【ElasticSearch】ES 5.6.15 向量插件支持

参考 :
https://github.com/lior-k/fast-elasticsearch-vector-scoring

下载插件
安装插件
插件目录：
elasticsearch/plugins,
安装后的目录如下
```
 plugins└── vector├── elasticsearch-binary-vector-scoring-5.6.9.jar└── plugin-descriptor.properties
```
修改 plugin-descriptor.properties 中的 elasticsearch.version 为 5.6.15（因为这里使用的是5.6.15版本ES），安装完成后重启ES。

构建测试索引

PUT /vector_test
{"settings": {"index": {"number_of_shards": 3,"number_of_replicas": 0}},"mappings": {"resume": {"dynamic": "strict","properties": {"file_hash": {"type": "keyword"},"embedding_vector": {"type": "binary","doc_values": true},"doc": {"type": "text"}}}}
}

构建测试数据

使用如下方法生成向量base64字符串

import base64
import numpy as npdfloat32 = np.dtype('>f4')def decode_float_list(base64_string):bytes = base64.b64decode(base64_string)return np.frombuffer(bytes, dtype=dfloat32).tolist()def encode_array(arr):base64_str = base64.b64encode(np.array(arr).astype(dfloat32)).decode("utf-8")return base64_strprint(encode_array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]))
print(encode_array([0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.010]))

将上述得到的结果放到下面内容(embedding_vector)中,这里 embedding_vector 要求传入上述方式base64生成的字符串

PUT /vector_test/resume/1
{"file_hash": "hash1","embedding_vector": "PczMzT5MzM0+mZmaPszMzT8AAAA/GZmaPzMzMz9MzM0/ZmZmP4AAAA==","doc": "This is the content of the first document."
}PUT /vector_test/resume/2
{"file_hash": "hash2","embedding_vector": "OoMSbzsDEm87RJumO4MSbzuj1wo7xJumO+VgQjwDEm88E3S8PCPXCg==","doc": "This is the content of the second document."
}

查询测试

POST /vector_test/resume/_search
{"query": {"function_score": {"boost_mode": "replace","script_score": {"script": {"source": "binary_vector_score","lang": "knn","params": {"cosine": true,"field": "embedding_vector","vector": [1.0,0.8,0.2223,0.7,0.6,0.5,0.4,0.3,0.2,0.1]}}}}},"size": 2,"_source": ["file_hash"]
}

查询结果

{"took": 1,"timed_out": false,"_shards": {"total": 5,"successful": 5,"skipped": 0,"failed": 0},"hits": {"total": 4,"max_score": 0.998783,"hits": [{"_index": "vector_test","_type": "resume","_id": "4","_score": 0.998783,"_source": {"file_hash": "hash4"}},{"_index": "vector_test","_type": "resume","_id": "1","_score": 0.5818508,"_source": {"file_hash": "hash1"}}]}
}