# RERANKER Spans
## Purpose
RERANKER spans represent reordering of retrieved documents (Cohere Rerank, cross-encoder models).
## Required Attributes
| Attribute | Type | Description | Required |
|-----------|------|-------------|----------|
| `openinference.span.kind` | String | Must be "RERANKER" | Yes |
## Attribute Reference
### Reranker Parameters
| Attribute | Type | Description |
|-----------|------|-------------|
| `reranker.model_name` | String | Reranker model identifier |
| `reranker.query` | String | Query used for reranking |
| `reranker.top_k` | Integer | Number of documents to return |
### Input Documents
| Attribute Pattern | Type | Description |
|-------------------|------|-------------|
| `reranker.input_documents.{i}.document.id` | String | Input document ID |
| `reranker.input_documents.{i}.document.content` | String | Input document content |
| `reranker.input_documents.{i}.document.score` | Float | Original retrieval score |
| `reranker.input_documents.{i}.document.metadata` | String (JSON) | Document metadata |
### Output Documents
| Attribute Pattern | Type | Description |
|-------------------|------|-------------|
| `reranker.output_documents.{i}.document.id` | String | Output document ID (reordered) |
| `reranker.output_documents.{i}.document.content` | String | Output document content |
| `reranker.output_documents.{i}.document.score` | Float | New reranker score |
| `reranker.output_documents.{i}.document.metadata` | String (JSON) | Document metadata |
### Score Comparison
Input scores (from retriever) vs. output scores (from reranker):
```json
{
"reranker.input_documents.0.document.id": "doc_A",
"reranker.input_documents.0.document.score": 0.7,
"reranker.input_documents.1.document.id": "doc_B",
"reranker.input_documents.1.document.score": 0.9,
"reranker.output_documents.0.document.id": "doc_B",
"reranker.output_documents.0.document.score": 0.95,
"reranker.output_documents.1.document.id": "doc_A",
"reranker.output_documents.1.document.score": 0.85
}
```
In this example:
- Input: doc_B (0.9) ranked higher than doc_A (0.7)
- Output: doc_B still highest but both scores increased
- Reranker confirmed retriever's ordering but refined scores
## Examples
### Complete Reranking Example
```json
{
"openinference.span.kind": "RERANKER",
"reranker.model_name": "cohere-rerank-v2",
"reranker.query": "What is machine learning?",
"reranker.top_k": 2,
"reranker.input_documents.0.document.id": "doc_123",
"reranker.input_documents.0.document.content": "Machine learning is a subset...",
"reranker.input_documents.1.document.id": "doc_456",
"reranker.input_documents.1.document.content": "Supervised learning algorithms...",
"reranker.input_documents.2.document.id": "doc_789",
"reranker.input_documents.2.document.content": "Neural networks are...",
"reranker.output_documents.0.document.id": "doc_456",
"reranker.output_documents.0.document.content": "Supervised learning algorithms...",
"reranker.output_documents.0.document.score": 0.95,
"reranker.output_documents.1.document.id": "doc_123",
"reranker.output_documents.1.document.content": "Machine learning is a subset...",
"reranker.output_documents.1.document.score": 0.88
}
```
