Ivan PleshkovandClaude Fable 5
7b96aefa82
* QuerySimd::dotprod_batch: score a contiguous run of vectors in one call
The entry point for scanning a contiguous run of encoded vectors at a
stride: `out[v]` ← score of the vector at `data[v * stride..]`. It
scores vector by vector for now; the SIMD batch kernels that share the
query loads across vectors follow.
Bench: `query{4,2,1}bit_dotprod_scan` — a hot query against runs of 512
consecutive vectors streaming from DRAM at the TurboQuant stride, per
vector and through `dotprod_batch`.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* QuerySimd: interleaved AVX-512 batch kernel with a fused reduction
Vectors up to four cache lines are scored in groups of four that share
every query block load and tail mask; the group's independent
accumulators keep `VPDPBUSD` saturated while one vector's reduction
overlaps with the next group's loads. Longer vectors keep the
per-vector walk, since the hardware prefetcher streams four interleaved
byte streams far worse than one (measured at the 4-bit width: +10 % at
dim 512, 2× slower at dim 1024).
The per-vector reduction fuses the query bytes before the horizontal
sum — `low + K · high` in i32 lanes, then one tree that widens to i64
at the end — for vectors within a per-width lane bound derived from the
encoding (2040 bytes at 4 bits, 1020 at 2, 255 for the wide 1-bit
query; unbounded for a one-byte query). A test pins the derivation to
the hand-computed 4-bit value and drives every width to its bound with
the heaviest possible inputs.
Bench: `batch_avx512_vnni` rows in the scan groups.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* QuerySimd: AVX2 batch kernel; one fused reduction for AVX2 and AVX-512
The AVX2 batch kernel scores vectors one at a time: its loop-carried
chain is a single `vpaddd` per accumulator (the `maddubs → madd`
products hang off the loads), so interleaving vectors only adds
register pressure on the 16 YMM registers — measured 10–15 % slower
with groups of two or four at the 4-bit width.
The AVX2 per-vector reduction now uses the same fused tree as the
AVX-512 one, within the same per-width lane bound; the bound test
drives both kernels.
Bench: `batch_avx2` rows in the scan groups.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* QuerySimd: copy the tail block in constant-size pieces
The SSE, AVX2 and NEON kernels run their last partial block on a
zero-padded copy of the remaining bytes. A `len`-byte copy compiles to
a `memcpy` call plus a `memset` for the padding — and the call forces
the accumulators out of their registers around it. Copy in power-of-
two pieces of constant size instead: `len` is the same for every vector
of a query, so the piece branches predict perfectly.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* QuerySimd: interleaved NEON batch kernels
The SDOT and plain NEON block loops take `N` vectors at a stride, and
the batch entry points score vectors up to four cache lines in groups
of four — the same policy as the AVX-512 kernel, with the group
threshold carried over from the AVX-512 measurement rather than tuned
on ARM hardware.
Bench: `batch_neon` and `batch_neon_sdot` rows in the scan groups.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Vector Search Engine for the next generation of AI applications
Qdrant (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.
With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
Qdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.
Quick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact
Getting Started
Agent Skills
Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.
Client-Server
To experience the full power of Qdrant locally, run the container with this command:
docker run -p 6333:6333 qdrant/qdrant
Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.
Now you can connect to the server with any client. For example, using Python:
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")
Before deploying Qdrant to production, be sure to read our installation and security guides.
Clients
Qdrant offers the following client libraries to help you integrate it into your application stack:
- Official:
- Community:
Qdrant Edge
Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.
To get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:
from qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation
shard = EdgeShard.create("./shard", EdgeConfig(
vectors={"my-vector": EdgeVectorParams(size=4, distance=Distance.Cosine)}
))
shard.update(UpdateOperation.upsert_points([
Point(id=1, vector={"my-vector": [0.1, 0.2, 0.3, 0.4]}, payload={"color": "red"})
]))
Where Do I Go from Here?
- Quick Start Guide
- Detailed Documentation
- Take the Qdrant Essentials course
- Follow this tutorial to create a semantic search engine with Qdrant
Demo Projects
Discover Semantic Text Search 🔍
Unlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!
Explore Similar Image Search - Food Discovery 🍕
There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!
Master Extreme Classification - E-Commerce Product Categorization 📺
Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!
API
REST
Qdrant provides a REST API with an OpenAPI 3.0 specification, enabling client generation for virtually any framework or programming language.
You can also download the raw OpenAPI definitions.
gRPC
For faster, production-tier searches, Qdrant also provides a gRPC interface.
Features
Dense, Sparse, and Multi Vector Search
Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.
Filtering on Payload
Attach any JSON payload to your vectors and filter on it using a rich set of conditions—keyword matching, full-text, numeric ranges, geo-locations, and more—combined with should, must, and must_not clauses.
Hybrid Search
Combine multiple vectors in a single query to get the best of semantic understanding and keyword precision, with results merged via configurable fusion strategies, such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).
Vector Quantization and On-Disk Storage
Built-in quantization cuts RAM usage by up to 97% and lets you tune the trade-off between search speed and precision.
Distributed Deployment
Scale horizontally with sharding and replication, and update or resize collections with zero downtime.
Highlighted Features
- Faceting - aggregate search results by payload values.
- Recommendation - use positive and negative examples to find similar points.
- Discovery - constrain search to a specific region of the vector space.
- Search Relevance Tuning - tools for adjusting search results, such as Maximal Marginal Relevance (MMR) and the Relevance Feedback Query.
- Multitenancy - scalable partitioning of data for multi-user environments.
- Observability - comprehensive metrics, telemetry, and audit logging for monitoring and debugging.
- Query Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.
- SIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
- GPU Support - for accelerated indexing, with support for NVIDIA and AMD GPUs.
- Async I/O - uses
io_uringto maximize disk throughput utilization even on network-attached storage. - Write-Ahead Logging - ensures data persistence with update confirmation, even during power outages.
Web UI
Web UI provides a visual way to interact with your data and monitor the health of your deployment. It enables you to explore your collections, manage data, interact with the REST API, and more.
Integrations
Qdrant integrates with the tools you're already using across every stage of your AI stack. You can connect to embedding providers, AI application frameworks, and data pipeline tools, as well as observability platforms for monitoring and tracing your vector search in production. No-code and low-code automation platforms are supported too. Refer to the Ecosystem page for the complete list.
Contributing
We are happy to receive your contributions! Before opening a pull request, please read our Contributing Guide.
Important
Our development branch is
dev, notmaster. Please fork the repo, branch fromdev, and open your pull request againstdev. PRs targetingmasterwill be asked to retarget.
Contacts
- Have questions? Join our Discord channel or mention @qdrant_engine on X
- Want to stay in touch with the latest releases? Subscribe to our Newsletters
- Looking for a managed cloud? Check pricing. Need something personalized? We're at info@qdrant.tech
License
Qdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.