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

✅ 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

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)

# 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

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