structure saas with tools
This commit is contained in:
Davidson Gomes
@@ -0,0 +1,450 @@
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"""
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Redis Semantic Cache implementation for LiteLLM
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The RedisSemanticCache provides semantic caching functionality using Redis as a backend.
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This cache stores responses based on the semantic similarity of prompts rather than
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exact matching, allowing for more flexible caching of LLM responses.
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This implementation uses RedisVL's SemanticCache to find semantically similar prompts
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and their cached responses.
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"""
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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, 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 (
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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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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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self,
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host: Optional[str] = None,
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port: Optional[str] = None,
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password: Optional[str] = None,
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redis_url: Optional[str] = None,
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similarity_threshold: Optional[float] = None,
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embedding_model: str = "text-embedding-ada-002",
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index_name: Optional[str] = None,
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**kwargs,
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):
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"""
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Initialize the Redis Semantic Cache.
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Args:
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host: Redis host address
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port: Redis port
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password: Redis password
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redis_url: Full Redis URL (alternative to separate host/port/password)
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similarity_threshold: Threshold for semantic similarity (0.0 to 1.0)
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where 1.0 requires exact matches and 0.0 accepts any match
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embedding_model: Model to use for generating embeddings
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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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"""
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from redisvl.extensions.llmcache import SemanticCache
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from redisvl.utils.vectorize import CustomTextVectorizer
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if index_name is None:
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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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self.distance_threshold = 1 - similarity_threshold
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self.embedding_model = embedding_model
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# Set up Redis connection
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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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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(
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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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print_verbose(f"Redis semantic-cache redis_url: {redis_url}")
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# Initialize the Redis vectorizer and cache
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cache_vectorizer = CustomTextVectorizer(self._get_embedding)
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self.llmcache = SemanticCache(
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name=index_name,
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redis_url=redis_url,
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vectorizer=cache_vectorizer,
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distance_threshold=self.distance_threshold,
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overwrite=False,
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)
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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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Returns:
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Optional[int]: The TTL value in seconds, or None if no TTL should be applied
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"""
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ttl = kwargs.get("ttl")
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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 = 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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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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if cached_response is None:
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return cached_response
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# Convert bytes to string if needed
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if isinstance(cached_response, bytes):
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cached_response = cached_response.decode("utf-8")
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# Convert string representation to Python object
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try:
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cached_response = json.loads(cached_response)
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except json.JSONDecodeError:
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try:
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cached_response = ast.literal_eval(cached_response)
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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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**kwargs: Additional arguments including 'messages' for the prompt
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and optional 'ttl' for time-to-live
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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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# 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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self.llmcache.store(prompt, value_str, ttl=int(ttl))
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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(
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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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print_verbose(f"Redis semantic-cache get_cache, kwargs: {kwargs}")
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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 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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# Return None if no similar prompts found
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if not results:
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return None
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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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print_verbose(
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f"Cache hit: similarity threshold: {self.similarity_threshold}, "
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f"actual similarity: {similarity}, "
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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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from litellm.proxy.proxy_server import llm_model_list, llm_router
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# Route the embedding request through the proxy if appropriate
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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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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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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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# Generate embedding directly
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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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# Extract and return the embedding vector
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return embedding_response["data"][0]["embedding"]
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except Exception as e:
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print_verbose(f"Error generating async embedding: {str(e)}")
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raise ValueError(f"Failed to generate embedding: {str(e)}") from e
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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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**kwargs: Additional arguments including 'messages' for the prompt
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and optional 'ttl' for time-to-live
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"""
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print_verbose(f"Async Redis semantic-cache set_cache, kwargs: {kwargs}")
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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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# 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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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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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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)
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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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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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print_verbose(f"Async Redis semantic-cache get_cache, kwargs: {kwargs}")
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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 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(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", {})[
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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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vector_distance = float(cache_hit["vector_distance"])
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# Convert vector distance back to similarity
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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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# 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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print_verbose(
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f"Cache hit: similarity threshold: {self.similarity_threshold}, "
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f"actual similarity: {similarity}, "
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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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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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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(
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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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"""
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try:
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tasks = []
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for val in cache_list:
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tasks.append(self.async_set_cache(val[0], val[1], **kwargs))
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await asyncio.gather(*tasks)
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except Exception as e:
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print_verbose(f"Error in async_set_cache_pipeline: {str(e)}")
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