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145 lines
3.3 KiB
Markdown
145 lines
3.3 KiB
Markdown
# Observe: Tracing Setup
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Configure tracing to capture data for evaluation.
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## Quick Setup
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```python
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# Python
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from phoenix.otel import register
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register(project_name="my-app", auto_instrument=True)
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```
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```typescript
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// TypeScript
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import { registerPhoenix } from "@arizeai/phoenix-otel";
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registerPhoenix({ projectName: "my-app", autoInstrument: true });
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```
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## Essential Attributes
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| Attribute | Why It Matters |
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| --------- | -------------- |
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| `input.value` | User's request |
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| `output.value` | Response to evaluate |
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| `retrieval.documents` | Context for faithfulness |
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| `tool.name`, `tool.parameters` | Agent evaluation |
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| `llm.model_name` | Track by model |
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## Custom Attributes for Evals
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```python
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span.set_attribute("metadata.client_type", "enterprise")
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span.set_attribute("metadata.query_category", "billing")
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```
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## Exporting for Evaluation
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### Spans (Python — DataFrame)
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```python
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from phoenix.client import Client
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# Client() works for local Phoenix (falls back to env vars or localhost:6006)
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# For remote/cloud: Client(base_url="https://app.phoenix.arize.com", api_key="...")
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client = Client()
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spans_df = client.spans.get_spans_dataframe(
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project_identifier="my-app", # NOT project_name= (deprecated)
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root_spans_only=True,
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)
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dataset = client.datasets.create_dataset(
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name="error-analysis-set",
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dataframe=spans_df[["input.value", "output.value"]],
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input_keys=["input.value"],
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output_keys=["output.value"],
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)
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```
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### Spans (TypeScript)
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```typescript
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import { getSpans } from "@arizeai/phoenix-client/spans";
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const { spans } = await getSpans({
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project: { projectName: "my-app" },
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parentId: null, // root spans only
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limit: 100,
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});
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```
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### Traces (Python — structured)
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Use `get_traces` when you need full trace trees (e.g., multi-turn conversations, agent workflows):
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```python
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from datetime import datetime, timedelta
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traces = client.traces.get_traces(
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project_identifier="my-app",
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start_time=datetime.now() - timedelta(hours=24),
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include_spans=True, # includes all spans per trace
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limit=100,
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)
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# Each trace has: trace_id, start_time, end_time, spans (when include_spans=True)
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```
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### Traces (TypeScript)
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```typescript
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import { getTraces } from "@arizeai/phoenix-client/traces";
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const { traces } = await getTraces({
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project: { projectName: "my-app" },
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startTime: new Date(Date.now() - 24 * 60 * 60 * 1000),
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includeSpans: true,
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limit: 100,
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});
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```
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## Uploading Evaluations as Annotations
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### Python
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```python
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from phoenix.evals import evaluate_dataframe
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from phoenix.evals.utils import to_annotation_dataframe
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# Run evaluations
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results_df = evaluate_dataframe(dataframe=spans_df, evaluators=[my_eval])
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# Format results for Phoenix annotations
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annotations_df = to_annotation_dataframe(results_df)
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# Upload to Phoenix
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client.spans.log_span_annotations_dataframe(dataframe=annotations_df)
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```
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### TypeScript
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```typescript
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import { logSpanAnnotations } from "@arizeai/phoenix-client/spans";
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await logSpanAnnotations({
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spanAnnotations: [
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{
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spanId: "abc123",
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name: "quality",
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label: "good",
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score: 0.95,
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annotatorKind: "LLM",
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},
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],
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});
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```
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Annotations are visible in the Phoenix UI alongside your traces.
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## Verify
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Required attributes: `input.value`, `output.value`, `status_code`
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For RAG: `retrieval.documents`
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For agents: `tool.name`, `tool.parameters`
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