mirror of
https://github.com/BerriAI/litellm.git
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f59cb46e71
* fix(__init__.py): add 'watsonx_text' as mapped llm api route Fixes https://github.com/BerriAI/litellm/issues/6663 * fix(opentelemetry.py): fix passing parallel tool calls to otel Fixes https://github.com/BerriAI/litellm/issues/6677 * refactor(test_opentelemetry_unit_tests.py): create a base set of unit tests for all logging integrations - test for parallel tool call handling reduces bugs in repo * fix(__init__.py): update provider-model mapping to include all known provider-model mappings Fixes https://github.com/BerriAI/litellm/issues/6669 * feat(anthropic): support passing document in llm api call * docs(anthropic.md): add pdf anthropic call to docs + expose new 'supports_pdf_input' function * fix(factory.py): fix linting error
73 lines
2 KiB
Python
73 lines
2 KiB
Python
import asyncio
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import httpx
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import json
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import pytest
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import sys
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from typing import Any, Dict, List
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from unittest.mock import MagicMock, Mock, patch
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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 litellm
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from litellm.exceptions import BadRequestError
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.utils import CustomStreamWrapper
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# test_example.py
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from abc import ABC, abstractmethod
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class BaseLLMChatTest(ABC):
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"""
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Abstract base test class that enforces a common test across all test classes.
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"""
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@abstractmethod
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def get_base_completion_call_args(self) -> dict:
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"""Must return the base completion call args"""
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pass
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def test_content_list_handling(self):
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"""Check if content list is supported by LLM API"""
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base_completion_call_args = self.get_base_completion_call_args()
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messages = [
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{
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"role": "user",
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"content": [{"type": "text", "text": "Hello, how are you?"}],
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}
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]
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response = litellm.completion(
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**base_completion_call_args,
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messages=messages,
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)
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assert response is not None
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@pytest.fixture
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def pdf_messages(self):
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import base64
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import requests
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# URL of the file
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url = "https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
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response = requests.get(url)
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file_data = response.content
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encoded_file = base64.b64encode(file_data).decode("utf-8")
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url = f"data:application/pdf;base64,{encoded_file}"
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image_content = [
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{"type": "text", "text": "What's this file about?"},
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{
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"type": "image_url",
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"image_url": {"url": url},
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},
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]
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image_messages = [{"role": "user", "content": image_content}]
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return image_messages
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