mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-24 18:24:20 +00:00
update redisvl dependency
This commit is contained in:
Tyler Hutcherson
parent
122ee634f4
commit
7864cd1f76
15 changed files with 1035 additions and 394 deletions
|
|
@ -4,7 +4,8 @@ python-dotenv
|
|||
tiktoken
|
||||
importlib_metadata
|
||||
cohere
|
||||
redis
|
||||
redis==5.2.1
|
||||
redisvl==0.4.1
|
||||
anthropic
|
||||
orjson==3.9.15
|
||||
pydantic==2.7.1
|
||||
|
|
|
|||
1
.gitignore
vendored
1
.gitignore
vendored
|
|
@ -1,3 +1,4 @@
|
|||
.python-version
|
||||
.venv
|
||||
.env
|
||||
.newenv
|
||||
|
|
|
|||
|
|
@ -37,9 +37,6 @@ RUN pip install dist/*.whl
|
|||
# install dependencies as wheels
|
||||
RUN pip wheel --no-cache-dir --wheel-dir=/wheels/ -r requirements.txt
|
||||
|
||||
# install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0
|
||||
RUN pip install redisvl==0.0.7 --no-deps
|
||||
|
||||
# ensure pyjwt is used, not jwt
|
||||
RUN pip uninstall jwt -y
|
||||
RUN pip uninstall PyJWT -y
|
||||
|
|
|
|||
|
|
@ -59,9 +59,6 @@ COPY --from=builder /wheels/ /wheels/
|
|||
# Install the built wheel using pip; again using a wildcard if it's the only file
|
||||
RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels
|
||||
|
||||
# install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0
|
||||
RUN pip install redisvl==0.0.7 --no-deps
|
||||
|
||||
# ensure pyjwt is used, not jwt
|
||||
RUN pip uninstall jwt -y
|
||||
RUN pip uninstall PyJWT -y
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ SHELL ["/bin/bash", "-o", "pipefail", "-c"]
|
|||
|
||||
# Install build dependencies
|
||||
RUN apt-get clean && apt-get update && \
|
||||
apt-get install -y gcc python3-dev && \
|
||||
apt-get install -y gcc g++ python3-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN pip install --no-cache-dir --upgrade pip && \
|
||||
|
|
@ -56,10 +56,8 @@ COPY --from=builder /wheels/ /wheels/
|
|||
# Install the built wheel using pip; again using a wildcard if it's the only file
|
||||
RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels
|
||||
|
||||
# install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0
|
||||
# ensure pyjwt is used, not jwt
|
||||
RUN pip install redisvl==0.0.7 --no-deps --no-cache-dir && \
|
||||
pip uninstall jwt -y && \
|
||||
RUN pip uninstall jwt -y && \
|
||||
pip uninstall PyJWT -y && \
|
||||
pip install PyJWT==2.9.0 --no-cache-dir
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ Install redis
|
|||
pip install redis
|
||||
```
|
||||
|
||||
For the hosted version you can setup your own Redis DB here: https://app.redislabs.com/
|
||||
For the hosted version you can setup your own Redis DB here: https://redis.io/try-free/
|
||||
|
||||
```python
|
||||
import litellm
|
||||
|
|
@ -37,11 +37,11 @@ litellm.cache = Cache(type="redis", host=<host>, port=<port>, password=<password
|
|||
|
||||
# Make completion calls
|
||||
response1 = completion(
|
||||
model="gpt-3.5-turbo",
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": "Tell me a joke."}]
|
||||
)
|
||||
response2 = completion(
|
||||
model="gpt-3.5-turbo",
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": "Tell me a joke."}]
|
||||
)
|
||||
|
||||
|
|
@ -91,12 +91,12 @@ response2 = completion(
|
|||
|
||||
<TabItem value="redis-sem" label="redis-semantic cache">
|
||||
|
||||
Install redis
|
||||
Install redisvl client
|
||||
```shell
|
||||
pip install redisvl==0.0.7
|
||||
pip install redisvl==0.4.1
|
||||
```
|
||||
|
||||
For the hosted version you can setup your own Redis DB here: https://app.redislabs.com/
|
||||
For the hosted version you can setup your own Redis DB here: https://redis.io/try-free/
|
||||
|
||||
```python
|
||||
import litellm
|
||||
|
|
@ -114,6 +114,7 @@ litellm.cache = Cache(
|
|||
port=os.environ["REDIS_PORT"],
|
||||
password=os.environ["REDIS_PASSWORD"],
|
||||
similarity_threshold=0.8, # similarity threshold for cache hits, 0 == no similarity, 1 = exact matches, 0.5 == 50% similarity
|
||||
ttl=120,
|
||||
redis_semantic_cache_embedding_model="text-embedding-ada-002", # this model is passed to litellm.embedding(), any litellm.embedding() model is supported here
|
||||
)
|
||||
response1 = completion(
|
||||
|
|
@ -471,11 +472,13 @@ def __init__(
|
|||
password: Optional[str] = None,
|
||||
namespace: Optional[str] = None,
|
||||
default_in_redis_ttl: Optional[float] = None,
|
||||
similarity_threshold: Optional[float] = None,
|
||||
redis_semantic_cache_use_async=False,
|
||||
redis_semantic_cache_embedding_model="text-embedding-ada-002",
|
||||
redis_flush_size=None,
|
||||
|
||||
# redis semantic cache params
|
||||
similarity_threshold: Optional[float] = None,
|
||||
redis_semantic_cache_embedding_model: str = "text-embedding-ada-002",
|
||||
redis_semantic_cache_index_name: Optional[str] = None,
|
||||
|
||||
# s3 Bucket, boto3 configuration
|
||||
s3_bucket_name: Optional[str] = None,
|
||||
s3_region_name: Optional[str] = None,
|
||||
|
|
|
|||
|
|
@ -88,16 +88,16 @@ class Cache:
|
|||
s3_aws_session_token: Optional[str] = None,
|
||||
s3_config: Optional[Any] = None,
|
||||
s3_path: Optional[str] = None,
|
||||
redis_semantic_cache_use_async=False,
|
||||
redis_semantic_cache_embedding_model="text-embedding-ada-002",
|
||||
redis_semantic_cache_embedding_model: str = "text-embedding-ada-002",
|
||||
redis_semantic_cache_index_name: Optional[str] = None,
|
||||
redis_flush_size: Optional[int] = None,
|
||||
redis_startup_nodes: Optional[List] = None,
|
||||
disk_cache_dir=None,
|
||||
disk_cache_dir: Optional[str] = None,
|
||||
qdrant_api_base: Optional[str] = None,
|
||||
qdrant_api_key: Optional[str] = None,
|
||||
qdrant_collection_name: Optional[str] = None,
|
||||
qdrant_quantization_config: Optional[str] = None,
|
||||
qdrant_semantic_cache_embedding_model="text-embedding-ada-002",
|
||||
qdrant_semantic_cache_embedding_model: str = "text-embedding-ada-002",
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
|
|
@ -170,8 +170,8 @@ class Cache:
|
|||
port=port,
|
||||
password=password,
|
||||
similarity_threshold=similarity_threshold,
|
||||
use_async=redis_semantic_cache_use_async,
|
||||
embedding_model=redis_semantic_cache_embedding_model,
|
||||
index_name=redis_semantic_cache_index_name,
|
||||
**kwargs,
|
||||
)
|
||||
elif type == LiteLLMCacheType.QDRANT_SEMANTIC:
|
||||
|
|
|
|||
|
|
@ -1,337 +1,437 @@
|
|||
"""
|
||||
Redis Semantic Cache implementation
|
||||
Redis Semantic Cache implementation for LiteLLM
|
||||
|
||||
Has 4 methods:
|
||||
- set_cache
|
||||
- get_cache
|
||||
- async_set_cache
|
||||
- async_get_cache
|
||||
The RedisSemanticCache provides semantic caching functionality using Redis as a backend.
|
||||
This cache stores responses based on the semantic similarity of prompts rather than
|
||||
exact matching, allowing for more flexible caching of LLM responses.
|
||||
|
||||
This implementation uses RedisVL's SemanticCache to find semantically similar prompts
|
||||
and their cached responses.
|
||||
"""
|
||||
|
||||
import ast
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Any
|
||||
import os
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import litellm
|
||||
from litellm._logging import print_verbose
|
||||
|
||||
from litellm.litellm_core_utils.prompt_templates.common_utils import get_str_from_messages
|
||||
from .base_cache import BaseCache
|
||||
|
||||
|
||||
class RedisSemanticCache(BaseCache):
|
||||
"""
|
||||
Redis-backed semantic cache for LLM responses.
|
||||
|
||||
This cache uses vector similarity to find semantically similar prompts that have been
|
||||
previously sent to the LLM, allowing for cache hits even when prompts are not identical
|
||||
but carry similar meaning.
|
||||
"""
|
||||
|
||||
DEFAULT_REDIS_INDEX_NAME: str = "litellm_semantic_cache_index"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
host=None,
|
||||
port=None,
|
||||
password=None,
|
||||
redis_url=None,
|
||||
similarity_threshold=None,
|
||||
use_async=False,
|
||||
embedding_model="text-embedding-ada-002",
|
||||
host: Optional[str] = None,
|
||||
port: Optional[str] = None,
|
||||
password: Optional[str] = None,
|
||||
redis_url: Optional[str] = None,
|
||||
similarity_threshold: Optional[float] = None,
|
||||
embedding_model: str = "text-embedding-ada-002",
|
||||
index_name: Optional[str] = None,
|
||||
**kwargs,
|
||||
):
|
||||
from redisvl.index import SearchIndex
|
||||
|
||||
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"}],
|
||||
"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 is 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 is 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
|
||||
Initialize the Redis Semantic Cache.
|
||||
|
||||
Args:
|
||||
host: Redis host address
|
||||
port: Redis port
|
||||
password: Redis password
|
||||
redis_url: Full Redis URL (alternative to separate host/port/password)
|
||||
similarity_threshold: Threshold for semantic similarity (0.0 to 1.0)
|
||||
where 1.0 requires exact matches and 0.0 accepts any match
|
||||
embedding_model: Model to use for generating embeddings
|
||||
index_name: Name for the Redis index
|
||||
ttl: Default time-to-live for cache entries in seconds
|
||||
**kwargs: Additional arguments passed to the Redis client
|
||||
|
||||
Raises:
|
||||
Exception: If similarity_threshold is not provided or required Redis
|
||||
connection information is missing
|
||||
"""
|
||||
from redisvl.extensions.llmcache import SemanticCache
|
||||
from redisvl.utils.vectorize import CustomTextVectorizer
|
||||
|
||||
if index_name is None:
|
||||
index_name = self.DEFAULT_REDIS_INDEX_NAME
|
||||
|
||||
print_verbose(f"Redis semantic-cache initializing index - {index_name}")
|
||||
|
||||
# Validate similarity threshold
|
||||
if similarity_threshold is None:
|
||||
raise ValueError("similarity_threshold must be provided, passed None")
|
||||
|
||||
# Store configuration
|
||||
self.similarity_threshold = similarity_threshold
|
||||
|
||||
# Convert similarity threshold [0,1] to distance threshold [0,2]
|
||||
# For cosine distance: 0 = most similar, 2 = least similar
|
||||
# While similarity: 1 = most similar, 0 = least similar
|
||||
self.distance_threshold = 1 - similarity_threshold
|
||||
self.embedding_model = embedding_model
|
||||
|
||||
# Set up Redis connection
|
||||
if redis_url is None:
|
||||
try:
|
||||
# Attempt to use provided parameters or fallback to environment variables
|
||||
host = host or os.environ['REDIS_HOST']
|
||||
port = port or os.environ['REDIS_PORT']
|
||||
password = password or os.environ['REDIS_PASSWORD']
|
||||
except KeyError as e:
|
||||
# Raise a more informative exception if any of the required keys are missing
|
||||
missing_var = e.args[0]
|
||||
raise ValueError(f"Missing required Redis configuration: {missing_var}. "
|
||||
f"Provide {missing_var} or redis_url.") from e
|
||||
|
||||
redis_url = f"redis://:{password}@{host}:{port}"
|
||||
|
||||
print_verbose(f"Redis semantic-cache redis_url: {redis_url}")
|
||||
|
||||
# Initialize the Redis vectorizer and cache
|
||||
cache_vectorizer = CustomTextVectorizer(self._get_embedding)
|
||||
|
||||
self.llmcache = SemanticCache(
|
||||
name=index_name,
|
||||
redis_url=redis_url,
|
||||
vectorizer=cache_vectorizer,
|
||||
distance_threshold=self.distance_threshold,
|
||||
overwrite=False,
|
||||
)
|
||||
|
||||
def _get_ttl(self, **kwargs) -> Optional[int]:
|
||||
"""
|
||||
Get the TTL (time-to-live) value for cache entries.
|
||||
|
||||
Args:
|
||||
**kwargs: Keyword arguments that may contain a custom TTL
|
||||
|
||||
Returns:
|
||||
Optional[int]: The TTL value in seconds, or None if no TTL should be applied
|
||||
"""
|
||||
ttl = kwargs.get("ttl")
|
||||
if ttl is not None:
|
||||
ttl = int(ttl)
|
||||
return ttl
|
||||
|
||||
def _get_embedding(self, prompt: str) -> List[float]:
|
||||
"""
|
||||
Generate an embedding vector for the given prompt using the configured embedding model.
|
||||
|
||||
Args:
|
||||
prompt: The text to generate an embedding for
|
||||
|
||||
Returns:
|
||||
List[float]: The embedding vector
|
||||
"""
|
||||
# Create an embedding from prompt
|
||||
embedding_response = litellm.embedding(
|
||||
model=self.embedding_model,
|
||||
input=prompt,
|
||||
cache={"no-store": True, "no-cache": True},
|
||||
)
|
||||
embedding = embedding_response["data"][0]["embedding"]
|
||||
return embedding
|
||||
|
||||
def _get_cache_logic(self, cached_response: Any) -> Any:
|
||||
"""
|
||||
Process the cached response to prepare it for use.
|
||||
|
||||
Args:
|
||||
cached_response: The raw cached response
|
||||
|
||||
Returns:
|
||||
The processed cache response, or None if input was None
|
||||
"""
|
||||
if cached_response is None:
|
||||
return cached_response
|
||||
|
||||
# check if cached_response is bytes
|
||||
# Convert bytes to string if needed
|
||||
if isinstance(cached_response, bytes):
|
||||
cached_response = cached_response.decode("utf-8")
|
||||
|
||||
# Convert string representation to Python object
|
||||
try:
|
||||
cached_response = json.loads(
|
||||
cached_response
|
||||
) # Convert string to dictionary
|
||||
except Exception:
|
||||
cached_response = ast.literal_eval(cached_response)
|
||||
cached_response = json.loads(cached_response)
|
||||
except json.JSONDecodeError:
|
||||
try:
|
||||
cached_response = ast.literal_eval(cached_response)
|
||||
except (ValueError, SyntaxError) as e:
|
||||
print_verbose(f"Error parsing cached response: {str(e)}")
|
||||
return None
|
||||
|
||||
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
|
||||
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
|
||||
|
||||
# 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 is 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_model_list, llm_router
|
||||
def set_cache(self, key: str, value: Any, **kwargs) -> None:
|
||||
"""
|
||||
Store a value in the semantic cache.
|
||||
|
||||
Args:
|
||||
key: The cache key (not directly used in semantic caching)
|
||||
value: The response value to cache
|
||||
**kwargs: Additional arguments including 'messages' for the prompt
|
||||
and optional 'ttl' for time-to-live
|
||||
"""
|
||||
print_verbose(f"Redis semantic-cache set_cache, kwargs: {kwargs}")
|
||||
|
||||
try:
|
||||
await self.index.acreate(overwrite=False) # don't overwrite existing index
|
||||
# Extract the prompt from messages
|
||||
messages = kwargs.get("messages", [])
|
||||
if not messages:
|
||||
print_verbose("No messages provided for semantic caching")
|
||||
return
|
||||
|
||||
prompt = get_str_from_messages(messages)
|
||||
value_str = str(value)
|
||||
|
||||
# Get TTL and store in Redis semantic cache
|
||||
ttl = self._get_ttl(**kwargs)
|
||||
if ttl is not None:
|
||||
self.llmcache.store(prompt, value_str, ttl=int(ttl))
|
||||
else:
|
||||
self.llmcache.store(prompt, value_str)
|
||||
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}")
|
||||
print_verbose(f"Error setting {value_str} in the Redis semantic cache: {str(e)}")
|
||||
|
||||
# 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},
|
||||
)
|
||||
def get_cache(self, key: str, **kwargs) -> Any:
|
||||
"""
|
||||
Retrieve a semantically similar cached response.
|
||||
|
||||
Args:
|
||||
key: The cache key (not directly used in semantic caching)
|
||||
**kwargs: Additional arguments including 'messages' for the prompt
|
||||
|
||||
Returns:
|
||||
The cached response if a semantically similar prompt is found, else None
|
||||
"""
|
||||
print_verbose(f"Redis semantic-cache get_cache, kwargs: {kwargs}")
|
||||
|
||||
# get the embedding
|
||||
embedding = embedding_response["data"][0]["embedding"]
|
||||
try:
|
||||
# Extract the prompt from messages
|
||||
messages = kwargs.get("messages", [])
|
||||
if not messages:
|
||||
print_verbose("No messages provided for semantic cache lookup")
|
||||
return None
|
||||
|
||||
prompt = get_str_from_messages(messages)
|
||||
# Check the cache for semantically similar prompts
|
||||
results = self.llmcache.check(prompt=prompt)
|
||||
|
||||
# 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
|
||||
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
|
||||
|
||||
from litellm.proxy.proxy_server import llm_model_list, llm_router
|
||||
|
||||
# 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 is None:
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
|
||||
return None
|
||||
if isinstance(results, list):
|
||||
if len(results) == 0:
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
|
||||
# Return None if no similar prompts found
|
||||
if not results:
|
||||
return None
|
||||
|
||||
vector_distance = results[0]["vector_distance"]
|
||||
vector_distance = float(vector_distance)
|
||||
similarity = 1 - vector_distance
|
||||
cached_prompt = results[0]["prompt"]
|
||||
# Process the best matching result
|
||||
cache_hit = results[0]
|
||||
vector_distance = float(cache_hit["vector_distance"])
|
||||
|
||||
# Convert vector distance back to similarity score
|
||||
# For cosine distance: 0 = most similar, 2 = least similar
|
||||
# While similarity: 1 = most similar, 0 = least similar
|
||||
similarity = 1 - vector_distance
|
||||
|
||||
cached_prompt = cache_hit["prompt"]
|
||||
cached_response = cache_hit["response"]
|
||||
|
||||
# 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}"
|
||||
)
|
||||
|
||||
# update kwargs["metadata"] with similarity, don't rewrite the original metadata
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
|
||||
|
||||
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}"
|
||||
f"Cache hit: similarity threshold: {self.similarity_threshold}, "
|
||||
f"actual similarity: {similarity}, "
|
||||
f"current prompt: {prompt}, "
|
||||
f"cached prompt: {cached_prompt}"
|
||||
)
|
||||
return self._get_cache_logic(cached_response=cached_value)
|
||||
else:
|
||||
# cache miss !
|
||||
return None
|
||||
pass
|
||||
|
||||
return self._get_cache_logic(cached_response=cached_response)
|
||||
except Exception as e:
|
||||
print_verbose(f"Error retrieving from Redis semantic cache: {str(e)}")
|
||||
|
||||
async def _get_async_embedding(self, prompt: str, **kwargs) -> List[float]:
|
||||
"""
|
||||
Asynchronously generate an embedding for the given prompt.
|
||||
|
||||
Args:
|
||||
prompt: The text to generate an embedding for
|
||||
**kwargs: Additional arguments that may contain metadata
|
||||
|
||||
Returns:
|
||||
List[float]: The embedding vector
|
||||
"""
|
||||
from litellm.proxy.proxy_server import llm_model_list, llm_router
|
||||
|
||||
async def _index_info(self):
|
||||
return await self.index.ainfo()
|
||||
# Route the embedding request through the proxy if appropriate
|
||||
router_model_names = (
|
||||
[m["model_name"] for m in llm_model_list]
|
||||
if llm_model_list is not None
|
||||
else []
|
||||
)
|
||||
|
||||
try:
|
||||
if llm_router is not None and self.embedding_model in router_model_names:
|
||||
# Use the router for embedding generation
|
||||
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:
|
||||
# Generate embedding directly
|
||||
embedding_response = await litellm.aembedding(
|
||||
model=self.embedding_model,
|
||||
input=prompt,
|
||||
cache={"no-store": True, "no-cache": True},
|
||||
)
|
||||
|
||||
async def async_set_cache_pipeline(self, cache_list, **kwargs):
|
||||
tasks = []
|
||||
for val in cache_list:
|
||||
tasks.append(self.async_set_cache(val[0], val[1], **kwargs))
|
||||
await asyncio.gather(*tasks)
|
||||
# Extract and return the embedding vector
|
||||
return embedding_response["data"][0]["embedding"]
|
||||
except Exception as e:
|
||||
print_verbose(f"Error generating async embedding: {str(e)}")
|
||||
raise ValueError(f"Failed to generate embedding: {str(e)}") from e
|
||||
|
||||
async def async_set_cache(self, key: str, value: Any, **kwargs) -> None:
|
||||
"""
|
||||
Asynchronously store a value in the semantic cache.
|
||||
|
||||
Args:
|
||||
key: The cache key (not directly used in semantic caching)
|
||||
value: The response value to cache
|
||||
**kwargs: Additional arguments including 'messages' for the prompt
|
||||
and optional 'ttl' for time-to-live
|
||||
"""
|
||||
print_verbose(f"Async Redis semantic-cache set_cache, kwargs: {kwargs}")
|
||||
|
||||
try:
|
||||
# Extract the prompt from messages
|
||||
messages = kwargs.get("messages", [])
|
||||
if not messages:
|
||||
print_verbose("No messages provided for semantic caching")
|
||||
return
|
||||
|
||||
prompt = get_str_from_messages(messages)
|
||||
value_str = str(value)
|
||||
|
||||
# Generate embedding for the value (response) to cache
|
||||
prompt_embedding = await self._get_async_embedding(prompt, **kwargs)
|
||||
|
||||
# Get TTL and store in Redis semantic cache
|
||||
ttl = self._get_ttl(**kwargs)
|
||||
if ttl is not None:
|
||||
await self.llmcache.astore(
|
||||
prompt,
|
||||
value_str,
|
||||
vector=prompt_embedding, # Pass through custom embedding
|
||||
ttl=ttl
|
||||
)
|
||||
else:
|
||||
await self.llmcache.astore(
|
||||
prompt,
|
||||
value_str,
|
||||
vector=prompt_embedding # Pass through custom embedding
|
||||
)
|
||||
except Exception as e:
|
||||
print_verbose(f"Error in async_set_cache: {str(e)}")
|
||||
|
||||
async def async_get_cache(self, key: str, **kwargs) -> Any:
|
||||
"""
|
||||
Asynchronously retrieve a semantically similar cached response.
|
||||
|
||||
Args:
|
||||
key: The cache key (not directly used in semantic caching)
|
||||
**kwargs: Additional arguments including 'messages' for the prompt
|
||||
|
||||
Returns:
|
||||
The cached response if a semantically similar prompt is found, else None
|
||||
"""
|
||||
print_verbose(f"Async Redis semantic-cache get_cache, kwargs: {kwargs}")
|
||||
|
||||
try:
|
||||
# Extract the prompt from messages
|
||||
messages = kwargs.get("messages", [])
|
||||
if not messages:
|
||||
print_verbose("No messages provided for semantic cache lookup")
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
|
||||
return None
|
||||
|
||||
prompt = get_str_from_messages(messages)
|
||||
|
||||
# Generate embedding for the prompt
|
||||
prompt_embedding = await self._get_async_embedding(prompt, **kwargs)
|
||||
|
||||
# Check the cache for semantically similar prompts
|
||||
results = await self.llmcache.acheck(
|
||||
prompt=prompt,
|
||||
vector=prompt_embedding
|
||||
)
|
||||
|
||||
# handle results / cache hit
|
||||
if not results:
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 # TODO why here but not above??
|
||||
return None
|
||||
|
||||
cache_hit = results[0]
|
||||
vector_distance = float(cache_hit["vector_distance"])
|
||||
|
||||
# Convert vector distance back to similarity
|
||||
# For cosine distance: 0 = most similar, 2 = least similar
|
||||
# While similarity: 1 = most similar, 0 = least similar
|
||||
similarity = 1 - vector_distance
|
||||
|
||||
cached_prompt = cache_hit["prompt"]
|
||||
cached_response = cache_hit["response"]
|
||||
|
||||
# update kwargs["metadata"] with similarity, don't rewrite the original metadata
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
|
||||
|
||||
print_verbose(
|
||||
f"Cache hit: similarity threshold: {self.similarity_threshold}, "
|
||||
f"actual similarity: {similarity}, "
|
||||
f"current prompt: {prompt}, "
|
||||
f"cached prompt: {cached_prompt}"
|
||||
)
|
||||
|
||||
return self._get_cache_logic(cached_response=cached_response)
|
||||
except Exception as e:
|
||||
print_verbose(f"Error in async_get_cache: {str(e)}")
|
||||
kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
|
||||
|
||||
async def _index_info(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Get information about the Redis index.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Information about the Redis index
|
||||
"""
|
||||
aindex = await self.llmcache._get_async_index()
|
||||
return await aindex.info()
|
||||
|
||||
async def async_set_cache_pipeline(self, cache_list: List[Tuple[str, Any]], **kwargs) -> None:
|
||||
"""
|
||||
Asynchronously store multiple values in the semantic cache.
|
||||
|
||||
Args:
|
||||
cache_list: List of (key, value) tuples to cache
|
||||
**kwargs: Additional arguments
|
||||
"""
|
||||
try:
|
||||
tasks = []
|
||||
for val in cache_list:
|
||||
tasks.append(self.async_set_cache(val[0], val[1], **kwargs))
|
||||
await asyncio.gather(*tasks)
|
||||
except Exception as e:
|
||||
print_verbose(f"Error in async_set_cache_pipeline: {str(e)}")
|
||||
|
|
|
|||
|
|
@ -1784,9 +1784,6 @@ class ProxyConfig:
|
|||
reset_color_code,
|
||||
cache_password,
|
||||
)
|
||||
if cache_type == "redis-semantic":
|
||||
# by default this should always be async
|
||||
cache_params.update({"redis_semantic_cache_use_async": True})
|
||||
|
||||
# users can pass os.environ/ variables on the proxy - we should read them from the env
|
||||
for key, value in cache_params.items():
|
||||
|
|
|
|||
467
poetry.lock
generated
467
poetry.lock
generated
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
|
|
@ -215,7 +215,7 @@ description = "Timeout context manager for asyncio programs"
|
|||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_full_version < \"3.11.3\" and extra == \"proxy\" or python_version < \"3.11\""
|
||||
markers = "python_full_version < \"3.11.3\" and (extra == \"extra-proxy\" or extra == \"proxy\" or python_version < \"3.11\")"
|
||||
files = [
|
||||
{file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
|
||||
{file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
|
||||
|
|
@ -428,8 +428,8 @@ files = [
|
|||
jmespath = ">=0.7.1,<2.0.0"
|
||||
python-dateutil = ">=2.1,<3.0.0"
|
||||
urllib3 = [
|
||||
{version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
|
||||
{version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""},
|
||||
{version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -671,6 +671,25 @@ files = [
|
|||
]
|
||||
markers = {main = "platform_system == \"Windows\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\""}
|
||||
|
||||
[[package]]
|
||||
name = "coloredlogs"
|
||||
version = "15.0.1"
|
||||
description = "Colored terminal output for Python's logging module"
|
||||
optional = true
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
|
||||
{file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
humanfriendly = ">=9.1"
|
||||
|
||||
[package.extras]
|
||||
cron = ["capturer (>=2.4)"]
|
||||
|
||||
[[package]]
|
||||
name = "cryptography"
|
||||
version = "43.0.3"
|
||||
|
|
@ -791,15 +810,15 @@ test = ["pytest (>=6)"]
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.115.11"
|
||||
version = "0.115.12"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"proxy\""
|
||||
files = [
|
||||
{file = "fastapi-0.115.11-py3-none-any.whl", hash = "sha256:32e1541b7b74602e4ef4a0260ecaf3aadf9d4f19590bba3e1bf2ac4666aa2c64"},
|
||||
{file = "fastapi-0.115.11.tar.gz", hash = "sha256:cc81f03f688678b92600a65a5e618b93592c65005db37157147204d8924bf94f"},
|
||||
{file = "fastapi-0.115.12-py3-none-any.whl", hash = "sha256:e94613d6c05e27be7ffebdd6ea5f388112e5e430c8f7d6494a9d1d88d43e814d"},
|
||||
{file = "fastapi-0.115.12.tar.gz", hash = "sha256:1e2c2a2646905f9e83d32f04a3f86aff4a286669c6c950ca95b5fd68c2602681"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1024,12 +1043,12 @@ files = [
|
|||
google-auth = ">=2.14.1,<3.0.0"
|
||||
googleapis-common-protos = ">=1.56.2,<2.0.0"
|
||||
grpcio = [
|
||||
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
grpcio-status = [
|
||||
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
proto-plus = [
|
||||
{version = ">=1.22.3,<2.0.0", markers = "python_version < \"3.13\""},
|
||||
|
|
@ -1135,7 +1154,7 @@ description = "HTTP/2-based RPC framework"
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
markers = "extra == \"extra-proxy\" and python_version < \"3.11\""
|
||||
files = [
|
||||
{file = "grpcio-1.70.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:95469d1977429f45fe7df441f586521361e235982a0b39e33841549143ae2851"},
|
||||
{file = "grpcio-1.70.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:ed9718f17fbdb472e33b869c77a16d0b55e166b100ec57b016dc7de9c8d236bf"},
|
||||
|
|
@ -1197,6 +1216,71 @@ files = [
|
|||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.70.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.71.0"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.11\" and extra == \"extra-proxy\""
|
||||
files = [
|
||||
{file = "grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:0ab8b2864396663a5b0b0d6d79495657ae85fa37dcb6498a2669d067c65c11ea"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c30f393f9d5ff00a71bb56de4aa75b8fe91b161aeb61d39528db6b768d7eac69"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f250ff44843d9a0615e350c77f890082102a0318d66a99540f54769c8766ab73"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e6d8de076528f7c43a2f576bc311799f89d795aa6c9b637377cc2b1616473804"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:9b91879d6da1605811ebc60d21ab6a7e4bae6c35f6b63a061d61eb818c8168f6"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f71574afdf944e6652203cd1badcda195b2a27d9c83e6d88dc1ce3cfb73b31a5"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-win32.whl", hash = "sha256:8997d6785e93308f277884ee6899ba63baafa0dfb4729748200fcc537858a509"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-win_amd64.whl", hash = "sha256:7d6ac9481d9d0d129224f6d5934d5832c4b1cddb96b59e7eba8416868909786a"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:d6aa986318c36508dc1d5001a3ff169a15b99b9f96ef5e98e13522c506b37eef"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-macosx_10_14_universal2.whl", hash = "sha256:d2c170247315f2d7e5798a22358e982ad6eeb68fa20cf7a820bb74c11f0736e7"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:e6f83a583ed0a5b08c5bc7a3fe860bb3c2eac1f03f1f63e0bc2091325605d2b7"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4be74ddeeb92cc87190e0e376dbc8fc7736dbb6d3d454f2fa1f5be1dee26b9d7"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dd0dfbe4d5eb1fcfec9490ca13f82b089a309dc3678e2edabc144051270a66e"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:a2242d6950dc892afdf9e951ed7ff89473aaf744b7d5727ad56bdaace363722b"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:0fa05ee31a20456b13ae49ad2e5d585265f71dd19fbd9ef983c28f926d45d0a7"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3d081e859fb1ebe176de33fc3adb26c7d46b8812f906042705346b314bde32c3"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-win32.whl", hash = "sha256:d6de81c9c00c8a23047136b11794b3584cdc1460ed7cbc10eada50614baa1444"},
|
||||
{file = "grpcio-1.71.0-cp311-cp311-win_amd64.whl", hash = "sha256:24e867651fc67717b6f896d5f0cac0ec863a8b5fb7d6441c2ab428f52c651c6b"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:0ff35c8d807c1c7531d3002be03221ff9ae15712b53ab46e2a0b4bb271f38537"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-macosx_10_14_universal2.whl", hash = "sha256:b78a99cd1ece4be92ab7c07765a0b038194ded2e0a26fd654591ee136088d8d7"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:dc1a1231ed23caac1de9f943d031f1bc38d0f69d2a3b243ea0d664fc1fbd7fec"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e6beeea5566092c5e3c4896c6d1d307fb46b1d4bdf3e70c8340b190a69198594"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d5170929109450a2c031cfe87d6716f2fae39695ad5335d9106ae88cc32dc84c"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:5b08d03ace7aca7b2fadd4baf291139b4a5f058805a8327bfe9aece7253b6d67"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:f903017db76bf9cc2b2d8bdd37bf04b505bbccad6be8a81e1542206875d0e9db"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:469f42a0b410883185eab4689060a20488a1a0a00f8bbb3cbc1061197b4c5a79"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-win32.whl", hash = "sha256:ad9f30838550695b5eb302add33f21f7301b882937460dd24f24b3cc5a95067a"},
|
||||
{file = "grpcio-1.71.0-cp312-cp312-win_amd64.whl", hash = "sha256:652350609332de6dac4ece254e5d7e1ff834e203d6afb769601f286886f6f3a8"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:cebc1b34ba40a312ab480ccdb396ff3c529377a2fce72c45a741f7215bfe8379"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-macosx_10_14_universal2.whl", hash = "sha256:85da336e3649a3d2171e82f696b5cad2c6231fdd5bad52616476235681bee5b3"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-manylinux_2_17_aarch64.whl", hash = "sha256:f9a412f55bb6e8f3bb000e020dbc1e709627dcb3a56f6431fa7076b4c1aab0db"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47be9584729534660416f6d2a3108aaeac1122f6b5bdbf9fd823e11fe6fbaa29"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7c9c80ac6091c916db81131d50926a93ab162a7e97e4428ffc186b6e80d6dda4"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:789d5e2a3a15419374b7b45cd680b1e83bbc1e52b9086e49308e2c0b5bbae6e3"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:1be857615e26a86d7363e8a163fade914595c81fec962b3d514a4b1e8760467b"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:a76d39b5fafd79ed604c4be0a869ec3581a172a707e2a8d7a4858cb05a5a7637"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-win32.whl", hash = "sha256:74258dce215cb1995083daa17b379a1a5a87d275387b7ffe137f1d5131e2cfbb"},
|
||||
{file = "grpcio-1.71.0-cp313-cp313-win_amd64.whl", hash = "sha256:22c3bc8d488c039a199f7a003a38cb7635db6656fa96437a8accde8322ce2366"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:c6a0a28450c16809f94e0b5bfe52cabff63e7e4b97b44123ebf77f448534d07d"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-macosx_10_14_universal2.whl", hash = "sha256:a371e6b6a5379d3692cc4ea1cb92754d2a47bdddeee755d3203d1f84ae08e03e"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:39983a9245d37394fd59de71e88c4b295eb510a3555e0a847d9965088cdbd033"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9182e0063112e55e74ee7584769ec5a0b4f18252c35787f48738627e23a62b97"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:693bc706c031aeb848849b9d1c6b63ae6bcc64057984bb91a542332b75aa4c3d"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:20e8f653abd5ec606be69540f57289274c9ca503ed38388481e98fa396ed0b41"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:8700a2a57771cc43ea295296330daaddc0d93c088f0a35cc969292b6db959bf3"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d35a95f05a8a2cbe8e02be137740138b3b2ea5f80bd004444e4f9a1ffc511e32"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-win32.whl", hash = "sha256:f9c30c464cb2ddfbc2ddf9400287701270fdc0f14be5f08a1e3939f1e749b455"},
|
||||
{file = "grpcio-1.71.0-cp39-cp39-win_amd64.whl", hash = "sha256:63e41b91032f298b3e973b3fa4093cbbc620c875e2da7b93e249d4728b54559a"},
|
||||
{file = "grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.71.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio-status"
|
||||
version = "1.70.0"
|
||||
|
|
@ -1204,7 +1288,7 @@ description = "Status proto mapping for gRPC"
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
markers = "extra == \"extra-proxy\" and python_version < \"3.11\""
|
||||
files = [
|
||||
{file = "grpcio_status-1.70.0-py3-none-any.whl", hash = "sha256:fc5a2ae2b9b1c1969cc49f3262676e6854aa2398ec69cb5bd6c47cd501904a85"},
|
||||
{file = "grpcio_status-1.70.0.tar.gz", hash = "sha256:0e7b42816512433b18b9d764285ff029bde059e9d41f8fe10a60631bd8348101"},
|
||||
|
|
@ -1215,6 +1299,24 @@ googleapis-common-protos = ">=1.5.5"
|
|||
grpcio = ">=1.70.0"
|
||||
protobuf = ">=5.26.1,<6.0dev"
|
||||
|
||||
[[package]]
|
||||
name = "grpcio-status"
|
||||
version = "1.71.0"
|
||||
description = "Status proto mapping for gRPC"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.11\" and extra == \"extra-proxy\""
|
||||
files = [
|
||||
{file = "grpcio_status-1.71.0-py3-none-any.whl", hash = "sha256:843934ef8c09e3e858952887467f8256aac3910c55f077a359a65b2b3cde3e68"},
|
||||
{file = "grpcio_status-1.71.0.tar.gz", hash = "sha256:11405fed67b68f406b3f3c7c5ae5104a79d2d309666d10d61b152e91d28fb968"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
googleapis-common-protos = ">=1.5.5"
|
||||
grpcio = ">=1.71.0"
|
||||
protobuf = ">=5.26.1,<6.0dev"
|
||||
|
||||
[[package]]
|
||||
name = "gunicorn"
|
||||
version = "23.0.0"
|
||||
|
|
@ -1332,6 +1434,22 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gr
|
|||
torch = ["safetensors[torch]", "torch"]
|
||||
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "humanfriendly"
|
||||
version = "10.0"
|
||||
description = "Human friendly output for text interfaces using Python"
|
||||
optional = true
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
|
||||
{file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.10"
|
||||
|
|
@ -1397,14 +1515,14 @@ type = ["pytest-mypy"]
|
|||
|
||||
[[package]]
|
||||
name = "iniconfig"
|
||||
version = "2.0.0"
|
||||
version = "2.1.0"
|
||||
description = "brain-dead simple config-ini parsing"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev"]
|
||||
files = [
|
||||
{file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
|
||||
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
|
||||
{file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
|
||||
{file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1659,6 +1777,45 @@ files = [
|
|||
{file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ml-dtypes"
|
||||
version = "0.4.1"
|
||||
description = ""
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "ml_dtypes-0.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:1fe8b5b5e70cd67211db94b05cfd58dace592f24489b038dc6f9fe347d2e07d5"},
|
||||
{file = "ml_dtypes-0.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c09a6d11d8475c2a9fd2bc0695628aec105f97cab3b3a3fb7c9660348ff7d24"},
|
||||
{file = "ml_dtypes-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9f5e8f75fa371020dd30f9196e7d73babae2abd51cf59bdd56cb4f8de7e13354"},
|
||||
{file = "ml_dtypes-0.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:15fdd922fea57e493844e5abb930b9c0bd0af217d9edd3724479fc3d7ce70e3f"},
|
||||
{file = "ml_dtypes-0.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2d55b588116a7085d6e074cf0cdb1d6fa3875c059dddc4d2c94a4cc81c23e975"},
|
||||
{file = "ml_dtypes-0.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e138a9b7a48079c900ea969341a5754019a1ad17ae27ee330f7ebf43f23877f9"},
|
||||
{file = "ml_dtypes-0.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:74c6cfb5cf78535b103fde9ea3ded8e9f16f75bc07789054edc7776abfb3d752"},
|
||||
{file = "ml_dtypes-0.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:274cc7193dd73b35fb26bef6c5d40ae3eb258359ee71cd82f6e96a8c948bdaa6"},
|
||||
{file = "ml_dtypes-0.4.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:827d3ca2097085cf0355f8fdf092b888890bb1b1455f52801a2d7756f056f54b"},
|
||||
{file = "ml_dtypes-0.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:772426b08a6172a891274d581ce58ea2789cc8abc1c002a27223f314aaf894e7"},
|
||||
{file = "ml_dtypes-0.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:126e7d679b8676d1a958f2651949fbfa182832c3cd08020d8facd94e4114f3e9"},
|
||||
{file = "ml_dtypes-0.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:df0fb650d5c582a9e72bb5bd96cfebb2cdb889d89daff621c8fbc60295eba66c"},
|
||||
{file = "ml_dtypes-0.4.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e35e486e97aee577d0890bc3bd9e9f9eece50c08c163304008587ec8cfe7575b"},
|
||||
{file = "ml_dtypes-0.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:560be16dc1e3bdf7c087eb727e2cf9c0e6a3d87e9f415079d2491cc419b3ebf5"},
|
||||
{file = "ml_dtypes-0.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad0b757d445a20df39035c4cdeed457ec8b60d236020d2560dbc25887533cf50"},
|
||||
{file = "ml_dtypes-0.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:ef0d7e3fece227b49b544fa69e50e607ac20948f0043e9f76b44f35f229ea450"},
|
||||
{file = "ml_dtypes-0.4.1.tar.gz", hash = "sha256:fad5f2de464fd09127e49b7fd1252b9006fb43d2edc1ff112d390c324af5ca7a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
{version = ">1.20", markers = "python_version < \"3.10\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "msal"
|
||||
version = "1.32.0"
|
||||
|
|
@ -1889,6 +2046,119 @@ files = [
|
|||
{file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.26.4"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.12\""
|
||||
files = [
|
||||
{file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
|
||||
{file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
|
||||
{file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
|
||||
{file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
|
||||
{file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
|
||||
{file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
|
||||
{file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
|
||||
{file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
|
||||
{file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.2.4"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = true
|
||||
python-versions = ">=3.10"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.12\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8146f3550d627252269ac42ae660281d673eb6f8b32f113538e0cc2a9aed42b9"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e642d86b8f956098b564a45e6f6ce68a22c2c97a04f5acd3f221f57b8cb850ae"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:a84eda42bd12edc36eb5b53bbcc9b406820d3353f1994b6cfe453a33ff101775"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:4ba5054787e89c59c593a4169830ab362ac2bee8a969249dc56e5d7d20ff8df9"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7716e4a9b7af82c06a2543c53ca476fa0b57e4d760481273e09da04b74ee6ee2"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:adf8c1d66f432ce577d0197dceaac2ac00c0759f573f28516246351c58a85020"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:218f061d2faa73621fa23d6359442b0fc658d5b9a70801373625d958259eaca3"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:df2f57871a96bbc1b69733cd4c51dc33bea66146b8c63cacbfed73eec0883017"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-win32.whl", hash = "sha256:a0258ad1f44f138b791327961caedffbf9612bfa504ab9597157806faa95194a"},
|
||||
{file = "numpy-2.2.4-cp310-cp310-win_amd64.whl", hash = "sha256:0d54974f9cf14acf49c60f0f7f4084b6579d24d439453d5fc5805d46a165b542"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e9e0a277bb2eb5d8a7407e14688b85fd8ad628ee4e0c7930415687b6564207a4"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9eeea959168ea555e556b8188da5fa7831e21d91ce031e95ce23747b7609f8a4"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:bd3ad3b0a40e713fc68f99ecfd07124195333f1e689387c180813f0e94309d6f"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:cf28633d64294969c019c6df4ff37f5698e8326db68cc2b66576a51fad634880"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2fa8fa7697ad1646b5c93de1719965844e004fcad23c91228aca1cf0800044a1"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4162988a360a29af158aeb4a2f4f09ffed6a969c9776f8f3bdee9b06a8ab7e5"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:892c10d6a73e0f14935c31229e03325a7b3093fafd6ce0af704be7f894d95687"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:db1f1c22173ac1c58db249ae48aa7ead29f534b9a948bc56828337aa84a32ed6"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-win32.whl", hash = "sha256:ea2bb7e2ae9e37d96835b3576a4fa4b3a97592fbea8ef7c3587078b0068b8f09"},
|
||||
{file = "numpy-2.2.4-cp311-cp311-win_amd64.whl", hash = "sha256:f7de08cbe5551911886d1ab60de58448c6df0f67d9feb7d1fb21e9875ef95e91"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a7b9084668aa0f64e64bd00d27ba5146ef1c3a8835f3bd912e7a9e01326804c4"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:dbe512c511956b893d2dacd007d955a3f03d555ae05cfa3ff1c1ff6df8851854"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:bb649f8b207ab07caebba230d851b579a3c8711a851d29efe15008e31bb4de24"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:f34dc300df798742b3d06515aa2a0aee20941c13579d7a2f2e10af01ae4901ee"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3f7ac96b16955634e223b579a3e5798df59007ca43e8d451a0e6a50f6bfdfba"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f92084defa704deadd4e0a5ab1dc52d8ac9e8a8ef617f3fbb853e79b0ea3592"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7a4e84a6283b36632e2a5b56e121961f6542ab886bc9e12f8f9818b3c266bfbb"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:11c43995255eb4127115956495f43e9343736edb7fcdb0d973defd9de14cd84f"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-win32.whl", hash = "sha256:65ef3468b53269eb5fdb3a5c09508c032b793da03251d5f8722b1194f1790c00"},
|
||||
{file = "numpy-2.2.4-cp312-cp312-win_amd64.whl", hash = "sha256:2aad3c17ed2ff455b8eaafe06bcdae0062a1db77cb99f4b9cbb5f4ecb13c5146"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1cf4e5c6a278d620dee9ddeb487dc6a860f9b199eadeecc567f777daace1e9e7"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1974afec0b479e50438fc3648974268f972e2d908ddb6d7fb634598cdb8260a0"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:79bd5f0a02aa16808fcbc79a9a376a147cc1045f7dfe44c6e7d53fa8b8a79392"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:3387dd7232804b341165cedcb90694565a6015433ee076c6754775e85d86f1fc"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6f527d8fdb0286fd2fd97a2a96c6be17ba4232da346931d967a0630050dfd298"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bce43e386c16898b91e162e5baaad90c4b06f9dcbe36282490032cec98dc8ae7"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:31504f970f563d99f71a3512d0c01a645b692b12a63630d6aafa0939e52361e6"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:81413336ef121a6ba746892fad881a83351ee3e1e4011f52e97fba79233611fd"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-win32.whl", hash = "sha256:f486038e44caa08dbd97275a9a35a283a8f1d2f0ee60ac260a1790e76660833c"},
|
||||
{file = "numpy-2.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:207a2b8441cc8b6a2a78c9ddc64d00d20c303d79fba08c577752f080c4007ee3"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8120575cb4882318c791f839a4fd66161a6fa46f3f0a5e613071aae35b5dd8f8"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a761ba0fa886a7bb33c6c8f6f20213735cb19642c580a931c625ee377ee8bd39"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:ac0280f1ba4a4bfff363a99a6aceed4f8e123f8a9b234c89140f5e894e452ecd"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:879cf3a9a2b53a4672a168c21375166171bc3932b7e21f622201811c43cdd3b0"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f05d4198c1bacc9124018109c5fba2f3201dbe7ab6e92ff100494f236209c960"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2f085ce2e813a50dfd0e01fbfc0c12bbe5d2063d99f8b29da30e544fb6483b8"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:92bda934a791c01d6d9d8e038363c50918ef7c40601552a58ac84c9613a665bc"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ee4d528022f4c5ff67332469e10efe06a267e32f4067dc76bb7e2cddf3cd25ff"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-win32.whl", hash = "sha256:05c076d531e9998e7e694c36e8b349969c56eadd2cdcd07242958489d79a7286"},
|
||||
{file = "numpy-2.2.4-cp313-cp313t-win_amd64.whl", hash = "sha256:188dcbca89834cc2e14eb2f106c96d6d46f200fe0200310fc29089657379c58d"},
|
||||
{file = "numpy-2.2.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7051ee569db5fbac144335e0f3b9c2337e0c8d5c9fee015f259a5bd70772b7e8"},
|
||||
{file = "numpy-2.2.4-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:ab2939cd5bec30a7430cbdb2287b63151b77cf9624de0532d629c9a1c59b1d5c"},
|
||||
{file = "numpy-2.2.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d0f35b19894a9e08639fd60a1ec1978cb7f5f7f1eace62f38dd36be8aecdef4d"},
|
||||
{file = "numpy-2.2.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:b4adfbbc64014976d2f91084915ca4e626fbf2057fb81af209c1a6d776d23e3d"},
|
||||
{file = "numpy-2.2.4.tar.gz", hash = "sha256:9ba03692a45d3eef66559efe1d1096c4b9b75c0986b5dff5530c378fb8331d4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "oauthlib"
|
||||
version = "3.2.2"
|
||||
|
|
@ -1909,14 +2179,14 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.66.3"
|
||||
version = "1.68.2"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "openai-1.66.3-py3-none-any.whl", hash = "sha256:a427c920f727711877ab17c11b95f1230b27767ba7a01e5b66102945141ceca9"},
|
||||
{file = "openai-1.66.3.tar.gz", hash = "sha256:8dde3aebe2d081258d4159c4cb27bdc13b5bb3f7ea2201d9bd940b9a89faf0c9"},
|
||||
{file = "openai-1.68.2-py3-none-any.whl", hash = "sha256:24484cb5c9a33b58576fdc5acf0e5f92603024a4e39d0b99793dfa1eb14c2b36"},
|
||||
{file = "openai-1.68.2.tar.gz", hash = "sha256:b720f0a95a1dbe1429c0d9bb62096a0d98057bcda82516f6e8af10284bdd5b19"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1932,6 +2202,7 @@ typing-extensions = ">=4.11,<5"
|
|||
[package.extras]
|
||||
datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
|
||||
realtime = ["websockets (>=13,<15)"]
|
||||
voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
|
|
@ -2249,24 +2520,24 @@ testing = ["google-api-core (>=1.31.5)"]
|
|||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
version = "5.29.3"
|
||||
version = "5.29.4"
|
||||
description = ""
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
files = [
|
||||
{file = "protobuf-5.29.3-cp310-abi3-win32.whl", hash = "sha256:3ea51771449e1035f26069c4c7fd51fba990d07bc55ba80701c78f886bf9c888"},
|
||||
{file = "protobuf-5.29.3-cp310-abi3-win_amd64.whl", hash = "sha256:a4fa6f80816a9a0678429e84973f2f98cbc218cca434abe8db2ad0bffc98503a"},
|
||||
{file = "protobuf-5.29.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:a8434404bbf139aa9e1300dbf989667a83d42ddda9153d8ab76e0d5dcaca484e"},
|
||||
{file = "protobuf-5.29.3-cp38-abi3-manylinux2014_aarch64.whl", hash = "sha256:daaf63f70f25e8689c072cfad4334ca0ac1d1e05a92fc15c54eb9cf23c3efd84"},
|
||||
{file = "protobuf-5.29.3-cp38-abi3-manylinux2014_x86_64.whl", hash = "sha256:c027e08a08be10b67c06bf2370b99c811c466398c357e615ca88c91c07f0910f"},
|
||||
{file = "protobuf-5.29.3-cp38-cp38-win32.whl", hash = "sha256:84a57163a0ccef3f96e4b6a20516cedcf5bb3a95a657131c5c3ac62200d23252"},
|
||||
{file = "protobuf-5.29.3-cp38-cp38-win_amd64.whl", hash = "sha256:b89c115d877892a512f79a8114564fb435943b59067615894c3b13cd3e1fa107"},
|
||||
{file = "protobuf-5.29.3-cp39-cp39-win32.whl", hash = "sha256:0eb32bfa5219fc8d4111803e9a690658aa2e6366384fd0851064b963b6d1f2a7"},
|
||||
{file = "protobuf-5.29.3-cp39-cp39-win_amd64.whl", hash = "sha256:6ce8cc3389a20693bfde6c6562e03474c40851b44975c9b2bf6df7d8c4f864da"},
|
||||
{file = "protobuf-5.29.3-py3-none-any.whl", hash = "sha256:0a18ed4a24198528f2333802eb075e59dea9d679ab7a6c5efb017a59004d849f"},
|
||||
{file = "protobuf-5.29.3.tar.gz", hash = "sha256:5da0f41edaf117bde316404bad1a486cb4ededf8e4a54891296f648e8e076620"},
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win32.whl", hash = "sha256:13eb236f8eb9ec34e63fc8b1d6efd2777d062fa6aaa68268fb67cf77f6839ad7"},
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win_amd64.whl", hash = "sha256:bcefcdf3976233f8a502d265eb65ea740c989bacc6c30a58290ed0e519eb4b8d"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:307ecba1d852ec237e9ba668e087326a67564ef83e45a0189a772ede9e854dd0"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-manylinux2014_aarch64.whl", hash = "sha256:aec4962f9ea93c431d5714ed1be1c93f13e1a8618e70035ba2b0564d9e633f2e"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-manylinux2014_x86_64.whl", hash = "sha256:d7d3f7d1d5a66ed4942d4fefb12ac4b14a29028b209d4bfb25c68ae172059922"},
|
||||
{file = "protobuf-5.29.4-cp38-cp38-win32.whl", hash = "sha256:1832f0515b62d12d8e6ffc078d7e9eb06969aa6dc13c13e1036e39d73bebc2de"},
|
||||
{file = "protobuf-5.29.4-cp38-cp38-win_amd64.whl", hash = "sha256:476cb7b14914c780605a8cf62e38c2a85f8caff2e28a6a0bad827ec7d6c85d68"},
|
||||
{file = "protobuf-5.29.4-cp39-cp39-win32.whl", hash = "sha256:fd32223020cb25a2cc100366f1dedc904e2d71d9322403224cdde5fdced0dabe"},
|
||||
{file = "protobuf-5.29.4-cp39-cp39-win_amd64.whl", hash = "sha256:678974e1e3a9b975b8bc2447fca458db5f93a2fb6b0c8db46b6675b5b5346812"},
|
||||
{file = "protobuf-5.29.4-py3-none-any.whl", hash = "sha256:3fde11b505e1597f71b875ef2fc52062b6a9740e5f7c8997ce878b6009145862"},
|
||||
{file = "protobuf-5.29.4.tar.gz", hash = "sha256:4f1dfcd7997b31ef8f53ec82781ff434a28bf71d9102ddde14d076adcfc78c99"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2520,6 +2791,22 @@ cffi = ">=1.4.1"
|
|||
docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
|
||||
tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pyreadline3"
|
||||
version = "3.5.4"
|
||||
description = "A python implementation of GNU readline."
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and sys_platform == \"win32\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
|
||||
{file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
dev = ["build", "flake8", "mypy", "pytest", "twine"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "7.4.4"
|
||||
|
|
@ -2543,6 +2830,25 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
|
|||
[package.extras]
|
||||
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-asyncio"
|
||||
version = "0.21.2"
|
||||
description = "Pytest support for asyncio"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["dev"]
|
||||
files = [
|
||||
{file = "pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b"},
|
||||
{file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pytest = ">=7.0.0"
|
||||
|
||||
[package.extras]
|
||||
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
|
||||
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-mock"
|
||||
version = "3.14.0"
|
||||
|
|
@ -2605,6 +2911,22 @@ files = [
|
|||
{file = "python_multipart-0.0.18.tar.gz", hash = "sha256:7a68db60c8bfb82e460637fa4750727b45af1d5e2ed215593f917f64694d34fe"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "python-ulid"
|
||||
version = "3.0.0"
|
||||
description = "Universally unique lexicographically sortable identifier"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "python_ulid-3.0.0-py3-none-any.whl", hash = "sha256:e4c4942ff50dbd79167ad01ac725ec58f924b4018025ce22c858bfcff99a5e31"},
|
||||
{file = "python_ulid-3.0.0.tar.gz", hash = "sha256:e50296a47dc8209d28629a22fc81ca26c00982c78934bd7766377ba37ea49a9f"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
pydantic = ["pydantic (>=2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pyyaml"
|
||||
version = "6.0.2"
|
||||
|
|
@ -2675,7 +2997,7 @@ description = "Python client for Redis database and key-value store"
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"proxy\""
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\" or extra == \"proxy\""
|
||||
files = [
|
||||
{file = "redis-5.2.1-py3-none-any.whl", hash = "sha256:ee7e1056b9aea0f04c6c2ed59452947f34c4940ee025f5dd83e6a6418b6989e4"},
|
||||
{file = "redis-5.2.1.tar.gz", hash = "sha256:16f2e22dff21d5125e8481515e386711a34cbec50f0e44413dd7d9c060a54e0f"},
|
||||
|
|
@ -2688,6 +3010,42 @@ async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\
|
|||
hiredis = ["hiredis (>=3.0.0)"]
|
||||
ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "redisvl"
|
||||
version = "0.4.1"
|
||||
description = "Python client library and CLI for using Redis as a vector database"
|
||||
optional = true
|
||||
python-versions = "<3.14,>=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "redisvl-0.4.1-py3-none-any.whl", hash = "sha256:6db5d5bc95b1fe8032a1cdae74ce1c65bc7fe9054e5429b5d34d5a91d28bae5f"},
|
||||
{file = "redisvl-0.4.1.tar.gz", hash = "sha256:fd6a36426ba94792c0efca20915c31232d4ee3cc58eb23794a62c142696401e6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
coloredlogs = ">=15.0,<16.0"
|
||||
ml-dtypes = ">=0.4.0,<0.5.0"
|
||||
numpy = [
|
||||
{version = ">=1,<2", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.0,<3", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
pydantic = ">=2,<3"
|
||||
python-ulid = ">=3.0.0,<4.0.0"
|
||||
pyyaml = ">=5.4,<7.0"
|
||||
redis = ">=5.0,<6.0"
|
||||
tabulate = ">=0.9.0,<0.10.0"
|
||||
tenacity = ">=8.2.2"
|
||||
|
||||
[package.extras]
|
||||
bedrock = ["boto3[bedrock] (>=1.36.0,<2.0.0)"]
|
||||
cohere = ["cohere (>=4.44)"]
|
||||
mistralai = ["mistralai (>=1.0.0)"]
|
||||
openai = ["openai (>=1.13.0,<2.0.0)"]
|
||||
sentence-transformers = ["scipy (<1.15)", "scipy (>=1.15,<2.0)", "sentence-transformers (>=3.4.0,<4.0.0)"]
|
||||
vertexai = ["google-cloud-aiplatform (>=1.26,<2.0)", "protobuf (>=5.29.1,<6.0.0)"]
|
||||
voyageai = ["voyageai (>=0.2.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "referencing"
|
||||
version = "0.35.1"
|
||||
|
|
@ -2961,15 +3319,15 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "rq"
|
||||
version = "2.1.0"
|
||||
version = "2.2.0"
|
||||
description = "RQ is a simple, lightweight, library for creating background jobs, and processing them."
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"proxy\""
|
||||
files = [
|
||||
{file = "rq-2.1.0-py3-none-any.whl", hash = "sha256:3c6892c6ca848e5fb47c1875399a66f13656bf0e123bf725d9aa9a12718e2fdf"},
|
||||
{file = "rq-2.1.0.tar.gz", hash = "sha256:764585b6cab69ef1412f4aee523347e5aa7ece3ca175c118b1d92223dd8c2826"},
|
||||
{file = "rq-2.2.0-py3-none-any.whl", hash = "sha256:dacbfe1ccb79a45c8cd95dec7951620679fa0195570b63da3f9347622d33accc"},
|
||||
{file = "rq-2.2.0.tar.gz", hash = "sha256:b636760f1e4c183022031c142faa0483e687885824e9732ba2953f994104e203"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3056,6 +3414,39 @@ typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""
|
|||
[package.extras]
|
||||
full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.9.0"
|
||||
description = "Pretty-print tabular data"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"},
|
||||
{file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
widechars = ["wcwidth"]
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "9.0.0"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.9\" and extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
|
||||
{file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx"]
|
||||
test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.7.0"
|
||||
|
|
@ -3234,15 +3625,15 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2025.1"
|
||||
version = "2025.2"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = true
|
||||
python-versions = ">=2"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"proxy\" and platform_system == \"Windows\""
|
||||
files = [
|
||||
{file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
|
||||
{file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
|
||||
{file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
|
||||
{file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3607,10 +3998,10 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
|
|||
type = ["pytest-mypy"]
|
||||
|
||||
[extras]
|
||||
extra-proxy = ["azure-identity", "azure-keyvault-secrets", "google-cloud-kms", "prisma", "resend"]
|
||||
extra-proxy = ["azure-identity", "azure-keyvault-secrets", "google-cloud-kms", "prisma", "redisvl", "resend"]
|
||||
proxy = ["PyJWT", "apscheduler", "backoff", "boto3", "cryptography", "fastapi", "fastapi-sso", "gunicorn", "orjson", "pynacl", "python-multipart", "pyyaml", "rq", "uvicorn", "uvloop", "websockets"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.8.1,<4.0, !=3.9.7"
|
||||
content-hash = "55078af47c1af79bd3ebadacb7ba92844d550a577bb0c49f5096693701ea4322"
|
||||
content-hash = "6850286db1cedd6507c4688767fde27c2f8cc8e657a0a0d792656664eec63d5d"
|
||||
|
|
|
|||
|
|
@ -53,6 +53,7 @@ resend = {version = "^0.8.0", optional = true}
|
|||
pynacl = {version = "^1.5.0", optional = true}
|
||||
websockets = {version = "^13.1.0", optional = true}
|
||||
boto3 = {version = "1.34.34", optional = true}
|
||||
redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
|
||||
|
||||
[tool.poetry.extras]
|
||||
proxy = [
|
||||
|
|
@ -80,6 +81,7 @@ extra_proxy = [
|
|||
"azure-keyvault-secrets",
|
||||
"google-cloud-kms",
|
||||
"resend",
|
||||
"redisvl"
|
||||
]
|
||||
|
||||
[tool.isort]
|
||||
|
|
@ -94,6 +96,7 @@ black = "^23.12.0"
|
|||
mypy = "^1.0"
|
||||
pytest = "^7.4.3"
|
||||
pytest-mock = "^3.12.0"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core", "wheel"]
|
||||
|
|
|
|||
|
|
@ -9,8 +9,8 @@ uvicorn==0.29.0 # server dep
|
|||
gunicorn==23.0.0 # server dep
|
||||
uvloop==0.21.0 # uvicorn dep, gives us much better performance under load
|
||||
boto3==1.34.34 # aws bedrock/sagemaker calls
|
||||
redis==5.0.0 # caching
|
||||
numpy==2.1.1 # semantic caching
|
||||
redis==5.2.1 # redis caching
|
||||
redisvl==0.4.1 # semantic caching
|
||||
prisma==0.11.0 # for db
|
||||
mangum==0.17.0 # for aws lambda functions
|
||||
pynacl==1.5.0 # for encrypting keys
|
||||
|
|
|
|||
|
|
@ -1,13 +1,8 @@
|
|||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
import respx
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
sys.path.insert(
|
||||
|
|
@ -18,9 +13,18 @@ from unittest.mock import AsyncMock
|
|||
from litellm.caching.redis_cache import RedisCache
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def redis_no_ping():
|
||||
"""Patch RedisCache initialization to prevent async ping tasks from being created"""
|
||||
with patch('asyncio.get_running_loop') as mock_get_loop:
|
||||
# Either raise an exception or return a mock that will handle the task creation
|
||||
mock_get_loop.side_effect = RuntimeError("No running event loop")
|
||||
yield
|
||||
|
||||
|
||||
@pytest.mark.parametrize("namespace", [None, "test"])
|
||||
@pytest.mark.asyncio
|
||||
async def test_redis_cache_async_increment(namespace, monkeypatch):
|
||||
async def test_redis_cache_async_increment(namespace, monkeypatch, redis_no_ping):
|
||||
monkeypatch.setenv("REDIS_HOST", "https://my-test-host")
|
||||
redis_cache = RedisCache(namespace=namespace)
|
||||
# Create an AsyncMock for the Redis client
|
||||
|
|
@ -47,10 +51,46 @@ async def test_redis_cache_async_increment(namespace, monkeypatch):
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_redis_client_init_with_socket_timeout(monkeypatch):
|
||||
async def test_redis_client_init_with_socket_timeout(monkeypatch, redis_no_ping):
|
||||
monkeypatch.setenv("REDIS_HOST", "my-fake-host")
|
||||
redis_cache = RedisCache(socket_timeout=1.0)
|
||||
assert redis_cache.redis_kwargs["socket_timeout"] == 1.0
|
||||
client = redis_cache.init_async_client()
|
||||
assert client is not None
|
||||
assert client.connection_pool.connection_kwargs["socket_timeout"] == 1.0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_redis_cache_async_batch_get_cache(monkeypatch, redis_no_ping):
|
||||
monkeypatch.setenv("REDIS_HOST", "https://my-test-host")
|
||||
redis_cache = RedisCache()
|
||||
|
||||
# Create an AsyncMock for the Redis client
|
||||
mock_redis_instance = AsyncMock()
|
||||
|
||||
# Make sure the mock can be used as an async context manager
|
||||
mock_redis_instance.__aenter__.return_value = mock_redis_instance
|
||||
mock_redis_instance.__aexit__.return_value = None
|
||||
|
||||
# Setup the return value for mget
|
||||
mock_redis_instance.mget.return_value = [
|
||||
b'{"key1": "value1"}',
|
||||
None,
|
||||
b'{"key3": "value3"}'
|
||||
]
|
||||
|
||||
test_keys = ["key1", "key2", "key3"]
|
||||
|
||||
with patch.object(
|
||||
redis_cache, "init_async_client", return_value=mock_redis_instance
|
||||
):
|
||||
# Call async_batch_get_cache
|
||||
result = await redis_cache.async_batch_get_cache(key_list=test_keys)
|
||||
|
||||
# Verify mget was called with the correct keys
|
||||
mock_redis_instance.mget.assert_called_once()
|
||||
|
||||
# Check that results were properly decoded
|
||||
assert result["key1"] == {"key1": "value1"}
|
||||
assert result["key2"] is None
|
||||
assert result["key3"] == {"key3": "value3"}
|
||||
|
|
|
|||
130
tests/litellm/caching/test_redis_semantic_cache.py
Normal file
130
tests/litellm/caching/test_redis_semantic_cache.py
Normal file
|
|
@ -0,0 +1,130 @@
|
|||
import os
|
||||
import sys
|
||||
from unittest.mock import MagicMock, patch, AsyncMock
|
||||
|
||||
import pytest
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../../..")
|
||||
) # Adds the parent directory to the system path
|
||||
|
||||
|
||||
# Tests for RedisSemanticCache
|
||||
def test_redis_semantic_cache_initialization(monkeypatch):
|
||||
# Mock the redisvl import
|
||||
semantic_cache_mock = MagicMock()
|
||||
with patch.dict("sys.modules", {
|
||||
"redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
|
||||
"redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=MagicMock())
|
||||
}):
|
||||
from litellm.caching.redis_semantic_cache import RedisSemanticCache
|
||||
|
||||
# Set environment variables
|
||||
monkeypatch.setenv("REDIS_HOST", "localhost")
|
||||
monkeypatch.setenv("REDIS_PORT", "6379")
|
||||
monkeypatch.setenv("REDIS_PASSWORD", "test_password")
|
||||
|
||||
# Initialize the cache with a similarity threshold
|
||||
redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
|
||||
|
||||
# Verify the semantic cache was initialized with correct parameters
|
||||
assert redis_semantic_cache.similarity_threshold == 0.8
|
||||
|
||||
# Use pytest.approx for floating point comparison to handle precision issues
|
||||
assert redis_semantic_cache.distance_threshold == pytest.approx(0.2, abs=1e-10)
|
||||
assert redis_semantic_cache.embedding_model == "text-embedding-ada-002"
|
||||
|
||||
# Test initialization with missing similarity_threshold
|
||||
with pytest.raises(ValueError, match="similarity_threshold must be provided"):
|
||||
RedisSemanticCache()
|
||||
|
||||
|
||||
def test_redis_semantic_cache_get_cache(monkeypatch):
|
||||
# Mock the redisvl import and embedding function
|
||||
semantic_cache_mock = MagicMock()
|
||||
custom_vectorizer_mock = MagicMock()
|
||||
|
||||
with patch.dict("sys.modules", {
|
||||
"redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
|
||||
"redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=custom_vectorizer_mock)
|
||||
}):
|
||||
from litellm.caching.redis_semantic_cache import RedisSemanticCache
|
||||
|
||||
# Set environment variables
|
||||
monkeypatch.setenv("REDIS_HOST", "localhost")
|
||||
monkeypatch.setenv("REDIS_PORT", "6379")
|
||||
monkeypatch.setenv("REDIS_PASSWORD", "test_password")
|
||||
|
||||
# Initialize cache
|
||||
redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
|
||||
|
||||
# Mock the llmcache.check method to return a result
|
||||
mock_result = [
|
||||
{
|
||||
"prompt": "What is the capital of France?",
|
||||
"response": '{"content": "Paris is the capital of France."}',
|
||||
"vector_distance": 0.1 # Distance of 0.1 means similarity of 0.9
|
||||
}
|
||||
]
|
||||
redis_semantic_cache.llmcache.check = MagicMock(return_value=mock_result)
|
||||
|
||||
# Mock the embedding function
|
||||
with patch("litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]}):
|
||||
# Test get_cache with a message
|
||||
result = redis_semantic_cache.get_cache(
|
||||
key="test_key",
|
||||
messages=[{"content": "What is the capital of France?"}]
|
||||
)
|
||||
|
||||
# Verify result is properly parsed
|
||||
assert result == {"content": "Paris is the capital of France."}
|
||||
|
||||
# Verify llmcache.check was called
|
||||
redis_semantic_cache.llmcache.check.assert_called_once()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_redis_semantic_cache_async_get_cache(monkeypatch):
|
||||
# Mock the redisvl import
|
||||
semantic_cache_mock = MagicMock()
|
||||
custom_vectorizer_mock = MagicMock()
|
||||
|
||||
with patch.dict("sys.modules", {
|
||||
"redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
|
||||
"redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=custom_vectorizer_mock)
|
||||
}):
|
||||
from litellm.caching.redis_semantic_cache import RedisSemanticCache
|
||||
|
||||
# Set environment variables
|
||||
monkeypatch.setenv("REDIS_HOST", "localhost")
|
||||
monkeypatch.setenv("REDIS_PORT", "6379")
|
||||
monkeypatch.setenv("REDIS_PASSWORD", "test_password")
|
||||
|
||||
# Initialize cache
|
||||
redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
|
||||
|
||||
# Mock the async methods
|
||||
mock_result = [
|
||||
{
|
||||
"prompt": "What is the capital of France?",
|
||||
"response": '{"content": "Paris is the capital of France."}',
|
||||
"vector_distance": 0.1 # Distance of 0.1 means similarity of 0.9
|
||||
}
|
||||
]
|
||||
|
||||
redis_semantic_cache.llmcache.acheck = AsyncMock(return_value=mock_result)
|
||||
redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
|
||||
|
||||
# Test async_get_cache with a message
|
||||
result = await redis_semantic_cache.async_get_cache(
|
||||
key="test_key",
|
||||
messages=[{"content": "What is the capital of France?"}],
|
||||
metadata={}
|
||||
)
|
||||
|
||||
# Verify result is properly parsed
|
||||
assert result == {"content": "Paris is the capital of France."}
|
||||
|
||||
# Verify methods were called
|
||||
redis_semantic_cache._get_async_embedding.assert_called_once()
|
||||
redis_semantic_cache.llmcache.acheck.assert_called_once()
|
||||
|
|
@ -794,7 +794,7 @@ def test_redis_cache_completion():
|
|||
response3 = completion(
|
||||
model="gpt-3.5-turbo", messages=messages, caching=True, temperature=0.5
|
||||
)
|
||||
response4 = completion(model="azure/chatgpt-v-2", messages=messages, caching=True)
|
||||
response4 = completion(model="gpt-4o-mini", messages=messages, caching=True)
|
||||
|
||||
print("\nresponse 1", response1)
|
||||
print("\nresponse 2", response2)
|
||||
|
|
@ -1690,20 +1690,12 @@ def test_cache_context_managers():
|
|||
print("VARS of litellm.cache", vars(litellm.cache))
|
||||
|
||||
|
||||
# test_cache_context_managers()
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="beta test - new redis semantic cache")
|
||||
def test_redis_semantic_cache_completion():
|
||||
litellm.set_verbose = True
|
||||
import logging
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
random_number = random.randint(
|
||||
1, 100000
|
||||
) # add a random number to ensure it's always adding /reading from cache
|
||||
|
||||
print("testing semantic caching")
|
||||
litellm.cache = Cache(
|
||||
type="redis-semantic",
|
||||
|
|
@ -1718,33 +1710,30 @@ def test_redis_semantic_cache_completion():
|
|||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"write a one sentence poem about: {random_number}",
|
||||
"content": "write a one sentence poem about summer",
|
||||
}
|
||||
],
|
||||
max_tokens=20,
|
||||
)
|
||||
print(f"response1: {response1}")
|
||||
|
||||
random_number = random.randint(1, 100000)
|
||||
|
||||
response2 = completion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"write a one sentence poem about: {random_number}",
|
||||
"content": "write a one sentence poem about summertime",
|
||||
}
|
||||
],
|
||||
max_tokens=20,
|
||||
)
|
||||
print(f"response2: {response1}")
|
||||
print(f"response2: {response2}")
|
||||
assert response1.id == response2.id
|
||||
|
||||
|
||||
# test_redis_cache_completion()
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="beta test - new redis semantic cache")
|
||||
@pytest.mark.asyncio
|
||||
async def test_redis_semantic_cache_acompletion():
|
||||
litellm.set_verbose = True
|
||||
|
|
@ -1752,38 +1741,32 @@ async def test_redis_semantic_cache_acompletion():
|
|||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
random_number = random.randint(
|
||||
1, 100000
|
||||
) # add a random number to ensure it's always adding / reading from cache
|
||||
|
||||
print("testing semantic caching")
|
||||
litellm.cache = Cache(
|
||||
type="redis-semantic",
|
||||
host=os.environ["REDIS_HOST"],
|
||||
port=os.environ["REDIS_PORT"],
|
||||
password=os.environ["REDIS_PASSWORD"],
|
||||
similarity_threshold=0.8,
|
||||
redis_semantic_cache_use_async=True,
|
||||
similarity_threshold=0.7,
|
||||
)
|
||||
response1 = await litellm.acompletion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"write a one sentence poem about: {random_number}",
|
||||
"content": "write a one sentence poem about summer",
|
||||
}
|
||||
],
|
||||
max_tokens=5,
|
||||
)
|
||||
print(f"response1: {response1}")
|
||||
|
||||
random_number = random.randint(1, 100000)
|
||||
response2 = await litellm.acompletion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"write a one sentence poem about: {random_number}",
|
||||
"content": "write a one sentence poem about summertime",
|
||||
}
|
||||
],
|
||||
max_tokens=5,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue