Wrong kNN similarity score when indexing with dynamic object

[meocong]

I have a search index which mapping like:
{ "settings" : { "index": { "knn": True, "knn.algo_param.m": 40, "knn.algo_param.ef_search" : 300, "knn.algo_param.ef_construction" : 500, "knn.space_type": "cosinesimil", "sort.field" : "created", "sort.order" : "desc" }, "translog.flush_threshold_size": "10gb" }, "mappings": { "embedding": { "type": "knn_vector", "dimension": embedding_size }, "metadata" : { "type" : "object", "dynamic" : True }, } } }

And I used elasticsearch-py as ES Client
When I index this embed with metadata, the query distance was wrong:
[ 0.037263173609972, -0.018216436728835106, -0.004558993503451347, 0.07959768176078796, 0.07989051192998886, 0.012814756482839584, 0.015803413465619087, 0.08274413645267487, 0.03609683737158775, -0.011350080370903015, -0.0053525823168456554, -0.0305563323199749, -0.06880204379558563, 0.03622889518737793, 0.050995949655771255, -0.025420812889933586, -0.06512117385864258, -0.008494238369166851, -0.019643913954496384, -0.07068609446287155, -0.05977993831038475, 0.04946266859769821, -0.07362569123506546, -0.016564900055527687, -0.08390657603740692, -0.05054805055260658, 0.018434297293424606, -0.03548923879861832, 0.04917234927415848, 0.09585133194923401, 0.015353847295045853, -0.022964736446738243, 0.015726866200566292, 0.010365299880504608, 0.0783892273902893, 0.006507471669465303, -0.04006418585777283, 0.06607645004987717, 0.03665514662861824, -0.03890605643391609, -0.011760648339986801, 0.024083642289042473, -0.009215307421982288, -0.0049561322666704655, 0.006363628897815943, 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The query embed:
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The result:
With dynamic=True metadata object: "similarity": 0.5550214
With dynamic=False metadata object: "similarity": 0.71384406

How can I fix this, or it's ODFE's problem, can you check, thank you for your help!

[meocong]

@jmazanec15 Can you take some time to check this issue, because I think it's critical.

[jmazanec15]

Hi @meocong, thanks for posting. It appears that when dynamic is set to True, it calculates the l2 distance (1/(1+euclidean^2)); when dynamic is set to False it calculates cosine.

We are having a similar discussion in the forums here: https://discuss.opendistrocommunity.dev/t/cosine-similarity-formula/4390/14.

I will take a closer look at this problem with the dynamic field in metadata and see if its related. I will update this issue when I find something.

[meocong]

@jmazanec15 Thank you for your quick reply, I know that it's holiday now but I am applying ODFE into Production, so hopefully you can debug it soon.

[jmazanec15]

Hi @meocong,

Verified that this bug has same root cause as https://discuss.opendistrocommunity.dev/t/cosine-similarity-formula/4390/14.

Here are reproduction steps:

curl -X PUT "localhost:9200/myindex" -H 'Content-Type: application/json' -d'
{
  "settings" : {
      "index": {
        "knn": "true",
        "knn.space_type": "cosinesimil"
      }
  },
  "mappings": {
    "properties": {
      "embedding": {
        "type": "knn_vector",
        "dimension": 2
      },
      "metadata" : {
        "type" : "object",
        "dynamic" : true
      }
    }
  }
}
'

curl -X POST "localhost:9200/myindex/_doc" -H 'Content-Type: application/json' -d'
{
  "metadata": {"test": "object"},
  "embedding":  [1, 2]
}
'

curl -X POST "localhost:9200/myindex/_search" -H 'Content-Type: application/json' -d'
{
  "size" : 1,
  "query": {
    "knn": {
      "embedding": {
        "vector": [3, 4],
        "k": 1
      }
    }
  }
}
'
{"took":4,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":1,"relation":"eq"},"max_score":0.26120389,"hits":[{"_index":"myindex","_type":"_doc","_id":"v7XLr3YBIf7zL2czQtly","_score":0.26120389,"_source":
{
  "metadata": {"test": "object"},
  "embedding":  [1, 2]
}
}]}}

The score returned is Euclidean: 1/(1 + sqrt((3-1)^2 + (4-2)^2)) = 0.26.

Making this change fixes the issue:

curl -X POST "localhost:9200/myindex/_search" -H 'Content-Type: application/json' -d'
{
  "size" : 1,
  "query": {
    "knn": {
      "embedding": {
        "vector": [3, 4],
        "k": 1
      }
    }
  }
}
'

{"took":87,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":1,"relation":"eq"},"max_score":0.984126,"hits":[{"_index":"myindex","_type":"_doc","_id":"O_fNr3YB8wZdPbAJ4nrv","_score":0.984126,"_source":
{
  "metadata": {"test": "object"},
  "embedding":  [1, 2]
}

I will submit a PR to fix this today. We will backport changes to older branches where this bug is present.

What ODFE version and artifact type (Docker, Deb, RPM, TAR, ...) are you using?

[meocong]

@jmazanec15 I am having this bug on versions 1.11.0 and 1.12.0 with docker image amazon/opendistro-for-elasticsearch
Thank you for your hardworking, hope it comes to 1.12.1 soon

[jmazanec15]

Hi @meocong, apologies for the delay. Unfortunately, we will not release a 1.12.1 Docker image with the patch.

As a workaround, you can build the patch into a custom Docker image with the following Dockerfile:

FROM amazon/opendistro-for-elasticsearch:1.12.0
RUN yum -y update \
    && yum install -y git \
    && yum clean all \
    && rm -rf /tmp/plugins \
    && mkdir /tmp/plugins \
    && git clone --recursive --branch opendistro-1.12 https://github.com/opendistro-for-elasticsearch/k-NN.git /usr/share/elasticsearch/k-NN \
    && cd /usr/share/elasticsearch/k-NN \
    && ./gradlew build -x integTest -x test -x jacocoTestReport \
    && cp build/distributions/opendistro-knn-1.12.0.0.zip /tmp/plugins/ \
    && rm -rf /usr/share/elasticsearch/k-NN \
    && yum remove git -y && yum clean all -y

WORKDIR /usr/share/elasticsearch

RUN elasticsearch-plugin remove opendistro-knn \
  && elasticsearch-plugin install --batch file:/tmp/plugins/opendistro-knn-1.12.0.0.zip

Running docker build --tag=odfe-1.12-knn-patch . will build it (assuming you are in the same directory as the Dockerfile).

[meocong]

@jmazanec15 I am really appreciate your work, I tested and I worked. Hope that the new version is coming soon.

[marcoaleixo]

When this fix will be available in the AWS Elasticsearch?
Sorry if you guys aren't related directly to AWS services.

[vamshin]

@marcoaleixo this fix would be available on AWS Elasticsearch around Feb15th as part of software update. If you need assistance with fix immediately or any other help, please feel free to cut support ticket to AWS Elasticsearch.