Apache Airflow Document Ingestion Pipeline for RAG Systems

2026-06-01 · Source: PyImageSearch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

This article details the design and implementation of a production-grade document ingestion pipeline for Retrieval-Augmented Generation (RAG) systems, leveraging Apache Airflow, FastAPI, and PostgreSQL. The architecture features a FastAPI service on port 8000 for PDF uploads, which computes SHA-256 hashes for deduplication and saves files to a shared Docker volume. Apache Airflow orchestrates processing via a DAG running every minute, executing tasks like document parsing (PyPDF), text chunking (512 words with 50-word overlap), and validation. PostgreSQL stores both Airflow's metadata and application-specific data, including document status and chunk details, ensuring idempotency and observability throughout the workflow.

Key takeaway

For MLOps Engineers building RAG systems, adopting an Apache Airflow-based ingestion pipeline is crucial for production reliability. This architecture ensures robust document processing with built-in deduplication, granular error handling, and full observability. You should prioritize idempotent task design and leverage shared volumes for efficient data transfer between services. This approach minimizes data loss and simplifies debugging in complex, real-world scenarios.

Key insights

Production RAG ingestion requires robust orchestration, idempotency, and observability for reliable document processing.

Principles

• Separate ingestion from heavy processing.
• Use content hashing for deduplication and idempotency.
• Design Airflow tasks for single responsibility and retries.

Method

Documents are uploaded via FastAPI, hashed, saved to a shared volume, and marked PENDING in PostgreSQL. An Airflow DAG then fetches, parses, chunks, validates, and marks documents COMPLETE.

In practice

• Implement SHA-256 hashing for file and chunk deduplication.
• Use `session_scope()` for transactional database operations in Airflow tasks.
• Pass large inter-task data via shared files, not Airflow XCom.

Topics

• Apache Airflow
• RAG Systems
• Document Ingestion
• FastAPI
• PostgreSQL
• Data Orchestration
• Idempotency

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by PyImageSearch.