Parent Document Retrieval: Context Without Noise

The Problem

Small chunks:

• ✅ Precise retrieval
• ❌ Missing context

Large chunks:

• ✅ Full context
• ❌ Noisy retrieval

Solution: Search small, return large.

How It Works

1. Index: Small chunks (200 tokens)
2. Search: Find relevant small chunks
3. Retrieve: Return parent document (2000 tokens)

Basic Implementation

DEVELOPERpython
import uuid

# Store chunks with parent reference
chunks = []
documents = []

for doc in raw_documents:
    parent_id = str(uuid.uuid4())

    # Store full document
    documents.append({
        "id": parent_id,
        "content": doc,
        "embedding": embed(doc)
    })

    # Create small chunks
    for chunk in split_into_chunks(doc, size=200):
        chunks.append({
            "id": str(uuid.uuid4()),
            "content": chunk,
            "embedding": embed(chunk),
            "parent_id": parent_id  # Link to parent
        })

# Index chunks only
vector_db.upsert(collection="chunks", documents=chunks)

Retrieval

DEVELOPERpython
def parent_document_retrieval(query, k=5):
    # Search small chunks
    chunk_results = vector_db.search(
        collection="chunks",
        query_vector=embed(query),
        limit=k
    )

    # Get parent document IDs
    parent_ids = [chunk["parent_id"] for chunk in chunk_results]

    # Fetch parent documents
    parent_docs = [
        doc for doc in documents
        if doc["id"] in parent_ids
    ]

    return parent_docs

Langchain Implementation

DEVELOPERpython
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Store for parent documents
docstore = InMemoryStore()

# Vector store for chunks
vectorstore = Chroma(embedding_function=embeddings)

# Splitters
child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)

# Create retriever
retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=docstore,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter
)

# Add documents
retriever.add_documents(documents)

# Retrieve (returns full parent docs)
results = retriever.get_relevant_documents("machine learning")

Multi-Level Hierarchy

DEVELOPERpython
# Book → Chapter → Paragraph structure
def create_hierarchy(book):
    book_id = str(uuid.uuid4())

    for chapter in book.chapters:
        chapter_id = str(uuid.uuid4())

        # Index paragraphs (small)
        for paragraph in chapter.paragraphs:
            vector_db.upsert({
                "id": str(uuid.uuid4()),
                "content": paragraph,
                "embedding": embed(paragraph),
                "parent_id": chapter_id,  # Chapter
                "grandparent_id": book_id  # Book
            })

        # Store chapter
        chapters[chapter_id] = chapter

    # Store book
    books[book_id] = book

def retrieve_with_context(query):
    # Find relevant paragraphs
    paragraphs = vector_db.search(embed(query), limit=3)

    # Get surrounding context
    results = []
    for p in paragraphs:
        chapter = chapters[p["parent_id"]]
        book = books[p["grandparent_id"]]

        results.append({
            "match": p["content"],
            "chapter": chapter,
            "book_title": book.title
        })

    return results

Windowed Retrieval

Return chunk + surrounding context:

DEVELOPERpython
def windowed_retrieval(query, window_size=2):
    # Find relevant chunk
    chunk_results = vector_db.search(embed(query), limit=5)

    # Get chunks before and after
    expanded_results = []
    for chunk in chunk_results:
        parent_doc = get_document(chunk["parent_id"])
        chunk_index = find_chunk_index(parent_doc, chunk["content"])

        # Get window
        start = max(0, chunk_index - window_size)
        end = min(len(parent_doc.chunks), chunk_index + window_size + 1)

        expanded_chunk = "".join(parent_doc.chunks[start:end])
        expanded_results.append(expanded_chunk)

    return expanded_results

Qdrant Implementation

DEVELOPERpython
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct

client = QdrantClient("localhost", port=6333)

# Create collection with parent ID in payload
client.create_collection(
    collection_name="chunks",
    vectors_config={"size": 1536, "distance": "Cosine"}
)

# Insert chunks with parent reference
points = []
for i, chunk in enumerate(chunks):
    points.append(PointStruct(
        id=i,
        vector=chunk["embedding"],
        payload={
            "content": chunk["content"],
            "parent_id": chunk["parent_id"]
        }
    ))

client.upsert(collection_name="chunks", points=points)

# Retrieve
def retrieve_parents(query):
    results = client.search(
        collection_name="chunks",
        query_vector=embed(query),
        limit=5
    )

    # Get unique parent IDs
    parent_ids = list(set([r.payload["parent_id"] for r in results]))

    # Fetch parents from document store
    parents = [get_document(pid) for pid in parent_ids]

    return parents

When to Use

✅ Use parent document retrieval when:

• Documents have clear structure
• You need full context for LLM
• Precision is important

❌ Don't use when:

• Documents are already small (< 500 tokens)
• You want to minimize token usage
• Context is not important

Parent document retrieval gives you precision without sacrificing context. Best of both worlds.