddings providers and store them in your database (e.g., to handle other content types such as images).
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- You can avoid pre-generating embeddings, and let vector search operations generate embeddings on-the-fly, while searching.
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- Embeddings can be used by other RavenDB AI features, e.g., [AI agents](../../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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- [See common embeddings-generation use cases](../../ai-integration/generating-embeddings/embeddings-generation_start#use-cases)
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</Admonition>
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<CardWithImageHorizontal title="Take over the world with AI and RavenDB" description="Practical AI integration with Oren Eini" url="https://www.youtube.com/watch?v=7DhbgfH_rSE" imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc" imgAlt="imgAlt" ctaLabel="Watch" />
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<CardWithImageHorizontal title="Vector search in RavenDB" description="The AI trend developers simply cannot ignore" url="https://www.youtube.com/watch?v=zZwid8LA-e4" imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc" imgAlt="imgAlt" ctaLabel="Watch" />
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