docs/DOCUMENTATION_INDEX.md

# RAG Capstone Project - TRACE Metrics Documentation Index

## 📚 Complete Documentation Suite

This document provides an index of all explanation materials for understanding how GPT Labeling Prompts are used to calculate TRACE metrics.

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## 📄 Documentation Files

### 1. **TRACE_METRICS_QUICK_REFERENCE.md** ⭐ START HERE
- **Size**: 8.4 KB
- **Purpose**: Quick reference guide with all key formulas
- **Contains**:
  - Executive summary
  - Complete data flow
  - 4 TRACE metric definitions
  - Mathematical formulas
  - Practical example with calculations
  - Key insights and advantages
- **Best For**: Quick lookup, understanding the basics

### 2. **TRACE_METRICS_EXPLANATION.md** 📖 DETAILED GUIDE
- **Size**: 16.7 KB
- **Purpose**: Comprehensive explanation of the entire process
- **Contains**:
  - Step-by-step breakdown (4 main steps)
  - GPT prompt generation details
  - LLM response format specification
  - JSON parsing procedure
  - Detailed calculation for each metric
  - Complete end-to-end example
  - Data flow diagram (text-based)
  - Code references with line numbers
- **Best For**: Deep understanding, implementation details

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## 🎨 Visual Diagrams

### 3. **TRACE_Metrics_Flow.png** 📊 PROCESS FLOW
- **Size**: 306 KB (300 DPI, high quality)
- **Purpose**: Visual representation of 8-step calculation process
- **Shows**:
  1. Input preparation
  2. Sentencization
  3. Prompt generation
  4. LLM API call
  5. JSON response
  6. Data extraction
  7. Metric calculation (4 metrics)
  8. Final output
- **Includes**: Example calculation with expected values
- **Best For**: Presentations, quick visual reference

### 4. **Sentence_Mapping_Example.png** 🎯 SENTENCE-LEVEL MAPPING
- **Size**: 255 KB (300 DPI, high quality)
- **Purpose**: Shows how sentences are mapped to support information
- **Shows**:
  - Retrieved documents (with relevance marking)
  - Response sentences
  - Support mapping (which docs support which sentences)
  - Metric calculations from the mapping
  - Color-coded legend
- **Best For**: Understanding sentence-level evaluation

### 5. **RAG_Architecture_Diagram.png** 🏗️ SYSTEM ARCHITECTURE
- **Size**: 872 KB (300 DPI, highest quality)
- **Purpose**: Complete system architecture with Judge component
- **Shows** 3 main sections:
  1. **Collection Creation** (left): Data ingestion through 6 chunking strategies and 8 embedding models
  2. **TRACE Evaluation Framework** (center): The 4 core metrics with formulas
  3. **Judge Evaluation** (right): LLM-based evaluation pipeline
- **Best For**: System overview, presentations, publications

### 6. **RAG_Data_Flow_Diagram.png** 🔄 END-TO-END DATA FLOW
- **Size**: 491 KB (300 DPI, high quality)
- **Purpose**: Detailed 7-step data flow from query to results
- **Shows**:
  1. Query Processing
  2. Retrieval
  3. Response Generation
  4. Evaluation Setup
  5. Judge Evaluation
  6. Metric Calculation
  7. Output
- **Includes**: Code file references for each step
- **Best For**: Understanding full pipeline, training materials

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## 🎤 Presentation Materials

### 7. **RAG_Capstone_Project_Presentation.pptx** 📽️ FULL PRESENTATION
- **Size**: 57.7 KB
- **Total Slides**: 20
- **Includes**:
  - Project overview
  - RAG pipeline architecture
  - 6 chunking strategies
  - 8 embedding models
  - RAG evaluation challenge
  - TRACE framework details
  - LLM-based evaluation methodology
  - Advanced features
  - Performance results
  - Use cases and future roadmap
- **Best For**: Presentations to stakeholders, conference talks

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## 🗺️ How to Navigate This Documentation

### 👨‍💼 For Managers/Stakeholders:
1. Start with: `RAG_Capstone_Project_Presentation.pptx`
2. Visualize: `RAG_Architecture_Diagram.png`
3. Details: `TRACE_METRICS_QUICK_REFERENCE.md`

### 👨‍💻 For Developers:
1. Start with: `TRACE_METRICS_QUICK_REFERENCE.md`
2. Deep dive: `TRACE_METRICS_EXPLANATION.md`
3. Code references in explanation documents
4. Visualize: `TRACE_Metrics_Flow.png` and `Sentence_Mapping_Example.png`

### 👨‍🔬 For Researchers:
1. Read: `TRACE_METRICS_EXPLANATION.md`
2. Review: `RAG_Data_Flow_Diagram.png`
3. Study: Code files in `advanced_rag_evaluator.py`
4. Reference: All visual diagrams for publications

### 👨‍🎓 For Learning/Training:
1. Start: `TRACE_METRICS_QUICK_REFERENCE.md`
2. Visual: `TRACE_Metrics_Flow.png`
3. Example: `Sentence_Mapping_Example.png`
4. Deep: `TRACE_METRICS_EXPLANATION.md`
5. Presentation: `RAG_Capstone_Project_Presentation.pptx`

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## 🔍 Quick Reference: What Each File Explains

| Document | Explains | Format |
|----------|----------|--------|
| Quick Reference | What, Why, How | Markdown |
| Detailed Explanation | Deep technical details | Markdown |
| TRACE Flow | Step-by-step process | Image (PNG) |
| Sentence Mapping | Sentence-level details | Image (PNG) |
| Architecture | System design | Image (PNG) |
| Data Flow | Complete pipeline | Image (PNG) |
| Presentation | Overview + business case | Slides (PPTX) |

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## 🎯 The Four TRACE Metrics (Quick Recap)

| Metric | Measures | Formula | Range |
|--------|----------|---------|-------|
| **R (Relevance)** | % of docs relevant to query | `\|relevant\| / 20` | [0,1] |
| **T (Utilization)** | % of relevant docs used | `\|used\| / \|relevant\|` | [0,1] |
| **C (Completeness)** | % of relevant info covered | `\|R∩T\| / \|R\|` | [0,1] |
| **A (Adherence)** | No hallucinations (boolean) | All fully_supported? | {0,1} |

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## 📊 Data Sources for Metrics

All metrics are calculated from the GPT Labeling Response JSON:

```
all_relevant_sentence_keys      → Used for R, T, C metrics
all_utilized_sentence_keys      → Used for T, C metrics
sentence_support_information[]  → Used for A metric (fully_supported flags)
overall_supported              → Metadata
```

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## 🔗 Related Code Files

The actual implementation can be found in:

- **`advanced_rag_evaluator.py`** - Main evaluation engine
  - Lines 305-350: GPT Labeling Prompt Template
  - Lines 470-552: Get & Parse GPT Response
  - Lines 554-609: Calculate TRACE Metrics

- **`llm_client.py`** - Groq API integration
  - LLM API calls
  - Rate limiting
  - Response handling

- **`streamlit_app.py`** - UI for viewing results
  - Evaluation display
  - Metric visualization
  - JSON download

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## 🚀 Using This Documentation

### For Implementation:
1. Read `TRACE_METRICS_QUICK_REFERENCE.md` for understanding
2. Reference `TRACE_METRICS_EXPLANATION.md` for details
3. Check code in `advanced_rag_evaluator.py` for actual implementation
4. Use flow diagrams for debugging/verification

### For Explanation:
1. Start with Quick Reference for overview
2. Use flow diagrams for visual explanation
3. Reference Detailed Explanation for specifics
4. Show Architecture/Data Flow diagrams for context

### For Documentation:
1. Include all diagrams in technical documentation
2. Use Presentation slides for stakeholder communication
3. Reference Quick Reference in README files
4. Link to Detailed Explanation in code comments

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## 📈 Document Quality

All documents are production-ready:
- ✅ Diagrams: 300 DPI high resolution
- ✅ Markdown: Properly formatted with code examples
- ✅ Presentation: 20 professional slides
- ✅ Content: Complete with examples and explanations
- ✅ Consistency: Aligned across all materials

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## 🎓 Learning Path Recommendation

**Beginner (2-3 hours):**
1. Presentation (5 min overview)
2. Quick Reference (15 min)
3. TRACE Flow diagram (10 min)
4. Sentence Mapping example (15 min)
5. Architecture diagram (10 min)

**Intermediate (1-2 days):**
1. All above materials
2. Detailed Explanation (30 min)
3. Code walkthrough (1 hour)
4. Run example evaluation (30 min)

**Advanced (Full understanding):**
1. All materials above
2. Implement custom evaluation
3. Modify prompts and metrics
4. Contribute improvements

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## 📞 Questions?

Refer to:
- **"What is TRACE?"** → Quick Reference or Presentation
- **"How is X calculated?"** → Detailed Explanation
- **"Show me the flow"** → Flow diagrams
- **"Why GPT labeling?"** → Architecture/Explanation docs
- **"How to implement?"** → Code files + Explanation

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## ✨ Summary

This documentation suite provides complete understanding of the GPT Labeling → TRACE Metrics calculation process from multiple angles:

- **Visual learners**: Diagrams and presentation
- **Detail-oriented**: Markdown explanations with examples
- **Implementers**: Code references with line numbers
- **Presenters**: Professional slides and diagrams
- **Researchers**: Detailed methodology and formulas

All materials are cross-referenced and ready for production use.