Package: recvani
python-recvaniThis contains the client side api for use with recvani server. Recvani is collabrative learning platform for any bussiness  where a user and item interact. So client can send score for user interactions and ask for recommendation for user. And all these things in real time.Check us out  athttps://www.recvani.comFeaturesEase to integration:Integrating is as simple as integrating a database.Real time system:User interaction are taken care within milliseconds for recommendationCan Scale very easily:It can easily support upto millions of users.Low on cost:Cost for deploying will be much less than your inhouse machine learning cost.Support categorization:Client can easily ask for specific category of item for a user.InstallationWe can directly install python client using pippip install recvaniExamplesClient need model name, client key and model key for connecting to recvani server. You can get these by contacting us.Creating connectionSetting up connection is quite easy.from recvani.rv_client import rv_client
#Don't forget to replace these by the keys you will get.
client = rv_client(CLIENT_KEY, MODEL_NAME, MODEL_KEY)Sending InteracationsSend Single Interactionfrom recvani.rv_requests import simple_interaction
import time

USER = "USER1"           # unique id for user
ITEM = "ITEM1"           # item id (item can be news id, video id, product id etc.)
SCORE = 1.0              # score client want to give for interaction. Should be devised intelligently
ITIME = int(time.time()) # time for the interaction in seconds

interaction = simple_interaction(USER, ITEM, SCORE, ITIME)

result = client.send(interaction)  #result will 1 on sucess otherwise exception will be thrown. Better to catch it.Send Batch Interactionsfrom recvani.rv_requests import batch_interaction, simple_interaction
import time

bi = []
bi.append(simple_interaction("USER1", "ITEM1", 0.0, int(time.time())))
bi.append(simple_interaction("USER2", "ITEM1", 1.0, int(time.time())))
bi.append(simple_interaction("USER1", "ITEM2", 0.0, int(time.time())))

bis = batch_interaction(bi)

result = client.send(bis)Sending ParametersWe can attach tags and expiry time for every itemSend expiry timefrom recvani.rv_requests import exp_request
import time

EXP_TIME = time.time() + 30*24*3600 # The time you want to expire the item. 
rexp = exp_request("ITEM1", EXP_TIME)
result = client.send(rexp)Send tagsfrom recvani.rv_requests import tag_request
TAGS = ["TAG1", "TAG2"]
trequest = tag_request("STORY1", TAGS)
result = client.send(trequest)Send in bulkfrom recvani.rv_requests import batch_param, item_param
 import time

 param1 = item_param("ITEM1", int(time.time()) + 365*24*3600, ["TAG1"])
 param2 = item_param("ITEM2", None, ["TAG2"])
 param3 = item_param("ITEM3", exp_time = int(time.time()) + 365*24*3600)

 bparam = batch_param([param1, param2, param3])

 result = client.send(bparam)Getting ResultWe can get the final recommendation for the user. We can filter history and send you items for particular tag.Overall Recommendationfrom recvani.rv_requests import rec_request 

USER = "USER1"      # USER ID
COUNT = 10          # Count of Recommended item to fetch
TAGS = []           # Tags of item, Empty for overall 
HISTORY = rec_request.FULL_HISTORY_FILTER     # Will not serve already serverd item.

rc = rec_request(USER, COUNT, [], HISTORY)
result = client.send(rc) # Result will be list of itemsTagged Recommedationrc = rec_request(USER, COUNT, [["TAG1"]], HISTORY)
result = client.send(rc) # Will give 10 stories with "TAG1" attached to itComplex QueriesTags can be used to make complex queries. For ExampleTAGS = [["TAG1", "TAG2"], ["TAG4"]]The inner lists provide intersection and outer lists provide union. The tags above will return  all time which are either marked "TAG4" or have both "TAG1" and "TAG2"  attached to it.For any help feel free to contactanshuman@recvani.com