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Copy file name to clipboardExpand all lines: docs/ai-integration/ai-agents/ai-agents_overview.mdx
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AI agents are designed to easily integrate AI capabilities into applications and workflows. They can interact with users, intelligently retrieve and process data from proprietary databases, and apply actions based on roles they are requested to take and the data they have access to. Some of the tasks and applications they can be tailored to perform include -
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***Customer support chatbot agents**
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Agents can answer customer queries based on information stored in databases and internal knowledge bases, provide troubleshooting steps, and guide users through processes in real time.
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#### <u>Customer support chatbot agents</u>
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Agents can answer customer queries based on information stored in databases and internal knowledge bases, provide troubleshooting steps, and guide users through processes in real time.
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***Data analysis and reporting agents**
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Agents can analyze large datasets to extract relevant data and present it in a user-friendly format, escalate customer issues and application output, create reports and highlight points of interest, and help businesses make informed decisions.
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#### <u>Data analysis and reporting agents</u>
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Agents can analyze large datasets to extract relevant data and present it in a user-friendly format, escalate customer issues and application output, create reports and highlight points of interest, and help businesses make informed decisions.
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***Content generation agents**
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Agents can generate summaries, add automated comments to articles and application-generated content, reference readers to related material, and create marketing content based on user input and stored information.
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#### <u>Content generation agents</u>
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Agents can generate summaries, add automated comments to articles and application-generated content, reference readers to related material, and create marketing content based on user input and stored information.
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***Workflow automation agents**
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Agents can automate repetitive tasks like email sorting, spam filtering, form filling, or file organization.
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#### <u>Workflow automation agents</u>
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Agents can automate repetitive tasks like email sorting, spam filtering, form filling, or file organization.
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***Intelligent recommendation agents**
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Agents can provide personalized recommendations based on user preferences and available data, e.g. a _library assistant_ suggesting books and other resources, an _HR office assistant_ recommending rewards for employees based on their performance and available facilities near their residence, or an _e-commerce assistant_ recommending products.
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#### <u>Intelligent recommendation agents</u>
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Agents can provide personalized recommendations based on user preferences and available data, e.g. a _library assistant_ suggesting books and other resources, an _HR office assistant_ recommending rewards for employees based on their performance and available facilities near their residence, or an _e-commerce assistant_ recommending products.
Copy file name to clipboardExpand all lines: docs/ai-integration/ai-integration_start.mdx
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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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- When running a vector search, your query is converted into an embedding and compared against document embeddings. The document embeddings can be either pre-generated (by an embeddings-generation task or an external provider) or generated on-the-fly while searching.
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- You can run vector search using either a dynamic query for ad-hoc or infrequent searches or a static index for optimized performance.
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- Vector search is designed so other RavenDB AI features can easily use it. A [GenAI task](../../ai-integration/gen-ai-integration/gen-ai_start), for example, can use vector search to find documents and update them with LLM-generated content, and an [AI agent](../../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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- Vector search is designed so other RavenDB AI features can easily use it. A [GenAI task](../ai-integration/gen-ai-integration/gen-ai_start), for example, can use vector search to find documents and update them with LLM-generated content, and an [AI agent](../ai-integration/ai-agents/ai-agents_start) can use vector search to retrieve relevant data requested by the LLM.
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<ColGridcolCount={3}>
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<CardWithImagetitle="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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<Admonitiontype="note"title="">
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[See common AI agents use cases](../ai-integration/ai-agents/ai-agents_overview#common-use-cases)
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See common AI agents use cases:
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[Customer support chatbot agents](../ai-integration/ai-agents/ai-agents_overview#customer-support-chatbot-agents)
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[Data analysis and reporting agents](../ai-integration/ai-agents/ai-agents_overview#data-analysis-and-reporting-agents)
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