mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
72e961af3c
* fix(utils.py): return citations for perplexity streaming Fixes https://github.com/BerriAI/litellm/issues/5535 * fix(anthropic/chat.py): support fallbacks for anthropic streaming (#5542) * fix(anthropic/chat.py): support fallbacks for anthropic streaming Fixes https://github.com/BerriAI/litellm/issues/5512 * fix(anthropic/chat.py): use module level http client if none given (prevents early client closure) * fix: fix linting errors * fix(http_handler.py): fix raise_for_status error handling * test: retry flaky test * fix otel type * fix(bedrock/embed): fix error raising * test(test_openai_batches_and_files.py): skip azure batches test (for now) quota exceeded * fix(test_router.py): skip azure batch route test (for now) - hit batch quota limits --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> * All `model_group_alias` should show up in `/models`, `/model/info` , `/model_group/info` (#5539) * fix(router.py): support returning model_alias model names in `/v1/models` * fix(proxy_server.py): support returning model alias'es on `/model/info` * feat(router.py): support returning model group alias for `/model_group/info` * fix(proxy_server.py): fix linting errors * fix(proxy_server.py): fix linting errors * build(model_prices_and_context_window.json): add amazon titan text premier pricing information Closes https://github.com/BerriAI/litellm/issues/5560 * feat(litellm_logging.py): log standard logging response object for pass through endpoints. Allows bedrock /invoke agent calls to be correctly logged to langfuse + s3 * fix(success_handler.py): fix linting error * fix(success_handler.py): fix linting errors * fix(team_endpoints.py): Allows admin to update team member budgets --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
419 lines
14 KiB
Python
419 lines
14 KiB
Python
import json
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import os
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import sys
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import io
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import os
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import os
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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import litellm
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from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.llms.prompt_templates.factory import anthropic_messages_pt
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# litellm.num_retries =3
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litellm.cache = None
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litellm.success_callback = []
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user_message = "Write a short poem about the sky"
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messages = [{"content": user_message, "role": "user"}]
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import logging
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from litellm._logging import verbose_logger
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def logger_fn(user_model_dict):
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print(f"user_model_dict: {user_model_dict}")
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@pytest.fixture(autouse=True)
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def reset_callbacks():
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print("\npytest fixture - resetting callbacks")
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litellm.success_callback = []
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litellm._async_success_callback = []
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litellm.failure_callback = []
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litellm.callbacks = []
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [True, False])
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async def test_completion_sagemaker(sync_mode):
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try:
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litellm.set_verbose = True
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verbose_logger.setLevel(logging.DEBUG)
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print("testing sagemaker")
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if sync_mode is True:
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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else:
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response = await litellm.acompletion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Add any assertions here to check the response
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print(response)
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cost = completion_cost(completion_response=response)
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print("calculated cost", cost)
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assert (
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cost > 0.0 and cost < 1.0
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) # should never be > $1 for a single completion call
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio()
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@pytest.mark.parametrize(
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"sync_mode",
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[True, False],
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)
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async def test_completion_sagemaker_messages_api(sync_mode):
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try:
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litellm.set_verbose = True
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verbose_logger.setLevel(logging.DEBUG)
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print("testing sagemaker")
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if sync_mode is True:
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resp = litellm.completion(
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model="sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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)
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print(resp)
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else:
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resp = await litellm.acompletion(
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model="sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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)
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print(resp)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [False, True])
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@pytest.mark.parametrize(
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"model",
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[
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"sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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"sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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],
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)
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@pytest.mark.flaky(retries=3, delay=1)
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async def test_completion_sagemaker_stream(sync_mode, model):
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try:
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from litellm.tests.test_streaming import streaming_format_tests
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litellm.set_verbose = False
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print("testing sagemaker")
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verbose_logger.setLevel(logging.DEBUG)
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full_text = ""
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if sync_mode is True:
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response = litellm.completion(
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model=model,
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messages=[
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{"role": "user", "content": "hi - what is ur name"},
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],
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temperature=0.2,
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stream=True,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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for idx, chunk in enumerate(response):
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print(chunk)
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streaming_format_tests(idx=idx, chunk=chunk)
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full_text += chunk.choices[0].delta.content or ""
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print("SYNC RESPONSE full text", full_text)
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else:
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response = await litellm.acompletion(
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model=model,
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messages=[
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{"role": "user", "content": "hi - what is ur name"},
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],
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stream=True,
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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print("streaming response")
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idx = 0
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async for chunk in response:
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print(chunk)
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streaming_format_tests(idx=idx, chunk=chunk)
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full_text += chunk.choices[0].delta.content or ""
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idx += 1
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print("ASYNC RESPONSE full text", full_text)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio
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async def test_acompletion_sagemaker_non_stream():
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mock_response = AsyncMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = await litellm.acompletion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = kwargs["json"]
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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@pytest.mark.asyncio
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async def test_completion_sagemaker_non_stream():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = kwargs["json"]
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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@pytest.mark.asyncio
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async def test_completion_sagemaker_prompt_template_non_stream():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n\n### Instruction:\nhi\n\n\n### Response:\n",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/deepseek_coder_6.7_instruct",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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hf_model_name="deepseek-ai/deepseek-coder-6.7b-instruct",
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = kwargs["json"]
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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@pytest.mark.asyncio
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async def test_completion_sagemaker_non_stream_with_aws_params():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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aws_access_key_id="gm",
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aws_secret_access_key="s",
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aws_region_name="us-west-5",
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = kwargs["json"]
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-5.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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