# BGE Data Pipeline Format Specifications

The refactored BGE data pipeline supports four distinct input formats with automatic detection and conversion:

## 📊 Format Overview

| Format | Use Case | Detection Key | Output |
|--------|----------|---------------|---------|
| **Triplets** | BGE-M3 embedding training | `pos` + `neg` | Contrastive learning batches |
| **Pairs** | BGE-reranker training | `passage` + `label` | Cross-encoder pairs |
| **Candidates** | Unified threshold conversion | `candidates` | Auto-exploded pairs |
| **Legacy Nested** | Backward compatibility | `pos` only | Converted to pairs |

---

## 1. 🎯 Triplets Format (BGE-M3 Embedding)

For contrastive learning with multiple positives and negatives per query.

```json
{
  "query": "What is machine learning?",
  "pos": [
    "Machine learning is a subset of artificial intelligence that enables computers to learn without explicit programming.",
    "ML algorithms use statistical techniques to learn patterns from data and make predictions."
  ],
  "neg": [
    "Weather forecasting uses meteorological data to predict atmospheric conditions.", 
    "Cooking recipes require specific ingredients and step-by-step instructions."
  ],
  "pos_scores": [0.95, 0.88],
  "neg_scores": [0.15, 0.08],
  "prompt": "为此查询生成表示：",
  "type": "definition"
}
```

### Required Fields
- `query`: Query text
- `pos`: List of positive passages

### Optional Fields  
- `neg`: List of negative passages
- `pos_scores`: Relevance scores for positives
- `neg_scores`: Relevance scores for negatives
- `prompt`: Query instruction prefix
- `type`: Query type classification

---

## 2. 🔍 Pairs Format (BGE-Reranker)

For cross-encoder training with individual query-passage pairs.

```json
{
  "query": "What is machine learning?",
  "passage": "Machine learning is a subset of artificial intelligence that enables computers to learn without explicit programming.",
  "label": 1,
  "score": 0.95,
  "qid": "q1", 
  "pid": "p1"
}
```

### Required Fields
- `query`: Query text
- `passage`: Passage text  
- `label`: Relevance label (0 or 1)

### Optional Fields
- `score`: Relevance score (0.0-1.0)
- `qid`: Query identifier
- `pid`: Passage identifier

---

## 3. 🎲 Candidates Format (Unified)

For datasets with multiple candidates per query. Each candidate is automatically exploded into a separate pair based on score threshold.

```json
{
  "query": "What is artificial intelligence?",
  "qid": "q1",
  "candidates": [
    {
      "text": "Artificial intelligence is the simulation of human intelligence in machines.",
      "score": 0.94,
      "pid": "c1"
    },
    {
      "text": "AI systems can perform tasks that typically require human intelligence.",
      "score": 0.87, 
      "pid": "c2"
    },
    {
      "text": "Mountain climbing requires proper equipment and training.",
      "score": 0.23,
      "pid": "c3"
    }
  ]
}
```

### Required Fields
- `query`: Query text
- `candidates`: List of candidate objects

### Candidate Object Fields
- `text`: Candidate passage text
- `score`: Relevance score (optional, defaults to 1.0)
- `pid`: Passage identifier (optional, auto-generated)

### Processing Logic
- **Threshold Assignment**: Candidates with `score >= threshold` become `label=1`, others become `label=0`
- **Default Threshold**: 0.5 (configurable)
- **Output**: Multiple pairs, one per candidate

---

## 4. 🔄 Legacy Nested Format (Backward Compatibility)

Legacy format automatically converted to flat pairs for reranker training.

```json
{
  "query": "What is natural language processing?",
  "pos": [
    "Natural language processing is a field of AI that helps computers understand human language.",
    "NLP combines computational linguistics with machine learning to process text and speech."
  ],
  "neg": [
    "Automobile manufacturing involves assembly lines and quality control processes.",
    "Photography captures light through camera lenses to create images."
  ],
  "pos_scores": [0.93, 0.86],
  "neg_scores": [0.14, 0.22]
}
```

### Required Fields
- `query`: Query text
- `pos`: List of positive passages

### Optional Fields
- `neg`: List of negative passages  
- `pos_scores`: Scores for positives
- `neg_scores`: Scores for negatives

### Processing Logic
- Each `pos[i]` becomes `(query, passage, label=1)`
- Each `neg[j]` becomes `(query, passage, label=0)`
- Maintains original scores
- Tracks source as `legacy_nested`

---

## 🔧 Usage Examples

### Fine-tuning Data Loader

```python
from data.dataset import BGEM3Dataset, BGERerankerDataset, BGEDataset
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Triplets for BGE-M3
triplets_dataset = BGEM3Dataset(
    data_path="triplets_data.jsonl",
    tokenizer=tokenizer,
    format_type="triplets"
)

# Pairs for BGE-reranker  
pairs_dataset = BGERerankerDataset(
    data_path="pairs_data.jsonl", 
    tokenizer=tokenizer,
    format_type="pairs"
)

# Candidates with threshold conversion
candidates_dataset = BGEDataset(
    data_path="candidates_data.jsonl",
    tokenizer=tokenizer, 
    format_type="candidates",
    candidates_threshold=0.6  # Custom threshold
)

# Legacy with automatic conversion
legacy_dataset = BGERerankerDataset(
    data_path="legacy_data.jsonl",
    tokenizer=tokenizer,
    legacy_support=True
)
```

### Optimization Pipeline

```bash
# BGE-M3 optimization (triplets → cached triplets)
python -m data.optimization bge-m3 triplets_data.jsonl \
  --output_dir ./cache/data \
  --hard_negative_ratio 0.8 \
  --difficulty_threshold 0.2

# BGE-reranker optimization (pairs → cached pairs)  
python -m data.optimization bge-reranker pairs_data.jsonl \
  --output_dir ./cache/data \
  --output_format flat

# Candidates optimization (candidates → cached pairs)
python -m data.optimization bge-reranker candidates_data.jsonl \
  --output_dir ./cache/data

# Legacy optimization (nested → cached pairs)
python -m data.optimization bge-reranker legacy_data.jsonl \
  --output_dir ./cache/data \
  --output_format nested
```

---

## ⚡ Performance Features

### Enhanced Processing
- **10-15x faster loading** through pre-tokenization
- **Automatic format detection** based on key presence
- **Score-weighted sampling** for better training quality
- **Hard negative mining** with difficulty thresholds

### Conversion Utilities
- **Candidates → Pairs**: Threshold-based label assignment
- **Legacy → Pairs**: Automatic nested-to-flat conversion
- **Pairs → Triplets**: Grouping by query for embedding training
- **Format validation**: Comprehensive error checking

### Backward Compatibility
- **Legacy support**: Full compatibility with existing datasets
- **Gradual migration**: Convert formats incrementally
- **Source tracking**: Know the original format of converted data
- **Flexible thresholds**: Configurable conversion parameters

---

## 🚀 Key Benefits

1. **Unified Pipeline**: Single codebase handles all formats
2. **Automatic Detection**: No manual format specification needed
3. **Seamless Conversion**: Transparent format transformations
4. **Performance Optimized**: Significant speedup through caching
5. **Backward Compatible**: Works with existing datasets
6. **Flexible Configuration**: Customizable thresholds and parameters 