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elasticsearchvectorelasticsearch-5elasticsearch-painless

How can I do this in painless script Elasticsearch 5.3

We're trying to replicate this ES plugin https://github.com/MLnick/elasticsearch-vector-scoring. The reason is AWS ES doesn't allow any custom plugin to be installed. The plugin is just doing dot product and cosine similarity so I'm guessing it should be really simple to replicate that in painless script. It looks like groovy scripting is deprecated in 5.0.

Here's the source code of the plugin.

    /**
     * @param params index that a scored are placed in this parameter. Initialize them here.
     */
    @SuppressWarnings("unchecked")
    private PayloadVectorScoreScript(Map<String, Object> params) {
        params.entrySet();
        // get field to score
        field = (String) params.get("field");
        // get query vector
        vector = (List<Double>) params.get("vector");
        // cosine flag
        Object cosineParam = params.get("cosine");
        if (cosineParam != null) {
            cosine = (boolean) cosineParam;
        }
        if (field == null || vector == null) {
            throw new IllegalArgumentException("cannot initialize " + SCRIPT_NAME + ": field or vector parameter missing!");
        }
        // init index
        index = new ArrayList<>(vector.size());
        for (int i = 0; i < vector.size(); i++) {
            index.add(String.valueOf(i));
        }
        if (vector.size() != index.size()) {
            throw new IllegalArgumentException("cannot initialize " + SCRIPT_NAME + ": index and vector array must have same length!");
        }
        if (cosine) {
            // compute query vector norm once
            for (double v: vector) {
                queryVectorNorm += Math.pow(v, 2.0);
            }
        }
    }

    @Override
    public Object run() {
        float score = 0;
        // first, get the ShardTerms object for the field.
        IndexField indexField = this.indexLookup().get(field);
        double docVectorNorm = 0.0f;
        for (int i = 0; i < index.size(); i++) {
            // get the vector value stored in the term payload
            IndexFieldTerm indexTermField = indexField.get(index.get(i), IndexLookup.FLAG_PAYLOADS);
            float payload = 0f;
            if (indexTermField != null) {
                Iterator<TermPosition> iter = indexTermField.iterator();
                if (iter.hasNext()) {
                    payload = iter.next().payloadAsFloat(0f);
                    if (cosine) {
                        // doc vector norm
                        docVectorNorm += Math.pow(payload, 2.0);
                    }
                }
            }
            // dot product
            score += payload * vector.get(i);
        }
        if (cosine) {
            // cosine similarity score
            if (docVectorNorm == 0 || queryVectorNorm == 0) return 0f;
            return score / (Math.sqrt(docVectorNorm) * Math.sqrt(queryVectorNorm));
        } else {
            // dot product score
            return score;
        }
    }

I'm trying to start with just getting a field from index. But I'm getting error.

Here's the shape of my index.

I've enabled delimited_payload_filter

"settings" : {
    "analysis": {
            "analyzer": {
               "payload_analyzer": {
                  "type": "custom",
                  "tokenizer":"whitespace",
                  "filter":"delimited_payload_filter"
                }
      }
    }
 }

And I have a field called @model_factor to store a vector.

{
    "movies" : {
        "properties" : {
            "@model_factor": {
                            "type": "text",
                            "term_vector": "with_positions_offsets_payloads",
                            "analyzer" : "payload_analyzer"
                     }
        }
    }
}

And this is the shape of the document 

{
    "@model_factor":"0|1.2 1|0.1 2|0.4 3|-0.2 4|0.3",
    "name": "Test 1"
}

Here's how I use the script

{
    "query": {
        "function_score": {
            "query" : {
                "query_string": {
                    "query": "*"
                }
            },
            "script_score": {
                "script": {
                    "inline": "def termInfo = doc['_index']['@model_factor'].get('1', 4);",
                    "lang": "painless",
                    "params": {
                        "field": "@model_factor",
                        "vector": [0.1,2.3,-1.6,0.7,-1.3],
                        "cosine" : true
                    }
                }
            },
            "boost_mode": "replace"
        }
    }
}

And this is the error I got. 

"failures": [
      {
        "shard": 2,
        "index": "test",
        "node": "ShL2G7B_Q_CMII5OvuFJNQ",
        "reason": {
          "type": "script_exception",
          "reason": "runtime error",
          "caused_by": {
            "type": "wrong_method_type_exception",
            "reason": "wrong_method_type_exception: cannot convert MethodHandle(List,int)int to (Object,String)String"
          },
          "script_stack": [
            "termInfo = doc['_index']['@model_factor'].get('1',4);",
            "              ^---- HERE"
          ],
          "script": "def termInfo = doc['_index']['@model_factor'].get('1',4);",
          "lang": "painless"
        }
      }
    ]

The question is how do I access the index field to get @model_factor in painless scripting?

Solution

  • Option 1

    Due to the fact that @model_factor is a text field, in painless scripting, it would be possible to access it, setting fielddata=true in the mapping. So the mapping should be: 

    {
    "movies" : {
        "properties" : {
            "@model_factor": {
                            "type": "text",
                            "term_vector": "with_positions_offsets_payloads",
                            "analyzer" : "payload_analyzer",
                            "fielddata" : true
                     }
        }
    }
}

    And then it can be scored accessing doc-values:

    {
    "query": {
        "function_score": {
            "query" : {
                "query_string": {
                    "query": "*"
                }
            },
            "script_score": {
                "script": {
                    "inline": "return Double.parseDouble(doc['@model_factor'].get(1)) * params.vector[1];",
                    "lang": "painless",
                    "params": {
                        "vector": [0.1,2.3,-1.6,0.7,-1.3]
                    }
                }
            },
            "boost_mode": "replace"
        }
    }
}

    Problems with Option 1

    So it is possible to access the field data value setting fielddata=true, but in this case, the value is the vector index as a term, not the value of the vector which is stored in the payload. Unfortunately, it looks like there is no way to access the Token Payload (where the real vector index value is stored) using painless scripting and doc-values. See the source code for elasticsearch and another similar question re: accessing term info.

    So the answer is that using painless scripting is NOT possible to access the payload.

    I tried also to store the vector values with a simple pattern tokenizer but when accessing the term vector values the order is not preserved, and this is probably the reason for which the author of the plugin decided to use the term as a string and then retrieve the position 0 of the vector as the term "0" and then find the real vector value in the payload.

    Option 2

    A very simple alternative is to use n fields in the documents, each of them represents a position in the vector, so in your example, we have a 5 dim vector with values stored in v0...v4 directly as double:

    {
    "@model_factor":"0|1.2 1|0.1 2|0.4 3|-0.2 4|0.3",
    "name": "Test 1",
    "v0" : 1.2,
    "v1" : 0.1,
    "v2" : 0.4,
    "v3" : -0.2,
    "v4" : 0.3
}

    and then the painless scripting should be:

    {
    "query": {
        "function_score": {
            "query" : {
                "query_string": {
                    "query": "*"
                }
            },
            "script_score": {
                "script": {
                    "inline": "return doc['v0'].getValue() * params.vector[0];",
                    "lang": "painless",
                    "params": {
                        "vector": [0.1,2.3,-1.6,0.7,-1.3]
                    }
                }
            },
            "boost_mode": "replace"
        }
    }
}

    It should be easily possible to iterate on the input vector length and get the fields dynamically to calculate the dot product modifying doc['v0'].getValue() * params.vector[0] that I wrote for simplicity.

    Problems with Option2

    Option 2 is viable as long as the vector dimension remains not big. I think that default Elasticsearch max number of fields per document is 1000, but it can be changed also in AWS environment:

    curl -X PUT \
  'https://.../indexName/_settings' \
  -H 'cache-control: no-cache' \
  -H 'content-type: application/json' 
  -d '{
"index.mapping.total_fields.limit": 2000
}'

    Moreover, it should be tested also the script speed on a large number of documents. Maybe in re-scoring / re-ranking scenarios, it is a viable solution.

    Option 3

    The third option is really an experiment and the most fascinating in my opinion. It tries to exploit the internal Elasticsearch representation of the Vector Space Model and does not use any scripting to score but reuse the default similarity score based on tf/idf.

    Lucene, that seats at Elasticsearch core, is already using internally a modification of the cosine similarity to calculate the similarity score between documents in his Vector Space Model representation of terms as the formula below, taken from the TFIDFSImilarity javadoc, shows:

    enter image description here

    In particular, the weights of the vector representing the field are the tf/idf values of the terms of that field.

    So we could index a document with termvectors, using as term the index of the vector. If we repeat it N times, we represent the value of the vector, exploiting the tf part of the scoring formula. This means that the domain of the vector should be transformed and rescaled in {1.. Infinite} Positive Integer numbers domain. We start from 1 so that we are sure that all the documents contain all the terms, it will make it easier to exploit the formula.

    For example, the vector: [21, 54, 45] can be indexed as a field in a document using a simple whitespace analyzer and the following value:

    {
    "@model_factor" : "0<repeated 21 times> 1<repeated 54 times> 2<repeated 45 times>",
    "name": "Test 1"
}

    then to query, i.e. calculate the dot product, we boost the single terms that represent the index position of the vector.

    So using the same example above the input vector: [45, 1, 1] will be transformed in the query:

    "should": [
        {
          "term": {
            "@model_factor": {
              "value": "0",
              "boost": 45 
            }
          }
        },
        {
          "term": {
            "@model_factor": "1" // boost:1 by default

          }
        },
        {
          "term": {
            "@model_factor": "2"  // boost:1 by default
          }
        }
      ]

    norm(t,d) should be disabled in the mapping so that it is not used in the formula above. The idf part is constant for all the documents because all of them contains all the terms (having all the vectors the same dimension).

    queryNorm(q) is the same for all the documents in the formula above so it is not a problem.

    coord(q,d) is a constant because all the documents contain all the terms.

    Problems with Option 3

    Need to be tested.

    It works only for positive numbers vectors, see this question in math stackoverflow for making it works also for negative numbers.

    It is not the exact same of a dot product but very close to find similar documents based on raw vectors.

    Scalability on large vector dimension can be an issue at querying time because this means we need to do a N dim terms query with different boosting.

    I will try it in a test index and edit this question with the results.