Highly recommend to prompt the following in LLM if you are trying to grasp Hybrid(Keyword, Filter + Vector) search or HNSW vs IVF flavour topics Graph-Based (H

Highly recommend to prompt the following in LLM if you are trying to grasp Hybrid(Keyword, Filter + Vector) search or HNSW vs IVF flavour topics Graph-Based (HNSW) vs Cluster-Based (IVF) Memory Usage: Higher for HNSW due to graph storage requirements vs Lowerfor IVF; stores cluster centroids and subsets of vectors Accuracy: High recall and precision, close to exact search vs Recall depends on the number of clusters and visited partitions Build Time: Slower for HNSW, Faster for IVF Best for: Real-time, high-accuracy tasks requiring dynamic updates vs Large, static datasets with strict memory constraints Notable Graph-Based (HNSW) Variants - Faiss HNSW or PANNG (Proximity and Navigable Neighbor Graph) Cluster-Based (IVF) Variants - IVF-PQ for memory-constrained environments, Multi-Index IVF partitions, IVF-GPU