mirror of
https://github.com/placeholder-soft/chroma.git
synced 2026-01-12 22:44:55 +08:00
69 lines
4.4 KiB
Python
69 lines
4.4 KiB
Python
import pandas as pd
|
|
from chroma_client import Chroma
|
|
|
|
chroma = Chroma()
|
|
chroma.reset()
|
|
chroma.set_model_space("sample_1_1")
|
|
|
|
print(chroma.heartbeat())
|
|
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/images/1"], ["training"], ["spoon"])
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/images/2"], ["training"], ["spoon"])
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/images/3"], ["training"], ["spoon"])
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/images/1"], ["training"], ["knife"])
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/images/4"], ["training"], ["knife"])
|
|
chroma.add([[1, 2, 3, 4, 5]], ["/prod/2"], ["test"], ["knife"])
|
|
|
|
process_task = chroma.process()
|
|
print(process_task)
|
|
|
|
print(chroma.get_task_status(process_task["task_id"]))
|
|
|
|
print("sleeping for 10s to wait for task to complete")
|
|
import time
|
|
|
|
time.sleep(10)
|
|
print(chroma.get_task_status(process_task["task_id"]))
|
|
print(chroma.get_results())
|
|
|
|
# print(chroma.raw_sql("SELECT * FROM results WHERE space_key = 'yolov3_1_1'"))
|
|
# knife_embedding = [0.2310010939836502, -0.3462161719799042, 0.29164767265319824, -0.09828940033912659, 1.814868450164795, -10.517369270324707, -13.531850814819336, -12.730537414550781, -13.011675834655762, -10.257010459899902, -13.779699325561523, -11.963963508605957, -13.948140144348145, -12.46799087524414, -14.569470405578613, -16.388280868530273, -13.76762580871582, -12.192169189453125, -12.204055786132812, -12.259000778198242, -13.696036338806152, -14.609177589416504, -16.951879501342773, -17.096384048461914, -14.355693817138672, -16.643482208251953, -14.270745277404785, -14.375198364257812, -14.381218910217285, -13.475995063781738, -12.694938659667969, -10.011992454528809, -9.770626068115234, -13.155019760131836, -16.136341094970703, -6.552414417266846, -11.243837356567383, -16.678457260131836, -14.629229545593262, -10.052337646484375, -15.451828956604004, -12.561151504516602, -11.68396282196045, -11.975972175598145, -11.09926986694336, -13.060500144958496, -12.075592994689941, -1.0808746814727783, 1.7046797275543213, -3.8080708980560303, -11.401922225952148, -12.184720039367676, -13.262567520141602, -11.299583435058594, -13.654638290405273, -10.767330169677734, -9.012763977050781, -10.202326774597168, -10.088111877441406, -13.247991561889648, -9.651527404785156, -11.903244972229004, -13.922954559326172, -17.37179946899414, -12.51513385772705, -7.8046746253967285, -14.406414985656738, -13.172696113586426, -11.194984436035156, -12.029500961303711, -10.996524810791016, -10.828441619873047, -8.673471450805664, -13.800869941711426, -9.680946350097656, -12.964024543762207, -9.694372177124023, -13.132003784179688, -9.38864803314209, -14.305071830749512, -14.4693603515625, -5.0566205978393555, -15.685358047485352, -12.493011474609375, -8.424881935119629]
|
|
|
|
# get_nearest_neighbors = chroma.get_nearest_neighbors(knife_embedding, 4, None, "training")
|
|
# res_df = pd.DataFrame(get_nearest_neighbors['embeddings'])
|
|
# print(res_df.head())
|
|
# chroma.set_context(app="yolov3", model_version="5", layer="1")
|
|
|
|
# chroma.add([[1,2,3,4,5]], ["/images/1"], ["training"], ['spoon'])
|
|
# chroma.add([[1,2,3,4,5]], ["/images/2"], ["training"], ['spoon'])
|
|
# chroma.add([[1,2,3,4,5]], ["/images/3"], ["training"], ['spoon'])
|
|
|
|
# chroma.set_context("test", "1", "55")
|
|
# chroma.add([[1,2,3,4,5]], ["/images/1"], ["training"], ['knife'])
|
|
# chroma.add([[1,2,3,4,5]], ["/images/4"], ["training"], ['knife'])
|
|
# chroma.add([[1,2,3,4,5]], ["/prod/2"], ["test"], ['knife'])
|
|
|
|
# print(chroma.raw_sql('SELECT DISTINCT space_key FROM embeddings;'))
|
|
# chroma.add([[1,2,3,4,5]], ["/images/4"], ["training"], ['spoon'])
|
|
# chroma.add([[1,2,3,4,5]], ["/prod/1"], ["test"], ['spoon'])
|
|
# chroma.add([[1,2,3,4,5]], ["/prod/2"], ["test"], ['spoon'])
|
|
# # print("context", chroma.get_context())
|
|
# print("context", chroma.heartbeat())
|
|
# print("layer 1", chroma.count(chroma.get_context()))
|
|
# print("fetch", chroma.fetch())
|
|
# print(chroma.process())
|
|
# print(chroma.get_nearest_neighbors([1,2,3,4,5], 2))
|
|
|
|
# chroma.set_context("test", "1", "2")
|
|
# chroma.add([[1,2,3,4,5]], ["/images/1"], ["training"], ['knife'])
|
|
# chroma.add([[1,2,3,4,5]], ["/images/4"], ["training"], ['knife'])
|
|
# chroma.add([[1,2,3,4,5]], ["/prod/2"], ["test"], ['knife'])
|
|
# print("context", chroma.get_context())
|
|
# print("context", chroma.heartbeat())
|
|
# print("layer 1", chroma.count(chroma.get_context()))
|
|
# # print("fetch", chroma.fetch())
|
|
# print(chroma.process())
|
|
# print(chroma.get_nearest_neighbors([1,2,3,4,5], 2))
|
|
|
|
# print(chroma.get_nearest_neighbors([1,2,3,4,5], 2, space_key="yolov3_5_1"))
|