How do you implement document ingestion pipelines for RAG at scale?
Scalable document ingestion handles diverse formats, large volumes, and continuous updates. Pipeline: source connectors (web crawlers, file system watchers, API integrations, email parsers), format processors (PDF extraction with PyMuPDF or PDFPlumber, DOCX with python-docx, HTML with BeautifulSoup, structured data with pandas), content cleaning (remove boilerplate, fix encoding, normalize whitespace, remove duplicate content), chunking (apply strategy appropriate to document type), embedding generation (batch embedding with rate management), and vector storage (upsert to vector database with metadata). Scale handling: implement a task queue (Celery, Bull, or cloud-native) for document processing, parallelize embedding generation (batch sizes of 100-500 texts per API call), and implement incremental processing (track document hashes to skip unchanged files). Change detection: for web sources implement last-modified header checking and content hashing, for file systems use file watcher events, and for databases use CDC (Change Data Capture). Metadata extraction: automatically extract title, author, creation date, language, and content type; implement custom extractors for domain-specific metadata. Deduplication: near-duplicate detection using MinHash/SimHash to prevent redundant chunks, exact duplicate removal using content hashes. Quality assurance: validate extraction quality with sample checks, log processing errors with document context for debugging, and implement a retry queue for transient failures. Monitoring: track ingestion throughput (documents/hour), processing errors by type and source, embedding generation costs, and index freshness (time from document update to searchable). Pipeline orchestration: use Apache Airflow or Prefect for complex pipelines with dependencies, implement circuit breakers for external services, and maintain an audit trail of all processed documents.