Handling Document Metadata and Filtering
Learn to attach, enrich, and filter document metadata so your RAG pipeline can scope retrieval to the right sources.
Why Metadata Matters
Every document chunk in LangChain carries a page_content string and a metadata dictionary. While the content feeds the embedding model, metadata drives filtering, attribution, and traceability.
- Source file or URL
- Page number or section
- Author, date, language
The Document Object
A LangChain Document is a lightweight container. You can construct one directly and pass any JSON-serializable values in metadata.
from langchain_core.documents import Document
doc = Document(
page_content="Annual revenue grew 12%.",
metadata={"source": "report.pdf", "page": 4, "year": 2025}
)
print(doc.metadata["source"])All lessons in this course
- Loading Diverse Document Types
- Understanding Text Splitting Strategies
- Customizing Document Splitting
- Handling Document Metadata and Filtering