forked from phoenix/litellm-mirror
b0be5bf3a1
* fix(anthropic/chat/transformation.py): add json schema as values: json_schema fixes passing pydantic obj to anthropic Fixes https://github.com/BerriAI/litellm/issues/6766 * (feat): Add timestamp_granularities parameter to transcription API (#6457) * Add timestamp_granularities parameter to transcription API * add param to the local test * fix(databricks/chat.py): handle max_retries optional param handling for openai-like calls Fixes issue with calling finetuned vertex ai models via databricks route * build(ui/): add team admins via proxy ui * fix: fix linting error * test: fix test * docs(vertex.md): refactor docs * test: handle overloaded anthropic model error * test: remove duplicate test * test: fix test * test: update test to handle model overloaded error --------- Co-authored-by: Show <35062952+BrunooShow@users.noreply.github.com>
128 lines
3.2 KiB
Python
128 lines
3.2 KiB
Python
# What is this?
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## Tests `litellm.transcription` endpoint. Outside litellm module b/c of audio file used in testing (it's ~700kb).
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import asyncio
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import logging
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import os
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import sys
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import time
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import traceback
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from typing import Optional
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import aiohttp
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import dotenv
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import pytest
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from dotenv import load_dotenv
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from openai import AsyncOpenAI
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import litellm
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from litellm.integrations.custom_logger import CustomLogger
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# Get the current directory of the file being run
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pwd = os.path.dirname(os.path.realpath(__file__))
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print(pwd)
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file_path = os.path.join(pwd, "gettysburg.wav")
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audio_file = open(file_path, "rb")
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file2_path = os.path.join(pwd, "eagle.wav")
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audio_file2 = open(file2_path, "rb")
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load_dotenv()
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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 litellm
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from litellm import Router
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@pytest.mark.parametrize(
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"model, api_key, api_base",
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[
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("whisper-1", None, None),
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# ("groq/whisper-large-v3", None, None),
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(
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"azure/azure-whisper",
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os.getenv("AZURE_EUROPE_API_KEY"),
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"https://my-endpoint-europe-berri-992.openai.azure.com/",
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),
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],
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)
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@pytest.mark.parametrize(
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"response_format, timestamp_granularities",
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[("json", None), ("vtt", None), ("verbose_json", ["word"])],
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)
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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async def test_transcription(
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model, api_key, api_base, response_format, sync_mode, timestamp_granularities
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):
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if sync_mode:
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transcript = litellm.transcription(
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model=model,
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file=audio_file,
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api_key=api_key,
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api_base=api_base,
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response_format=response_format,
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timestamp_granularities=timestamp_granularities,
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drop_params=True,
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)
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else:
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transcript = await litellm.atranscription(
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model=model,
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file=audio_file,
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api_key=api_key,
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api_base=api_base,
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response_format=response_format,
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drop_params=True,
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)
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print(f"transcript: {transcript.model_dump()}")
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print(f"transcript: {transcript._hidden_params}")
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assert transcript.text is not None
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@pytest.mark.asyncio()
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async def test_transcription_caching():
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import litellm
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from litellm.caching.caching import Cache
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litellm.set_verbose = True
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litellm.cache = Cache()
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# make raw llm api call
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response_1 = await litellm.atranscription(
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model="whisper-1",
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file=audio_file,
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)
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await asyncio.sleep(5)
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# cache hit
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response_2 = await litellm.atranscription(
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model="whisper-1",
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file=audio_file,
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)
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print("response_1", response_1)
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print("response_2", response_2)
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print("response2 hidden params", response_2._hidden_params)
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assert response_2._hidden_params["cache_hit"] is True
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# cache miss
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response_3 = await litellm.atranscription(
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model="whisper-1",
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file=audio_file2,
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)
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print("response_3", response_3)
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print("response3 hidden params", response_3._hidden_params)
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assert response_3._hidden_params.get("cache_hit") is not True
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assert response_3.text != response_2.text
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litellm.cache = None
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