Client
A module for writing and querying vectors to Postgres
_= load_dotenv(find_dotenv(), override=True)service_url= os.environ['TIMESCALE_SERVICE_URL']uuid_from_time
uuid_from_time (time_arg=None, node=None, clock_seq=None)
Converts a datetime or timestamp to a type 1uuid.UUID.
| Type | Default | Details | |
|---|---|---|---|
| time_arg | NoneType | None | The time to use for the timestamp portion of the UUID. This can either be a datetime object or a timestamp in seconds(as returned from time.time()). |
| node | NoneType | None | Bytes for the UUID (up to 48 bits). If not specified, this field is randomized. |
| clock_seq | NoneType | None | Clock sequence field for the UUID (up to 14 bits). If not specified, a random sequence is generated. |
| Returns | uuid.UUID: For the given time, node, and clock sequence |
Index Definitions
DiskAnnIndex
DiskAnnIndex (search_list_size:Optional[int]=None, num_neighbors:Optional[int]=None, max_alpha:Optional[float]=None, storage_layout:Optional[str]=None, num_dimensions:Optional[int]=None, num_bits_per_dimension:Optional[int]=None)
Timescale’s vector index.
HNSWIndex
HNSWIndex (m:Optional[int]=None, ef_construction:Optional[int]=None)
Pgvector’s hnsw index.
IvfflatIndex
IvfflatIndex (num_records:Optional[int]=None, num_lists:Optional[int]=None)
Pgvector’s ivfflat index.
BaseIndex
BaseIndex ()
Initialize self. See help(type(self)) for accurate signature.
Query Params
HNSWIndexParams
HNSWIndexParams (ef_search:int)
Initialize self. See help(type(self)) for accurate signature.
IvfflatIndexParams
IvfflatIndexParams (probes:int)
Initialize self. See help(type(self)) for accurate signature.
DiskAnnIndexParams
DiskAnnIndexParams (search_list_size:Optional[int]=None, rescore:Optional[int]=None)
Initialize self. See help(type(self)) for accurate signature.
QueryParams
QueryParams (params:dict[str,typing.Any])
Initialize self. See help(type(self)) for accurate signature.
Query Builder
UUIDTimeRange
UUIDTimeRange (start_date:Union[datetime.datetime,str,NoneType]=None, end_date:Union[datetime.datetime,str,NoneType]=None, time_delta:Optional[datetime.timedelta]=None, start_inclusive=True, end_inclusive=False)
*A UUIDTimeRange is a time range predicate on the UUID Version 1 timestamps.
Note that naive datetime objects are interpreted as local time on the python client side and converted to UTC before being sent to the database.*
Predicates
Predicates (*clauses:Union[ForwardRef('Predicates'),Tuple[str,Union[str,i nt,float,datetime.datetime,list,tuple]],Tuple[str,str,Union[s tr,int,float,datetime.datetime,list,tuple]],str,int,float,dat etime.datetime,list,tuple], operator:str='AND')
Predicates class defines predicates on the object metadata. Predicates can be combined using logical operators (&, |, and ~).
| Type | Default | Details | |
|---|---|---|---|
| clauses | Union | Predicate clauses. Can be either another Predicates object or a tuple of the form (field, operator, value) or (field, value). | |
| operator | str | AND |
QueryBuilder
QueryBuilder (table_name:str, num_dimensions:int, distance_type:str, id_type:str, time_partition_interval:Optional[datetime.timedelta], infer_filters:bool, schema_name:Optional[str])
Initializes a base Vector object to generate queries for vector clients.
| Type | Details | |
|---|---|---|
| table_name | str | The name of the table. |
| num_dimensions | int | The number of dimensions for the embedding vector. |
| distance_type | str | The distance type for indexing. |
| id_type | str | The type of the id column. Can be either ‘UUID’ or ‘TEXT’. |
| time_partition_interval | Optional | The time interval for partitioning the table (optional). |
| infer_filters | bool | Whether to infer start and end times from the special __start_date and __end_date filters. |
| schema_name | Optional | The schema name for the table (optional, uses the database’s default schema if not specified). |
| Returns | None |
QueryBuilder.get_create_query
QueryBuilder.get_create_query ()
Generates a query to create the tables, indexes, and extensions needed to store the vector data.
Async Client
Async
Async (service_url:str, table_name:str, num_dimensions:int, distance_type:str='cosine', id_type='UUID', time_partition_interval:Optional[datetime.timedelta]=None, max_db_connections:Optional[int]=None, infer_filters:bool=True, schema_name:Optional[str]=None)
Initializes a async client for storing vector data.
| Type | Default | Details | |
|---|---|---|---|
| service_url | str | The connection string for the database. | |
| table_name | str | The name of the table. | |
| num_dimensions | int | The number of dimensions for the embedding vector. | |
| distance_type | str | cosine | The distance type for indexing. |
| id_type | str | UUID | The type of the id column. Can be either ‘UUID’ or ‘TEXT’. |
| time_partition_interval | Optional | None | The time interval for partitioning the table (optional). |
| max_db_connections | Optional | None | |
| infer_filters | bool | True | Whether to infer start and end times from the special __start_date and __end_date filters. |
| schema_name | Optional | None | The schema name for the table (optional, uses the database’s default schema if not specified). |
| Returns | None |
Async.create_tables
Async.create_tables ()
Creates necessary tables.
Async.create_tables
Async.create_tables ()
Creates necessary tables.
Async.search
Async.search (query_embedding:Optional[List[float]]=None, limit:int=10, filter:Union[Dict[str,str],List[Dict[str,str]],NoneType]=No ne, predicates:Optional[__main__.Predicates]=None, uuid_time_filter:Optional[__main__.UUIDTimeRange]=None, query_params:Optional[__main__.QueryParams]=None)
Retrieves similar records using a similarity query.
| Type | Default | Details | |
|---|---|---|---|
| query_embedding | Optional | None | The query embedding vector. |
| limit | int | 10 | The number of nearest neighbors to retrieve. |
| filter | Union | None | A filter for metadata. Should be specified as a key-value object or a list of key-value objects (where any objects in the list are matched). |
| predicates | Optional | None | A Predicates object to filter the results. Predicates support more complex queries than the filter parameter. Predicates can be combined using logical operators (&, |, and ~). |
| uuid_time_filter | Optional | None | A UUIDTimeRange object to filter the results by time using the id column. |
| query_params | Optional | None | |
| Returns | List: List of similar records. |
Usage Example
for schemain ["tschema",None]: vec= Async(service_url,"data_table",2, schema_name=schema)await vec.create_tables() empty=await vec.table_is_empty()assert emptyawait vec.upsert([(uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2])]) empty=await vec.table_is_empty()assertnot emptyawait vec.upsert([ (uuid.uuid4(),'''{"key":"val"}''',"the brown fox", [1.0,1.3]), (uuid.uuid4(),'''{"key":"val2", "key_10": "10", "key_11": "11.3"}''',"the brown fox", [1.0,1.4]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.5]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.6]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.6]), (uuid.uuid4(),'''{"key2":"val2"}''',"the brown fox", [1.0,1.7]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.9]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,100.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,101.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key_1":"val_1", "key_2":"val_2"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key0": [1,2,3,4]}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key0": [8,9,"A"]}''',"the brown fox", [1.0,1.8]),# mixed types (uuid.uuid4(),'''{"key0": [5,6,7], "key3": 3}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key0": ["B", "C"]}''',"the brown fox", [1.0,1.8]), ])await vec.create_embedding_index(IvfflatIndex())await vec.drop_embedding_index()await vec.create_embedding_index(IvfflatIndex(100))await vec.drop_embedding_index()await vec.create_embedding_index(HNSWIndex())await vec.drop_embedding_index()await vec.create_embedding_index(HNSWIndex(20,125))await vec.drop_embedding_index()await vec.create_embedding_index(DiskAnnIndex())await vec.drop_embedding_index()await vec.create_embedding_index(DiskAnnIndex(50,50,1.5,"memory_optimized",2,1)) rec=await vec.search([1.0,2.0])assertlen(rec)==10 rec=await vec.search([1.0,2.0], limit=4)assertlen(rec)==4 rec=await vec.search(limit=4)assertlen(rec)==4 rec=await vec.search([1.0,2.0], limit=4,filter={"key2":"val2"})assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4,filter={"key2":"does not exist"})assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4,filter={"key_1":"val_1"})assertlen(rec)==1 rec=await vec.search([1.0,2.0],filter={"key_1":"val_1","key_2":"val_2"})assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4,filter={"key_1":"val_1","key_2":"val_3"})assertlen(rec)==0 rec=await vec.search(limit=4,filter={"key_1":"val_1","key_2":"val_3"})assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==2 rec=await vec.search(limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==2 rec=await vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}, {"no such key":"no such val"}])assertlen(rec)==2assertisinstance(rec[0][SEARCH_RESULT_METADATA_IDX],dict)assertisinstance(rec[0]["metadata"],dict)assert rec[0]["contents"]=="the brown fox" rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(("key","val2")))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(("key","==","val2")))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key","==","val2"))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<",100))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<",10))assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<=",10))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<=",10.0))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_11","<=",11.3))assertlen(rec)==1 rec=await vec.search(limit=4, predicates=Predicates("key_11",">=",11.29999))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_11","<",11.299999))assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [1,2]))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [3,7]))assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [42]))assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [4]))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [9,"A"]))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", ["A"]))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", ("C","B")))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(*[("key","val2"), ("key_10","<",100)]))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(("key","val2"), ("key_10","<",100), operator='AND'))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(("key","val2"), ("key_2","val_2"), operator='OR'))assertlen(rec)==2 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<",100)& (Predicates("key","==","val2",)| Predicates("key_2","==","val_2")))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key_10","<",100)and (Predicates("key","==","val2")or Predicates("key_2","==","val_2")))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [6,7])and Predicates("key3","==",3))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates("key0","@>", [6,7])and Predicates("key3","==",6))assertlen(rec)==0 rec=await vec.search(limit=4, predicates=~Predicates(("key","val2"), ("key_10","<",100)))assertlen(rec)==4 raised=Falsetry:# can't upsert using both keys and dictionariesawait vec.upsert([ (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.2]) ])exceptValueErroras e: raised=Trueassert raised raised=Falsetry:# can't upsert using both keys and dictionaries opposite orderawait vec.upsert([ (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.2]), (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]) ])exceptBaseExceptionas e: raised=Trueassert raised rec=await vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==2await vec.delete_by_ids([rec[0]["id"]]) rec=await vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==1await vec.delete_by_metadata([{"key_1":"val_1"}, {"key2":"val2"}]) rec=await vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4,filter=[{"key2":"val"}])assertlen(rec)==4await vec.delete_by_metadata([{"key2":"val"}]) rec=await vec.search([1.0,2.0], limit=4,filter=[{"key2":"val"}])assertlen(rec)==0assertnotawait vec.table_is_empty()await vec.delete_all()assertawait vec.table_is_empty()await vec.drop_table()await vec.close() vec= Async(service_url,"data_table",2, id_type="TEXT")await vec.create_tables() empty=await vec.table_is_empty()assert emptyawait vec.upsert([("Not a valid UUID", {"key":"val"},"the brown fox", [1.0,1.2])]) empty=await vec.table_is_empty()assertnot emptyawait vec.delete_by_ids(["Not a valid UUID"]) empty=await vec.table_is_empty()assert emptyawait vec.drop_table()await vec.close() vec= Async(service_url,"data_table",2, time_partition_interval=timedelta(seconds=60))await vec.create_tables() empty=await vec.table_is_empty()assert emptyid= uuid.uuid1()await vec.upsert([(id, {"key":"val"},"the brown fox", [1.0,1.2])]) empty=await vec.table_is_empty()assertnot emptyawait vec.delete_by_ids([id]) empty=await vec.table_is_empty()assert empty raised=Falsetry:# can't upsert with uuid type 4 in time partitioned tableawait vec.upsert([ (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]) ])exceptBaseExceptionas e: raised=Trueassert raised specific_datetime= datetime(2018,8,10,15,30,0)await vec.upsert([# current time (uuid.uuid1(), {"key":"val"},"the brown fox", [1.0,1.2]),#time in 2018 (uuid_from_time(specific_datetime), {"key":"val"},"the brown fox", [1.0,1.2]) ])assertnotawait vec.table_is_empty()#check all the possible ways to specify a date rangeasyncdef search_date(start_date, end_date, expected):#using uuid_time_filter rec=await vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(start_date, end_date))assertlen(rec)== expected rec=await vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(str(start_date),str(end_date)))assertlen(rec)== expected#using filtersfilter= {}if start_dateisnotNone:filter["__start_date"]= start_dateif end_dateisnotNone:filter["__end_date"]= end_date rec=await vec.search([1.0,2.0], limit=4,filter=filter)assertlen(rec)== expected#using filters with string datesfilter= {}if start_dateisnotNone:filter["__start_date"]=str(start_date)if end_dateisnotNone:filter["__end_date"]=str(end_date) rec=await vec.search([1.0,2.0], limit=4,filter=filter)assertlen(rec)== expected#using predicates predicates= []if start_dateisnotNone: predicates.append(("__uuid_timestamp",">=", start_date))if end_dateisnotNone: predicates.append(("__uuid_timestamp","<", end_date)) rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(*predicates))assertlen(rec)== expected#using predicates with string dates predicates= []if start_dateisnotNone: predicates.append(("__uuid_timestamp",">=",str(start_date)))if end_dateisnotNone: predicates.append(("__uuid_timestamp","<",str(end_date))) rec=await vec.search([1.0,2.0], limit=4, predicates=Predicates(*predicates))assertlen(rec)== expectedawait search_date(specific_datetime-timedelta(days=7), specific_datetime+timedelta(days=7),1)await search_date(specific_datetime-timedelta(days=7),None,2)await search_date(None, specific_datetime+timedelta(days=7),1)await search_date(specific_datetime-timedelta(days=7), specific_datetime-timedelta(days=2),0)#check timedelta handling rec=await vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(start_date=specific_datetime, time_delta=timedelta(days=7)))assertlen(rec)==1#end is exclusive rec=await vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(end_date=specific_datetime, time_delta=timedelta(days=7)))assertlen(rec)==0 rec=await vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(end_date=specific_datetime+timedelta(seconds=1), time_delta=timedelta(days=7)))assertlen(rec)==1 rec=await vec.search([1.0,2.0], limit=4, query_params=DiskAnnIndexParams(10,5))assertlen(rec)==2 rec=await vec.search([1.0,2.0], limit=4, query_params=DiskAnnIndexParams(100))assertlen(rec)==2await vec.drop_table()await vec.close()Sync Client
Sync
Sync (service_url:str, table_name:str, num_dimensions:int, distance_type:str='cosine', id_type='UUID', time_partition_interval:Optional[datetime.timedelta]=None, max_db_connections:Optional[int]=None, infer_filters:bool=True, schema_name:Optional[str]=None)
Initializes a sync client for storing vector data.
| Type | Default | Details | |
|---|---|---|---|
| service_url | str | The connection string for the database. | |
| table_name | str | The name of the table. | |
| num_dimensions | int | The number of dimensions for the embedding vector. | |
| distance_type | str | cosine | The distance type for indexing. |
| id_type | str | UUID | The type of the primary id column. Can be either ‘UUID’ or ‘TEXT’. |
| time_partition_interval | Optional | None | The time interval for partitioning the table (optional). |
| max_db_connections | Optional | None | |
| infer_filters | bool | True | Whether to infer start and end times from the special __start_date and __end_date filters. |
| schema_name | Optional | None | The schema name for the table (optional, uses the database’s default schema if not specified). |
| Returns | None |
Sync.create_tables
Sync.create_tables ()
Creates necessary tables.
Sync.upsert
Sync.upsert (records)
Performs upsert operation for multiple records.
| Type | Details | |
|---|---|---|
| records | Records to upsert. | |
| Returns | None |
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/fastcore/docscrape.py:230: UserWarning: potentially wrong underline length... Returns -------- in Retrieves similar records using a similarity query.... else: warn(msg)Sync.search
Sync.search (query_embedding:Optional[List[float]]=None, limit:int=10, filter:Union[Dict[str,str],List[Dict[str,str]],NoneType]=Non e, predicates:Optional[__main__.Predicates]=None, uuid_time_filter:Optional[__main__.UUIDTimeRange]=None, query_params:Optional[__main__.QueryParams]=None)
Retrieves similar records using a similarity query.
| Type | Default | Details | |
|---|---|---|---|
| query_embedding | Optional | None | The query embedding vector. |
| limit | int | 10 | The number of nearest neighbors to retrieve. |
| filter | Union | None | A filter for metadata. Should be specified as a key-value object or a list of key-value objects (where any objects in the list are matched). |
| predicates | Optional | None | A Predicates object to filter the results. Predicates support more complex queries than the filter parameter. Predicates can be combined using logical operators (&, |, and ~). |
| uuid_time_filter | Optional | None | |
| query_params | Optional | None | |
| Returns | List: List of similar records. |
Usage Example:
for schemain [None,"tschema"]: vec= Sync(service_url,"data_table",2, schema_name=schema) vec.create_tables() empty= vec.table_is_empty()assert empty vec.upsert([(uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2])]) empty= vec.table_is_empty()assertnot empty vec.upsert([ (uuid.uuid4(),'''{"key":"val"}''',"the brown fox", [1.0,1.3]), (uuid.uuid4(),'''{"key":"val2"}''',"the brown fox", [1.0,1.4]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.5]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.6]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.6]), (uuid.uuid4(),'''{"key2":"val2"}''',"the brown fox", [1.0,1.7]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.9]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,100.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,101.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key_1":"val_1", "key_2":"val_2"}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key0": [1,2,3,4]}''',"the brown fox", [1.0,1.8]), (uuid.uuid4(),'''{"key0": [5,6,7], "key3": 3}''',"the brown fox", [1.0,1.8]), ]) vec.create_embedding_index(IvfflatIndex()) vec.drop_embedding_index() vec.create_embedding_index(IvfflatIndex(100)) vec.drop_embedding_index() vec.create_embedding_index(HNSWIndex()) vec.drop_embedding_index() vec.create_embedding_index(HNSWIndex(20,125)) vec.drop_embedding_index() vec.create_embedding_index(DiskAnnIndex()) vec.drop_embedding_index() vec.create_embedding_index(DiskAnnIndex(50,50,1.5)) rec= vec.search([1.0,2.0])assertlen(rec)==10 rec= vec.search(np.array([1.0,2.0]))assertlen(rec)==10 rec= vec.search([1.0,2.0], limit=4)assertlen(rec)==4 rec= vec.search(limit=4)assertlen(rec)==4 rec= vec.search([1.0,2.0], limit=4,filter={"key2":"val2"})assertlen(rec)==1 rec= vec.search([1.0,2.0], limit=4,filter={"key2":"does not exist"})assertlen(rec)==0 rec= vec.search(limit=4,filter={"key2":"does not exist"})assertlen(rec)==0 rec= vec.search([1.0,2.0], limit=4,filter={"key_1":"val_1"})assertlen(rec)==1 rec= vec.search([1.0,2.0],filter={"key_1":"val_1","key_2":"val_2"})assertlen(rec)==1 rec= vec.search([1.0,2.0], limit=4,filter={"key_1":"val_1","key_2":"val_3"})assertlen(rec)==0 rec= vec.search([1.0,2.0], limit=4,filter=[ {"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==2 rec= vec.search([1.0,2.0], limit=4,filter=[{"key_1":"val_1"}, {"key2":"val2"}, {"no such key":"no such val"}])assertlen(rec)==2 raised=Falsetry:# can't upsert using both keys and dictionariesawait vec.upsert([ (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]), (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.2]) ])exceptValueErroras e: raised=Trueassert raised raised=Falsetry:# can't upsert using both keys and dictionaries opposite orderawait vec.upsert([ (uuid.uuid4(),'''{"key2":"val"}''',"the brown fox", [1.0,1.2]), (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]) ])exceptBaseExceptionas e: raised=Trueassert raised rec= vec.search([1.0,2.0],filter={"key_1":"val_1","key_2":"val_2"})assert rec[0][SEARCH_RESULT_CONTENTS_IDX]=='the brown fox'assert rec[0]["contents"]=='the brown fox'assert rec[0][SEARCH_RESULT_METADATA_IDX]== {'key_1':'val_1','key_2':'val_2'}assert rec[0]["metadata"]== {'key_1':'val_1','key_2':'val_2'}assertisinstance(rec[0][SEARCH_RESULT_METADATA_IDX],dict)assert rec[0][SEARCH_RESULT_DISTANCE_IDX]==0.0009438353921149556assert rec[0]["distance"]==0.0009438353921149556 rec= vec.search([1.0,2.0], limit=4, predicates=Predicates("key","==","val2"))assertlen(rec)==1 rec= vec.search([1.0,2.0], limit=4,filter=[ {"key_1":"val_1"}, {"key2":"val2"}])len(rec)==2 vec.delete_by_ids([rec[0][SEARCH_RESULT_ID_IDX]]) rec= vec.search([1.0,2.0], limit=4,filter=[ {"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==1 vec.delete_by_metadata([{"key_1":"val_1"}, {"key2":"val2"}]) rec= vec.search([1.0,2.0], limit=4,filter=[ {"key_1":"val_1"}, {"key2":"val2"}])assertlen(rec)==0 rec= vec.search([1.0,2.0], limit=4,filter=[{"key2":"val"}])assertlen(rec)==4 vec.delete_by_metadata([{"key2":"val"}]) rec= vec.search([1.0,2.0], limit=4,filter=[{"key2":"val"}])len(rec)==0assertnot vec.table_is_empty() vec.delete_all()assert vec.table_is_empty() vec.drop_table() vec.close() vec= Sync(service_url,"data_table",2, id_type="TEXT", schema_name=schema) vec.create_tables()assert vec.table_is_empty() vec.upsert([("Not a valid UUID", {"key":"val"},"the brown fox", [1.0,1.2])])assertnot vec.table_is_empty() vec.delete_by_ids(["Not a valid UUID"])assert vec.table_is_empty() vec.drop_table() vec.close() vec= Sync(service_url,"data_table",2, time_partition_interval=timedelta(seconds=60), schema_name=schema) vec.create_tables()assert vec.table_is_empty()id= uuid.uuid1() vec.upsert([(id, {"key":"val"},"the brown fox", [1.0,1.2])])assertnot vec.table_is_empty() vec.delete_by_ids([id])assert vec.table_is_empty() raised=Falsetry:# can't upsert with uuid type 4 in time partitioned table vec.upsert([ (uuid.uuid4(), {"key":"val"},"the brown fox", [1.0,1.2]) ])#passexceptBaseExceptionas e: raised=Trueassert raised specific_datetime= datetime(2018,8,10,15,30,0) vec.upsert([# current time (uuid.uuid1(), {"key":"val"},"the brown fox", [1.0,1.2]),#time in 2018 (uuid_from_time(specific_datetime), {"key":"val"},"the brown fox", [1.0,1.2]) ])def search_date(start_date, end_date, expected):#using uuid_time_filter rec= vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(start_date, end_date))assertlen(rec)== expected rec= vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(str(start_date),str(end_date)))assertlen(rec)== expected#using filtersfilter= {}if start_dateisnotNone:filter["__start_date"]= start_dateif end_dateisnotNone:filter["__end_date"]= end_date rec= vec.search([1.0,2.0], limit=4,filter=filter)assertlen(rec)== expected#using filters with string datesfilter= {}if start_dateisnotNone:filter["__start_date"]=str(start_date)if end_dateisnotNone:filter["__end_date"]=str(end_date) rec= vec.search([1.0,2.0], limit=4,filter=filter)assertlen(rec)== expected#using predicates predicates= []if start_dateisnotNone: predicates.append(("__uuid_timestamp",">=", start_date))if end_dateisnotNone: predicates.append(("__uuid_timestamp","<", end_date)) rec= vec.search([1.0,2.0], limit=4, predicates=Predicates(*predicates))assertlen(rec)== expected#using predicates with string dates predicates= []if start_dateisnotNone: predicates.append(("__uuid_timestamp",">=",str(start_date)))if end_dateisnotNone: predicates.append(("__uuid_timestamp","<",str(end_date))) rec= vec.search([1.0,2.0], limit=4, predicates=Predicates(*predicates))assertlen(rec)== expectedassertnot vec.table_is_empty() search_date(specific_datetime-timedelta(days=7), specific_datetime+timedelta(days=7),1) search_date(specific_datetime-timedelta(days=7),None,2) search_date(None, specific_datetime+timedelta(days=7),1) search_date(specific_datetime-timedelta(days=7), specific_datetime-timedelta(days=2),0)#check timedelta handling rec= vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(start_date=specific_datetime, time_delta=timedelta(days=7)))assertlen(rec)==1#end is exclusive rec= vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(end_date=specific_datetime, time_delta=timedelta(days=7)))assertlen(rec)==0 rec= vec.search([1.0,2.0], limit=4, uuid_time_filter=UUIDTimeRange(end_date=specific_datetime+timedelta(seconds=1), time_delta=timedelta(days=7)))assertlen(rec)==1 rec= vec.search([1.0,2.0], limit=4, query_params=DiskAnnIndexParams(10,5))assertlen(rec)==2 rec= vec.search([1.0,2.0], limit=4, query_params=DiskAnnIndexParams(100, rescore=2))assertlen(rec)==2 vec.drop_table() vec.close()