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Edge 0.7 documentation updates (#2391)

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* docs(edge): add "Modify the Vector Schema" step to quickstart

Documents the new Edge 0.7 API for adding and removing named vector
fields on an existing shard without recreating it, with Python and
Rust snippets.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* docs(edge): mention quantization support in quickstart

Adds a note after the EdgeConfig snippet that Edge supports all four
quantization methods (Scalar, Product, Binary, TurboQuant), with a
link to the quantization guide.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* docs(edge): add On-Device BM25 page for Edge 0.7

New guide covering the built-in BM25 sparse embedder: configuring a
sparse vector shard, creating a Bm25/EdgeBm25 embedder, embedding and
upserting documents, and querying. Includes Python and Rust snippets.
Also adds the page to the Edge index navigation table.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* docs(edge): add WAL segment size configuration to quickstart

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Upgrade code snippets to Edge 0.7.2

* Review feedback

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Abdon Pijpelink
2026-06-03 15:51:20 +02:00
committed by GitHub
co-authored by Claude Sonnet 4.6
parent 56211977fb
commit ffe50340fa
37 changed files with 924 additions and 642 deletions
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qdrant-landing/content/documentation/edge/edge-fastembed-embeddings.md
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@@ -10,6 +10,8 @@ partition: develop
When using Python, you can use the [FastEmbed](/documentation/fastembed/) library to generate embeddings for use with Qdrant Edge. FastEmbed provides multimodal models that run efficiently on edge devices to generate vector embeddings from text and images.
<aside role="status">To generate sparse BM25 embeddings for keyword search, see <a href="/documentation/edge/edge-bm25/">BM25 Embeddings on Qdrant Edge</a>.</aside>
# Provision the Device
Assuming the devices on which you will run Qdrant Edge have intermittent or no internet connectivity, you need to provision them with the necessary dependencies and model files ahead of time. First, install FastEmbed and the Qdrant Edge Python bindings: