forked from phoenix/litellm-mirror
8673f2541e
* fix(key_management_endpoints.py): fix user-membership check when creating team key * docs: add deprecation notice on original `/v1/messages` endpoint + add better swagger tags on pass-through endpoints * fix(gemini/): fix image_url handling for gemini Fixes https://github.com/BerriAI/litellm/issues/6897 * fix(teams.tsx): fix member add when role is 'user' * fix(team_endpoints.py): /team/member_add fix adding several new members to team * test(test_vertex.py): remove redundant test * test(test_proxy_server.py): fix team member add tests
247 lines
8.1 KiB
Python
247 lines
8.1 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 (
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CustomStreamWrapper,
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get_supported_openai_params,
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get_optional_params,
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)
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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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try:
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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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except litellm.InternalServerError:
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pytest.skip("Model is overloaded")
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# for OpenAI the content contains the JSON schema, so we need to assert that the content is not None
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assert response.choices[0].message.content is not None
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def test_message_with_name(self):
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base_completion_call_args = self.get_base_completion_call_args()
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messages = [
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{"role": "user", "content": "Hello", "name": "test_name"},
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]
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response = litellm.completion(**base_completion_call_args, messages=messages)
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assert response is not None
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def test_json_response_format(self):
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"""
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Test that the JSON response format is supported by the LLM API
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"""
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base_completion_call_args = self.get_base_completion_call_args()
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litellm.set_verbose = True
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messages = [
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{
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"role": "system",
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"content": "Your output should be a JSON object with no additional properties. ",
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},
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{
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"role": "user",
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"content": "Respond with this in json. city=San Francisco, state=CA, weather=sunny, temp=60",
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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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response_format={"type": "json_object"},
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)
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print(response)
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# OpenAI guarantees that the JSON schema is returned in the content
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# relevant issue: https://github.com/BerriAI/litellm/issues/6741
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assert response.choices[0].message.content is not None
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@pytest.mark.flaky(retries=6, delay=1)
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def test_json_response_pydantic_obj(self):
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litellm.set_verbose = True
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from pydantic import BaseModel
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from litellm.utils import supports_response_schema
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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class TestModel(BaseModel):
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first_response: str
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base_completion_call_args = self.get_base_completion_call_args()
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if not supports_response_schema(base_completion_call_args["model"], None):
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pytest.skip("Model does not support response schema")
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try:
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res = litellm.completion(
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**base_completion_call_args,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "What is the capital of France?",
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},
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],
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response_format=TestModel,
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)
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assert res is not None
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print(res.choices[0].message)
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assert res.choices[0].message.content is not None
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assert res.choices[0].message.tool_calls is None
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except litellm.InternalServerError:
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pytest.skip("Model is overloaded")
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def test_json_response_format_stream(self):
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"""
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Test that the JSON response format with streaming is supported by the LLM API
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"""
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base_completion_call_args = self.get_base_completion_call_args()
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litellm.set_verbose = True
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messages = [
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{
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"role": "system",
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"content": "Your output should be a JSON object with no additional properties. ",
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},
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{
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"role": "user",
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"content": "Respond with this in json. city=San Francisco, state=CA, weather=sunny, temp=60",
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},
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]
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try:
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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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response_format={"type": "json_object"},
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stream=True,
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)
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except litellm.InternalServerError:
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pytest.skip("Model is overloaded")
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print(response)
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content = ""
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for chunk in response:
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content += chunk.choices[0].delta.content or ""
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print("content=", content)
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# OpenAI guarantees that the JSON schema is returned in the content
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# relevant issue: https://github.com/BerriAI/litellm/issues/6741
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# we need to assert that the JSON schema was returned in the content, (for Anthropic we were returning it as part of the tool call)
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assert content is not None
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assert len(content) > 0
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@pytest.fixture
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def tool_call_no_arguments(self):
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return {
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"id": "call_2c384bc6-de46-4f29-8adc-60dd5805d305",
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"function": {"name": "Get-FAQ", "arguments": "{}"},
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"type": "function",
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}
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],
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}
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@abstractmethod
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def test_tool_call_no_arguments(self, tool_call_no_arguments):
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"""Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
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pass
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def test_image_url(self):
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litellm.set_verbose = True
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from litellm.utils import supports_vision
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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base_completion_call_args = self.get_base_completion_call_args()
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if not supports_vision(base_completion_call_args["model"], None):
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pytest.skip("Model does not support image input")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://i.pinimg.com/736x/b4/b1/be/b4b1becad04d03a9071db2817fc9fe77.jpg"
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},
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},
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],
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}
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]
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response = litellm.completion(**base_completion_call_args, messages=messages)
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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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