Principle:Googleapis Python genai Document Management
| Knowledge Sources | |
|---|---|
| Domains | RAG, Document_Management |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
Pattern for managing documents within retrieval-augmented generation (RAG) stores to provide grounding context for generative AI.
Description
Document Management is the practice of organizing, indexing, retrieving, and lifecycle-managing documents within a knowledge store. In RAG workflows, documents are ingested into a search store where they are chunked, embedded, and indexed. Subsequent generation requests can retrieve relevant document chunks to ground model responses in factual data. Management operations include listing, getting metadata, and deleting documents from stores.
Usage
Use this principle when building RAG pipelines that require programmatic management of the document corpus, such as refreshing indexed content, auditing stored documents, or cleaning up stale entries from a file search store.
Theoretical Basis
The document management lifecycle follows a CRUD pattern over an indexed corpus:
# Pseudo-code for document management in a RAG store
store = create_search_store(name, config)
upload_document(store, file_path)
documents = list_documents(store)
doc_metadata = get_document(doc_name)
delete_document(doc_name)
Key properties:
- Indexed storage: Documents are chunked and embedded for retrieval
- Metadata-rich: Each document carries name, type, and state metadata
- Store-scoped: Documents belong to a parent file search store