Index Tuning: Balancing Speed and Accuracy

In a vector database, "Fast" is easy, and "Accurate" is easy. Fast AND Accurate is where the engineering happens. Most vector databases use HNSW (Hierarchical Navigable Small World) by default, and tuning its parameters is the single most effective way to optimize performance.

In this lesson, we learn how to tune your index for production-grade speed.

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1. The HNSW Knobs: M and efConstruction

When you create an index in a database like Chroma, Weaviate, or Pinecone, you often encounter these two variables:

1. M (Max connections): The number of "links" each vector has to its neighbors.
   • Higher M: Better accuracy (more paths to the answer), but higher RAM usage and slower build times.
2. efConstruction: How many neighbors to explore during index building.
   • Higher efConstruction: Higher index quality, but significantly slower ingestion.

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2. The Query Knob: efSearch

This is the most important parameter for query performance. efSearch determines how many neighbors to check at query time.

• Low efSearch (e.g., 40): Blazing fast search, but might miss the "Best" result (Lower Recall).
• High efSearch (e.g., 400): Slower search, but guaranteed to find the true nearest neighbors.

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3. Visualizing the Trade-off: The Pareto Curve

In performance tuning, we look for the "Sweet Spot" on the Pareto frontier where we get 95%+ Recall without a massive spike in Latency.

graph LR
    A[Low efSearch] --> B(Fast / Low Accuracy)
    C[High efSearch] --> D(Slow / High Accuracy)
    E[The Sweet Spot] --> F(Optimal Production Performance)

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4. Implementation: Tuning in Python (ChromaDB example)

While many managed services hide these settings, local databases like Chroma (via HNSWlib) allow tuning:

# During collection creation
collection = client.create_collection(
    name="tuned_collection",
    metadata={
        "hnsw:space": "cosine",
        "hnsw:construction_ef": 200, # Better index quality
        "hnsw:M": 16,               # Standard link density
    }
)

# During Query
results = collection.query(
    query_texts=["AI performance"],
    n_results=10,
    # Some clients allow passing 'ef' at query time
)

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5. Summary and Key Takeaways

1. Parameter M: Control your memory footprint and link complexity.
2. efConstruction: Use high values for static data where you ingest once and search many times.
3. efSearch: This is your primary lever for balancing latency vs. accuracy in real-time.
4. Benchmarks: Never tune blindly. Use a gold-standard dataset to measure "Recall" (Module 11) while you change parameters.

In the next lesson, we’ll look at Batch Ingestion—the secret to loading millions of vectors without crashing your client.

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Congratulations on completing Module 14 Lesson 1! You are now tuning your vector DB like a pro.