# Phase 1: Core Infrastructure (Week 1-2)
## Build RAG Foundation: Document Processing & Basic Retrieval

### 🎯 Phase Objectives
- Set up project structure and configuration system
- Implement document processing pipeline
- Create vector database integration
- Build BM25 indexing system
- Establish logging and monitoring basics

### 📋 Deliverables Checklist
- [ ] Project directory structure
- [ ] Configuration management system
- [ ] Document processor for multiple formats
- [ ] Vector database setup (ChromaDB/Qdrant)
- [ ] BM25 index implementation
- [ ] Basic logging system
- [ ] Unit tests for core components

### 🛠 Technical Implementation

#### 1. Project Setup (Day 1)
```bash
# Create directory structure
mkdir -p {src/{core,api,evaluation,utils,web},data/{documents,embeddings,evaluation},config,tests/{unit,integration,evaluation},.github/workflows,docker,requirements}

# Set up virtual environment and dependencies
pip install -r requirements/base.txt
```

#### 2. Configuration System (Day 1-2)
**File**: `src/utils/config.py`
- YAML-based configuration
- Environment variable overrides
- Validation with Pydantic
- Support for dev/staging/prod environments

#### 3. Document Processor (Day 2-4)
**File**: `src/core/document_processor.py`
**Features**:
- Multi-format support (PDF, DOCX, TXT, MD, HTML)
- Intelligent chunking with overlap
- Metadata extraction (title, author, date, file type)
- Text cleaning and normalization
- Duplicate detection

**Key Methods**:
```python
def process_document(file_path: str) -> List[DocumentChunk]
def extract_text(file_path: str) -> str
def chunk_text(text: str, metadata: dict) -> List[DocumentChunk]
def extract_metadata(file_path: str) -> dict
```

#### 4. Vector Database Integration (Day 5-7)
**File**: `src/utils/database.py`
**Components**:
- ChromaDB for development
- Qdrant for production scaling
- Embedding generation via Ollama
- Batch operations for efficiency
- Metadata filtering capabilities

#### 5. BM25 Implementation (Day 8-10)
**File**: `src/core/bm25_index.py`
**Features**:
- Document indexing with preprocessing
- Query processing and scoring
- Index persistence and loading
- Parameter tuning (k1, b values)

#### 6. Basic Logging (Day 11-12)
**File**: `src/utils/logging.py`
- Structured logging with JSON format
- Performance metrics collection
- Error tracking and alerting
- Request/response logging

#### 7. Testing Infrastructure (Day 13-14)
- Unit tests for all components
- Integration tests for database operations
- Performance benchmarks
- Test data fixtures

### 🔧 Dependencies
```txt
# Core dependencies
chromadb>=0.4.0
sentence-transformers>=2.2.0
rank-bm25>=0.2.2
pydantic>=2.0.0
pyyaml>=6.0
nltk>=3.8
spacy>=3.6.0
python-magic>=0.4.27
PyPDF2>=3.0.0
python-docx>=0.8.11
markdown>=3.4.0
beautifulsoup4>=4.12.0
```

### 📊 Success Criteria
- [ ] Process 1000+ documents without memory issues
- [ ] Sub-second document indexing for typical documents
- [ ] 100% test coverage for core components
- [ ] Configurable via YAML without code changes
- [ ] Clean logging output with proper levels

### 🧪 Testing Strategy
1. **Unit Tests**: Each component in isolation
2. **Integration Tests**: Database operations end-to-end
3. **Performance Tests**: Large document processing
4. **Configuration Tests**: All config scenarios

### 🚀 Deployment Preparation
- Docker containerization setup
- Environment-specific configs
- Health check endpoints
- Basic monitoring hooks

### ⏭ Phase 1 → Phase 2 Handoff
**Phase 1 Output**:
- Processed documents in vector database
- BM25 index ready for queries
- Configuration system operational
- Logging and monitoring active

**Phase 2 Input Requirements**:
- Populated vector database
- Functional BM25 index
- Query interface contracts defined
- Performance baseline metrics