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[8.15] Updates refs to LLMs (backport #5806) (#5815)
* Updates refs to LLMs (#5806) * updates refs to LLMs * Update docs/AI-for-security/ai-security-assistant.asciidoc Co-authored-by: Joe Peeples <[email protected]> * Update docs/serverless/AI-for-security/ai-assistant.mdx Co-authored-by: Joe Peeples <[email protected]> --------- Co-authored-by: Joe Peeples <[email protected]> (cherry picked from commit d7ebe83) # Conflicts: # docs/serverless/AI-for-security/ai-assistant.mdx # docs/serverless/AI-for-security/connect-to-azure-openai.mdx * Delete docs/serverless directory and its contents --------- Co-authored-by: Benjamin Ironside Goldstein <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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docs/AI-for-security/ai-security-assistant.asciidoc

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.Recommended models
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[sidebar]
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While AI Assistant is compatible with many different models, our testing found increased quality with Azure 32k, and faster, more cost-effective responses with Claude 3 Haiku and OpenAI GPT4 Turbo. For more information, refer to the <<llm-performance-matrix>>.
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While AI Assistant is compatible with many different models, refer to the <<llm-performance-matrix>> to select models that perform well with your desired use cases.
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docs/AI-for-security/connect-to-azure-openai.asciidoc

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. From within your Azure OpenAI deployment, select **Model deployments**, then click **Manage deployments**.
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. On the **Deployments** page, select **Create new deployment**.
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. Under **Select a model**, choose `gpt-4` or `gpt-4-32k`.
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** If you select `gpt-4`, set the **Model version** to `0125-Preview`.
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** If you select `gpt-4-32k`, set the **Model version** to `default`.
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. Under **Select a model**, choose `gpt-4o` or `gpt-4 turbo`.
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. Set the model version to "Auto-update to default".
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IMPORTANT: The models available to you depend on https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability[region availability]. For best results, use `GPT-4o 2024-05-13` with the maximum Tokens-Per-Minute (TPM) capacity. For more information on how different models perform for different tasks, refer to the <<llm-performance-matrix>>.
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