forked from phoenix/litellm-mirror
6d4cf2d908
* add support for response_format=json anthropic * add test_json_response_format to baseLLM ChatTest * fix test_litellm_anthropic_prompt_caching_tools * fix test_anthropic_function_call_with_no_schema * test test_create_json_tool_call_for_response_format
107 lines
3.1 KiB
Python
107 lines
3.1 KiB
Python
import asyncio
|
|
import httpx
|
|
import json
|
|
import pytest
|
|
import sys
|
|
from typing import Any, Dict, List
|
|
from unittest.mock import MagicMock, Mock, patch
|
|
import os
|
|
|
|
sys.path.insert(
|
|
0, os.path.abspath("../..")
|
|
) # Adds the parent directory to the system path
|
|
import litellm
|
|
from litellm.exceptions import BadRequestError
|
|
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
|
|
from litellm.utils import CustomStreamWrapper
|
|
|
|
|
|
# test_example.py
|
|
from abc import ABC, abstractmethod
|
|
|
|
|
|
class BaseLLMChatTest(ABC):
|
|
"""
|
|
Abstract base test class that enforces a common test across all test classes.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def get_base_completion_call_args(self) -> dict:
|
|
"""Must return the base completion call args"""
|
|
pass
|
|
|
|
def test_content_list_handling(self):
|
|
"""Check if content list is supported by LLM API"""
|
|
base_completion_call_args = self.get_base_completion_call_args()
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": "Hello, how are you?"}],
|
|
}
|
|
]
|
|
response = litellm.completion(
|
|
**base_completion_call_args,
|
|
messages=messages,
|
|
)
|
|
assert response is not None
|
|
|
|
def test_message_with_name(self):
|
|
base_completion_call_args = self.get_base_completion_call_args()
|
|
messages = [
|
|
{"role": "user", "content": "Hello", "name": "test_name"},
|
|
]
|
|
response = litellm.completion(**base_completion_call_args, messages=messages)
|
|
assert response is not None
|
|
|
|
def test_json_response_format(self):
|
|
"""
|
|
Test that the JSON response format is supported by the LLM API
|
|
"""
|
|
base_completion_call_args = self.get_base_completion_call_args()
|
|
litellm.set_verbose = True
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": "Your output should be a JSON object with no additional properties. ",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "Respond with this in json. city=San Francisco, state=CA, weather=sunny, temp=60",
|
|
},
|
|
]
|
|
|
|
response = litellm.completion(
|
|
**base_completion_call_args,
|
|
messages=messages,
|
|
response_format={"type": "json_object"},
|
|
)
|
|
|
|
print(response)
|
|
|
|
@pytest.fixture
|
|
def pdf_messages(self):
|
|
import base64
|
|
|
|
import requests
|
|
|
|
# URL of the file
|
|
url = "https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
|
|
|
|
response = requests.get(url)
|
|
file_data = response.content
|
|
|
|
encoded_file = base64.b64encode(file_data).decode("utf-8")
|
|
url = f"data:application/pdf;base64,{encoded_file}"
|
|
|
|
image_content = [
|
|
{"type": "text", "text": "What's this file about?"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": url},
|
|
},
|
|
]
|
|
|
|
image_messages = [{"role": "user", "content": image_content}]
|
|
|
|
return image_messages
|