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mighty article
This commit is contained in:
Andre Bogus
@@ -24,7 +24,27 @@ We're always on the lookout for interesting services to combine with Qdrant to c
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For mighty, we start up a [docker container](https://hub.docker.com/layers/maxdotio/mighty-sentence-transformers/0.9.9/images/sha256-0d92a89fbdc2c211d927f193c2d0d34470ecd963e8179798d8d391a4053f6caf?context=explore) with an open port 5050. We can check that it works by calling `curl https://<address>:5050/sentence-transformer?q=hello+mighty`. This will give us a result like (formatted via `jq`):
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For mighty, we start up a [docker container](https://hub.docker.com/layers/maxdotio/mighty-sentence-transformers/0.9.9/images/sha256-0d92a89fbdc2c211d927f193c2d0d34470ecd963e8179798d8d391a4053f6caf?context=explore) with an open port 5050. Just loading the port in a window shows the following:
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```
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{
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"name": "sentence-transformers/all-MiniLM-L6-v2",
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"architectures": [
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"BertModel"
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],
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"model_type": "bert",
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"max_position_embeddings": 512,
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"labels": null,
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"named_entities": null,
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"image_size": null,
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"source": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"
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}
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}
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```
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We note that this uses huggingface's MiniLM-L6 v2 model. If we look at huggingface's site, we find that the model "maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search". Below, it tells us that the distance measure to use is cosine similarity.
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We can check that it works by calling `curl https://<address>:5050/sentence-transformer?q=hello+mighty`. This will give us a result like (formatted via `jq`):
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```json
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```json
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{
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{
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@@ -33,7 +53,7 @@ For mighty, we start up a [docker container](https://hub.docker.com/layers/maxdo
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-0.05019686743617058,
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-0.05019686743617058,
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0.051746174693107605,
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0.051746174693107605,
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0.048117730766534805,
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0.048117730766534805,
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...
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... (381 values skipped)
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]
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]
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],
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],
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"shape": [
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"shape": [
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@@ -79,8 +99,7 @@ pub async fn get_mighty_embedding(
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}
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}
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let embeddings: Result<EmbeddingsResponse, _> = response.json().await?;
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let embeddings: Result<EmbeddingsResponse, _> = response.json().await?;
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Ok(embeddings[0]) // we ignore multiple embeddings at the moment
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Ok(embeddings[0])
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}
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}
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```
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```
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After Width: | Height: | Size: 6.9 KiB |
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Block a user