How do I handle document deduplication in LlamaIndex?

To handle document deduplication in LlamaIndex, you can use built-in tools and custom logic to identify and remove redundant content. LlamaIndex processes documents by splitting them into “nodes” (text chunks), and deduplication typically occurs at this node level. The framework provides a DeduplicatePostProcessor class, which you can integrate into your indexing or query pipeline. This post-processor compares nodes using similarity metrics like cosine distance or exact hashing, removing duplicates based on a threshold. For example, you might configure it to discard nodes with a similarity score above 0.95, ensuring only unique content remains in your dataset.

One common approach involves using embeddings to detect semantic duplicates. For instance, you could generate embeddings for each node with a model like Sentence-BERT and compute pairwise similarities. Nodes exceeding a similarity threshold are flagged as duplicates. Alternatively, for exact duplicates, you can hash node text (e.g., using MD5 or SHA-256) and remove entries with identical hashes. LlamaIndex simplifies this by allowing you to pass a dedupe_fn to the post-processor. For example, dedupe_fn=lambda text: hashlib.md5(text.encode()).hexdigest() would trigger exact match deduplication. You might combine both methods—using hashing for exact matches and embeddings for near-duplicates—to balance precision and computational cost.

Implementation steps typically involve adding the post-processor to your indexing or query pipeline. Here’s a basic example:

from llama_index.core import VectorStoreIndex, ServiceContext
from llama_index.core.postprocessor import DeduplicatePostProcessor

# Create index
service_context = ServiceContext.from_defaults()
index = VectorStoreIndex.from_documents(documents, service_context=service_context)

# Add deduplication during querying
query_engine = index.as_query_engine(
 node_postprocessors=[DeduplicatePostProcessor()]
)

Consider applying deduplication during indexing to reduce storage costs or during querying to ensure real-time results. Be mindful of thresholds: too strict may remove valid content, too lenient risks redundancy. Test different similarity metrics (e.g., cosine vs. Jaccard) to align with your data’s characteristics. For large datasets, optimize performance by batching comparisons or using approximate nearest neighbor libraries like FAISS.