1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
// This file is autogenerated, DO NOT EDIT
// search/search-your-data/retrievers-examples.asciidoc:715
[source, python]
----
resp = client.indices.create(
index="retrievers_example_nested",
settings={
"number_of_shards": 1
},
mappings={
"properties": {
"nested_field": {
"type": "nested",
"properties": {
"paragraph_id": {
"type": "keyword"
},
"nested_vector": {
"type": "dense_vector",
"dims": 3,
"similarity": "l2_norm",
"index": True,
"index_options": {
"type": "flat"
}
}
}
},
"topic": {
"type": "keyword"
}
}
},
)
print(resp)
resp1 = client.index(
index="retrievers_example_nested",
id="1",
document={
"nested_field": [
{
"paragraph_id": "1a",
"nested_vector": [
-1.12,
-0.59,
0.78
]
},
{
"paragraph_id": "1b",
"nested_vector": [
-0.12,
1.56,
0.42
]
},
{
"paragraph_id": "1c",
"nested_vector": [
1,
-1,
0
]
}
],
"topic": [
"ai"
]
},
)
print(resp1)
resp2 = client.index(
index="retrievers_example_nested",
id="2",
document={
"nested_field": [
{
"paragraph_id": "2a",
"nested_vector": [
0.23,
1.24,
0.65
]
}
],
"topic": [
"information_retrieval"
]
},
)
print(resp2)
resp3 = client.index(
index="retrievers_example_nested",
id="3",
document={
"topic": [
"ai"
]
},
)
print(resp3)
resp4 = client.indices.refresh(
index="retrievers_example_nested",
)
print(resp4)
----
|
