Annotations Overview
Annotations allow you to add human or automated feedback to traces, spans, documents, and sessions. Annotations are essential for evaluation, quality assessment, and building training datasets.
Annotation Types
Phoenix supports four types of annotations:
| Type | Target | Purpose | Example Use Case |
|---|---|---|---|
| Span Annotation | Individual span | Feedback on a specific operation | "This LLM response was accurate" |
| Document Annotation | Document within a RETRIEVER span | Feedback on retrieved document relevance | "This document was not helpful" |
| Trace Annotation | Entire trace | Feedback on end-to-end interaction | "User was satisfied with result" |
| Session Annotation | User session | Feedback on multi-turn conversation | "Session ended successfully" |
Annotation Fields
Every annotation has these fields:
Required Fields
| Field | Type | Description |
|---|---|---|
| Entity ID | String | ID of the target entity (span_id, trace_id, session_id, or document_position) |
name |
String | Annotation name/label (e.g., "quality", "relevance", "helpfulness") |
Result Fields (At Least One Required)
| Field | Type | Description |
|---|---|---|
label |
String (optional) | Categorical value (e.g., "good", "bad", "relevant", "irrelevant") |
score |
Float (optional) | Numeric value (typically 0-1, but can be any range) |
explanation |
String (optional) | Free-text explanation of the annotation |
At least one of label, score, or explanation must be provided.
Optional Fields
| Field | Type | Description |
|---|---|---|
annotator_kind |
String | Who created this annotation: "HUMAN", "LLM", or "CODE" (default: "HUMAN") |
identifier |
String | Unique identifier for upsert behavior (updates existing if same name+entity+identifier) |
metadata |
Object | Custom metadata as key-value pairs |
Annotator Kinds
| Kind | Description | Example |
|---|---|---|
HUMAN |
Manual feedback from a person | User ratings, expert labels |
LLM |
Automated feedback from an LLM | GPT-4 evaluating response quality |
CODE |
Automated feedback from code | Rule-based checks, heuristics |
Examples
Quality Assessment:
quality- Overall quality (label: good/fair/poor, score: 0-1)correctness- Factual accuracy (label: correct/incorrect, score: 0-1)helpfulness- User satisfaction (label: helpful/not_helpful, score: 0-1)
RAG-Specific:
relevance- Document relevance to query (label: relevant/irrelevant, score: 0-1)faithfulness- Answer grounded in context (label: faithful/unfaithful, score: 0-1)
Safety:
toxicity- Contains harmful content (score: 0-1)pii_detected- Contains personally identifiable information (label: yes/no)