krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-26 06:28:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

mighty article

Browse Source
This commit is contained in:
Andre Bogus
2023-07-04 09:33:19 +02:00
parent 4cc973493b
commit 3fcedb46e2
2 changed files with 220 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/mighty-integration.md
+23 -4
View File
@@ -24,7 +24,27 @@ We're always on the lookout for interesting services to combine with Qdrant to c
![max.io logo](/articles_data/mighty-integration/maxio-logo.svg)
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`):
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:
```
{
"name": "sentence-transformers/all-MiniLM-L6-v2",
"architectures": [
"BertModel"
],
"model_type": "bert",
"max_position_embeddings": 512,
"labels": null,
"named_entities": null,
"image_size": null,
"source": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"
}
}
```
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.
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`):
```json
{
@@ -33,7 +53,7 @@ For mighty, we start up a [docker container](https://hub.docker.com/layers/maxdo
-0.05019686743617058,
0.051746174693107605,
0.048117730766534805,
...
... (381 values skipped)
]
],
"shape": [
@@ -79,8 +99,7 @@ pub async fn get_mighty_embedding(
}
let embeddings: Result<EmbeddingsResponse, _> = response.json().await?;
Ok(embeddings[0])
Ok(embeddings[0]) // we ignore multiple embeddings at the moment
}
```