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294 | class TimescaleVectorStore(BasePydanticVectorStore):
"""Timescale vector store.
Examples:
`pip install llama-index-vector-stores-timescalevector`
```python
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
# Set up the Timescale service URL
TIMESCALE_SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"
# Create a TimescaleVectorStore instance
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="your_table_name_here",
num_dimensions=1536,
)
```
"""
stores_text: bool = True
flat_metadata: bool = False
service_url: str
table_name: str
num_dimensions: int
time_partition_interval: Optional[timedelta]
_sync_client: client.Sync = PrivateAttr()
_async_client: client.Async = PrivateAttr()
def __init__(
self,
service_url: str,
table_name: str,
num_dimensions: int = DEFAULT_EMBEDDING_DIM,
time_partition_interval: Optional[timedelta] = None,
) -> None:
table_name = table_name.lower()
super().__init__(
service_url=service_url,
table_name=table_name,
num_dimensions=num_dimensions,
time_partition_interval=time_partition_interval,
)
self._create_clients()
self._create_tables()
@classmethod
def class_name(cls) -> str:
return "TimescaleVectorStore"
@property
def client(self) -> Any:
"""Get client."""
return self._sync_client
async def close(self) -> None:
self._sync_client.close()
await self._async_client.close()
@classmethod
def from_params(
cls,
service_url: str,
table_name: str,
num_dimensions: int = DEFAULT_EMBEDDING_DIM,
time_partition_interval: Optional[timedelta] = None,
) -> "TimescaleVectorStore":
return cls(
service_url=service_url,
table_name=table_name,
num_dimensions=num_dimensions,
time_partition_interval=time_partition_interval,
)
def _create_clients(self) -> None:
# in the normal case doesn't restrict the id type to even uuid.
# Allow arbitrary text
id_type = "TEXT"
if self.time_partition_interval is not None:
# for time partitioned tables, the id type must be UUID v1
id_type = "UUID"
self._sync_client = client.Sync(
self.service_url,
self.table_name,
self.num_dimensions,
id_type=id_type,
time_partition_interval=self.time_partition_interval,
)
self._async_client = client.Async(
self.service_url,
self.table_name,
self.num_dimensions,
id_type=id_type,
time_partition_interval=self.time_partition_interval,
)
def _create_tables(self) -> None:
self._sync_client.create_tables()
def _node_to_row(self, node: BaseNode) -> Any:
metadata = node_to_metadata_dict(
node,
remove_text=True,
flat_metadata=self.flat_metadata,
)
# reuse the node id in the common case
id = node.node_id
if self.time_partition_interval is not None:
# for time partitioned tables, the id must be a UUID v1,
# so generate one if it's not already set
try:
# Attempt to parse the UUID from the string
parsed_uuid = uuid.UUID(id)
if parsed_uuid.version != 1:
id = str(uuid.uuid1())
except ValueError:
id = str(uuid.uuid1())
return [
id,
metadata,
node.get_content(metadata_mode=MetadataMode.NONE),
node.embedding,
]
def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
rows_to_insert = [self._node_to_row(node) for node in nodes]
ids = [result[0] for result in rows_to_insert]
self._sync_client.upsert(rows_to_insert)
return ids
async def async_add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
rows_to_insert = [self._node_to_row(node) for node in nodes]
ids = [result.node_id for result in nodes]
await self._async_client.upsert(rows_to_insert)
return ids
def _filter_to_dict(
self, metadata_filters: Optional[MetadataFilters]
) -> Optional[Dict[str, str]]:
if metadata_filters is None or len(metadata_filters.legacy_filters()) <= 0:
return None
res = {}
for filter in metadata_filters.legacy_filters():
res[filter.key] = filter.value
return res
def _db_rows_to_query_result(self, rows: List) -> VectorStoreQueryResult:
nodes = []
similarities = []
ids = []
for row in rows:
try:
node = metadata_dict_to_node(row[client.SEARCH_RESULT_METADATA_IDX])
node.set_content(str(row[client.SEARCH_RESULT_CONTENTS_IDX]))
except Exception:
# NOTE: deprecated legacy logic for backward compatibility
node = TextNode(
id_=row[client.SEARCH_RESULT_ID_IDX],
text=row[client.SEARCH_RESULT_CONTENTS_IDX],
metadata=row[client.SEARCH_RESULT_METADATA_IDX],
)
similarities.append(row[client.SEARCH_RESULT_DISTANCE_IDX])
ids.append(row[client.SEARCH_RESULT_ID_IDX])
nodes.append(node)
return VectorStoreQueryResult(
nodes=nodes,
similarities=similarities,
ids=ids,
)
def date_to_range_filter(self, **kwargs: Any) -> Any:
constructor_args = {
key: kwargs[key]
for key in [
"start_date",
"end_date",
"time_delta",
"start_inclusive",
"end_inclusive",
]
if key in kwargs
}
if not constructor_args or len(constructor_args) == 0:
return None
return client.UUIDTimeRange(**constructor_args)
def _query_with_score(
self,
embedding: Optional[List[float]],
limit: int = 10,
metadata_filters: Optional[MetadataFilters] = None,
**kwargs: Any,
) -> VectorStoreQueryResult:
filter = self._filter_to_dict(metadata_filters)
res = self._sync_client.search(
embedding,
limit,
filter,
uuid_time_filter=self.date_to_range_filter(**kwargs),
)
return self._db_rows_to_query_result(res)
async def _aquery_with_score(
self,
embedding: Optional[List[float]],
limit: int = 10,
metadata_filters: Optional[MetadataFilters] = None,
**kwargs: Any,
) -> VectorStoreQueryResult:
filter = self._filter_to_dict(metadata_filters)
res = await self._async_client.search(
embedding,
limit,
filter,
uuid_time_filter=self.date_to_range_filter(**kwargs),
)
return self._db_rows_to_query_result(res)
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
return self._query_with_score(
query.query_embedding, query.similarity_top_k, query.filters, **kwargs
)
async def aquery(
self, query: VectorStoreQuery, **kwargs: Any
) -> VectorStoreQueryResult:
return await self._aquery_with_score(
query.query_embedding,
query.similarity_top_k,
query.filters,
**kwargs,
)
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
filter: Dict[str, str] = {"doc_id": ref_doc_id}
self._sync_client.delete_by_metadata(filter)
DEFAULT_INDEX_TYPE: ClassVar = IndexType.TIMESCALE_VECTOR
def create_index(
self, index_type: IndexType = DEFAULT_INDEX_TYPE, **kwargs: Any
) -> None:
if index_type == IndexType.PGVECTOR_IVFFLAT:
self._sync_client.create_embedding_index(client.IvfflatIndex(**kwargs))
if index_type == IndexType.PGVECTOR_HNSW:
self._sync_client.create_embedding_index(client.HNSWIndex(**kwargs))
if index_type == IndexType.TIMESCALE_VECTOR:
self._sync_client.create_embedding_index(
client.TimescaleVectorIndex(**kwargs)
)
def drop_index(self) -> None:
self._sync_client.drop_embedding_index()
|
