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6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
297 lines
9.1 KiB
Python
297 lines
9.1 KiB
Python
import time
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import traceback
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from litellm._uuid import uuid
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from dotenv import load_dotenv
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load_dotenv()
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import asyncio
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import hashlib
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import random
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import pytest
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import litellm
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from litellm import aembedding, completion, embedding
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from litellm.caching.caching import Cache
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from unittest.mock import AsyncMock, patch, MagicMock
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from litellm.caching.caching_handler import LLMCachingHandler, CachingHandlerResponse
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from litellm.caching.caching import LiteLLMCacheType
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from litellm.types.utils import CallTypes
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from litellm.types.rerank import RerankResponse
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from litellm.types.utils import (
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ModelResponse,
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EmbeddingResponse,
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TextCompletionResponse,
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TranscriptionResponse,
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Embedding,
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)
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from datetime import timedelta, datetime
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
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from litellm.litellm_core_utils.model_param_helper import ModelParamHelper
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from litellm._logging import verbose_logger
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import logging
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def test_get_kwargs_for_cache_key():
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_cache = litellm.Cache()
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relevant_kwargs = ModelParamHelper._get_all_llm_api_params()
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print(relevant_kwargs)
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def test_get_cache_key_chat_completion():
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cache = Cache()
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kwargs = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"temperature": 0.7,
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}
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cache_key_1 = cache.get_cache_key(**kwargs)
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assert isinstance(cache_key_1, str)
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assert len(cache_key_1) > 0
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kwargs_2 = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"max_completion_tokens": 100,
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}
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cache_key_2 = cache.get_cache_key(**kwargs_2)
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assert cache_key_1 != cache_key_2
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kwargs_3 = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"max_completion_tokens": 100,
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}
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cache_key_3 = cache.get_cache_key(**kwargs_3)
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assert cache_key_2 == cache_key_3
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def test_get_cache_key_embedding():
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cache = Cache()
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kwargs = {
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"model": "text-embedding-3-small",
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"input": "Hello, world!",
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"dimensions": 1536,
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}
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cache_key_1 = cache.get_cache_key(**kwargs)
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assert isinstance(cache_key_1, str)
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assert len(cache_key_1) > 0
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kwargs_2 = {
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"model": "text-embedding-3-small",
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"input": "Hello, world!",
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"dimensions": 1539,
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}
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cache_key_2 = cache.get_cache_key(**kwargs_2)
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assert cache_key_1 != cache_key_2
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kwargs_3 = {
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"model": "text-embedding-3-small",
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"input": "Hello, world!",
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"dimensions": 1539,
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}
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cache_key_3 = cache.get_cache_key(**kwargs_3)
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assert cache_key_2 == cache_key_3
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def test_get_cache_key_text_completion():
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cache = Cache()
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kwargs = {
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"model": "gpt-3.5-turbo",
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"prompt": "Hello, world! here is a second line",
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"best_of": 3,
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"logit_bias": {"123": 1},
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"seed": 42,
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}
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cache_key_1 = cache.get_cache_key(**kwargs)
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assert isinstance(cache_key_1, str)
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assert len(cache_key_1) > 0
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kwargs_2 = {
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"model": "gpt-3.5-turbo",
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"prompt": "Hello, world! here is a second line",
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"best_of": 30,
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}
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cache_key_2 = cache.get_cache_key(**kwargs_2)
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assert cache_key_1 != cache_key_2
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kwargs_3 = {
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"model": "gpt-3.5-turbo",
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"prompt": "Hello, world! here is a second line",
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"best_of": 30,
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}
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cache_key_3 = cache.get_cache_key(**kwargs_3)
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assert cache_key_2 == cache_key_3
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def test_get_cache_key_responses_api():
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"""
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Regression test: two /v1/responses calls that differ only in
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`instructions` (or any Responses-API-only param) must produce
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different cache keys. Mirrors the chat / embedding / text-completion
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cache-key tests above.
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"""
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cache = Cache()
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base_kwargs = {
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"model": "openai/gpt-4.1",
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"input": [{"role": "user", "content": "what is the weather"}],
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"temperature": 0.3,
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}
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kwargs_a = {
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**base_kwargs,
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"instructions": "summarize the weather on 10th May",
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}
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kwargs_b = {
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**base_kwargs,
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"instructions": "summarize the weather on 7th May",
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}
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key_a = cache.get_cache_key(**kwargs_a)
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key_b = cache.get_cache_key(**kwargs_b)
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assert isinstance(key_a, str) and len(key_a) > 0
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assert key_a != key_b, "instructions must be part of the Responses API cache key"
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# Sanity: identical payloads must still collide (cache hits still work)
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key_a_again = cache.get_cache_key(**kwargs_a)
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assert key_a == key_a_again
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# Spot-check a handful of other Responses-only params individually.
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for param, value_x, value_y in [
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("previous_response_id", "resp_aaa", "resp_bbb"),
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("reasoning", {"effort": "low"}, {"effort": "high"}),
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("include", ["reasoning.encrypted_content"], []),
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("max_output_tokens", 100, 500),
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("background", True, False),
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]:
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kx = {**base_kwargs, param: value_x}
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ky = {**base_kwargs, param: value_y}
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assert cache.get_cache_key(**kx) != cache.get_cache_key(
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**ky
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), f"Responses-API param `{param}` is not part of the cache key"
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def test_get_hashed_cache_key():
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cache = Cache()
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cache_key = "model:gpt-3.5-turbo,messages:Hello world"
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hashed_key = Cache._get_hashed_cache_key(cache_key)
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assert len(hashed_key) == 64 # SHA-256 produces a 64-character hex string
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def test_add_namespace_to_cache_key():
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cache = Cache(namespace="test_namespace")
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hashed_key = "abcdef1234567890"
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# Test with class-level namespace
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result = cache._add_namespace_to_cache_key(hashed_key)
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assert result == "test_namespace:abcdef1234567890"
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# Test with metadata namespace
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kwargs = {"metadata": {"redis_namespace": "custom_namespace"}}
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result = cache._add_namespace_to_cache_key(hashed_key, **kwargs)
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assert result == "custom_namespace:abcdef1234567890"
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# Test with cache control namespace
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kwargs = {"cache": {"namespace": "cache_control_namespace"}}
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result = cache._add_namespace_to_cache_key(hashed_key, **kwargs)
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assert result == "cache_control_namespace:abcdef1234567890"
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kwargs = {"cache": {"namespace": "cache_control_namespace-2"}}
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result = cache._add_namespace_to_cache_key(hashed_key, **kwargs)
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assert result == "cache_control_namespace-2:abcdef1234567890"
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def test_get_model_param_value():
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cache = Cache()
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# Test with regular model
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kwargs = {"model": "gpt-3.5-turbo"}
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assert cache._get_model_param_value(kwargs) == "gpt-3.5-turbo"
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# Test with model_group
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kwargs = {"model": "gpt-3.5-turbo", "metadata": {"model_group": "gpt-group"}}
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assert cache._get_model_param_value(kwargs) == "gpt-group"
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# Test with caching_group
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kwargs = {
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"model": "gpt-3.5-turbo",
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"metadata": {
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"model_group": "openai-gpt-3.5-turbo",
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"caching_groups": [("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")],
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},
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}
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assert (
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cache._get_model_param_value(kwargs)
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== "('openai-gpt-3.5-turbo', 'azure-gpt-3.5-turbo')"
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)
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kwargs = {
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"model": "gpt-3.5-turbo",
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"metadata": {
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"model_group": "azure-gpt-3.5-turbo",
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"caching_groups": [("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")],
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},
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}
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assert (
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cache._get_model_param_value(kwargs)
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== "('openai-gpt-3.5-turbo', 'azure-gpt-3.5-turbo')"
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)
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kwargs = {
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"model": "gpt-3.5-turbo",
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"metadata": {
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"model_group": "not-in-caching-group-gpt-3.5-turbo",
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"caching_groups": [("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")],
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},
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}
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assert cache._get_model_param_value(kwargs) == "not-in-caching-group-gpt-3.5-turbo"
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def test_preset_cache_key():
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"""
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Test that the preset cache key is used if it is set in kwargs["litellm_params"]
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"""
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cache = Cache()
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kwargs = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"temperature": 0.7,
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"litellm_params": {"preset_cache_key": "preset-cache-key"},
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}
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assert cache.get_cache_key(**kwargs) == "preset-cache-key"
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def test_generate_streaming_content():
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cache = Cache()
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content = "Hello, this is a test message."
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generator = cache.generate_streaming_content(content)
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full_response = ""
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chunk_count = 0
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for chunk in generator:
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chunk_count += 1
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assert "choices" in chunk
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assert len(chunk["choices"]) == 1
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assert "delta" in chunk["choices"][0]
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assert "role" in chunk["choices"][0]["delta"]
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assert chunk["choices"][0]["delta"]["role"] == "assistant"
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assert "content" in chunk["choices"][0]["delta"]
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chunk_content = chunk["choices"][0]["delta"]["content"]
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full_response += chunk_content
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# Check that each chunk is no longer than 5 characters
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assert len(chunk_content) <= 5
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print("full_response from generate_streaming_content", full_response)
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# Check that the full content is reconstructed correctly
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assert full_response == content
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# Check that there were multiple chunks
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assert chunk_count > 1
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print(f"Number of chunks: {chunk_count}")
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