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feat(llm-monitoring): Docs for LLM monitoring (#1285)
* Docs for LLM monitoring * Add notes field to table
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title: 'LLM Monitoring' | ||
--- | ||
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Sentry auto-generates LLM Monitoring data for common providers in Python, but you may need to manually annotate spans for other frameworks. | ||
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## Span conventions | ||
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### Span Operations | ||
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| Span OP | Description | | ||
|:------------------------|:-------------------------------------------------------------------------------------| | ||
| `ai.pipeline.*` | The top-level span which corresponds to one or more AI operations & helper functions | | ||
| `ai.run.*` | A unit of work - a tool call, LLM execution, or helper method. | | ||
| `ai.chat_completions.*` | A LLM chat operation | | ||
| `ai.embeddings.*` | An LLM embedding creation operation | | ||
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### Span Data | ||
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| Attribute | Type | Description | Examples | Notes | | ||
|-----------------------------|---------|-------------------------------------------------------|------------------------------------------|------------------------------------------| | ||
| `ai.input_messages` | string | The input messages sent to the model | `[{"role": "user", "message": "hello"}]` | | | ||
| `ai.completion_tоkens.used` | int | The number of tokens used to respond to the message | `10` | required for cost calculation | | ||
| `ai.prompt_tоkens.used` | int | The number of tokens used to process just the prompt | `20` | required for cost calculation | | ||
| `ai.total_tоkens.used` | int | The total number of tokens used to process the prompt | `30` | required for charts and cost calculation | | ||
| `ai.model_id` | list | The vendor-specific ID of the model used | `"gpt-4"` | required for cost calculation | | ||
| `ai.streaming` | boolean | Whether the request was streamed back | `true` | | | ||
| `ai.responses` | list | The response messages sent back by the AI model | `["hello", "world"]` | | | ||
| `ai.pipeline.name` | string | The description of the parent ai.pipeline span | `My AI pipeline` | required for charts | | ||
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## Instrumentation | ||
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When a user creates a new AI pipeline, the SDK automatically creates spans that instrument both the pipeline and its AI operations. | ||
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**Example** | ||
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```python | ||
from sentry_sdk.ai.monitoring import ai_track | ||
from openai import OpenAI | ||
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sentry.init(...) | ||
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openai = OpenAI() | ||
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@ai_track(description="My AI pipeline") | ||
def invoke_pipeline(): | ||
result = openai.chat.completions.create( | ||
model="some-model", messages=[{"role": "system", "content": "hello"}] | ||
).choices[0].message.content | ||
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return openai.chat.completions.create( | ||
model="some-model", messages=[{"role": "system", "content": result}] | ||
).choices[0].message.content | ||
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``` | ||
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This should result in the following spans. | ||
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``` | ||
<span op:"ai.pipeline" description:"My AI pipeline"> | ||
<span op:"ai.chat_completions.openai" description:"OpenAI Chat Completion" data[ai.total_tokens.used]:15 data[ai.pipeline.name]:"My AI pipeline" /> | ||
<span op:"ai.chat_completions.openai" description:"OpenAI Chat Completion" data[ai.total_tokens.used]:20 data[ai.pipeline.name]:"My AI pipeline" /> | ||
</span> | ||
``` | ||
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Notice that the ai.pipeline.name span of the children spans is the description of the `ai.pipeline.*` span parent. |