Coverage for mindsdb / integrations / handlers / pinecone_handler / connection_args.py: 0%

1from collections import OrderedDict 

2 

3from mindsdb.integrations.libs.const import HANDLER_CONNECTION_ARG_TYPE as ARG_TYPE 

4 

5 

6connection_args = OrderedDict( 

7 api_key={ 

8 "type": ARG_TYPE.STR, 

9 "description": "The API key that can be found in your pinecone account", 

10 "required": True, 

11 "secret": True 

12 }, 

13 environment={ 

14 "type": ARG_TYPE.STR, 

15 "description": "The environment name corresponding to the `api_key`", 

16 "required": True, 

17 }, 

18 dimension={ 

19 "type": ARG_TYPE.INT, 

20 "description": "dimensions of the vectors to be stored in the index (default=8)", 

21 "required": False, 

22 }, 

23 metric={ 

24 "type": ARG_TYPE.STR, 

25 "description": "distance metric to be used for similarity search (default='cosine')", 

26 "required": False, 

27 }, 

28 pods={ 

29 "type": ARG_TYPE.INT, 

30 "description": "number of pods for the index to use, including replicas (default=1)", 

31 "required": False, 

32 }, 

33 replicas={ 

34 "type": ARG_TYPE.INT, 

35 "description": "the number of replicas. replicas duplicate your index. they provide higher availability and throughput (default=1)", 

36 "required": False, 

37 }, 

38 pod_type={ 

39 "type": ARG_TYPE.STR, 

40 "description": "the type of pod to use, refer to pinecone documentation (default='p1')", 

41 "required": False, 

42 }, 

43) 

44 

45connection_args_example = OrderedDict( 

46 api_key="00000000-0000-0000-0000-000000000000", 

47 environment="gcp-starter", 

48 dimension=8, 

49 metric="cosine", 

50 pods=1, 

51 replicas=1, 

52 pod_type='p1', 

53)