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LangChain / RAG / Vector DBs · Lesson

Handling Document Metadata and Filtering

Learn to attach, enrich, and filter document metadata so your RAG pipeline can scope retrieval to the right sources.

Handling Document Metadata and Filtering is a free LangChain / RAG / Vector DBs lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LangChain / RAG / Vector DBs learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

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"])

Automatic Metadata from Loaders

Most loaders inject metadata for free. A PyPDFLoader adds source and page, while a WebBaseLoader adds the URL and page title.

from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader("handbook.pdf")
pages = loader.load()
print(pages[0].metadata)
# {"source": "handbook.pdf", "page": 0}

Enriching Metadata After Load

You often need to add fields the loader does not know about, like a category or tenant id. Iterate and mutate the dictionary.

for d in pages:
    d.metadata["department"] = "finance"
    d.metadata["sensitive"] = False
print(pages[0].metadata["department"])

Metadata Survives Splitting

When you split documents, the splitter copies the parent metadata onto each child chunk. This means filters set before splitting still apply afterward.

from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(chunk_size=200)
chunks = splitter.split_documents(pages)
print(chunks[0].metadata["source"])

Filtering at Retrieval Time

Vector stores accept a filter argument so you only search the relevant slice. This is faster and reduces irrelevant matches.

results = vectorstore.similarity_search(
    "vacation policy",
    k=3,
    filter={"department": "hr"}
)

Self-Query Retrieval

A SelfQueryRetriever lets the LLM translate a natural-language query into both a semantic search and a metadata filter automatically.

You describe the metadata schema once, and the model decides when to filter.

Cleaning Noisy Metadata

Loaders sometimes produce verbose or nested metadata that vector stores reject. Flatten or whitelist the keys you need.

def clean(d):
    keep = {"source", "page", "department"}
    d.metadata = {k: v for k, v in d.metadata.items() if k in keep}
    return d

cleaned = [clean(d) for d in chunks]

Metadata for Citations

Storing the source and page lets you cite where an answer came from. After retrieval, format the metadata into a human-readable reference.

for r in results:
    src = r.metadata["source"]
    pg = r.metadata.get("page", "?")
    print(f"[{src} p.{pg}]")

Type Constraints

Many vector databases only allow scalar metadata values: strings, numbers, and booleans. Lists or dicts must be serialized to JSON strings or removed.

  • Good: {"page": 4}
  • Reject: {"tags": ["a","b"]}

A Practical Filter Pipeline

Combine enrichment, cleaning, and filtered search into one flow so every query is scoped to the correct subset of your corpus.

docs = PyPDFLoader("policy.pdf").load()
for d in docs:
    d.metadata["region"] = "EU"
chunks = splitter.split_documents(docs)
# index chunks, then:
vectorstore.similarity_search("data retention", filter={"region": "EU"})

Quick Check

Test your understanding of metadata handling.

Recap

You learned to work with document metadata:

  • Loaders auto-add fields like source and page
  • Enrich and clean metadata for filtering and citations
  • Metadata propagates through splitting
  • Use filter or a self-query retriever to scope searches

Frequently asked questions

Is the “Handling Document Metadata and Filtering” lesson free?

Yes — the full text of “Handling Document Metadata and Filtering” is free to read here on the web, and the LangChain / RAG / Vector DBs course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LangChain / RAG / Vector DBs course, upgrade to CoddyKit PRO.

What will I learn in “Handling Document Metadata and Filtering”?

Learn to attach, enrich, and filter document metadata so your RAG pipeline can scope retrieval to the right sources. You practise LangChain / RAG / Vector DBs with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start LangChain / RAG / Vector DBs?

No prior experience is required. LangChain / RAG / Vector DBs on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Handling Document Metadata and Filtering” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this LangChain / RAG / Vector DBs lesson?

Yes. Every LangChain / RAG / Vector DBs lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Loading Diverse Document Types
  2. Understanding Text Splitting Strategies
  3. Customizing Document Splitting
  4. Handling Document Metadata and Filtering
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