# Test Commands for Refactored Bicorder

Run these tests in order to verify the refactored code works correctly.

## Test 1: Dry Run - Single Protocol

Test that prompts are generated correctly with protocol context:

```bash
python3 scripts/bicorder_query.py data/synthetic_20251116/protocols_edited.csv 1 --dry-run | head -80
```

**Expected result:**
- Should show "DRY RUN: Row 1, 23 gradients"
- Should show protocol descriptor and description
- Each prompt should include full protocol context
- Should show 23 gradient prompts

## Test 2: Verify CSV Structure

Check that the analyze script still creates proper CSV structure:

```bash
python3 scripts/bicorder_analyze.py data/synthetic_20251116/protocols_edited.csv -o test_output.csv
head -1 test_output.csv | tr ',' '\n' | grep -E "(explicit|precise|elite)" | head -5
```

**Expected result:**
- Should show gradient column names like:
  - Design_explicit_vs_implicit
  - Design_precise_vs_interpretive
  - Design_elite_vs_vernacular

## Test 3: Single Gradient Query (Real LLM Call)

Query just one protocol to test the full pipeline:

```bash
python3 scripts/bicorder_query.py test_output.csv 1 -m gpt-4o-mini
```

**Expected result:**
- Should show "Protocol: [name]"
- Should show "[1/23] Querying: Design explicit vs implicit..."
- Should complete all 23 gradients
- Should show "✓ CSV updated: test_output.csv"
- Each gradient should show a value 1-9

**Verify the output:**
```bash
# Check that values were written
head -2 test_output.csv | tail -1 | tr ',' '\n' | tail -25 | head -5
```

## Test 4: Check for No Conversation State

Verify that the tool doesn't create any conversation files:

```bash
# Before running test
llm logs list | grep -i bicorder

# Run a query
python3 scripts/bicorder_query.py test_output.csv 2 -m gpt-4o-mini

# After running test
llm logs list | grep -i bicorder
```

**Expected result:**
- Should not see any "bicorder_row_*" or similar conversation IDs
- Each query should be independent

## Test 5: Batch Processing (Small Set)

Test batch processing on rows 1-3:

```bash
python3 scripts/bicorder_batch.py data/synthetic_20251116/protocols_edited.csv -o test_batch_output.csv --start 1 --end 3 -m gpt-4o-mini
```

**Expected result:**
- Should process 3 protocols
- Should show progress for each row
- Should show "Successful: 3" at the end
- No mention of "initializing conversation"

**Verify outputs:**
```bash
# Check that all 3 rows have values
python3 -c "
import csv
with open('test_batch_output.csv') as f:
    reader = csv.DictReader(f)
    for i, row in enumerate(reader, 1):
        if i > 3:
            break
        gradient_cols = [k for k in row.keys() if '_vs_' in k]
        filled = sum(1 for k in gradient_cols if row[k])
        print(f'Row {i}: {filled}/23 gradients filled')
"
```

## Test 6: Dry Run with Different Model

Test that model parameter works in dry run:

```bash
python3 scripts/bicorder_query.py data/synthetic_20251116/protocols_edited.csv 5 --dry-run -m mistral | head -50
```

**Expected result:**
- Should show prompts (model doesn't matter in dry run, but flag should be accepted)

## Test 7: Error Handling

Test with invalid row number:

```bash
python3 scripts/bicorder_query.py test_output.csv 999
```

**Expected result:**
- Should show error: "Error: Row 999 not found in CSV"

## Test 8: Compare Prompt Structure

Compare the new standalone prompts vs old system prompt approach:

```bash
# New approach - protocol context in each prompt
python3 scripts/bicorder_query.py data/synthetic_20251116/protocols_edited.csv 1 --dry-run | grep -A 5 "Analyze this protocol"

# Old approach would have had protocol in system prompt only (no longer used)
# Verify that protocol context appears in EVERY gradient prompt
python3 scripts/bicorder_query.py data/synthetic_20251116/protocols_edited.csv 1 --dry-run | grep -c "Analyze this protocol"
```

**Expected result:**
- Should show "23" (protocol context appears in all 23 prompts)

## Cleanup

Remove test files:

```bash
rm -f test_output.csv test_batch_output.csv
```

## Success Criteria

✅ All 23 gradients queried for each protocol
✅ No conversation IDs created or referenced
✅ Protocol context included in every prompt
✅ CSV values properly written (1-9)
✅ Batch processing works without initialization step
✅ Error handling works correctly
