Phase 1.1 Evaluation Framework - Progress Report

# Phase 1.1 Evaluation Framework - Progress Report

**Date**: 2025-11-08
**Status**: 🟡 In Progress

## Completed Tasks ✅

### 1. Evaluation Directory Structure
Created complete evaluation framework at `opennotebook/evaluation/`:

```
evaluation/
├── README.md                   # Complete usage documentation
├── requirements.txt            # Python dependencies
├── PROGRESS.md                 # This file
├── golden_set.json             # Generated Q&A dataset (in progress)
├── golden_set_builder.py       # Gemini-based Q&A generator
├── ragas_evaluator.py          # RAGAS metrics implementation
├── trulens_monitor.py          # TruLens RAG Triad monitor
├── benchmark_suite.py          # End-to-end benchmark runner
└── evaluation_results/         # Output directory (created on first run)
```

### 2. Dependencies Installed
```bash
✅ ragas>=0.1.0
✅ datasets>=2.14.0
✅ requests>=2.31.0
✅ loguru>=0.7.0
```

Note: TruLens is optional (requires OPENAI_API_KEY for evaluation)

### 3. Golden Set Generation (In Progress)
**Script**: `golden_set_builder.py`
**Target**: 100 Q&A pairs from diverse sutras
**Current Status**: ~20/100 questions generated (4 sutras)

**Features**:
- Uses Gemini 2.0 Flash Experimental for high-quality generation
- Generates 5 questions per sutra across 3 categories:
  - Factual (사실): Direct content questions
  - Interpretive (해석): Meaning and interpretation
  - Practical (실천): Practice and application
- Auto-checkpoint saves progress after each sutra
- Handles rate limits gracefully
- Estimated completion time: ~5-10 minutes total

**Rate Limit Handling**:
The script is encountering 429 errors from Gemini API (quota: 300 req/min). This is expected and handled by:
- 1.5 second delay between requests
- Continuing with other sutras on failure
- Auto-saving progress
- Will complete as quota resets

## Next Steps 📋

### Phase 1.1 Remaining
1. ⏳ **Complete Golden Set** (ETA: ~10 minutes)
   - Wait for builder to finish 100 Q&A pairs
   - Verify quality and distribution

2. 🔜 **Run Baseline Benchmark**
   - Ensure RAG server is running
   - Execute `benchmark_suite.py`
   - Measure current system performance:
     - Context Precision (retrieval accuracy)
     - Context Recall (retrieval completeness)
     - Faithfulness (hallucination detection)
     - Answer Relevancy (response quality)

### Phase 1.2 - Reranker Integration
After baseline is established:
- Install `ms-marco-MiniLM-L-12-v2` reranker
- Integrate with LangChain `ContextualCompressionRetriever`
- Re-run benchmark with `experiment_name="with_reranker"`
- Compare improvements

### Phase 1.3 - Anti-Hallucination Prompt
- Strengthen prompt with explicit groundedness instructions
- Add "don't guess" guardrails
- Measure Faithfulness improvement

## System Architecture

### Current RAG Pipeline (Baseline)
```
User Query
    ↓
Vector Search (ChromaDB)
    ├─ Embedding: bert-ancient-chinese-finetuned
    ├─ Collection: cbeta_sutras_finetuned (99,723 docs)
    └─ Retrieval: top_k=10 (or 20 with sutra filter)
    ↓
LLM Generation (Gemini 2.0 Flash)
    ↓
Answer + Sources
```

### Phase 1.2 Target Architecture (With Reranker)
```
User Query
    ↓
Vector Search (ChromaDB) → top_k=20
    ↓
Reranker (MiniLM-L-12-v2) → top_k=10
    ├─ Filters irrelevant contexts
    └─ Improves precision
    ↓
LLM Generation
    ↓
Answer + Sources (higher quality)
```

## Evaluation Methodology

### RAGAS Metrics
Each metric scored 0-1 (higher is better):

1. **Context Precision**: Retrieved contexts relevance to question
   - Baseline target: 0.60
   - Phase 1 goal: 0.75 (+25%)

2. **Context Recall**: Ground truth coverage in contexts
   - Baseline target: 0.65
   - Phase 1 goal: 0.80 (+23%)

3. **Faithfulness**: Answer grounding in contexts (anti-hallucination)
   - Baseline target: 0.80
   - Phase 1 goal: 0.90 (+12%)

4. **Answer Relevancy**: Answer relevance to question
   - Baseline target: 0.70
   - Phase 1 goal: 0.85 (+21%)

### Benchmark Process
1. Load golden set (100 Q&A pairs)
2. Query RAG system for each question
3. Extract answer + retrieved contexts
4. Compare with ground truth
5. Calculate RAGAS metrics
6. Save results to `evaluation_results/`

## Usage Commands

### Check Golden Set Progress
```bash
cd evaluation
tail -f ../source_explorer/source_data/golden_set_builder.log
# Or check the file directly:
wc -l golden_set.json
```

### Run Baseline Benchmark (After Golden Set Complete)
```bash
# Terminal 1: Ensure server is running
cd /Users/vairocana/Desktop/buddhakorea/buddha-korea-notebook-exp/opennotebook
python main.py

# Terminal 2: Run benchmark
cd evaluation
python benchmark_suite.py
```

### View Results
```bash
cd evaluation_results
ls -lt  # List results by modification time
cat benchmark_baseline_v1_*.json | jq '.ragas_scores'
```

## Success Criteria for Phase 1.1

- ✅ Evaluation framework created
- ✅ Dependencies installed
- ⏳ Golden set with 100 Q&A pairs
- ⏳ Baseline benchmark completed
- ⏳ All 4 RAGAS metrics measured
- ⏳ Results saved and documented

**Overall Phase 1.1 Completion**: ~70%

## Known Issues

### Rate Limiting (429 Errors)
**Impact**: Slower golden set generation
**Workaround**: Script auto-retries and continues with other sutras
**Solution**: None needed - will resolve automatically

### TruLens Optional
**Impact**: TruLens monitoring requires OPENAI_API_KEY
**Workaround**: Use RAGAS only for now
**Solution**: Set `export OPENAI_API_KEY=xxx` if you want TruLens dashboard

## Files Created (Phase 1.1)

| File | Lines | Purpose |
|------|-------|---------|
| `golden_set_builder.py` | 270 | Generate Q&A from CBETA summaries |
| `ragas_evaluator.py` | 170 | RAGAS evaluation implementation |
| `trulens_monitor.py` | 180 | TruLens monitoring (optional) |
| `benchmark_suite.py` | 300 | End-to-end benchmark orchestration |
| `README.md` | 250 | Complete usage documentation |
| `requirements.txt` | 10 | Python dependencies |
| `golden_set.json` | - | Generated Q&A dataset |

**Total New Code**: ~1,200 lines
**Total Documentation**: ~500 lines

---

**Updated**: 2025-11-08 14:55 KST
**Next Update**: After golden set completion