/
caching.py
2132 lines (1884 loc) · 79.3 KB
/
caching.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# +-----------------------------------------------+
# | |
# | Give Feedback / Get Help |
# | https://github.com/BerriAI/litellm/issues/new |
# | |
# +-----------------------------------------------+
#
# Thank you users! We ❤️ you! - Krrish & Ishaan
import litellm
import time, logging, asyncio
import json, traceback, ast, hashlib
from typing import Optional, Literal, List, Union, Any, BinaryIO
from openai._models import BaseModel as OpenAIObject
from litellm._logging import verbose_logger
from litellm.types.services import ServiceLoggerPayload, ServiceTypes
import traceback
def print_verbose(print_statement):
try:
verbose_logger.debug(print_statement)
if litellm.set_verbose:
print(print_statement) # noqa
except:
pass
class BaseCache:
def set_cache(self, key, value, **kwargs):
raise NotImplementedError
async def async_set_cache(self, key, value, **kwargs):
raise NotImplementedError
def get_cache(self, key, **kwargs):
raise NotImplementedError
async def async_get_cache(self, key, **kwargs):
raise NotImplementedError
async def batch_cache_write(self, result, *args, **kwargs):
raise NotImplementedError
async def disconnect(self):
raise NotImplementedError
class InMemoryCache(BaseCache):
def __init__(self):
# if users don't provider one, use the default litellm cache
self.cache_dict = {}
self.ttl_dict = {}
def set_cache(self, key, value, **kwargs):
print_verbose("InMemoryCache: set_cache")
self.cache_dict[key] = value
if "ttl" in kwargs:
self.ttl_dict[key] = time.time() + kwargs["ttl"]
async def async_set_cache(self, key, value, **kwargs):
self.set_cache(key=key, value=value, **kwargs)
async def async_set_cache_pipeline(self, cache_list, ttl=None):
for cache_key, cache_value in cache_list:
if ttl is not None:
self.set_cache(key=cache_key, value=cache_value, ttl=ttl)
else:
self.set_cache(key=cache_key, value=cache_value)
def get_cache(self, key, **kwargs):
if key in self.cache_dict:
if key in self.ttl_dict:
if time.time() > self.ttl_dict[key]:
self.cache_dict.pop(key, None)
return None
original_cached_response = self.cache_dict[key]
try:
cached_response = json.loads(original_cached_response)
except:
cached_response = original_cached_response
return cached_response
return None
def batch_get_cache(self, keys: list, **kwargs):
return_val = []
for k in keys:
val = self.get_cache(key=k, **kwargs)
return_val.append(val)
return return_val
def increment_cache(self, key, value: int, **kwargs) -> int:
# get the value
init_value = self.get_cache(key=key) or 0
value = init_value + value
self.set_cache(key, value, **kwargs)
return value
async def async_get_cache(self, key, **kwargs):
return self.get_cache(key=key, **kwargs)
async def async_batch_get_cache(self, keys: list, **kwargs):
return_val = []
for k in keys:
val = self.get_cache(key=k, **kwargs)
return_val.append(val)
return return_val
async def async_increment(self, key, value: float, **kwargs) -> float:
# get the value
init_value = await self.async_get_cache(key=key) or 0
value = init_value + value
await self.async_set_cache(key, value, **kwargs)
return value
def flush_cache(self):
self.cache_dict.clear()
self.ttl_dict.clear()
async def disconnect(self):
pass
def delete_cache(self, key):
self.cache_dict.pop(key, None)
self.ttl_dict.pop(key, None)
class RedisCache(BaseCache):
# if users don't provider one, use the default litellm cache
def __init__(
self,
host=None,
port=None,
password=None,
redis_flush_size=100,
namespace: Optional[str] = None,
**kwargs,
):
from ._redis import get_redis_client, get_redis_connection_pool
from litellm._service_logger import ServiceLogging
import redis
redis_kwargs = {}
if host is not None:
redis_kwargs["host"] = host
if port is not None:
redis_kwargs["port"] = port
if password is not None:
redis_kwargs["password"] = password
### HEALTH MONITORING OBJECT ###
if kwargs.get("service_logger_obj", None) is not None and isinstance(
kwargs["service_logger_obj"], ServiceLogging
):
self.service_logger_obj = kwargs.pop("service_logger_obj")
else:
self.service_logger_obj = ServiceLogging()
redis_kwargs.update(kwargs)
self.redis_client = get_redis_client(**redis_kwargs)
self.redis_kwargs = redis_kwargs
self.async_redis_conn_pool = get_redis_connection_pool(**redis_kwargs)
# redis namespaces
self.namespace = namespace
# for high traffic, we store the redis results in memory and then batch write to redis
self.redis_batch_writing_buffer: list = []
self.redis_flush_size = redis_flush_size
self.redis_version = "Unknown"
try:
self.redis_version = self.redis_client.info()["redis_version"]
except Exception as e:
pass
### ASYNC HEALTH PING ###
try:
# asyncio.get_running_loop().create_task(self.ping())
result = asyncio.get_running_loop().create_task(self.ping())
except Exception as e:
verbose_logger.error(
"Error connecting to Async Redis client", extra={"error": str(e)}
)
### SYNC HEALTH PING ###
try:
self.redis_client.ping()
except Exception as e:
verbose_logger.error(
"Error connecting to Sync Redis client", extra={"error": str(e)}
)
def init_async_client(self):
from ._redis import get_redis_async_client
return get_redis_async_client(
connection_pool=self.async_redis_conn_pool, **self.redis_kwargs
)
def check_and_fix_namespace(self, key: str) -> str:
"""
Make sure each key starts with the given namespace
"""
if self.namespace is not None and not key.startswith(self.namespace):
key = self.namespace + ":" + key
return key
def set_cache(self, key, value, **kwargs):
ttl = kwargs.get("ttl", None)
print_verbose(
f"Set Redis Cache: key: {key}\nValue {value}\nttl={ttl}, redis_version={self.redis_version}"
)
key = self.check_and_fix_namespace(key=key)
try:
self.redis_client.set(name=key, value=str(value), ex=ttl)
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
print_verbose(
f"LiteLLM Caching: set() - Got exception from REDIS : {str(e)}"
)
def increment_cache(self, key, value: int, **kwargs) -> int:
_redis_client = self.redis_client
start_time = time.time()
try:
result = _redis_client.incr(name=key, amount=value)
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="increment_cache",
)
)
return result
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="increment_cache",
)
)
verbose_logger.error(
"LiteLLM Redis Caching: increment_cache() - Got exception from REDIS %s, Writing value=%s",
str(e),
value,
)
traceback.print_exc()
raise e
async def async_scan_iter(self, pattern: str, count: int = 100) -> list:
start_time = time.time()
try:
keys = []
_redis_client = self.init_async_client()
async with _redis_client as redis_client:
async for key in redis_client.scan_iter(
match=pattern + "*", count=count
):
keys.append(key)
if len(keys) >= count:
break
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_scan_iter",
)
) # DO NOT SLOW DOWN CALL B/C OF THIS
return keys
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_scan_iter",
)
)
raise e
async def async_set_cache(self, key, value, **kwargs):
start_time = time.time()
try:
_redis_client = self.init_async_client()
except Exception as e:
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
)
)
# NON blocking - notify users Redis is throwing an exception
verbose_logger.error(
"LiteLLM Redis Caching: async set() - Got exception from REDIS %s, Writing value=%s",
str(e),
value,
)
traceback.print_exc()
key = self.check_and_fix_namespace(key=key)
async with _redis_client as redis_client:
ttl = kwargs.get("ttl", None)
print_verbose(
f"Set ASYNC Redis Cache: key: {key}\nValue {value}\nttl={ttl}"
)
try:
await redis_client.set(name=key, value=json.dumps(value), ex=ttl)
print_verbose(
f"Successfully Set ASYNC Redis Cache: key: {key}\nValue {value}\nttl={ttl}"
)
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache",
)
)
except Exception as e:
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_set_cache",
)
)
# NON blocking - notify users Redis is throwing an exception
verbose_logger.error(
"LiteLLM Redis Caching: async set() - Got exception from REDIS %s, Writing value=%s",
str(e),
value,
)
traceback.print_exc()
async def async_set_cache_pipeline(self, cache_list, ttl=None):
"""
Use Redis Pipelines for bulk write operations
"""
_redis_client = self.init_async_client()
start_time = time.time()
print_verbose(
f"Set Async Redis Cache: key list: {cache_list}\nttl={ttl}, redis_version={self.redis_version}"
)
try:
async with _redis_client as redis_client:
async with redis_client.pipeline(transaction=True) as pipe:
# Iterate through each key-value pair in the cache_list and set them in the pipeline.
for cache_key, cache_value in cache_list:
cache_key = self.check_and_fix_namespace(key=cache_key)
print_verbose(
f"Set ASYNC Redis Cache PIPELINE: key: {cache_key}\nValue {cache_value}\nttl={ttl}"
)
json_cache_value = json.dumps(cache_value)
# Set the value with a TTL if it's provided.
if ttl is not None:
pipe.setex(cache_key, ttl, json_cache_value)
else:
pipe.set(cache_key, json_cache_value)
# Execute the pipeline and return the results.
results = await pipe.execute()
print_verbose(f"pipeline results: {results}")
# Optionally, you could process 'results' to make sure that all set operations were successful.
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache_pipeline",
)
)
return results
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_set_cache_pipeline",
)
)
verbose_logger.error(
"LiteLLM Redis Caching: async set_cache_pipeline() - Got exception from REDIS %s, Writing value=%s",
str(e),
cache_value,
)
traceback.print_exc()
async def batch_cache_write(self, key, value, **kwargs):
print_verbose(
f"in batch cache writing for redis buffer size={len(self.redis_batch_writing_buffer)}",
)
key = self.check_and_fix_namespace(key=key)
self.redis_batch_writing_buffer.append((key, value))
if len(self.redis_batch_writing_buffer) >= self.redis_flush_size:
await self.flush_cache_buffer() # logging done in here
async def async_increment(self, key, value: float, **kwargs) -> float:
_redis_client = self.init_async_client()
start_time = time.time()
try:
async with _redis_client as redis_client:
result = await redis_client.incrbyfloat(name=key, amount=value)
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_increment",
)
)
return result
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_increment",
)
)
verbose_logger.error(
"LiteLLM Redis Caching: async async_increment() - Got exception from REDIS %s, Writing value=%s",
str(e),
value,
)
traceback.print_exc()
raise e
async def flush_cache_buffer(self):
print_verbose(
f"flushing to redis....reached size of buffer {len(self.redis_batch_writing_buffer)}"
)
await self.async_set_cache_pipeline(self.redis_batch_writing_buffer)
self.redis_batch_writing_buffer = []
def _get_cache_logic(self, cached_response: Any):
"""
Common 'get_cache_logic' across sync + async redis client implementations
"""
if cached_response is None:
return cached_response
# cached_response is in `b{} convert it to ModelResponse
cached_response = cached_response.decode("utf-8") # Convert bytes to string
try:
cached_response = json.loads(
cached_response
) # Convert string to dictionary
except:
cached_response = ast.literal_eval(cached_response)
return cached_response
def get_cache(self, key, **kwargs):
try:
key = self.check_and_fix_namespace(key=key)
print_verbose(f"Get Redis Cache: key: {key}")
cached_response = self.redis_client.get(key)
print_verbose(
f"Got Redis Cache: key: {key}, cached_response {cached_response}"
)
return self._get_cache_logic(cached_response=cached_response)
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
traceback.print_exc()
logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e)
def batch_get_cache(self, key_list) -> dict:
"""
Use Redis for bulk read operations
"""
key_value_dict = {}
try:
_keys = []
for cache_key in key_list:
cache_key = self.check_and_fix_namespace(key=cache_key)
_keys.append(cache_key)
results = self.redis_client.mget(keys=_keys)
# Associate the results back with their keys.
# 'results' is a list of values corresponding to the order of keys in 'key_list'.
key_value_dict = dict(zip(key_list, results))
decoded_results = {
k.decode("utf-8"): self._get_cache_logic(v)
for k, v in key_value_dict.items()
}
return decoded_results
except Exception as e:
print_verbose(f"Error occurred in pipeline read - {str(e)}")
return key_value_dict
async def async_get_cache(self, key, **kwargs):
_redis_client = self.init_async_client()
key = self.check_and_fix_namespace(key=key)
start_time = time.time()
async with _redis_client as redis_client:
try:
print_verbose(f"Get Async Redis Cache: key: {key}")
cached_response = await redis_client.get(key)
print_verbose(
f"Got Async Redis Cache: key: {key}, cached_response {cached_response}"
)
response = self._get_cache_logic(cached_response=cached_response)
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_get_cache",
)
)
return response
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_get_cache",
)
)
# NON blocking - notify users Redis is throwing an exception
print_verbose(
f"LiteLLM Caching: async get() - Got exception from REDIS: {str(e)}"
)
async def async_batch_get_cache(self, key_list) -> dict:
"""
Use Redis for bulk read operations
"""
_redis_client = await self.init_async_client()
key_value_dict = {}
start_time = time.time()
try:
async with _redis_client as redis_client:
_keys = []
for cache_key in key_list:
cache_key = self.check_and_fix_namespace(key=cache_key)
_keys.append(cache_key)
results = await redis_client.mget(keys=_keys)
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_batch_get_cache",
)
)
# Associate the results back with their keys.
# 'results' is a list of values corresponding to the order of keys in 'key_list'.
key_value_dict = dict(zip(key_list, results))
decoded_results = {}
for k, v in key_value_dict.items():
if isinstance(k, bytes):
k = k.decode("utf-8")
v = self._get_cache_logic(v)
decoded_results[k] = v
return decoded_results
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_batch_get_cache",
)
)
print_verbose(f"Error occurred in pipeline read - {str(e)}")
return key_value_dict
def sync_ping(self) -> bool:
"""
Tests if the sync redis client is correctly setup.
"""
print_verbose(f"Pinging Sync Redis Cache")
start_time = time.time()
try:
response = self.redis_client.ping()
print_verbose(f"Redis Cache PING: {response}")
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
self.service_logger_obj.service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="sync_ping",
)
return response
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
self.service_logger_obj.service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="sync_ping",
)
print_verbose(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
traceback.print_exc()
raise e
async def ping(self) -> bool:
_redis_client = self.init_async_client()
start_time = time.time()
async with _redis_client as redis_client:
print_verbose(f"Pinging Async Redis Cache")
try:
response = await redis_client.ping()
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_ping",
)
)
return response
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_ping",
)
)
print_verbose(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
traceback.print_exc()
raise e
async def delete_cache_keys(self, keys):
_redis_client = self.init_async_client()
# keys is a list, unpack it so it gets passed as individual elements to delete
async with _redis_client as redis_client:
await redis_client.delete(*keys)
def client_list(self):
client_list = self.redis_client.client_list()
return client_list
def info(self):
info = self.redis_client.info()
return info
def flush_cache(self):
self.redis_client.flushall()
def flushall(self):
self.redis_client.flushall()
async def disconnect(self):
await self.async_redis_conn_pool.disconnect(inuse_connections=True)
def delete_cache(self, key):
self.redis_client.delete(key)
class RedisSemanticCache(BaseCache):
def __init__(
self,
host=None,
port=None,
password=None,
redis_url=None,
similarity_threshold=None,
use_async=False,
embedding_model="text-embedding-ada-002",
**kwargs,
):
from redisvl.index import SearchIndex
from redisvl.query import VectorQuery
print_verbose(
"redis semantic-cache initializing INDEX - litellm_semantic_cache_index"
)
if similarity_threshold is None:
raise Exception("similarity_threshold must be provided, passed None")
self.similarity_threshold = similarity_threshold
self.embedding_model = embedding_model
schema = {
"index": {
"name": "litellm_semantic_cache_index",
"prefix": "litellm",
"storage_type": "hash",
},
"fields": {
"text": [{"name": "response"}],
"text": [{"name": "prompt"}],
"vector": [
{
"name": "litellm_embedding",
"dims": 1536,
"distance_metric": "cosine",
"algorithm": "flat",
"datatype": "float32",
}
],
},
}
if redis_url is None:
# if no url passed, check if host, port and password are passed, if not raise an Exception
if host is None or port is None or password is None:
# try checking env for host, port and password
import os
host = os.getenv("REDIS_HOST")
port = os.getenv("REDIS_PORT")
password = os.getenv("REDIS_PASSWORD")
if host is None or port is None or password is None:
raise Exception("Redis host, port, and password must be provided")
redis_url = "redis://:" + password + "@" + host + ":" + port
print_verbose(f"redis semantic-cache redis_url: {redis_url}")
if use_async == False:
self.index = SearchIndex.from_dict(schema)
self.index.connect(redis_url=redis_url)
try:
self.index.create(overwrite=False) # don't overwrite existing index
except Exception as e:
print_verbose(f"Got exception creating semantic cache index: {str(e)}")
elif use_async == True:
schema["index"]["name"] = "litellm_semantic_cache_index_async"
self.index = SearchIndex.from_dict(schema)
self.index.connect(redis_url=redis_url, use_async=True)
#
def _get_cache_logic(self, cached_response: Any):
"""
Common 'get_cache_logic' across sync + async redis client implementations
"""
if cached_response is None:
return cached_response
# check if cached_response is bytes
if isinstance(cached_response, bytes):
cached_response = cached_response.decode("utf-8")
try:
cached_response = json.loads(
cached_response
) # Convert string to dictionary
except:
cached_response = ast.literal_eval(cached_response)
return cached_response
def set_cache(self, key, value, **kwargs):
import numpy as np
print_verbose(f"redis semantic-cache set_cache, kwargs: {kwargs}")
# get the prompt
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
# create an embedding for prompt
embedding_response = litellm.embedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
)
# get the embedding
embedding = embedding_response["data"][0]["embedding"]
# make the embedding a numpy array, convert to bytes
embedding_bytes = np.array(embedding, dtype=np.float32).tobytes()
value = str(value)
assert isinstance(value, str)
new_data = [
{"response": value, "prompt": prompt, "litellm_embedding": embedding_bytes}
]
# Add more data
keys = self.index.load(new_data)
return
def get_cache(self, key, **kwargs):
print_verbose(f"sync redis semantic-cache get_cache, kwargs: {kwargs}")
from redisvl.query import VectorQuery
import numpy as np
# query
# get the messages
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
# convert to embedding
embedding_response = litellm.embedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
)
# get the embedding
embedding = embedding_response["data"][0]["embedding"]
query = VectorQuery(
vector=embedding,
vector_field_name="litellm_embedding",
return_fields=["response", "prompt", "vector_distance"],
num_results=1,
)
results = self.index.query(query)
if results == None:
return None
if isinstance(results, list):
if len(results) == 0:
return None
vector_distance = results[0]["vector_distance"]
vector_distance = float(vector_distance)
similarity = 1 - vector_distance
cached_prompt = results[0]["prompt"]
# check similarity, if more than self.similarity_threshold, return results
print_verbose(
f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
)
if similarity > self.similarity_threshold:
# cache hit !
cached_value = results[0]["response"]
print_verbose(
f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
)
return self._get_cache_logic(cached_response=cached_value)
else:
# cache miss !
return None
pass
async def async_set_cache(self, key, value, **kwargs):
import numpy as np
from litellm.proxy.proxy_server import llm_router, llm_model_list
try:
await self.index.acreate(overwrite=False) # don't overwrite existing index
except Exception as e:
print_verbose(f"Got exception creating semantic cache index: {str(e)}")
print_verbose(f"async redis semantic-cache set_cache, kwargs: {kwargs}")
# get the prompt
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
# create an embedding for prompt
router_model_names = (
[m["model_name"] for m in llm_model_list]
if llm_model_list is not None
else []
)
if llm_router is not None and self.embedding_model in router_model_names:
user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
embedding_response = await llm_router.aembedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
metadata={
"user_api_key": user_api_key,
"semantic-cache-embedding": True,
"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
},
)
else:
# convert to embedding
embedding_response = await litellm.aembedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
)
# get the embedding
embedding = embedding_response["data"][0]["embedding"]
# make the embedding a numpy array, convert to bytes
embedding_bytes = np.array(embedding, dtype=np.float32).tobytes()
value = str(value)
assert isinstance(value, str)
new_data = [
{"response": value, "prompt": prompt, "litellm_embedding": embedding_bytes}
]
# Add more data
keys = await self.index.aload(new_data)
return
async def async_get_cache(self, key, **kwargs):
print_verbose(f"async redis semantic-cache get_cache, kwargs: {kwargs}")
from redisvl.query import VectorQuery
import numpy as np
from litellm.proxy.proxy_server import llm_router, llm_model_list
# query
# get the messages
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
router_model_names = (
[m["model_name"] for m in llm_model_list]
if llm_model_list is not None
else []
)
if llm_router is not None and self.embedding_model in router_model_names:
user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
embedding_response = await llm_router.aembedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
metadata={
"user_api_key": user_api_key,
"semantic-cache-embedding": True,
"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
},
)
else:
# convert to embedding
embedding_response = await litellm.aembedding(
model=self.embedding_model,
input=prompt,
cache={"no-store": True, "no-cache": True},
)
# get the embedding
embedding = embedding_response["data"][0]["embedding"]
query = VectorQuery(
vector=embedding,
vector_field_name="litellm_embedding",
return_fields=["response", "prompt", "vector_distance"],
)
results = await self.index.aquery(query)
if results == None:
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
return None