design/document_processing.md

# Document Processing Pipeline Design

This document outlines the design for the document processing pipeline of our Norwegian RAG-based chatbot. The pipeline will transform raw documents into embeddings that can be efficiently retrieved during the chat process.

## Pipeline Overview

```
Raw Documents → Text Extraction → Text Chunking → Text Cleaning → Embedding Generation → Vector Storage
```

## Components

### 1. Text Extraction

**Purpose**: Extract plain text from various document formats.

**Supported Formats**:
- PDF (.pdf)
- Word Documents (.docx, .doc)
- Text files (.txt)
- HTML (.html, .htm)
- Markdown (.md)

**Implementation**:
- Use PyPDF2 for PDF extraction
- Use python-docx for Word documents
- Use BeautifulSoup for HTML parsing
- Direct reading for text and markdown files

### 2. Text Chunking

**Purpose**: Split documents into manageable chunks for more precise retrieval.

**Chunking Strategies**:
- Fixed size chunks (512 tokens recommended for Norwegian text)
- Semantic chunking (split at paragraph or section boundaries)
- Overlapping chunks (100-token overlap recommended)

**Implementation**:
- Use LangChain's text splitters
- Implement custom Norwegian-aware chunking logic

### 3. Text Cleaning

**Purpose**: Normalize and clean text to improve embedding quality.

**Cleaning Operations**:
- Remove excessive whitespace
- Normalize Norwegian characters (æ, ø, å)
- Remove irrelevant content (headers, footers, page numbers)
- Handle special characters and symbols

**Implementation**:
- Custom text cleaning functions
- Norwegian-specific normalization rules

### 4. Embedding Generation

**Purpose**: Generate vector representations of text chunks.

**Embedding Model**:
- Primary: NbAiLab/nb-sbert-base (768 dimensions)
- Alternative: FFI/SimCSE-NB-BERT-large

**Implementation**:
- Use sentence-transformers library
- Batch processing for efficiency
- Caching mechanism for frequently embedded chunks

### 5. Vector Storage

**Purpose**: Store and index embeddings for efficient retrieval.

**Storage Options**:
- Primary: FAISS (Facebook AI Similarity Search)
- Alternative: Milvus (for larger deployments)

**Implementation**:
- FAISS IndexFlatIP (Inner Product) for cosine similarity
- Metadata storage for mapping vectors to original text
- Serialization for persistence

## Processing Flow

1. **Document Ingestion**:
   - Accept documents via upload interface
   - Store original documents in a document store
   - Extract document metadata (title, date, source)

2. **Processing Pipeline Execution**:
   - Process documents through the pipeline components
   - Track processing status and errors
   - Generate unique IDs for each chunk

3. **Index Management**:
   - Create and update vector indices
   - Implement versioning for indices
   - Provide reindexing capabilities

## Norwegian Language Considerations

- **Character Encoding**: Ensure proper handling of Norwegian characters (UTF-8)
- **Tokenization**: Use tokenizers that properly handle Norwegian word structures
- **Stopwords**: Implement Norwegian stopword filtering for improved retrieval
- **Stemming/Lemmatization**: Consider Norwegian-specific stemming or lemmatization

## Implementation Plan

1. Create document processor class structure
2. Implement text extraction for different formats
3. Develop chunking strategies optimized for Norwegian
4. Build text cleaning and normalization functions
5. Integrate with embedding model
6. Set up vector storage and retrieval mechanisms
7. Create a unified API for the entire pipeline

## Code Structure

```python
# Example structure for the document processing pipeline

class DocumentProcessor:
    def __init__(self, embedding_model, vector_store):
        self.embedding_model = embedding_model
        self.vector_store = vector_store
        
    def process_document(self, document_path):
        # Extract text based on document type
        raw_text = self._extract_text(document_path)
        
        # Split text into chunks
        chunks = self._chunk_text(raw_text)
        
        # Clean and normalize text chunks
        cleaned_chunks = [self._clean_text(chunk) for chunk in chunks]
        
        # Generate embeddings
        embeddings = self._generate_embeddings(cleaned_chunks)
        
        # Store in vector database
        self._store_embeddings(embeddings, cleaned_chunks)
        
    def _extract_text(self, document_path):
        # Implementation for different document types
        pass
        
    def _chunk_text(self, text):
        # Implementation of chunking strategy
        pass
        
    def _clean_text(self, text):
        # Text normalization and cleaning
        pass
        
    def _generate_embeddings(self, chunks):
        # Use embedding model to generate vectors
        pass
        
    def _store_embeddings(self, embeddings, chunks):
        # Store in vector database with metadata
        pass
```

## Next Steps

1. Implement the document processor class
2. Create test documents in Norwegian
3. Evaluate chunking strategies for Norwegian text
4. Benchmark embedding generation performance
5. Test retrieval accuracy with Norwegian queries