Preprocessing and Conditioning Layer

Raw data needs cleaning and normalization before embedding. This layer ensures quality.

Preprocessing Pipeline

graph LR
    A[Raw Data] --> B{Data Type}
    B -->|PDF| C[Extract Text + OCR]
    B -->|Image| D[OCR/Vision]
    B -->|Audio| E[Transcribe]
    B -->|Video| F[Extract Frames + Audio]
    
    C & D & E & F --> G[Clean & Normalize]
    G --> H[Ready for Embedding]

Key Operations

Text Cleaning

• Remove formatting artifacts
• Fix encoding issues
• Normalize whitespace
• Language detection

Deduplication

• Content hashing
• Fuzzy matching
• Version control

Metadata Enrichment

• Extract dates, authors
• Classify documents
• Add tags
• Generate summaries

Example

def preprocess_document(doc):
    # Clean text
    text = remove_artifacts(doc.content)
    text = normalize_whitespace(text)
    
    # Detect language
    lang = detect_language(text)
    
    # Extract metadata
    metadata = {
        'title': extract_title(doc),
        'date': extract_date(doc),
        'language': lang,
        'word_count': len(text.split())
    }
    
    return {
        'text': text,
        'metadata': metadata
    }

Next: Embedding and indexing.