qdrant-hybrid-search
Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to combine results from multiple searches?' and 'combining multiple representations'
git clone --depth 1 https://github.com/qdrant/skills /tmp/qdrant-hybrid-search && cp -r /tmp/qdrant-hybrid-search/skills/qdrant-search-quality/search-strategies/hybrid-search ~/.claude/skills/qdrant-hybrid-searchSKILL.md
# Hybrid Search in Qdrant Hybrid search means running two or more different searches in parallel and combining their results into one. In Qdrant this is powered by the Query API via `prefetch`: each `prefetch` runs exactly one type of search independently, and the outer `query` combines results from parallel prefetches. Prefetches can be nested and searches can be multi-stage, all pipeline happening in one request through Query API. See [Universal Query API](https://skills.qdrant.tech/md/course/essentials/day-5/universal-query-api/) for examples. Identify the user's problem and pick building blocks: - What can go into one prefetch, e.g. power one search, in [Search Types](search-types/SKILL.md) - How to combine results of these searches (RRF, DBSF, FormulaQuery, reranking) in [Combining Searches](combining-searches/SKILL.md) Based on what you've picked, test your approach: 1. Configure Qdrant collection with [named vectors](https://skills.qdrant.tech/md/documentation/manage-data/vectors/?s=named-vectors), where each named vector usually corresponds to one representation (different embedding models or different vector types) of a data point. 2. Construct a hybrid search request with Query API from your building blocks. You can search independently among one type of vectors, with `prefetch` + `using`, like shown in examples in [Hybrid Queries documentation](https://skills.qdrant.tech/md/documentation/search/hybrid-queries). 3. Evaluate hybrid search quality on real user data and provide user with improvements and tradeoffs (speed/resources). ## How Isolated Are Parallel Searches? Use when: different tenants share one collection and you need to understand hybrid search isolation guarantees. If user wants to isolate/share hybrid search pipelines between tenants, consider that: - Indexes (sparse, payload and dense) and [IDF modifier](https://skills.qdrant.tech/md/documentation/manage-data/indexing/?s=idf-modifier) for sparse vectors are computed independently **per shard**, not per **tenant**. - Prefetch runs independently per shard to retrieve #limit results, so for collection-level prefetches if collection has several shards, Qdrant will always prefetch under the hood #limit * #shard results. Final results are merged based on scores. - In nested prefetches (deeper than 1 level), methods described in "Combining Searches" might be done on a shard level first, then per-shards results once again will be merged based on scores. ## What NOT to Do - Choose a hybrid search pattern based on "vibes" without any [hybrid search quality evaluation](https://skills.qdrant.tech/md/articles/hybrid-search/?s=how-effective-is-your-search-system) in-place. - Create too many named vectors without a need. An unfilled named vector might take as much resources as a filled one.
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.
Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.
Guides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, and log centralization. Use when someone asks 'how to set up monitoring', 'Prometheus config', 'Grafana dashboard', 'health check endpoints', 'how to scrape Hybrid Cloud', 'what alerts to set', 'how to centralize logs', or 'audit logging'.
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.