Elasticsearch RequestError(400) 'search_phase_execution_exception'

Question

I have a simple python app that uses Elasticsearch to store documents for Pokemon using this mapping:

{


"mappings": {
    "properties": {
      "id": {
        "type": "integer"
      },
      "name": {
        "type": "object"
      },
      "type": {
        "type": "keyword"
      },
      "base": {
        "type": "object"
      },
      "species": {
        "type": "text"
      },
      "description": {
        "type": "text"
      },
      "evolution": {
        "type": "object"
      },
      "profile": {
        "type": "nested",
        "properties": {
          "ability": {
            "type": "keyword"  
          }
        }
      },
      "image": {
        "type": "object"
      },
      "embedding": {  
        "type": "dense_vector",
        "dims": 384
      }
    }
  }
}

When I query by a property for example name in english from my python code:

term_query = {
    "size": 5,
    "query": {
        "term": {
            "name.english": "Pikachu"
        }
    }
}

response = es_client.search(index="pokemon", body=term_query)
print(response)

I do get back the document for Pikachu. This is the response:

{'took': 16, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 1, 'relation': 'eq'}, 'max_score': 2.5902672, 'hits': [{'_index': 'pokemon', '_type': '_doc', '_id': 'uTSUE5EB_ikN9OuVrCYz', '_score': 2.5902672, '_source': {'id': 25, 'name': {'english': 'Pikachu', 'japanese': 'ピカチュウ', 'chinese': '皮卡丘', 'french': 'Pikachu'}, 'type': ['Electric'], 'base': {'HP': 35, 'Attack': 55, 'Defense': 40, 'Sp. Attack': 50, 'Sp. Defense': 50, 'Speed': 90}, 'species': 'Mouse Pokémon', 'description': 'While sleeping, it generates electricity in the sacs in its cheeks. If it’s not getting enough sleep, it will be able to use only weak electricity.', 'evolution': {'prev': ['172', 'high Friendship'], 'next': [['26', 'use Thunder Stone']]}, 'profile': {'height': '0.4 m', 'weight': '6 kg', 'egg': ['Field', 'Fairy'], 'ability': [['Static', 'false'], ['Lightning Rod', 'true']], 'gender': '50:50'}, 'image': {'sprite': 'https://raw.githubusercontent.com/Purukitto/pokemon-data.json/master/images/pokedex/sprites/025.png', 'thumbnail': 'https://raw.githubusercontent.com/Purukitto/pokemon-data.json/master/images/pokedex/thumbnails/025.png', 'hires': 'https://raw.githubusercontent.com/Purukitto/pokemon-data.json/master/images/pokedex/hires/025.png'}, 'embedding': [-0.08073285967111588, 0.06939519941806793, 0.12187427282333374, -0.004082795698195696, -0.12248878180980682, -0.20900841057300568, 0.24315859377384186, 0.17423027753829956, 0.16088330745697021, -0.11973902583122253, 0.219094380736351, -0.1787237524986267, 0.14656412601470947, 0.26914048194885254, -0.0900934562087059, -0.02184007130563259, 0.08033201843500137, 0.22495900094509125, -0.2534410059452057, 0.041292887181043625, 0.06425879895687103, -0.23064647614955902, 0.13032366335391998, -0.18455955386161804, -0.27389195561408997, -0.039019424468278885, 0.35004937648773193, -0.015063440427184105, -0.046075183898210526, 0.06129240244626999, -0.2043248414993286, 0.06172535941004753, 0.04312220215797424, 0.08780308812856674, 0.13448253273963928, -0.05551649630069733, -0.1422523856163025, -0.027074327692389488, 0.04502112418413162, -0.1769474595785141, -0.008200257085263729, -0.25519609451293945, 0.13449157774448395, 0.2700392007827759, 0.006515479180961847, 0.5337464809417725, 0.1946619153022766, -0.3672884404659271, -0.12406620383262634, -0.09569848328828812, -0.007241903804242611, 0.3416319191455841, -0.05228937789797783, 0.036399926990270615, -0.22220291197299957, -0.061945606023073196, 0.11898031830787659, -0.18378591537475586, -0.041961733251810074, -0.047705043107271194, 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I checked all the properties and they look correct. The embedding also has the right lenght 384 which is what this model I am using produces: embeddings_model = SentenceTransformer("paraphrase-MiniLM-L6-v2")

However when I try to run an embedding cosine similarity query like:

query = "Pikachu"
query_embedding = embeddings_model.encode([query])[0].tolist()
script_query_cosine = {
    "size": 5,
    "query": {
        "script_score": {
            "query": {
                "match_all": {}
            },
            "script": {
                "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
                "params": {
                    "query_vector": query_embedding
                }
            }
        }
    }
}

response = es_client.search(index="pokemon", body=script_query_cosine)
print(response)

I get this error:

RequestError(400, 'search_phase_execution_exception', 'runtime error')

I checked the embedding size and it is correct. I checked if any documents are missing the embeddings fields and I found no documents that miss it. I have no clue what this error is.

Did you try to print script_query_cosine and execute from the Kibana ? it seems like query formation have some issue. Please print query and see if it correct or not. — Sagar Patel
– Sagar Patel, Commented Aug 2, 2024 at 15:37
Hi @SagarPatel what do you mean print the query string in my python code? Or print the query the client sends? How do I do this? Thank you! — KZiovas
– KZiovas, Commented Aug 3, 2024 at 17:17

Lisan Al Gaib · Accepted Answer · 2024-08-19 11:47:31Z

0

Term is an exact/perfect matching query and Match is fuzzy matching query.

I would suggest you to use match query, instead of term for your case.

GET /pokemon/_search
{
  "size": 5,
  "query": {
    "match": {
      "name.english": "Pikachu"
    }
  }
}

answered Aug 19, 2024 at 11:47

Lisan Al Gaib

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