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
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153 | class RelytVectorStore(BasePydanticVectorStore):
"""Relyt Vector Store.
Examples:
`pip install llama-index-vector-stores-relyt`
```python
from llama_index.vector_stores.relyt import RelytVectorStore
# Setup relyt client
from pgvecto_rs.sdk import PGVectoRs
import os
URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
port=os.getenv("RELYT_PORT", "5432"),
host=os.getenv("RELYT_HOST", "localhost"),
username=os.getenv("RELYT_USER", "postgres"),
password=os.getenv("RELYT_PASS", "mysecretpassword"),
db_name=os.getenv("RELYT_NAME", "postgres"),
)
client = PGVectoRs(
db_url=URL,
collection_name="example",
dimension=1536, # Using OpenAI’s text-embedding-ada-002
)
# Initialize RelytVectorStore
vector_store = RelytVectorStore(client=client)
```
"""
stores_text = True
_client: "PGVectoRs" = PrivateAttr()
_collection_name: str = PrivateAttr()
def __init__(self, client: "PGVectoRs", collection_name: str) -> None:
self._client: PGVectoRs = client
self._collection_name = collection_name
self.init_index()
super().__init__()
@classmethod
def class_name(cls) -> str:
return "RelytStore"
def init_index(self):
index_name = f"idx_{self._collection_name}_embedding"
with self._client._engine.connect() as conn:
with conn.begin():
index_query = text(
f"""
SELECT 1
FROM pg_indexes
WHERE indexname = '{index_name}';
"""
)
result = conn.execute(index_query).scalar()
if not result:
index_statement = text(
f"""
CREATE INDEX {index_name}
ON collection_{self._collection_name}
USING vectors (embedding vector_l2_ops)
WITH (options = $$
optimizing.optimizing_threads = 30
segment.max_growing_segment_size = 2000
segment.max_sealed_segment_size = 30000000
[indexing.hnsw]
m=30
ef_construction=500
$$);
"""
)
conn.execute(index_statement)
@property
def client(self) -> Any:
return self._client
def add(
self,
nodes: List[BaseNode],
) -> List[str]:
records = [
Record(
id=node.id_,
text=node.get_content(metadata_mode=MetadataMode.NONE),
meta=node_to_metadata_dict(node, remove_text=True),
embedding=node.get_embedding(),
)
for node in nodes
]
self._client.insert(records)
return [node.id_ for node in nodes]
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
self._client.delete(meta_contains({"ref_doc_id": ref_doc_id}))
def drop(self) -> None:
self._client.drop()
# TODO: the more filter type(le, ne, ge ...) will add later, after the base api supported,
# now only support eq filter for meta information
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
results = self._client.search(
embedding=query.query_embedding,
top_k=query.similarity_top_k,
filter=(
meta_contains(
{pair.key: pair.value for pair in query.filters.legacy_filters()}
)
if query.filters is not None
else None
),
)
nodes = [
metadata_dict_to_node(record.meta, text=record.text)
for record, _ in results
]
return VectorStoreQueryResult(
nodes=nodes,
similarities=[score for _, score in results],
ids=[str(record.id) for record, _ in results],
)
|