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)

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