mirror of
https://github.com/BerriAI/litellm.git
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build(pyproject.toml): add new dev dependencies - for type checking (#9631)
* build(pyproject.toml): add new dev dependencies - for type checking * build: reformat files to fit black * ci: reformat to fit black * ci(test-litellm.yml): make tests run clear * build(pyproject.toml): add ruff * fix: fix ruff checks * build(mypy/): fix mypy linting errors * fix(hashicorp_secret_manager.py): fix passing cert for tls auth * build(mypy/): resolve all mypy errors * test: update test * fix: fix black formatting * build(pre-commit-config.yaml): use poetry run black * fix(proxy_server.py): fix linting error * fix: fix ruff safe representation error
This commit is contained in:
Krish Dholakia
GitHub
parent
95e5dfae5a
commit
9b7ebb6a7d
214 changed files with 1553 additions and 1433 deletions
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@ -13,23 +13,27 @@ import ast
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import asyncio
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import json
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import os
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from typing import Any, Dict, List, Optional, Tuple
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from typing import Any, Dict, List, Optional, Tuple, cast
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import litellm
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from litellm._logging import print_verbose
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from litellm.litellm_core_utils.prompt_templates.common_utils import get_str_from_messages
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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get_str_from_messages,
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)
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from litellm.types.utils import EmbeddingResponse
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from .base_cache import BaseCache
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class RedisSemanticCache(BaseCache):
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"""
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Redis-backed semantic cache for LLM responses.
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This cache uses vector similarity to find semantically similar prompts that have been
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Redis-backed semantic cache for LLM responses.
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This cache uses vector similarity to find semantically similar prompts that have been
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previously sent to the LLM, allowing for cache hits even when prompts are not identical
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but carry similar meaning.
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"""
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DEFAULT_REDIS_INDEX_NAME: str = "litellm_semantic_cache_index"
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def __init__(
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@ -57,7 +61,7 @@ class RedisSemanticCache(BaseCache):
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index_name: Name for the Redis index
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ttl: Default time-to-live for cache entries in seconds
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**kwargs: Additional arguments passed to the Redis client
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Raises:
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Exception: If similarity_threshold is not provided or required Redis
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connection information is missing
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@ -69,14 +73,14 @@ class RedisSemanticCache(BaseCache):
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index_name = self.DEFAULT_REDIS_INDEX_NAME
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print_verbose(f"Redis semantic-cache initializing index - {index_name}")
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# Validate similarity threshold
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if similarity_threshold is None:
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raise ValueError("similarity_threshold must be provided, passed None")
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# Store configuration
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self.similarity_threshold = similarity_threshold
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# Convert similarity threshold [0,1] to distance threshold [0,2]
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# For cosine distance: 0 = most similar, 2 = least similar
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# While similarity: 1 = most similar, 0 = least similar
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@ -87,14 +91,16 @@ class RedisSemanticCache(BaseCache):
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if redis_url is None:
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try:
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# Attempt to use provided parameters or fallback to environment variables
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host = host or os.environ['REDIS_HOST']
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port = port or os.environ['REDIS_PORT']
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password = password or os.environ['REDIS_PASSWORD']
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host = host or os.environ["REDIS_HOST"]
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port = port or os.environ["REDIS_PORT"]
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password = password or os.environ["REDIS_PASSWORD"]
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except KeyError as e:
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# Raise a more informative exception if any of the required keys are missing
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missing_var = e.args[0]
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raise ValueError(f"Missing required Redis configuration: {missing_var}. "
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f"Provide {missing_var} or redis_url.") from e
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raise ValueError(
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f"Missing required Redis configuration: {missing_var}. "
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f"Provide {missing_var} or redis_url."
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) from e
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redis_url = f"redis://:{password}@{host}:{port}"
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@ -114,7 +120,7 @@ class RedisSemanticCache(BaseCache):
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def _get_ttl(self, **kwargs) -> Optional[int]:
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"""
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Get the TTL (time-to-live) value for cache entries.
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Args:
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**kwargs: Keyword arguments that may contain a custom TTL
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@ -125,22 +131,25 @@ class RedisSemanticCache(BaseCache):
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if ttl is not None:
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ttl = int(ttl)
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return ttl
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def _get_embedding(self, prompt: str) -> List[float]:
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"""
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Generate an embedding vector for the given prompt using the configured embedding model.
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Args:
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prompt: The text to generate an embedding for
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Returns:
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List[float]: The embedding vector
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"""
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# Create an embedding from 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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embedding_response = cast(
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EmbeddingResponse,
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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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)
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embedding = embedding_response["data"][0]["embedding"]
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return embedding
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@ -148,10 +157,10 @@ class RedisSemanticCache(BaseCache):
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def _get_cache_logic(self, cached_response: Any) -> Any:
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"""
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Process the cached response to prepare it for use.
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Args:
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cached_response: The raw cached response
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Returns:
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The processed cache response, or None if input was None
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"""
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@ -171,13 +180,13 @@ class RedisSemanticCache(BaseCache):
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except (ValueError, SyntaxError) as e:
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print_verbose(f"Error parsing cached response: {str(e)}")
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return None
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return cached_response
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def set_cache(self, key: str, value: Any, **kwargs) -> None:
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"""
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Store a value in the semantic cache.
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Args:
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key: The cache key (not directly used in semantic caching)
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value: The response value to cache
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@ -186,13 +195,14 @@ class RedisSemanticCache(BaseCache):
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"""
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print_verbose(f"Redis semantic-cache set_cache, kwargs: {kwargs}")
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value_str: Optional[str] = None
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try:
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# Extract the prompt from messages
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messages = kwargs.get("messages", [])
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if not messages:
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print_verbose("No messages provided for semantic caching")
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return
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prompt = get_str_from_messages(messages)
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value_str = str(value)
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@ -203,16 +213,18 @@ class RedisSemanticCache(BaseCache):
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else:
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self.llmcache.store(prompt, value_str)
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except Exception as e:
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print_verbose(f"Error setting {value_str} in the Redis semantic cache: {str(e)}")
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print_verbose(
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f"Error setting {value_str or value} in the Redis semantic cache: {str(e)}"
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)
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def get_cache(self, key: str, **kwargs) -> Any:
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"""
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Retrieve a semantically similar cached response.
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Args:
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key: The cache key (not directly used in semantic caching)
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**kwargs: Additional arguments including 'messages' for the prompt
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Returns:
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The cached response if a semantically similar prompt is found, else None
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"""
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@ -224,7 +236,7 @@ class RedisSemanticCache(BaseCache):
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if not messages:
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print_verbose("No messages provided for semantic cache lookup")
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return None
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prompt = get_str_from_messages(messages)
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# Check the cache for semantically similar prompts
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results = self.llmcache.check(prompt=prompt)
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@ -236,12 +248,12 @@ class RedisSemanticCache(BaseCache):
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# Process the best matching result
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cache_hit = results[0]
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vector_distance = float(cache_hit["vector_distance"])
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# Convert vector distance back to similarity score
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# For cosine distance: 0 = most similar, 2 = least similar
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# While similarity: 1 = most similar, 0 = least similar
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similarity = 1 - vector_distance
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cached_prompt = cache_hit["prompt"]
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cached_response = cache_hit["response"]
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@ -251,19 +263,19 @@ class RedisSemanticCache(BaseCache):
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f"current prompt: {prompt}, "
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f"cached prompt: {cached_prompt}"
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)
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return self._get_cache_logic(cached_response=cached_response)
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except Exception as e:
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print_verbose(f"Error retrieving from Redis semantic cache: {str(e)}")
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async def _get_async_embedding(self, prompt: str, **kwargs) -> List[float]:
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"""
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Asynchronously generate an embedding for the given prompt.
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Args:
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prompt: The text to generate an embedding for
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**kwargs: Additional arguments that may contain metadata
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Returns:
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List[float]: The embedding vector
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"""
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@ -275,7 +287,7 @@ class RedisSemanticCache(BaseCache):
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if llm_model_list is not None
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else []
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)
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try:
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if llm_router is not None and self.embedding_model in router_model_names:
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# Use the router for embedding generation
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@ -307,7 +319,7 @@ class RedisSemanticCache(BaseCache):
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async def async_set_cache(self, key: str, value: Any, **kwargs) -> None:
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"""
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Asynchronously store a value in the semantic cache.
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Args:
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key: The cache key (not directly used in semantic caching)
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value: The response value to cache
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@ -322,13 +334,13 @@ class RedisSemanticCache(BaseCache):
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if not messages:
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print_verbose("No messages provided for semantic caching")
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return
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prompt = get_str_from_messages(messages)
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value_str = str(value)
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# Generate embedding for the value (response) to cache
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prompt_embedding = await self._get_async_embedding(prompt, **kwargs)
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# Get TTL and store in Redis semantic cache
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ttl = self._get_ttl(**kwargs)
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if ttl is not None:
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@ -336,13 +348,13 @@ class RedisSemanticCache(BaseCache):
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prompt,
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value_str,
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vector=prompt_embedding, # Pass through custom embedding
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ttl=ttl
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ttl=ttl,
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)
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else:
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await self.llmcache.astore(
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prompt,
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value_str,
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vector=prompt_embedding # Pass through custom embedding
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vector=prompt_embedding, # Pass through custom embedding
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)
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except Exception as e:
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print_verbose(f"Error in async_set_cache: {str(e)}")
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@ -350,11 +362,11 @@ class RedisSemanticCache(BaseCache):
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async def async_get_cache(self, key: str, **kwargs) -> Any:
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"""
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Asynchronously retrieve a semantically similar cached response.
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Args:
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key: The cache key (not directly used in semantic caching)
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**kwargs: Additional arguments including 'messages' for the prompt
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Returns:
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The cached response if a semantically similar prompt is found, else None
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"""
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@ -367,21 +379,20 @@ class RedisSemanticCache(BaseCache):
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print_verbose("No messages provided for semantic cache lookup")
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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prompt = get_str_from_messages(messages)
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# Generate embedding for the prompt
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prompt_embedding = await self._get_async_embedding(prompt, **kwargs)
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# Check the cache for semantically similar prompts
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results = await self.llmcache.acheck(
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prompt=prompt,
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vector=prompt_embedding
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)
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results = await self.llmcache.acheck(prompt=prompt, vector=prompt_embedding)
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# handle results / cache hit
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if not results:
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 # TODO why here but not above??
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kwargs.setdefault("metadata", {})[
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"semantic-similarity"
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] = 0.0 # TODO why here but not above??
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return None
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cache_hit = results[0]
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@ -404,7 +415,7 @@ class RedisSemanticCache(BaseCache):
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f"current prompt: {prompt}, "
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f"cached prompt: {cached_prompt}"
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)
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return self._get_cache_logic(cached_response=cached_response)
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except Exception as e:
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print_verbose(f"Error in async_get_cache: {str(e)}")
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@ -413,17 +424,19 @@ class RedisSemanticCache(BaseCache):
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async def _index_info(self) -> Dict[str, Any]:
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"""
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Get information about the Redis index.
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Returns:
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Dict[str, Any]: Information about the Redis index
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"""
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aindex = await self.llmcache._get_async_index()
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return await aindex.info()
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async def async_set_cache_pipeline(self, cache_list: List[Tuple[str, Any]], **kwargs) -> None:
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async def async_set_cache_pipeline(
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self, cache_list: List[Tuple[str, Any]], **kwargs
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) -> None:
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"""
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Asynchronously store multiple values in the semantic cache.
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Args:
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cache_list: List of (key, value) tuples to cache
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**kwargs: Additional arguments
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