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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
80 lines
3.0 KiB
Markdown
80 lines
3.0 KiB
Markdown
# LLM Spans
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Represent calls to language models (OpenAI, Anthropic, local models, etc.).
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## Required Attributes
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| Attribute | Type | Description |
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|-----------|------|-------------|
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| `openinference.span.kind` | String | Must be "LLM" |
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| `llm.model_name` | String | Model identifier (e.g., "gpt-4", "claude-3-5-sonnet-20241022") |
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## Key Attributes
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| Category | Attributes | Example |
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|----------|------------|---------|
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| **Model** | `llm.model_name`, `llm.provider` | "gpt-4-turbo", "openai" |
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| **Tokens** | `llm.token_count.prompt`, `llm.token_count.completion`, `llm.token_count.total` | 25, 8, 33 |
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| **Cost** | `llm.cost.prompt`, `llm.cost.completion`, `llm.cost.total` | 0.0021, 0.0045, 0.0066 |
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| **Parameters** | `llm.invocation_parameters` (JSON) | `{"temperature": 0.7, "max_tokens": 1024}` |
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| **Messages** | `llm.input_messages.{i}.*`, `llm.output_messages.{i}.*` | See examples below |
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| **Tools** | `llm.tools.{i}.tool.json_schema` | Function definitions |
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## Cost Tracking
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**Core attributes:**
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- `llm.cost.prompt` - Total input cost (USD)
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- `llm.cost.completion` - Total output cost (USD)
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- `llm.cost.total` - Total cost (USD)
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**Detailed cost breakdown:**
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- `llm.cost.prompt_details.{input,cache_read,cache_write,audio}` - Input cost components
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- `llm.cost.completion_details.{output,reasoning,audio}` - Output cost components
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## Messages
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**Input messages:**
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- `llm.input_messages.{i}.message.role` - "user", "assistant", "system", "tool"
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- `llm.input_messages.{i}.message.content` - Text content
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- `llm.input_messages.{i}.message.contents.{j}` - Multimodal (text + images)
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- `llm.input_messages.{i}.message.tool_calls` - Tool invocations
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**Output messages:** Same structure as input messages.
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## Example: Basic LLM Call
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```json
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{
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"openinference.span.kind": "LLM",
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"llm.model_name": "claude-3-5-sonnet-20241022",
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"llm.invocation_parameters": "{\"temperature\": 0.7, \"max_tokens\": 1024}",
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"llm.input_messages.0.message.role": "system",
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"llm.input_messages.0.message.content": "You are a helpful assistant.",
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"llm.input_messages.1.message.role": "user",
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"llm.input_messages.1.message.content": "What is the capital of France?",
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"llm.output_messages.0.message.role": "assistant",
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"llm.output_messages.0.message.content": "The capital of France is Paris.",
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"llm.token_count.prompt": 25,
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"llm.token_count.completion": 8,
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"llm.token_count.total": 33
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}
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```
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## Example: LLM with Tool Calls
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```json
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{
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"openinference.span.kind": "LLM",
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"llm.model_name": "gpt-4-turbo",
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"llm.input_messages.0.message.content": "What's the weather in SF?",
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"llm.output_messages.0.message.tool_calls.0.tool_call.function.name": "get_weather",
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"llm.output_messages.0.message.tool_calls.0.tool_call.function.arguments": "{\"location\": \"San Francisco\"}",
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"llm.tools.0.tool.json_schema": "{\"type\": \"function\", \"function\": {\"name\": \"get_weather\"}}"
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}
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```
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## See Also
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- **Instrumentation:** `instrumentation-auto-python.md`, `instrumentation-manual-python.md`
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- **Full spec:** https://github.com/Arize-ai/openinference/blob/main/spec/semantic_conventions.md
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