BadRequestError(400, 'search_phase_execution_exception', 'runtime error') when trying to do l2 similarity search

Elastic Stack Elasticsearch
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1

Hello, Im trying to implement a vector similarity search for face recognition using Elasticsearch in python.

This is my mapping for creating an index:

mapping = {
    "mappings": {
        "properties": {
            "embedding_aaaa":{
                "type": "dense_vector",
                "dims": 128
            },
            "title_name": {"type": "keyword"}
        }
    }
}

once this index is created, i insert:

    video_details = {
        "filename": (unique_filename + ".mp4"),
        "video_title": video_title,
        "tags": tags.split(",") if tags else [],
        "created_at": str(datetime.datetime.now()),
        "created_by": createdBy,
        "uploded_file_size_in_mb": round(os.path.getsize(file_path) / (1024 * 1024), 2)
    }

This much data. once that is complete, i use a face recognition model to generate an embedding for each frame i extract from this video and append the frame's timestamp along with the embedding before finally inserting that data onto the same document by using update method.

embedding_per_frame = []
 ds = {
                "timestamp": timestamp,
                "embedding": embedding
            }
        embedding_per_frame.append(ds.copy())

        update_body = {
        "script": {
            "source": "ctx._source." + "title_vector_aaaa" + " = params." + "title_vector_aaaa",
            "lang": "painless",
            "params": {
                "title_vector_aaa": embedding_per_frame,
            }
     }
    }
        resp = es.update(index=index_name, id=document_id, body=update_body)

Now comes the search part where i use l2 distance to find similar vectors:

        for embed in embeddings:
            query = {
                    "size": 5,
                    "query": {
                    "script_score": {
                        "query": {
                            "match_all": {}
                        },
                        
                        "script": {
                            "source": "1 / (1 + l2norm(params.queryVector, 'embedding_aaaa'))",
                            "params": {
                                "queryVector": list(embed)
                            }
                        }
                    }
                }}

            res = es.search(index="videos, body=query)

This is where the error happens.
if i replace the source statement with this:

source": "doc['embedding_aaaa'].size() == 0 ? 0 :1 / (1 + l2norm(params.queryVector, 'embedding_aaaa'))", #euclidean distance

the error goes away but all the scores are 0.0 which i assume is from the first half of the source statement.

where did i do wrong?. any help is very much appreciated

2

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