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[Optimize] Optimize the code in caching file

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This commit is contained in:
Rahul Kataria 2024-05-12 15:04:45 +05:30
parent c5ca2619f9
commit 9b77b8c90b
1 changed files with 7 additions and 14 deletions
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21
litellm/caching.py
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@ -374,10 +374,11 @@ class RedisCache(BaseCache):
f"Set ASYNC Redis Cache PIPELINE: key: {cache_key}\nValue {cache_value}\nttl={ttl}" f"Set ASYNC Redis Cache PIPELINE: key: {cache_key}\nValue {cache_value}\nttl={ttl}"
) )
# Set the value with a TTL if it's provided. # Set the value with a TTL if it's provided.
json_cache_value = json.dumps(cache_value)
if ttl is not None: if ttl is not None:
pipe.setex(cache_key, ttl, json.dumps(cache_value)) pipe.setex(cache_key, ttl, json_cache_value)
else: else:
pipe.set(cache_key, json.dumps(cache_value)) pipe.set(cache_key, json_cache_value)
# Execute the pipeline and return the results. # Execute the pipeline and return the results.
results = await pipe.execute() results = await pipe.execute()
@ -810,9 +811,7 @@ class RedisSemanticCache(BaseCache):
# get the prompt # get the prompt
messages = kwargs["messages"] messages = kwargs["messages"]
prompt = "" prompt = "".join(message["content"] for message in messages)
for message in messages:
prompt += message["content"]
# create an embedding for prompt # create an embedding for prompt
embedding_response = litellm.embedding( embedding_response = litellm.embedding(
@ -847,9 +846,7 @@ class RedisSemanticCache(BaseCache):
# get the messages # get the messages
messages = kwargs["messages"] messages = kwargs["messages"]
prompt = "" prompt = "".join(message["content"] for message in messages)
for message in messages:
prompt += message["content"]
# convert to embedding # convert to embedding
embedding_response = litellm.embedding( embedding_response = litellm.embedding(
@ -909,9 +906,7 @@ class RedisSemanticCache(BaseCache):
# get the prompt # get the prompt
messages = kwargs["messages"] messages = kwargs["messages"]
prompt = "" prompt = "".join(message["content"] for message in messages)
for message in messages:
prompt += message["content"]
# create an embedding for prompt # create an embedding for prompt
router_model_names = ( router_model_names = (
[m["model_name"] for m in llm_model_list] [m["model_name"] for m in llm_model_list]
@ -964,9 +959,7 @@ class RedisSemanticCache(BaseCache):
# get the messages # get the messages
messages = kwargs["messages"] messages = kwargs["messages"]
prompt = "" prompt = "".join(message["content"] for message in messages)
for message in messages:
prompt += message["content"]
router_model_names = ( router_model_names = (
[m["model_name"] for m in llm_model_list] [m["model_name"] for m in llm_model_list]