Ingestion Layer

The ingestion layer connects your RAG system to data sources and handles the initial data collection.

Key Responsibilities

graph LR
    A[Data Sources] --> B[Ingestion Layer]
    B --> C[File System Monitor]
    B --> D[API Connectors]
    B --> E[Cloud Storage Sync]
    B --> F[Database Queries]
    
    C & D & E & F --> G[Preprocessing Queue]

Ingestion Patterns

Batch Ingestion

• Process large volumes at once
• Scheduled runs (daily, weekly)
• Initial system setup

Streaming Ingestion

• Real-time document processing
• File system watchers
• Event-driven updates

Incremental Updates

• Only process changed files
• Track versions
• Minimize reprocessing

Implementation

# Conceptual ingestion pipeline
class IngestionPipeline:
    def ingest_from_s3(self, bucket, prefix):
        files = s3.list_objects(bucket, prefix)
        for file in files:
            if not already_indexed(file):
                content = s3.download(file)
                yield {
                    'content': content,
                    'metadata': extract_metadata(file),
                    'source': f's3://{bucket}/{file.key}'
                }

Data Source Connectors

Common integrations:

• S3/Cloud Storage
• SharePoint/Google Drive
• Databases (PostgreSQL, MongoDB)
• APIs (REST, GraphQL)
• File systems
• Email (IMAP)
• Slack/Teams

Next lesson covers preprocessing.