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(refactor) - caching use separate files for each cache class (#6251)
* fix remove qdrant semantic caching to it's own folder * refactor use 1 file for s3 caching * fix use sep files for in mem and redis caching * fix refactor caching * add readme.md for caching folder
This commit is contained in:
Ishaan Jaff
GitHub
parent
773795e981
commit
e79136f481
11 changed files with 2339 additions and 2159 deletions
333
litellm/caching/redis_semantic_cache.py
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333
litellm/caching/redis_semantic_cache.py
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"""
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Redis Semantic Cache implementation
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Has 4 methods:
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- set_cache
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- get_cache
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- async_set_cache
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- async_get_cache
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"""
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import ast
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import json
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from typing import Any
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import litellm
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from litellm._logging import print_verbose
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from .base_cache import BaseCache
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class RedisSemanticCache(BaseCache):
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def __init__(
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self,
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host=None,
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port=None,
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password=None,
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redis_url=None,
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similarity_threshold=None,
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use_async=False,
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embedding_model="text-embedding-ada-002",
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**kwargs,
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):
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from redisvl.index import SearchIndex
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from redisvl.query import VectorQuery
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print_verbose(
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"redis semantic-cache initializing INDEX - litellm_semantic_cache_index"
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)
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if similarity_threshold is None:
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raise Exception("similarity_threshold must be provided, passed None")
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self.similarity_threshold = similarity_threshold
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self.embedding_model = embedding_model
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schema = {
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"index": {
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"name": "litellm_semantic_cache_index",
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"prefix": "litellm",
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"storage_type": "hash",
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},
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"fields": {
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"text": [{"name": "response"}],
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"vector": [
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{
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"name": "litellm_embedding",
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"dims": 1536,
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"distance_metric": "cosine",
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"algorithm": "flat",
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"datatype": "float32",
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}
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],
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},
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}
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if redis_url is None:
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# if no url passed, check if host, port and password are passed, if not raise an Exception
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if host is None or port is None or password is None:
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# try checking env for host, port and password
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import os
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host = os.getenv("REDIS_HOST")
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port = os.getenv("REDIS_PORT")
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password = os.getenv("REDIS_PASSWORD")
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if host is None or port is None or password is None:
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raise Exception("Redis host, port, and password must be provided")
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redis_url = "redis://:" + password + "@" + host + ":" + port
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print_verbose(f"redis semantic-cache redis_url: {redis_url}")
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if use_async is False:
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self.index = SearchIndex.from_dict(schema)
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self.index.connect(redis_url=redis_url)
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try:
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self.index.create(overwrite=False) # don't overwrite existing index
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except Exception as e:
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print_verbose(f"Got exception creating semantic cache index: {str(e)}")
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elif use_async is True:
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schema["index"]["name"] = "litellm_semantic_cache_index_async"
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self.index = SearchIndex.from_dict(schema)
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self.index.connect(redis_url=redis_url, use_async=True)
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#
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def _get_cache_logic(self, cached_response: Any):
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"""
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Common 'get_cache_logic' across sync + async redis client implementations
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"""
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if cached_response is None:
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return cached_response
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# check if cached_response is bytes
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if isinstance(cached_response, bytes):
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cached_response = cached_response.decode("utf-8")
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try:
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cached_response = json.loads(
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cached_response
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) # Convert string to dictionary
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except Exception:
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cached_response = ast.literal_eval(cached_response)
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return cached_response
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def set_cache(self, key, value, **kwargs):
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import numpy as np
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print_verbose(f"redis semantic-cache set_cache, kwargs: {kwargs}")
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# get the prompt
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messages = kwargs["messages"]
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prompt = "".join(message["content"] for message in messages)
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# create an embedding for prompt
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embedding_response = litellm.embedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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# make the embedding a numpy array, convert to bytes
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embedding_bytes = np.array(embedding, dtype=np.float32).tobytes()
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value = str(value)
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assert isinstance(value, str)
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new_data = [
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{"response": value, "prompt": prompt, "litellm_embedding": embedding_bytes}
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]
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# Add more data
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self.index.load(new_data)
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return
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def get_cache(self, key, **kwargs):
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print_verbose(f"sync redis semantic-cache get_cache, kwargs: {kwargs}")
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import numpy as np
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from redisvl.query import VectorQuery
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# query
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# get the messages
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messages = kwargs["messages"]
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prompt = "".join(message["content"] for message in messages)
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# convert to embedding
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embedding_response = litellm.embedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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query = VectorQuery(
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vector=embedding,
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vector_field_name="litellm_embedding",
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return_fields=["response", "prompt", "vector_distance"],
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num_results=1,
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)
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results = self.index.query(query)
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if results is None:
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return None
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if isinstance(results, list):
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if len(results) == 0:
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return None
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vector_distance = results[0]["vector_distance"]
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vector_distance = float(vector_distance)
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similarity = 1 - vector_distance
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cached_prompt = results[0]["prompt"]
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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
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)
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if similarity > self.similarity_threshold:
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# cache hit !
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cached_value = results[0]["response"]
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print_verbose(
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f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
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)
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return self._get_cache_logic(cached_response=cached_value)
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else:
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# cache miss !
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return None
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pass
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async def async_set_cache(self, key, value, **kwargs):
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import numpy as np
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from litellm.proxy.proxy_server import llm_model_list, llm_router
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try:
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await self.index.acreate(overwrite=False) # don't overwrite existing index
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except Exception as e:
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print_verbose(f"Got exception creating semantic cache index: {str(e)}")
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print_verbose(f"async redis semantic-cache set_cache, kwargs: {kwargs}")
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# get the prompt
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messages = kwargs["messages"]
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prompt = "".join(message["content"] for message in messages)
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# create an embedding for prompt
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router_model_names = (
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[m["model_name"] for m in llm_model_list]
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if llm_model_list is not None
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else []
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)
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if llm_router is not None and self.embedding_model in router_model_names:
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user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
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embedding_response = await llm_router.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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metadata={
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"user_api_key": user_api_key,
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"semantic-cache-embedding": True,
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"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
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},
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)
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else:
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# convert to embedding
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embedding_response = await litellm.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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# make the embedding a numpy array, convert to bytes
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embedding_bytes = np.array(embedding, dtype=np.float32).tobytes()
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value = str(value)
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assert isinstance(value, str)
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new_data = [
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{"response": value, "prompt": prompt, "litellm_embedding": embedding_bytes}
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]
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# Add more data
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await self.index.aload(new_data)
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return
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async def async_get_cache(self, key, **kwargs):
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print_verbose(f"async redis semantic-cache get_cache, kwargs: {kwargs}")
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import numpy as np
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from redisvl.query import VectorQuery
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from litellm.proxy.proxy_server import llm_model_list, llm_router
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# query
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# get the messages
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messages = kwargs["messages"]
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prompt = "".join(message["content"] for message in messages)
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router_model_names = (
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[m["model_name"] for m in llm_model_list]
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if llm_model_list is not None
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else []
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)
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if llm_router is not None and self.embedding_model in router_model_names:
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user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
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embedding_response = await llm_router.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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metadata={
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"user_api_key": user_api_key,
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"semantic-cache-embedding": True,
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"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
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},
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)
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else:
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# convert to embedding
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embedding_response = await litellm.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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query = VectorQuery(
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vector=embedding,
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vector_field_name="litellm_embedding",
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return_fields=["response", "prompt", "vector_distance"],
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)
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results = await self.index.aquery(query)
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if results is None:
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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if isinstance(results, list):
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if len(results) == 0:
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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vector_distance = results[0]["vector_distance"]
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vector_distance = float(vector_distance)
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similarity = 1 - vector_distance
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cached_prompt = results[0]["prompt"]
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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
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)
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# update kwargs["metadata"] with similarity, don't rewrite the original metadata
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kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
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if similarity > self.similarity_threshold:
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# cache hit !
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cached_value = results[0]["response"]
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print_verbose(
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f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
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)
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return self._get_cache_logic(cached_response=cached_value)
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else:
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# cache miss !
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return None
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pass
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async def _index_info(self):
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return await self.index.ainfo()
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