Sort Relevance

Sort Relevance stage showing document reordering by relevance scores

The Sort Relevance stage reorders documents based on their relevance scores from previous stages. This is useful when documents have been modified or filtered and need to be re-sorted by their original search scores.

Stage Category: SORT (Reorders documents)Transformation: N documents → N documents (reordered by relevance)

When to Use

Use Case	Description
Post-filter sorting	Re-sort after attribute_filter removes documents
Score normalization	Apply consistent sorting after multi-stage processing
Restoring order	Return to relevance order after other sort operations
Combining scores	Sort by combined scores from multiple sources

When NOT to Use

Scenario	Recommended Alternative
Sorting by metadata field	`sort_by_field`
Neural re-scoring	`rerank`
Results already sorted	Skip this stage
Diversifying results	`mmr`

Parameters

Parameter	Type	Default	Description
`score_field`	string	`score`	Field containing relevance score
`direction`	string	`desc`	Sort direction: `desc` (highest first) or `asc`
`missing_score`	string	`bottom`	Where docs lacking a score go: `bottom`, `top`, or `preserve`

Configuration Examples

{
  "stage_name": "sort_relevance",
  "stage_type": "sort",
  "config": {
    "stage_id": "sort_relevance",
    "parameters": {
      "direction": "desc"
    }
  }
}

{
  "stage_name": "sort_relevance",
  "stage_type": "sort",
  "config": {
    "stage_id": "sort_relevance",
    "parameters": {
      "score_field": "search_score",
      "direction": "desc"
    }
  }
}

{
  "stage_name": "sort_relevance",
  "stage_type": "sort",
  "config": {
    "stage_id": "sort_relevance",
    "parameters": {
      "score_field": "score",
      "direction": "desc",
      "missing_score": "bottom"
    }
  }
}

{
  "stage_name": "sort_relevance",
  "stage_type": "sort",
  "config": {
    "stage_id": "sort_relevance",
    "parameters": {
      "score_field": "distance",
      "direction": "asc"
    }
  }
}

How It Works

Extract Scores: Read the score field from each document
Sort: Order documents by score (descending by default)
Optionally Normalize: Scale scores to 0-1 range
Return: Documents in new order

Output Schema

{
  "document_id": "doc_123",
  "content": "Document content...",
  "score": 0.95,
  "sort_relevance": {
    "original_position": 3,
    "new_position": 1,
    "normalized_score": 1.0
  }
}

Performance

Metric	Value
Latency	< 5ms
Memory	O(N)
Cost	Free
Complexity	O(N log N)

Common Pipeline Patterns

Search + Filter + Re-sort

[
  {
    "stage_name": "semantic_search",
    "stage_type": "filter",
    "config": {
      "stage_id": "feature_search",
      "parameters": {
        "searches": [
          { "feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": { "input_mode": "text", "value": "{{INPUT.query}}" }, "top_k": 100 }
        ],
        "final_top_k": 100
      }
    }
  },
  {
    "stage_name": "structured_filter",
    "stage_type": "filter",
    "config": {
      "stage_id": "attribute_filter",
      "parameters": {
        "conditions": {
          "field": "metadata.status",
          "operator": "eq",
          "value": "published"
        }
      }
    }
  },
  {
    "stage_name": "sort_relevance",
    "stage_type": "sort",
    "config": {
      "stage_id": "sort_relevance",
      "parameters": {
        "direction": "desc"
      }
    }
  }
]

Multi-Stage with Score Normalization

[
  {
    "stage_name": "hybrid_search",
    "stage_type": "filter",
    "config": {
      "stage_id": "feature_search",
      "parameters": {
        "searches": [
          { "feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": { "input_mode": "text", "value": "{{INPUT.query}}" }, "top_k": 50 }
        ],
        "final_top_k": 50
      }
    }
  },
  {
    "stage_name": "llm_enrichment",
    "stage_type": "enrich",
    "config": {
      "stage_id": "llm_enrich",
      "parameters": {
        "model": "gpt-4o-mini",
        "prompt": "Extract key topics",
        "output_field": "topics"
      }
    }
  },
  {
    "stage_name": "sort_relevance",
    "stage_type": "sort",
    "config": {
      "stage_id": "sort_relevance",
      "parameters": {
        "direction": "desc"
      }
    }
  }
]

Comparison with Other Sort Stages

Stage	Purpose	Score Source
`sort_relevance`	Relevance scores	Search/fusion scores
`sort_by_field`	Metadata values	Any document field
`rerank`	Neural re-scoring	Cross-encoder model
`mmr`	Diversity	Relevance + diversity

Error Handling

Error	Behavior
Missing score field	Use 0 as default
Non-numeric score	Move to end
Empty input	Return empty
Equal scores	Maintain original order

Rerank - Neural re-scoring
Sort Attribute - Metadata sorting
MMR - Diversity-aware sorting

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Resources

When to Use

When NOT to Use

Parameters

Configuration Examples

How It Works

Output Schema

Performance

Common Pipeline Patterns

Search + Filter + Re-sort

Multi-Stage with Score Normalization

Comparison with Other Sort Stages

Error Handling

​When to Use

​When NOT to Use

​Parameters

​Configuration Examples

​How It Works

​Output Schema

​Performance

​Common Pipeline Patterns

​Search + Filter + Re-sort

​Multi-Stage with Score Normalization

​Comparison with Other Sort Stages

​Error Handling

​Related

When to Use

When NOT to Use

Parameters

Configuration Examples

How It Works

Output Schema

Performance

Common Pipeline Patterns

Search + Filter + Re-sort

Multi-Stage with Score Normalization

Comparison with Other Sort Stages

Error Handling

Related