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kanungleandAbdon Pijpelink
544708f293
* create Develop and Deploy tabs; move Operations; re-weight pages * move capacity planning page; create section dropdown content * added aliases to frontmatter * update link references to new canonical links; maintain anchoring * address remaining link issues and errors * fix outlier tutorial reference issue * Treat 'develop' and 'deploy' as a unified search space * fix some frontmatter aliases * add section header redirects * fix 'Operations' redirect to go to 'Deploy' tab * update redirects file for * pattern * add :splat to redirect references * Add wildcard to each entry in _redirects file --------- Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
52 lines
4.5 KiB
Markdown
52 lines
4.5 KiB
Markdown
---
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title: "Qdrant Edge"
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weight: 220
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partition: develop
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---
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<aside role="status">Qdrant Edge is in beta. The API and functionality may change in future releases.</aside>
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# What Is Qdrant Edge?
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Qdrant Edge is a lightweight, embedded vector search engine for in-process retrieval with a minimal memory footprint and no background services. Qdrant Edge is designed for applications requiring low-latency vector search in environments with limited or intermittent connectivity, such as robots, kiosks, home assistants, and mobile phones.
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Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Think of it as SQLite, but for vector search. Data is stored and queried locally, ensuring low-latency access and enhanced privacy since data does not need to be transmitted to an external server. That said, Qdrant Edge provides APIs to [synchronize data with a Qdrant server](/documentation/edge/edge-data-synchronization-patterns/). This enables you to offload heavy computations such as indexing to more powerful server instances, back up and restore data, and centrally aggregate data from multiple edge devices.
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## Qdrant Edge Shard
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Qdrant Edge is built around the concept of an **Edge Shard**: a self-contained storage unit that can operate independently. Each Edge Shard manages its own data, including vector and payload storage, and can perform local search and retrieval operations.
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To work with a Qdrant Edge Shard, use the [Python Bindings for Qdrant Edge](https://pypi.org/project/qdrant-edge-py/) package or the [`qdrant-edge` Rust crate](https://crates.io/crates/qdrant-edge). This library provides an `EdgeShard` class with methods to manage data, query it, and restore snapshots:
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- `new` (Rust) / `create` (Python): Creates a new Edge Shard at the given path with the provided configuration. Fails if the path already contains data.
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- `load`: Initializes an Edge Shard by reading existing data and optionally the configuration from disk.
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- `update`: Updates the data.
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- `query`: Queries the data.
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- `facet`: Returns the top N distinct values of a payload field, sorted by the number of points that have each value.
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- `scroll`: Returns all points.
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- `count`: Returns the number of points.
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- `retrieve`: Retrieves points with the given IDs.
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- `flush`: Flushes the data to ensure that all writes have been persisted to disk.
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- `close`: Cleanly destroys the shard instance, ensuring the data is flushed (Python). The data is persisted on disk and can be used to create another shard. In Rust, use the `Drop` trait to ensure the shard is closed when it goes out of scope.
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- `optimize`: Optimizes the Edge Shard by removing data marked for deletion, merging segments, and creating indexes.
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- `info`: Returns metadata information about the shard.
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- `unpack_snapshot`: Unpacks a snapshot on disk.
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- `snapshot_manifest`: Returns the current shard’s snapshot manifest.
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- `recover_partial_snapshot` (Rust) / `update_from_snapshot` (Python): Applies a snapshot to the shard.
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## Using Qdrant Edge
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| Type | Guide | What you'll learn |
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|--------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
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| **Beginner** | [Qdrant Edge Quickstart](/documentation/edge/edge-quickstart/) | Get started with Qdrant Edge and learn the basics of managing and querying data |
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| **Beginner** | [On-Device Embeddings](/documentation/edge/edge-fastembed-embeddings/) | Generate vector embeddings directly on edge devices using FastEmbed |
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| **Reference** | [Data Synchronization Patterns](/documentation/edge/edge-data-synchronization-patterns/) | Overview of patterns for synchronizing data between Edge Shards and Qdrant server collections |
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| **Advanced** | [Synchronize with a Server](/documentation/edge/edge-synchronization-guide/) | Synchronize an Edge Shard with a Qdrant server collection to offload indexing and synchronize data between devices |
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### More Examples
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The Qdrant GitHub repository contains examples of using the Qdrant Edge API in [Python](https://github.com/qdrant/qdrant/tree/dev/lib/edge/python/examples) and [Rust](https://github.com/qdrant/qdrant/tree/dev/lib/edge/publish/examples).
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