What is Data Ingestion?
Data ingestion is the process of loading documents and information into an AI system's memory or knowledge base, making it available for retrieval and use by agents. This process encompasses several steps: extracting content from various source formats (PDFs, web pages, databases, APIs), preprocessing and cleaning the text, chunking it into appropriate segments, generating embeddings, and storing both the embeddings and original content in a retrieval system.
The ingestion pipeline must handle diverse data sources and formats, each with their own challenges. Structured data from databases may need to be converted into natural language descriptions, PDFs may require OCR or layout parsing, web pages need HTML stripping and content extraction, and APIs may return data in various formats that need normalization. The pipeline must also manage data quality issues, handle incremental updates, and often implement deduplication to avoid storing redundant information.
Effective data ingestion is critical for the performance of RAG systems and agent memory. Poor ingestion – such as inadequate preprocessing, inappropriate chunking, or incomplete metadata extraction – can significantly degrade retrieval quality even with sophisticated search algorithms. Modern ingestion pipelines often include features like automatic metadata extraction, document structure preservation, quality filtering, and monitoring to ensure that the knowledge base remains current and accurate.