mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
ac9f03beae
* fix(litellm_proxy/chat/transformation.py): support 'thinking' param Fixes https://github.com/BerriAI/litellm/issues/9380 * feat(azure/gpt_transformation.py): add azure audio model support Closes https://github.com/BerriAI/litellm/issues/6305 * fix(utils.py): use provider_config in common functions * fix(utils.py): add missing provider configs to get_chat_provider_config * test: fix test * fix: fix path * feat(utils.py): make bedrock invoke nova config baseconfig compatible * fix: fix linting errors * fix(azure_ai/transformation.py): remove buggy optional param filtering for azure ai Removes incorrect check for support tool choice when calling azure ai - prevented calling models with response_format unless on litell model cost map * fix(amazon_cohere_transformation.py): fix bedrock invoke cohere transformation to inherit from coherechatconfig * test: fix azure ai tool choice mapping * fix: fix model cost map to add 'supports_tool_choice' to cohere models * fix(get_supported_openai_params.py): check if custom llm provider in llm providers * fix(get_supported_openai_params.py): fix llm provider in list check * fix: fix ruff check errors * fix: support defs when calling bedrock nova * fix(factory.py): fix test
391 lines
12 KiB
Python
391 lines
12 KiB
Python
import json
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import os
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import sys
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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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from datetime import datetime
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from unittest.mock import AsyncMock
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from dotenv import load_dotenv
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load_dotenv()
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import httpx
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import pytest
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from respx import MockRouter
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from unittest.mock import patch, MagicMock, AsyncMock
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import litellm
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from litellm import Choices, Message, ModelResponse
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# Adds the parent directory to the system path
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def return_mocked_response(model: str):
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if model == "bedrock/mistral.mistral-large-2407-v1:0":
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return {
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"metrics": {"latencyMs": 316},
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"output": {
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"message": {
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"content": [{"text": "Hello! How are you doing today? How can"}],
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"role": "assistant",
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}
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},
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"stopReason": "max_tokens",
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"usage": {"inputTokens": 5, "outputTokens": 10, "totalTokens": 15},
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}
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@pytest.mark.parametrize(
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"model",
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[
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"bedrock/mistral.mistral-large-2407-v1:0",
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],
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)
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@pytest.mark.asyncio()
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async def test_bedrock_max_completion_tokens(model: str):
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"""
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Tests that:
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- max_completion_tokens is passed as max_tokens to bedrock models
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"""
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
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litellm.set_verbose = True
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client = AsyncHTTPHandler()
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mock_response = return_mocked_response(model)
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_model = model.split("/")[1]
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print("\n\nmock_response: ", mock_response)
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with patch.object(client, "post") as mock_client:
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try:
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response = await litellm.acompletion(
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model=model,
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max_completion_tokens=10,
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messages=[{"role": "user", "content": "Hello!"}],
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = json.loads(mock_client.call_args.kwargs["data"])
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print("request_body: ", request_body)
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assert request_body == {
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"messages": [{"role": "user", "content": [{"text": "Hello!"}]}],
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"additionalModelRequestFields": {},
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"system": [],
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"inferenceConfig": {"maxTokens": 10},
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}
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@pytest.mark.parametrize(
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"model",
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["anthropic/claude-3-sonnet-20240229", "anthropic/claude-3-opus-20240229"],
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)
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@pytest.mark.asyncio()
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async def test_anthropic_api_max_completion_tokens(model: str):
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"""
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Tests that:
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- max_completion_tokens is passed as max_tokens to anthropic models
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"""
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litellm.set_verbose = True
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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mock_response = {
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"content": [{"text": "Hi! My name is Claude.", "type": "text"}],
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"id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
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"model": "claude-3-5-sonnet-20240620",
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"role": "assistant",
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"stop_reason": "end_turn",
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"stop_sequence": None,
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"type": "message",
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"usage": {"input_tokens": 2095, "output_tokens": 503},
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}
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client = HTTPHandler()
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print("\n\nmock_response: ", mock_response)
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with patch.object(client, "post") as mock_client:
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try:
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response = await litellm.acompletion(
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model=model,
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max_completion_tokens=10,
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messages=[{"role": "user", "content": "Hello!"}],
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client=client,
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)
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except Exception as e:
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print(f"Error: {e}")
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs["json"]
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print("request_body: ", request_body)
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assert request_body == {
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"messages": [
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{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}
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],
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"max_tokens": 10,
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"model": model.split("/")[-1],
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}
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def test_all_model_configs():
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from litellm.llms.vertex_ai.vertex_ai_partner_models.ai21.transformation import (
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VertexAIAi21Config,
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)
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from litellm.llms.vertex_ai.vertex_ai_partner_models.llama3.transformation import (
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VertexAILlama3Config,
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)
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assert (
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"max_completion_tokens"
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in VertexAILlama3Config().get_supported_openai_params(model="llama3")
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)
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assert VertexAILlama3Config().map_openai_params(
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{"max_completion_tokens": 10}, {}, "llama3", drop_params=False
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) == {"max_tokens": 10}
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assert "max_completion_tokens" in VertexAIAi21Config().get_supported_openai_params(
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model="jamba-1.5-mini@001"
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)
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assert VertexAIAi21Config().map_openai_params(
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{"max_completion_tokens": 10}, {}, "jamba-1.5-mini@001", drop_params=False
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) == {"max_tokens": 10}
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from litellm.llms.fireworks_ai.chat.transformation import (
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FireworksAIConfig,
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)
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assert "max_completion_tokens" in FireworksAIConfig().get_supported_openai_params(
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model="llama3"
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)
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assert FireworksAIConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.nvidia_nim.chat import NvidiaNimConfig
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assert "max_completion_tokens" in NvidiaNimConfig().get_supported_openai_params(
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model="llama3"
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)
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assert NvidiaNimConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.ollama_chat import OllamaChatConfig
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assert "max_completion_tokens" in OllamaChatConfig().get_supported_openai_params(
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model="llama3"
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)
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assert OllamaChatConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"num_predict": 10}
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from litellm.llms.predibase.chat.transformation import PredibaseConfig
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assert "max_completion_tokens" in PredibaseConfig().get_supported_openai_params(
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model="llama3"
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)
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assert PredibaseConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_new_tokens": 10}
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from litellm.llms.codestral.completion.transformation import (
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CodestralTextCompletionConfig,
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)
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assert (
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"max_completion_tokens"
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in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3")
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)
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assert CodestralTextCompletionConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.volcengine import VolcEngineConfig
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assert "max_completion_tokens" in VolcEngineConfig().get_supported_openai_params(
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model="llama3"
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)
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assert VolcEngineConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.ai21.chat.transformation import AI21ChatConfig
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assert "max_completion_tokens" in AI21ChatConfig().get_supported_openai_params(
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"jamba-1.5-mini@001"
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)
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assert AI21ChatConfig().map_openai_params(
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model="jamba-1.5-mini@001",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIConfig
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assert "max_completion_tokens" in AzureOpenAIConfig().get_supported_openai_params(
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model="gpt-3.5-turbo"
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)
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assert AzureOpenAIConfig().map_openai_params(
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model="gpt-3.5-turbo",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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api_version="2022-12-01",
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drop_params=False,
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) == {"max_completion_tokens": 10}
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from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig
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assert (
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"max_completion_tokens"
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in AmazonConverseConfig().get_supported_openai_params(
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model="anthropic.claude-3-sonnet-20240229-v1:0"
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)
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)
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assert AmazonConverseConfig().map_openai_params(
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model="anthropic.claude-3-sonnet-20240229-v1:0",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"maxTokens": 10}
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from litellm.llms.codestral.completion.transformation import (
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CodestralTextCompletionConfig,
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)
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assert (
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"max_completion_tokens"
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in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3")
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)
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assert CodestralTextCompletionConfig().map_openai_params(
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model="llama3",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm import (
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AmazonAnthropicClaude3Config,
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AmazonAnthropicConfig,
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)
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assert (
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"max_completion_tokens"
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in AmazonAnthropicClaude3Config().get_supported_openai_params(
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model="anthropic.claude-3-sonnet-20240229-v1:0"
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)
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)
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assert AmazonAnthropicClaude3Config().map_openai_params(
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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model="anthropic.claude-3-sonnet-20240229-v1:0",
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drop_params=False,
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) == {"max_tokens": 10}
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assert (
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"max_completion_tokens"
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in AmazonAnthropicConfig().get_supported_openai_params(model="")
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)
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assert AmazonAnthropicConfig().map_openai_params(
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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model="",
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drop_params=False,
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) == {"max_tokens_to_sample": 10}
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from litellm.llms.databricks.chat.transformation import DatabricksConfig
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assert "max_completion_tokens" in DatabricksConfig().get_supported_openai_params()
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assert DatabricksConfig().map_openai_params(
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model="databricks/llama-3-70b-instruct",
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drop_params=False,
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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) == {"max_tokens": 10}
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from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (
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VertexAIAnthropicConfig,
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)
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assert (
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"max_completion_tokens"
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in VertexAIAnthropicConfig().get_supported_openai_params(
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model="claude-3-5-sonnet-20240620"
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)
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)
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assert VertexAIAnthropicConfig().map_openai_params(
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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model="claude-3-5-sonnet-20240620",
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drop_params=False,
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) == {"max_tokens": 10}
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from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
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VertexGeminiConfig,
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)
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from litellm.llms.gemini.chat.transformation import GoogleAIStudioGeminiConfig
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assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params(
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model="gemini-1.0-pro"
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)
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assert VertexGeminiConfig().map_openai_params(
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model="gemini-1.0-pro",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_output_tokens": 10}
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assert (
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"max_completion_tokens"
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in GoogleAIStudioGeminiConfig().get_supported_openai_params(
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model="gemini-1.0-pro"
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)
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)
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assert GoogleAIStudioGeminiConfig().map_openai_params(
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model="gemini-1.0-pro",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_output_tokens": 10}
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assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params(
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model="gemini-1.0-pro"
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)
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assert VertexGeminiConfig().map_openai_params(
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model="gemini-1.0-pro",
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non_default_params={"max_completion_tokens": 10},
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optional_params={},
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drop_params=False,
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) == {"max_output_tokens": 10}
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