"mappings": {
"properties": {
"nominee_text": {"type": "text"},
"nominee_vector": {
"type": "knn_vector",
"dimension": 4096,
"method": {
"engine": "nmslib",
"space_type": "cosinesimil",
"name": "hnsw",
"parameters": {"ef_construction": 512, "m": 16},
},
},
}
},
"settings": {
"index": {
"number_of_shards": 2,
"knn.algo_param": {"ef_search": 512},
"knn": True,
}
},
} Any solution?
"mappings": {
"properties": {
"nominee_text": {"type": "text"},
"nominee_vector": {
"type": "knn_vector",
"dimension": 4096,
"method": {
"engine": "nmslib",
"space_type": "cosinesimil",
"name": "hnsw",
"parameters": {"ef_construction": 512, "m": 16},
},
},
}
},
"settings": {
"index": {
"number_of_shards": 2,
"knn.algo_param": {"ef_search": 512},
"knn": True,
}
},
} Error:
RequestError: RequestError(400, 'mapper_parsing_exception', "failed to parse field [nominee_vector] of type [knn_vector] in document with id '55nxY40Bdoi9X56tiIna'. Preview of field's value: 'null'")
Any solution for the above mentioned problem?
I tried to store my embedding and text to opensearchserverless collection by using this index. But getting this error everytimes
These kind of error usually occurs from the mismatch of vector dimension. You should find out the dimension length, and configure when you create the OpenSearch index. I hope it helps you.