# Research Framework v2.0 - Implementation Summary

This document summarizes the optimizations implemented to transform the Research Development Framework from a "Searchable Archive" into an "Active Research Agent."

---

## What Was Implemented

### Phase 1: Foundation

#### 1.1 Schema Updates (`database/schema_updates_v2.sql`)
New database structures for intelligent organization:

| Addition | Purpose |
|----------|---------|
| `documents.metadata_source` | Track classification origin (filename/llm/manual) |
| `documents.classification_confidence` | LLM confidence score (0-1) |
| `documents.primary_category` | Auto-assigned category |
| `documents.content_type` | lecture/essay/book_chapter/etc. |
| `documents.difficulty_level` | introductory/intermediate/advanced/expert |
| `document_clusters` table | Semantic cluster definitions |
| `document_cluster_membership` | Document-to-cluster links |
| `chunks.parent_chunk_id` | Hierarchical chunk relationships |
| `chunks.chunk_level` | parent/child/standard indicator |
| `chunk_connections` table | Cross-document semantic links |
| `chat_sessions/messages` | RAG conversation tracking |

#### 1.2 Auto-Taxonomist (`pipeline/taxonomist.py`)
Intelligent document classification module:

```python
from taxonomist import Taxonomist

taxonomist = Taxonomist()
classification = taxonomist.classify_document(text)
# Returns: {
#   'primary_category': 'Philosophy',
#   'specific_topics': ['Consciousness Studies', 'Epistemology'],
#   'key_concepts': ['thinking', 'cognition', 'self-awareness'],
#   'confidence': 0.87,
#   'classification_source': 'llm'
# }

taxonomist.sync_to_database(classification, document_id)
folder = taxonomist.suggest_folder_path(classification)
# Returns: 'Philosophy/Rudolf_Steiner'
```

Features:
- LLM-powered classification (gpt-4o-mini)
- Rule-based fallback when no API key
- Auto-creates topics/concepts (flagged for review)
- Suggests logical folder organization

#### 1.3 Enhanced Ingestion (`pipeline/ingest_documents.py`)
Updated document processing with classification:

```bash
# Standard ingestion with auto-classification
python ingest_documents.py

# Skip classification
python ingest_documents.py --no-classify

# Disable logical folder organization
python ingest_documents.py --no-logical-org
```

New flow:
1. Extract text
2. Quality assessment
3. **Auto-classify with Taxonomist** (NEW)
4. Register in database with classification fields
5. **Sync topics/concepts** (NEW)
6. Move to **logical folder** (e.g., `Philosophy/Steiner/`) (NEW)

---

### Phase 2: Intelligence Layer

#### 2.1 Semantic Clusterer (`pipeline/cluster_documents.py`)
Automatic document grouping based on embedding similarity:

```bash
# Auto-detect optimal clusters
python cluster_documents.py

# Specify number of clusters
python cluster_documents.py --n-clusters 20

# Use DBSCAN (density-based)
python cluster_documents.py --method dbscan

# Also find cross-document connections
python cluster_documents.py --find-connections
```

Features:
- K-means or DBSCAN clustering
- Automatic cluster naming via LLM
- Coherence scoring
- Cross-document connection discovery

---

### Phase 3: Research Interface

#### 3.1 RAG Chat Endpoint (`web/app.py` - `/api/chat`)
Question-answering over your document library:

```bash
curl -X POST http://localhost:5000/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What are the indications for teaching writing before reading?",
    "max_sources": 5
  }'
```

Response:
```json
{
  "answer": "According to Steiner's lectures [Source 1], writing engages the will forces and should precede reading because...",
  "sources": [
    {
      "source_number": 1,
      "document_id": "DOC_045",
      "title": "Practical Advice to Teachers",
      "excerpt": "...",
      "similarity": 0.87
    }
  ],
  "search_time_ms": 45.2,
  "generation_time_ms": 1200.5
}
```

#### 3.2 Faceted Search (`/api/search/faceted`)
Advanced filtering for researchers:

```bash
curl -X POST http://localhost:5000/api/search/faceted \
  -H "Content-Type: application/json" \
  -d '{
    "query": "consciousness",
    "filters": {
      "categories": ["Philosophy", "Psychology"],
      "year_range": [1900, 1950],
      "content_types": ["lecture"],
      "clusters": [1, 3]
    }
  }'
```

Returns results plus facet counts for UI refinement.

#### 3.3 Cluster Endpoints
Browse and search within semantic clusters:

| Endpoint | Method | Purpose |
|----------|--------|---------|
| `/api/clusters` | GET | List all clusters |
| `/api/clusters/<id>` | GET | Cluster details + documents |
| `/api/clusters/<id>/search` | POST | Search within cluster |

---

### Phase 4: Hierarchical Chunking

#### 4.1 Adaptive Chunker (`pipeline/chunk_documents.py`)
Parent-child chunk architecture for better RAG:

```bash
# Standard chunking (750 tokens)
python chunk_documents.py

# Hierarchical chunking (parent + child)
python chunk_documents.py --hierarchical

# Force rechunk all
python chunk_documents.py --rechunk --hierarchical
```

Hierarchy:
```
┌─────────────────────────────────────────────────────────────────────┐
│                    PARENT CHUNK (2000 tokens)                        │
│  Full context for LLM generation and understanding                  │
│                                                                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │ CHILD (200) │  │ CHILD (200) │  │ CHILD (200) │  │ CHILD (200) │ │
│  │ precise     │  │ search hit  │  │ specific    │  │ quote       │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
│  Used for: Precise search, quote finding                            │
└─────────────────────────────────────────────────────────────────────┘
```

Benefits:
- Search finds precise child chunks
- Parent provides full context for RAG
- Section titles preserved

---

## New Files Created

| File | Purpose |
|------|---------|
| `database/schema_updates_v2.sql` | Schema extensions |
| `pipeline/taxonomist.py` | Auto-classification module |
| `pipeline/cluster_documents.py` | Semantic clustering |
| `docs/OPTIMIZATION_ROADMAP.md` | Detailed technical roadmap |
| `docs/IMPLEMENTATION_SUMMARY.md` | This file |

## Modified Files

| File | Changes |
|------|---------|
| `pipeline/ingest_documents.py` | Added Taxonomist integration, logical organization |
| `pipeline/chunk_documents.py` | Added AdaptiveChunker, hierarchical mode |
| `web/app.py` | Added RAG chat, faceted search, cluster endpoints |

---

## API Endpoints Summary

### Core (v1.0)
| Method | Endpoint | Description |
|--------|----------|-------------|
| GET | `/api/health` | System health |
| GET | `/api/stats` | Database statistics |
| POST | `/api/search` | Search documents |
| GET | `/api/documents` | List documents |
| GET | `/api/documents/<id>` | Document details |
| GET | `/api/concepts` | List concepts |
| GET | `/api/topics` | List topics |

### New (v2.0)
| Method | Endpoint | Description |
|--------|----------|-------------|
| POST | `/api/chat` | RAG-powered Q&A |
| POST | `/api/search/faceted` | Faceted search with filters |
| GET | `/api/clusters` | List semantic clusters |
| GET | `/api/clusters/<id>` | Cluster details |
| POST | `/api/clusters/<id>/search` | Search within cluster |

---

## Usage Examples

### Complete Ingestion Workflow
```bash
# 1. Drop files in NEW_DOCS folder (root directory - easy access!)
cp *.pdf NEW_DOCS/
# Or use legacy location: library/NEW_DOCS/incoming/

# 2. Ingest with auto-classification
python pipeline/ingest_documents.py

# 3. Chunk documents (hierarchical for RAG)
python pipeline/chunk_documents.py --hierarchical

# 4. Generate embeddings (optional - for semantic search)
python pipeline/generate_embeddings.py

# 5. Run clustering
python pipeline/cluster_documents.py --find-connections
# Or use TF-IDF for offline mode:
python pipeline/cluster_documents.py --tfidf --find-connections

# 6. Start API server
python web/app.py
```

### Research with RAG
```python
import requests

# Ask a question
response = requests.post('http://localhost:5000/api/chat', json={
    'question': 'What is the relationship between thinking and consciousness according to Steiner?',
    'max_sources': 5
})

print(response.json()['answer'])
# "According to Steiner [Source 1], thinking is the activity through which..."

# Get sources
for source in response.json()['sources']:
    print(f"- {source['title']}: {source['excerpt'][:100]}...")
```

### Browse Clusters
```python
# List clusters
clusters = requests.get('http://localhost:5000/api/clusters').json()

for c in clusters['clusters']:
    print(f"{c['name']}: {c['document_count']} documents")

# Search within a cluster
results = requests.post('http://localhost:5000/api/clusters/1/search', json={
    'query': 'child development',
    'limit': 10
}).json()
```

---

## Configuration Additions

Add to `config/project.yaml`:

```yaml
# v2.0 Classification settings
classification:
  enabled: true
  model: "gpt-4o-mini"
  confidence_threshold: 0.6
  auto_create_topics: true

# Clustering settings
clustering:
  enabled: true
  min_cluster_size: 5
  method: "kmeans"

# Chunking mode
chunking:
  mode: "hierarchical"  # or "standard"
  parent_tokens: 2000
  child_tokens: 200

# RAG settings
rag:
  enabled: true
  model: "gpt-4o"
  max_sources: 5
  temperature: 0.3
```

---

## Database Migration

To apply schema updates:

```bash
psql -U research_dev_user -d research_dev_db -f database/schema_updates_v2.sql
```

---

## Required Libraries by Intelligence Tier

The framework supports three intelligence modes with different library requirements:

### Core Requirements (All Tiers)

```
psycopg2-binary>=2.9.0    # PostgreSQL driver
pgvector>=0.2.0           # Vector extension
PyYAML>=6.0.0             # Configuration
python-dotenv>=1.0.0      # Environment variables
flask>=3.0.0              # Web framework
flask-cors>=4.0.0         # CORS support
pypdf>=3.0.0              # PDF processing
python-docx>=1.0.0        # Word documents
tiktoken>=0.5.0           # Token counting
```

### Statistical Tier (Offline Mode)

```
scikit-learn>=1.3.0       # TF-IDF, K-means clustering
multi-rake>=0.0.2         # RAKE keyword extraction
yake>=0.4.8               # YAKE keyword extraction
sumy>=0.11.0              # Extractive summarization
nltk>=3.8.0               # Natural language toolkit
```

**NLTK Data Required:**
```bash
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords')"
```

### Local Tier (Ollama)

```
openai>=1.0.0             # OpenAI-compatible client
requests>=2.31.0          # HTTP client for health checks
```

**Prerequisites:**
1. Install Ollama: https://ollama.ai/download
2. Pull a model: `ollama pull llama3`
3. Start server: `ollama serve`

### Cloud Tier (OpenAI)

```
openai>=1.0.0             # OpenAI API client
tiktoken>=0.5.0           # Token counting
```

**Prerequisites:**
1. Get API key from https://platform.openai.com/api-keys
2. Add to `.env` file: `OPENAI_API_KEY=sk-your-key-here`

### Quick Install Commands

```bash
# Install all dependencies at once
pip install -r requirements.txt

# Statistical tier only (minimal)
pip install scikit-learn multi-rake sumy nltk yake
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords')"

# Cloud tier only
pip install openai tiktoken
```

---

## Cost Estimates (Cloud Tier Only)

| Operation | Model | Cost per 1000 docs |
|-----------|-------|-------------------|
| Classification | gpt-4o-mini | ~$0.50 |
| Embeddings | text-embedding-3-small | ~$0.40 |
| Cluster naming | gpt-4o-mini | ~$0.10 |
| RAG query | gpt-4o | ~$0.02/query |

**Statistical and Local tiers have no API costs.**

---

## Transformation Summary

```
BEFORE (v1.0 - Searchable Archive)           AFTER (v2.0 - Research Agent)
═══════════════════════════════════════      ═══════════════════════════════════════

Drop file → Extract → Chunk → Search         Drop file → Extract → AUTO-CLASSIFY →
                                             → Chunk (hierarchical) → CLUSTER →
                                             → Search/CHAT/DISCOVER

Files in: ORGANIZED/PDF/                     Files in: ORGANIZED/Philosophy/Steiner/

Search returns: List of chunks               Search returns: Synthesized answers
                                             with citations

Organization: Manual topics only             Organization: Auto topics + clusters +
                                             cross-document connections

Book compilation: Manual selection           Book compilation: AI-suggested outlines
```
