chore: import upstream snapshot with attribution

examples/eval-max-score-selection/README.md

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+# eval-max-score-selection (Max-Score Selection)
+
+You can run this example with:
+
+```bash
+npx promptfoo@latest init --example eval-max-score-selection
+cd eval-max-score-selection
+```
+
+This example demonstrates the `max-score` assertion type for objective output selection based on aggregated scores from other assertions.
+
+## Overview
+
+The `max-score` assertion provides a deterministic way to select the best output from multiple providers by:
+
+- Aggregating scores from other assertions (correctness, quality, documentation, etc.)
+- Applying configurable weights to different assertion types
+- Selecting the output with the highest weighted score
+- Providing objective, reproducible selection criteria
+
+## Key Differences from `select-best`
+
+- **Objective**: Uses quantifiable scores rather than LLM judgment
+- **Deterministic**: Same inputs always produce same selection
+- **Transparent**: Clear scoring methodology based on weighted assertions
+- **Cost-effective**: No additional LLM calls for selection
+
+## Configuration
+
+```yaml
+- type: max-score
+  value:
+    method: average # 'average' (default) or 'sum'
+    weights:
+      python: 3 # Weight for Python code correctness tests
+      llm-rubric: 1 # Weight for LLM-evaluated quality rubrics
+      javascript: 2 # Weight for JavaScript tests
+      contains: 0.5 # Weight for simple string matching
+    threshold: 0.7 # Optional minimum score threshold
+```
+
+### Options
+
+- **method**: How to aggregate scores
+  - `average` (default): Weighted average of assertion scores
+  - `sum`: Weighted sum of assertion scores
+- **weights**: Map of assertion types to their weights (default: 1.0)
+- **threshold**: Minimum score required for selection (optional)
+
+## Usage
+
+### Basic Example
+
+```bash
+# Run the main example (requires API keys for OpenAI/Anthropic)
+npx promptfoo@latest eval
+```
+
+## How It Works
+
+1. **Multiple Outputs Generated**: Each provider generates a solution
+2. **Assertions Evaluated**: All assertions run on each output:
+   - Python tests verify correctness (pass=1, fail=0)
+   - LLM rubrics evaluate quality aspects (0-1 score)
+   - Other assertions contribute their scores
+3. **Scores Aggregated**: Max-score calculates weighted score for each output
+4. **Best Selected**: Output with highest score is marked as passing
+5. **Results Shown**: Clear indication of which output won and why
+
+## Example Scoring
+
+Given three outputs with these assertion results:
+
+- Output A: python=1.0, documentation=0.5, efficiency=0.7
+- Output B: python=1.0, documentation=0.9, efficiency=0.8
+- Output C: python=0.0, documentation=1.0, efficiency=1.0
+
+With weights: python=3, llm-rubric=1
+
+- Output A: (3×1.0 + 1×0.5 + 1×0.7) / 5 = 0.84
+- Output B: (3×1.0 + 1×0.9 + 1×0.8) / 5 = 0.94 ✓ (selected)
+- Output C: (3×0.0 + 1×1.0 + 1×1.0) / 5 = 0.40
+
+## When to Use max-score
+
+Use `max-score` when:
+
+- You have objective criteria (tests, metrics)
+- You want reproducible results
+- You need to weight different aspects differently
+- You want to avoid additional API costs
+
+Use `select-best` when:
+
+- You need subjective judgment
+- The criteria are hard to quantify
+- You want nuanced evaluation of quality

examples/eval-max-score-selection/promptfooconfig.yaml

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+# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
+description: Max-score assertion for objective output selection
+
+# This example demonstrates how max-score can objectively select the best implementation
+# based on weighted scores from multiple assertions (correctness, documentation, efficiency)
+
+prompts:
+  - 'Generate a Python function to {{task}}'
+  - 'Write an efficient Python function to {{task}}'
+  - 'Create a well-documented Python function to {{task}}'
+
+providers:
+  - openai:o4-mini
+  - anthropic:claude-sonnet-4-20250514
+  - google:gemini-2.5-flash
+
+tests:
+  - vars:
+      task: 'merge two sorted lists into one sorted list'
+    assert:
+      # Correctness test
+      - type: python
+        value: |
+          # Test the merge function
+          list1 = [1, 3, 5, 7, 9]
+          list2 = [2, 4, 6, 8, 10]
+          result = merge_sorted_lists(list1, list2)
+          assert result == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], f"Expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], got {result}"
+
+          # Test with empty lists
+          assert merge_sorted_lists([], [1, 2, 3]) == [1, 2, 3]
+          assert merge_sorted_lists([1, 2, 3], []) == [1, 2, 3]
+          assert merge_sorted_lists([], []) == []
+
+      # Documentation test
+      - type: llm-rubric
+        value: 'Code includes a clear docstring explaining parameters, return value, and complexity'
+
+      # Code structure and efficiency
+      - type: llm-rubric
+        value: 'The implementation uses an efficient algorithm (O(m+n) time complexity) and follows Python best practices'
+
+      # Max-score selects the best implementation objectively
+      - type: max-score
+        value:
+          weights:
+            python: 3 # Correctness is most important
+            llm-rubric: 1.5 # Documentation and code quality weighted together
+
+  - vars:
+      task: 'check if a string is a palindrome (ignoring case and spaces)'
+    assert:
+      # Correctness
+      - type: python
+        value: |
+          assert is_palindrome("racecar") == True
+          assert is_palindrome("A man a plan a canal Panama") == True
+          assert is_palindrome("race a car") == False
+          assert is_palindrome("hello") == False
+          assert is_palindrome("") == True
+
+      # Edge case handling
+      - type: llm-rubric
+        value: 'Handles edge cases like empty strings and single characters correctly'
+
+      # Efficiency
+      - type: llm-rubric
+        value: 'Uses an efficient algorithm (O(n) time complexity)'
+
+      # Simple max-score (all assertions equally weighted)
+      - type: max-score