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1b367e2b62
The tree was a debranded snapshot of upstream taken around 2026-02-20, and upstream has moved about five months since. This replaces it with upstream master, mechanically re-derived: bin/debrand does the rename, and the pipeline and squashed migrations are restored from the shipped tree. Not a merge. The previous import replaced the tree wholesale in a single commit, so there is no shared content history to merge against. Reproducing the rename on the new tree is what actually converges. What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md, and insights/migrations. The squashed initial migration is load-bearing — the live database records it by name, so upstream's ~918 individual migrations cannot come back. Nine products are dropped as unreferenced. Twenty-one more that we do not run are kept anyway because the core app still imports them; unpicking those is a refactor and does not belong in a rebase. This base does not build yet, and the reasons are not in the rename: upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against our 4.2, and the dependency manifests still name upstream packages that we republish under our own names. Those are decisions, not renames.
256 lines
11 KiB
TypeScript
256 lines
11 KiB
TypeScript
import { useMDXComponents } from 'scenes/onboarding/shared/OnboardingDocsContentWrapper'
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const propertyColumnStyle = { minWidth: '150px' }
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export const EmbeddingEvent = (): JSX.Element => {
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const { Markdown, dedent } = useMDXComponents()
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return (
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<>
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<Markdown>
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{dedent`
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An embedding is a single call to an embedding model to convert text into a vector representation.
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**Event name**: \`$ai_embedding\`
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### Core properties
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`}
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</Markdown>
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<div className="Markdown">
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<table className="my-4">
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<thead>
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<tr>
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<th style={propertyColumnStyle}>Property</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_trace_id</code>
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</td>
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<td>
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<p>
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The trace ID (a UUID to group related AI events together). Must contain only
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letters, numbers, and special characters: <code>-</code>, <code>_</code>,{' '}
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<code>~</code>, <code>.</code>, <code>@</code>, <code>(</code>, <code>)</code>,{' '}
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<code>!</code>, <code>'</code>, <code>:</code>, <code>|</code>
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<br />
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Example: <code>d9222e05-8708-41b8-98ea-d4a21849e761</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_session_id</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Groups related traces together. Use this to organize traces by
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whatever grouping makes sense for your application (user sessions, workflows,
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conversations, or other logical boundaries).
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<br />
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Example: <code>session-abc-123</code>, <code>conv-user-456</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_span_id</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Unique identifier for this embedding operation
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_span_name</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Name given to this embedding operation
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<br />
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Example: <code>embed_user_query</code>, <code>index_document</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_parent_id</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Parent span ID for tree-view grouping
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_model</code>
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</td>
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<td>
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<p>
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The embedding model used
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<br />
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Example: <code>text-embedding-3-small</code>, <code>text-embedding-ada-002</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_provider</code>
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</td>
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<td>
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<p>
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The LLM provider
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<br />
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Example: <code>openai</code>, <code>cohere</code>, <code>voyage</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_input</code>
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</td>
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<td>
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<p>
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The text to embed
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<br />
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Example: <code>"Tell me a fun fact about mascots"</code> or array of strings for
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batch embeddings
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_input_tokens</code>
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</td>
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<td>
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<p>The number of tokens in the input</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_latency</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> The latency of the embedding call in seconds
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_http_status</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> The HTTP status code of the response
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_base_url</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> The base URL of the LLM provider
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<br />
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Example: <code>https://api.openai.com/v1</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_request_url</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> The full URL of the request made to the embedding API
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<br />
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Example: <code>https://api.openai.com/v1/embeddings</code>
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_is_error</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Boolean to indicate if the request was an error
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_error</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> The error message or object if the embedding failed
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</p>
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</td>
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</tr>
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</tbody>
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</table>
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</div>
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<Markdown>
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{dedent`
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### Cost properties
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Cost properties are optional as we can automatically calculate them from model and token counts. If you want, you can provide your own cost property instead.
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`}
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</Markdown>
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<div className="Markdown">
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<table className="my-4">
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<thead>
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<tr>
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<th style={propertyColumnStyle}>Property</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_input_cost_usd</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Cost in USD for input tokens
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_output_cost_usd</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Cost in USD for output tokens (usually 0 for embeddings)
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</p>
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</td>
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</tr>
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<tr>
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<td style={propertyColumnStyle}>
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<code>$ai_total_cost_usd</code>
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</td>
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<td>
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<p>
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<em>(Optional)</em> Total cost in USD
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</p>
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</td>
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</tr>
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</tbody>
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</table>
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</div>
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</>
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)
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}
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