Qdrant Essentials | Fast Vector Search with Qdrant HNSW Indexing
Understanding Vector Search and HNSW
Introduction to Vector Search
- The course begins with a recap of day one, focusing on how vector search retrieves similar vectors using distance metrics like Euclidean distance and cosine similarity.
- Brute force search is introduced as a method for comparing every vector to the query vector, but it is inefficient for large datasets due to the high number of calculations required.
Limitations of Brute Force Search
- The inefficiency of brute force search becomes apparent when dealing with millions or billions of vectors, making it impractical for large-scale applications.
Introduction to HNSW Algorithm
- Quadrant employs an efficient indexing algorithm called HNSW (Hierarchical Navigable Small World), which enhances vector search efficiency at scale.
- An analogy is drawn between searching for a book in a disorganized library (brute force) versus an organized library where books are categorized, illustrating the need for structured searches.
How HNSW Works
- HNSW builds a multi-layered graph where each vector acts as a node. The top layer has fewer nodes connected broadly, while lower layers have more nodes with specific connections.
- When querying, HNSW starts from the top layer and navigates down through the graph, refining its search by moving from broader categories to more specific ones until reaching the most relevant results.
Query Process Example
- For example, if searching for "striped blue shirt made of cotton," the algorithm begins at broad categories like clothing and narrows down through layers until it identifies the closest match.
- Each layer refines the results further based on characteristics such as color and pattern until arriving at an exact match in the final layer.
Fine-Tuning HNSW Parameters
- The performance of HNSW can be adjusted by fine-tuning parameters that balance speed, accuracy, memory usage, and index-building time.
- The M parameter controls connections per node; increasing it improves accuracy but requires more memory and longer indexing times. Conversely, reducing M speeds up insertion but may decrease search quality.
Inserting New Vectors into HNSW
- Adding new vectors involves integrating them efficiently into the existing structure without sacrificing accuracy or performance during indexing.
- The EF construct parameter influences this process by determining how thoroughly neighbors are considered during insertion.
Understanding the EF Construct in Graph Indexing
The Impact of Higher EF Values
- A higher EF (Exploration Factor) construct results in evaluating more neighbors, leading to a more comprehensive and accurate graph. However, this increases the indexing process's computational demands and slows it down.
Balancing Speed and Accuracy
- Lowering the EF construct speeds up indexing but compromises the quality of connections within the graph, potentially affecting search accuracy. Finding an optimal balance between indexing speed and graph quality is crucial.
Search Parameters and Their Effects
- The number of neighbors evaluated during a search can be adjusted using the EF parameter. Higher values yield more accurate results by searching through larger neighborhoods but also increase query time.
Trade-offs in Query Performance
- Lower EF values can decrease search accuracy due to fewer neighbors being considered, yet they can enhance query speed. Recommended F parameter values range from 50 to 200 based on desired accuracy versus speed trade-offs.
Tuning HNSW for Optimal Performance
- Proper tuning of HNSW (Hierarchical Navigable Small World graphs) significantly influences system performance regarding accuracy and speed. Quadrant optimizes memory usage by only storing indexed vectors in the HNSW graph.
Indexing Threshold Considerations
- Smaller collections may not trigger HSW indexing due to set thresholds, necessitating alternative search methods like brute force until indexing becomes necessary to determine vector usability within collections.