AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：We choose embedding models, dimensions, and indexes. The geometry usually comes with the package. But why use a flat space, and what else could we choose? What Is a Manifold, and Where Do Our Vectors Live? A manifold is the space our embeddings live in. For embeddings, we care about the geometry we give that space. It determines how we measure distance, what the shortest path looks like, and how much room there is as we move outward.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：A search result can look plausible and still be wrong. Dense retrieval can return a document on the right topic but miss an exact identifier copied into the query. Sparse retrieval can miss a relevant document when the query describes it with terms the corpus doesn’t use. Either way, your logs record a successful query. Hybrid search runs dense and sparse retrieval over the same query, then merges their result lists. Dense retrieval adds semantic similarity, so paraphrases can rank together. Sparse retrieval adds weighted term matching for exact words and identifiers.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Once a collection no longer fits in RAM, the kernel evicts vector pages, and the next query waits on a disk read to get them back. Quantization buys that memory back. Qdrant keeps a compressed copy of each dense vector in RAM and moves the full-precision originals to disk. TurboQuant is the method measured here. It rotates each vector before compressing it, which spreads the error evenly across coordinates, and its bits parameter sets the depth from bits4 down to bits1. Start at bits4, a good default for many workloads at eight times compression.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Before you tune a reranker, use the pre-tuning checks to verify index state and set a labeled baseline. Your candidate list can already contain documents your ranking never shows. Score those candidates as if they were perfectly ordered, then compare that with the score your pipeline returns today. The gap between the two is everything a better ranking stage could recover, so measure it before you reach for a model. Use nDCG@10, which grades the top 10 results and gives more credit to relevant documents near the top.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Before you tune fusion, use the pre-tuning checks to verify index state and set a labeled baseline. Hybrid search retrieves dense and sparse candidate lists, then fuses them into one ranking. The dense prefetch finds similar meaning; the sparse prefetch finds matching keywords. Fusion reorders the candidates the prefetches return, so a document missing from both lists cannot appear in the result. Confirm Fusion Beats Either Prefetch Before tuning, compare dense retrieval, sparse retrieval, and default Reciprocal Rank Fusion (RRF) at k=2 and equal weights. Score all three with nDCG@10, which grades the top 10 results and gives more credit to relevant documents near the top.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Before you tune candidate depth, use the pre-tuning checks to verify index state and set a labeled baseline. Everything below measures against that baseline. Candidate depth is the number of candidates a retrieval stage passes to a later ranking stage. It matters only when a later stage can use the extra candidates. In hybrid search, every prefetch carries its own limit, and a multi-stage query that nests one prefetch inside another sets a depth at each level. In dense-only or sparse-only search, it is the number of candidates you pass to a reranker or other downstream stage.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Before you change a setting, decide what better retrieval means for your workload. The right document at rank one, more candidates for a reranker, lower latency, and a smaller memory footprint each favor different settings, so pick your goal first. If your labeled queries can’t detect the improvement you’re chasing, you won’t be able to tell whether a change helped. Some settings are there to verify correctness, not to tune performance. If a vector is unindexed, a sparse vector is missing the IDF modifier, or the BM25 average length is wrong, the results are invalid. Any benchmark or comparison you run after that will reflect a broken setup. This article shows you how to check each setting and what the correct state looks like.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Filtered vector search breaks when metadata filters turn a healthy nearest-neighbor graph into scattered islands. HNSW’s m parameter controls how many links each point gets. At Qdrant’s default m=16, the one-million-point collection benchmarked below averaged about 21 links per node on layer 0. Filter out 96% of the points and fewer than one link per node survives on average, so traversal can get stranded before it reaches the true nearest matches.