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>
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title, weight, partition
| title | weight | partition |
|---|---|---|
| On-Device Embeddings | 15 | develop |
On-Device Embeddings with Qdrant Edge and FastEmbed
When using Python, you can use the 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.
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:
pip install fastembed qdrant-edge-py
Next, download the embedding models and save them locally on the device. Instantiate instances of ImageEmbedding and TextEmbedding, setting the cache_dir parameter to a local directory:
{{< code-snippet path="/documentation/headless/snippets/edge/fastembed/" block="download-models" >}}
The models will be downloaded and cached in the specified MODELS_DIR directory, from where you can use them to generate embeddings.
Generate Image Embeddings
First, initialize an Edge Shard as described in the Qdrant Edge Quickstart Guide.
Details
{{< code-snippet path="/documentation/headless/snippets/edge/fastembed/" block="initialize-edge-shard" >}}
Assuming you have an image file temp.jpg, you can generate an embedding for it using FastEmbed's ImageEmbedding class and then store it in the Edge Shard:
{{< code-snippet path="/documentation/headless/snippets/edge/fastembed/" block="embed-and-store-image" >}}
Note the use of cache_dir=MODELS_DIR and local_files_only=True to load the image embedding model from the local directory where it was previously downloaded.
Generate Text Embeddings
At query time, you can generate text embeddings using FastEmbed's TextEmbedding class. For example, to query the Edge Shard:
{{< code-snippet path="/documentation/headless/snippets/edge/fastembed/" block="query-with-text-embedding" >}}
Again, using cache_dir=MODELS_DIR and local_files_only=True ensures the text embedding model is loaded from the local directory.