mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 10:44:24 +00:00
a65bfab697
* fix(anthropic_claude3_transformation.py): fix amazon anthropic claude 3 tool calling transformation on invoke route
move to using anthropic config as base
* fix(utils.py): expose anthropic config via providerconfigmanager
* fix(llm_http_handler.py): support json mode on async completion calls
* fix(invoke_handler/make_call): support json mode for anthropic called via bedrock invoke
* fix(anthropic/): handle 'response_format: {"type": "text"}` + migrate amazon claude 3 invoke config to inherit from anthropic config
Prevents error when passing in 'response_format: {"type": "text"}
* test: fix test
* fix(utils.py): fix base invoke provider check
* fix(anthropic_claude3_transformation.py): don't pass 'stream' param
* fix: fix linting errors
* fix(converse_transformation.py): handle response_format type=text for converse
1370 lines
46 KiB
Python
1370 lines
46 KiB
Python
#### What this tests ####
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# This tests if get_optional_params works as expected
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import asyncio
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import inspect
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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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import pytest
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sys.path.insert(0, os.path.abspath("../.."))
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from unittest.mock import MagicMock, patch
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import litellm
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from litellm.litellm_core_utils.prompt_templates.factory import map_system_message_pt
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from litellm.types.completion import (
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ChatCompletionMessageParam,
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ChatCompletionSystemMessageParam,
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ChatCompletionUserMessageParam,
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)
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from litellm.utils import (
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get_optional_params,
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get_optional_params_embeddings,
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get_optional_params_image_gen,
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)
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## get_optional_params_embeddings
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### Models: OpenAI, Azure, Bedrock
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### Scenarios: w/ optional params + litellm.drop_params = True
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def test_supports_system_message():
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"""
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Check if litellm.completion(...,supports_system_message=False)
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"""
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messages = [
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ChatCompletionSystemMessageParam(role="system", content="Listen here!"),
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ChatCompletionUserMessageParam(role="user", content="Hello there!"),
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]
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new_messages = map_system_message_pt(messages=messages)
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assert len(new_messages) == 1
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assert new_messages[0]["role"] == "user"
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## confirm you can make a openai call with this param
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response = litellm.completion(
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model="gpt-3.5-turbo", messages=new_messages, supports_system_message=False
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)
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assert isinstance(response, litellm.ModelResponse)
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@pytest.mark.parametrize(
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"stop_sequence, expected_count", [("\n", 0), (["\n"], 0), (["finish_reason"], 1)]
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)
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def test_anthropic_optional_params(stop_sequence, expected_count):
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"""
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Test if whitespace character optional param is dropped by anthropic
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"""
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litellm.drop_params = True
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optional_params = get_optional_params(
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model="claude-3", custom_llm_provider="anthropic", stop=stop_sequence
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)
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assert len(optional_params) == expected_count
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def test_bedrock_optional_params_embeddings():
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litellm.drop_params = True
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optional_params = get_optional_params_embeddings(
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model="", user="John", encoding_format=None, custom_llm_provider="bedrock"
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)
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assert len(optional_params) == 0
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@pytest.mark.parametrize(
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"model",
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[
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"us.anthropic.claude-3-haiku-20240307-v1:0",
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"us.meta.llama3-2-11b-instruct-v1:0",
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"anthropic.claude-3-haiku-20240307-v1:0",
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],
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)
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def test_bedrock_optional_params_completions(model):
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tools = [
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{
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"type": "function",
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"function": {
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"name": "structure_output",
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"description": "Send structured output back to the user",
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"strict": True,
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"parameters": {
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"type": "object",
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"properties": {
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"reasoning": {"type": "string"},
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"sentiment": {"type": "string"},
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},
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"required": ["reasoning", "sentiment"],
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"additionalProperties": False,
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},
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"additionalProperties": False,
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},
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}
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]
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optional_params = get_optional_params(
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model=model,
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max_tokens=10,
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temperature=0.1,
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tools=tools,
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custom_llm_provider="bedrock",
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)
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print(f"optional_params: {optional_params}")
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assert len(optional_params) == 4
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assert optional_params == {
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"maxTokens": 10,
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"stream": False,
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"temperature": 0.1,
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"tools": tools,
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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/amazon.titan-large",
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"bedrock/meta.llama3-2-11b-instruct-v1:0",
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"bedrock/ai21.j2-ultra-v1",
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"bedrock/cohere.command-nightly",
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"bedrock/mistral.mistral-7b",
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],
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)
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def test_bedrock_optional_params_simple(model):
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litellm.drop_params = True
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get_optional_params(
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model=model,
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max_tokens=10,
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temperature=0.1,
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custom_llm_provider="bedrock",
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)
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@pytest.mark.parametrize(
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"model, expected_dimensions, dimensions_kwarg",
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[
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("bedrock/amazon.titan-embed-text-v1", False, None),
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("bedrock/amazon.titan-embed-image-v1", True, "embeddingConfig"),
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("bedrock/amazon.titan-embed-text-v2:0", True, "dimensions"),
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("bedrock/cohere.embed-multilingual-v3", False, None),
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],
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)
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def test_bedrock_optional_params_embeddings_dimension(
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model, expected_dimensions, dimensions_kwarg
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):
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litellm.drop_params = True
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optional_params = get_optional_params_embeddings(
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model=model,
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user="John",
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encoding_format=None,
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dimensions=20,
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custom_llm_provider="bedrock",
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)
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if expected_dimensions:
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assert len(optional_params) == 1
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else:
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assert len(optional_params) == 0
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if dimensions_kwarg is not None:
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assert dimensions_kwarg in optional_params
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def test_google_ai_studio_optional_params_embeddings():
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optional_params = get_optional_params_embeddings(
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model="",
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user="John",
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encoding_format=None,
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custom_llm_provider="gemini",
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drop_params=True,
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)
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assert len(optional_params) == 0
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def test_openai_optional_params_embeddings():
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litellm.drop_params = True
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optional_params = get_optional_params_embeddings(
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model="", user="John", encoding_format=None, custom_llm_provider="openai"
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)
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assert len(optional_params) == 1
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assert optional_params["user"] == "John"
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def test_azure_optional_params_embeddings():
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litellm.drop_params = True
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optional_params = get_optional_params_embeddings(
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model="chatgpt-v-2",
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user="John",
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encoding_format=None,
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custom_llm_provider="azure",
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)
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assert len(optional_params) == 1
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assert optional_params["user"] == "John"
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def test_databricks_optional_params():
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litellm.drop_params = True
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optional_params = get_optional_params(
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model="",
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user="John",
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custom_llm_provider="databricks",
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max_tokens=10,
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temperature=0.2,
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stream=True,
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)
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print(f"optional_params: {optional_params}")
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assert len(optional_params) == 3
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assert "user" not in optional_params
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def test_azure_ai_mistral_optional_params():
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litellm.drop_params = True
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optional_params = get_optional_params(
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model="mistral-large-latest",
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user="John",
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custom_llm_provider="openai",
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max_tokens=10,
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temperature=0.2,
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)
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assert "user" not in optional_params
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def test_vertex_ai_llama_3_optional_params():
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litellm.vertex_llama3_models = ["meta/llama3-405b-instruct-maas"]
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litellm.drop_params = True
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optional_params = get_optional_params(
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model="meta/llama3-405b-instruct-maas",
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user="John",
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custom_llm_provider="vertex_ai",
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max_tokens=10,
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temperature=0.2,
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)
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assert "user" not in optional_params
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def test_vertex_ai_mistral_optional_params():
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litellm.vertex_mistral_models = ["mistral-large@2407"]
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litellm.drop_params = True
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optional_params = get_optional_params(
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model="mistral-large@2407",
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user="John",
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custom_llm_provider="vertex_ai",
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max_tokens=10,
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temperature=0.2,
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)
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assert "user" not in optional_params
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assert "max_tokens" in optional_params
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assert "temperature" in optional_params
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def test_azure_gpt_optional_params_gpt_vision():
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# for OpenAI, Azure all extra params need to get passed as extra_body to OpenAI python. We assert we actually set extra_body here
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optional_params = litellm.utils.get_optional_params(
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model="",
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user="John",
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custom_llm_provider="azure",
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max_tokens=10,
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temperature=0.2,
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enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
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dataSources=[
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{
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"type": "AzureComputerVision",
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"parameters": {
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"endpoint": "<your_computer_vision_endpoint>",
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"key": "<your_computer_vision_key>",
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},
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}
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],
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)
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print(optional_params)
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assert optional_params["max_tokens"] == 10
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assert optional_params["temperature"] == 0.2
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assert optional_params["extra_body"] == {
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"enhancements": {"ocr": {"enabled": True}, "grounding": {"enabled": True}},
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"dataSources": [
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{
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"type": "AzureComputerVision",
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"parameters": {
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"endpoint": "<your_computer_vision_endpoint>",
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"key": "<your_computer_vision_key>",
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},
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}
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],
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}
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# test_azure_gpt_optional_params_gpt_vision()
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def test_azure_gpt_optional_params_gpt_vision_with_extra_body():
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# if user passes extra_body, we should not over write it, we should pass it along to OpenAI python
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optional_params = litellm.utils.get_optional_params(
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model="",
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user="John",
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custom_llm_provider="azure",
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max_tokens=10,
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temperature=0.2,
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extra_body={
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"meta": "hi",
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},
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enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
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dataSources=[
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{
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"type": "AzureComputerVision",
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"parameters": {
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"endpoint": "<your_computer_vision_endpoint>",
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"key": "<your_computer_vision_key>",
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},
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}
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],
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)
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print(optional_params)
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assert optional_params["max_tokens"] == 10
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assert optional_params["temperature"] == 0.2
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assert optional_params["extra_body"] == {
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"enhancements": {"ocr": {"enabled": True}, "grounding": {"enabled": True}},
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"dataSources": [
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{
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"type": "AzureComputerVision",
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"parameters": {
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"endpoint": "<your_computer_vision_endpoint>",
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"key": "<your_computer_vision_key>",
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},
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}
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],
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"meta": "hi",
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}
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# test_azure_gpt_optional_params_gpt_vision_with_extra_body()
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def test_openai_extra_headers():
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optional_params = litellm.utils.get_optional_params(
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model="",
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user="John",
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custom_llm_provider="openai",
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max_tokens=10,
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temperature=0.2,
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extra_headers={"AI-Resource Group": "ishaan-resource"},
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)
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print(optional_params)
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assert optional_params["max_tokens"] == 10
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assert optional_params["temperature"] == 0.2
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assert optional_params["extra_headers"] == {"AI-Resource Group": "ishaan-resource"}
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@pytest.mark.parametrize(
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"api_version",
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[
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"2024-02-01",
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"2024-07-01", # potential future version with tool_choice="required" supported
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"2023-07-01-preview",
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"2024-03-01-preview",
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],
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)
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def test_azure_tool_choice(api_version):
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"""
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Test azure tool choice on older + new version
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"""
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litellm.drop_params = True
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optional_params = litellm.utils.get_optional_params(
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model="chatgpt-v-2",
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user="John",
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custom_llm_provider="azure",
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max_tokens=10,
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temperature=0.2,
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extra_headers={"AI-Resource Group": "ishaan-resource"},
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tool_choice="required",
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api_version=api_version,
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)
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print(f"{optional_params}")
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if api_version == "2024-07-01":
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assert optional_params["tool_choice"] == "required"
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else:
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assert (
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"tool_choice" not in optional_params
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), "tool choice should not be present. Got - tool_choice={} for api version={}".format(
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optional_params["tool_choice"], api_version
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)
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@pytest.mark.parametrize("drop_params", [True, False, None])
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def test_dynamic_drop_params(drop_params):
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"""
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Make a call to cohere w/ drop params = True vs. false.
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"""
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if drop_params is True:
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optional_params = litellm.utils.get_optional_params(
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model="command-r",
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custom_llm_provider="cohere",
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response_format={"type": "json"},
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drop_params=drop_params,
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)
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else:
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try:
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optional_params = litellm.utils.get_optional_params(
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model="command-r",
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custom_llm_provider="cohere",
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response_format={"type": "json"},
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drop_params=drop_params,
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)
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pytest.fail("Expected to fail")
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except Exception as e:
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pass
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|
|
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def test_dynamic_drop_params_e2e():
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post", new=MagicMock()
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) as mock_response:
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try:
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response = litellm.completion(
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model="command-r",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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response_format={"key": "value"},
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drop_params=True,
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)
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except Exception as e:
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pass
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mock_response.assert_called_once()
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print(mock_response.call_args.kwargs["data"])
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assert "response_format" not in mock_response.call_args.kwargs["data"]
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|
|
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@pytest.mark.parametrize(
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"model, provider, should_drop",
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[("command-r", "cohere", True), ("gpt-3.5-turbo", "openai", False)],
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|
)
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|
def test_drop_params_parallel_tool_calls(model, provider, should_drop):
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"""
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https://github.com/BerriAI/litellm/issues/4584
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|
"""
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response = litellm.utils.get_optional_params(
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model=model,
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custom_llm_provider=provider,
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response_format={"type": "json"},
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parallel_tool_calls=True,
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drop_params=True,
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)
|
|
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print(response)
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|
|
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if should_drop:
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assert "response_format" not in response
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|
assert "parallel_tool_calls" not in response
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|
else:
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assert "response_format" in response
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|
assert "parallel_tool_calls" in response
|
|
|
|
|
|
def test_dynamic_drop_params_parallel_tool_calls():
|
|
"""
|
|
https://github.com/BerriAI/litellm/issues/4584
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|
"""
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|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post", new=MagicMock()
|
|
) as mock_response:
|
|
try:
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|
response = litellm.completion(
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model="command-r",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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|
parallel_tool_calls=True,
|
|
drop_params=True,
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|
)
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|
except Exception as e:
|
|
pass
|
|
|
|
mock_response.assert_called_once()
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|
print(mock_response.call_args.kwargs["data"])
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|
assert "parallel_tool_calls" not in mock_response.call_args.kwargs["data"]
|
|
|
|
|
|
@pytest.mark.parametrize("drop_params", [True, False, None])
|
|
def test_dynamic_drop_additional_params(drop_params):
|
|
"""
|
|
Make a call to cohere, dropping 'response_format' specifically
|
|
"""
|
|
if drop_params is True:
|
|
optional_params = litellm.utils.get_optional_params(
|
|
model="command-r",
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|
custom_llm_provider="cohere",
|
|
response_format={"type": "json"},
|
|
additional_drop_params=["response_format"],
|
|
)
|
|
else:
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|
try:
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|
optional_params = litellm.utils.get_optional_params(
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|
model="command-r",
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custom_llm_provider="cohere",
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|
response_format={"type": "json"},
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)
|
|
pytest.fail("Expected to fail")
|
|
except Exception as e:
|
|
pass
|
|
|
|
|
|
def test_dynamic_drop_additional_params_stream_options():
|
|
"""
|
|
Make a call to vertex ai, dropping 'stream_options' specifically
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|
"""
|
|
optional_params = litellm.utils.get_optional_params(
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model="mistral-large-2411@001",
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|
custom_llm_provider="vertex_ai",
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|
stream_options={"include_usage": True},
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|
additional_drop_params=["stream_options"],
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|
)
|
|
|
|
assert "stream_options" not in optional_params
|
|
|
|
|
|
def test_dynamic_drop_additional_params_e2e():
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post", new=MagicMock()
|
|
) as mock_response:
|
|
try:
|
|
response = litellm.completion(
|
|
model="command-r",
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
response_format={"key": "value"},
|
|
additional_drop_params=["response_format"],
|
|
)
|
|
except Exception as e:
|
|
pass
|
|
|
|
mock_response.assert_called_once()
|
|
print(mock_response.call_args.kwargs["data"])
|
|
assert "response_format" not in mock_response.call_args.kwargs["data"]
|
|
assert "additional_drop_params" not in mock_response.call_args.kwargs["data"]
|
|
|
|
|
|
def test_get_optional_params_image_gen():
|
|
response = litellm.utils.get_optional_params_image_gen(
|
|
aws_region_name="us-east-1", custom_llm_provider="openai"
|
|
)
|
|
|
|
print(response)
|
|
|
|
assert "aws_region_name" not in response
|
|
response = litellm.utils.get_optional_params_image_gen(
|
|
aws_region_name="us-east-1", custom_llm_provider="bedrock"
|
|
)
|
|
|
|
print(response)
|
|
|
|
assert "aws_region_name" in response
|
|
|
|
|
|
def test_bedrock_optional_params_embeddings_provider_specific_params():
|
|
optional_params = get_optional_params_embeddings(
|
|
model="my-custom-model",
|
|
custom_llm_provider="huggingface",
|
|
wait_for_model=True,
|
|
)
|
|
assert len(optional_params) == 1
|
|
|
|
|
|
def test_get_optional_params_num_retries():
|
|
"""
|
|
Relevant issue - https://github.com/BerriAI/litellm/issues/5124
|
|
"""
|
|
with patch("litellm.main.get_optional_params", new=MagicMock()) as mock_client:
|
|
_ = litellm.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "Hello world"}],
|
|
num_retries=10,
|
|
)
|
|
|
|
mock_client.assert_called()
|
|
|
|
print(f"mock_client.call_args: {mock_client.call_args}")
|
|
assert mock_client.call_args.kwargs["max_retries"] == 10
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"provider",
|
|
[
|
|
"vertex_ai",
|
|
"vertex_ai_beta",
|
|
],
|
|
)
|
|
def test_vertex_safety_settings(provider):
|
|
litellm.vertex_ai_safety_settings = [
|
|
{
|
|
"category": "HARM_CATEGORY_HARASSMENT",
|
|
"threshold": "BLOCK_NONE",
|
|
},
|
|
{
|
|
"category": "HARM_CATEGORY_HATE_SPEECH",
|
|
"threshold": "BLOCK_NONE",
|
|
},
|
|
{
|
|
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
|
|
"threshold": "BLOCK_NONE",
|
|
},
|
|
{
|
|
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
|
|
"threshold": "BLOCK_NONE",
|
|
},
|
|
]
|
|
|
|
optional_params = get_optional_params(
|
|
model="gemini-1.5-pro", custom_llm_provider=provider
|
|
)
|
|
assert len(optional_params) == 1
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, provider, expectedAddProp",
|
|
[("gemini-1.5-pro", "vertex_ai_beta", False), ("gpt-3.5-turbo", "openai", True)],
|
|
)
|
|
def test_parse_additional_properties_json_schema(model, provider, expectedAddProp):
|
|
optional_params = get_optional_params(
|
|
model=model,
|
|
custom_llm_provider=provider,
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "math_reasoning",
|
|
"schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"steps": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"explanation": {"type": "string"},
|
|
"output": {"type": "string"},
|
|
},
|
|
"required": ["explanation", "output"],
|
|
"additionalProperties": False,
|
|
},
|
|
},
|
|
"final_answer": {"type": "string"},
|
|
},
|
|
"required": ["steps", "final_answer"],
|
|
"additionalProperties": False,
|
|
},
|
|
"strict": True,
|
|
},
|
|
},
|
|
)
|
|
|
|
print(optional_params)
|
|
|
|
if provider == "vertex_ai_beta":
|
|
schema = optional_params["response_schema"]
|
|
elif provider == "openai":
|
|
schema = optional_params["response_format"]["json_schema"]["schema"]
|
|
assert ("additionalProperties" in schema) == expectedAddProp
|
|
|
|
|
|
def test_o1_model_params():
|
|
optional_params = get_optional_params(
|
|
model="o1-preview-2024-09-12",
|
|
custom_llm_provider="openai",
|
|
seed=10,
|
|
user="John",
|
|
)
|
|
assert optional_params["seed"] == 10
|
|
assert optional_params["user"] == "John"
|
|
|
|
|
|
def test_azure_o1_model_params():
|
|
optional_params = get_optional_params(
|
|
model="o1-preview",
|
|
custom_llm_provider="azure",
|
|
seed=10,
|
|
user="John",
|
|
)
|
|
assert optional_params["seed"] == 10
|
|
assert optional_params["user"] == "John"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"temperature, expected_error",
|
|
[(0.2, True), (1, False), (0, True)],
|
|
)
|
|
@pytest.mark.parametrize("provider", ["openai", "azure"])
|
|
def test_o1_model_temperature_params(provider, temperature, expected_error):
|
|
if expected_error:
|
|
with pytest.raises(litellm.UnsupportedParamsError):
|
|
get_optional_params(
|
|
model="o1-preview",
|
|
custom_llm_provider=provider,
|
|
temperature=temperature,
|
|
)
|
|
else:
|
|
get_optional_params(
|
|
model="o1-preview-2024-09-12",
|
|
custom_llm_provider="openai",
|
|
temperature=temperature,
|
|
)
|
|
|
|
|
|
def test_unmapped_gemini_model_params():
|
|
"""
|
|
Test if unmapped gemini model optional params are translated correctly
|
|
"""
|
|
optional_params = get_optional_params(
|
|
model="gemini-new-model",
|
|
custom_llm_provider="vertex_ai",
|
|
stop="stop_word",
|
|
)
|
|
assert optional_params["stop_sequences"] == ["stop_word"]
|
|
|
|
|
|
def _check_additional_properties(schema):
|
|
if isinstance(schema, dict):
|
|
# Remove the 'additionalProperties' key if it exists and is set to False
|
|
if "additionalProperties" in schema or "strict" in schema:
|
|
raise ValueError(
|
|
"additionalProperties and strict should not be in the schema"
|
|
)
|
|
|
|
# Recursively process all dictionary values
|
|
for key, value in schema.items():
|
|
_check_additional_properties(value)
|
|
|
|
elif isinstance(schema, list):
|
|
# Recursively process all items in the list
|
|
for item in schema:
|
|
_check_additional_properties(item)
|
|
|
|
return schema
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"provider, model",
|
|
[
|
|
("hosted_vllm", "my-vllm-model"),
|
|
("gemini", "gemini-1.5-pro"),
|
|
("vertex_ai", "gemini-1.5-pro"),
|
|
],
|
|
)
|
|
def test_drop_nested_params_add_prop_and_strict(provider, model):
|
|
"""
|
|
Relevant issue - https://github.com/BerriAI/litellm/issues/5288
|
|
|
|
Relevant issue - https://github.com/BerriAI/litellm/issues/6136
|
|
"""
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "structure_output",
|
|
"description": "Send structured output back to the user",
|
|
"strict": True,
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"reasoning": {"type": "string"},
|
|
"sentiment": {"type": "string"},
|
|
},
|
|
"required": ["reasoning", "sentiment"],
|
|
"additionalProperties": False,
|
|
},
|
|
"additionalProperties": False,
|
|
},
|
|
}
|
|
]
|
|
tool_choice = {"type": "function", "function": {"name": "structure_output"}}
|
|
optional_params = get_optional_params(
|
|
model=model,
|
|
custom_llm_provider=provider,
|
|
temperature=0.2,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
additional_drop_params=[
|
|
["tools", "function", "strict"],
|
|
["tools", "function", "additionalProperties"],
|
|
],
|
|
)
|
|
|
|
_check_additional_properties(optional_params["tools"])
|
|
|
|
|
|
def test_hosted_vllm_tool_param():
|
|
"""
|
|
Relevant issue - https://github.com/BerriAI/litellm/issues/6228
|
|
"""
|
|
optional_params = get_optional_params(
|
|
model="my-vllm-model",
|
|
custom_llm_provider="hosted_vllm",
|
|
temperature=0.2,
|
|
tools=None,
|
|
tool_choice=None,
|
|
)
|
|
assert "tools" not in optional_params
|
|
assert "tool_choice" not in optional_params
|
|
|
|
|
|
def test_unmapped_vertex_anthropic_model():
|
|
optional_params = get_optional_params(
|
|
model="claude-3-5-sonnet-v250@20241022",
|
|
custom_llm_provider="vertex_ai",
|
|
max_retries=10,
|
|
)
|
|
assert "max_retries" not in optional_params
|
|
|
|
|
|
@pytest.mark.parametrize("provider", ["anthropic", "vertex_ai"])
|
|
def test_anthropic_parallel_tool_calls(provider):
|
|
optional_params = get_optional_params(
|
|
model="claude-3-5-sonnet-v250@20241022",
|
|
custom_llm_provider=provider,
|
|
parallel_tool_calls=True,
|
|
)
|
|
print(f"optional_params: {optional_params}")
|
|
assert optional_params["tool_choice"]["disable_parallel_tool_use"] is False
|
|
|
|
|
|
def test_anthropic_computer_tool_use():
|
|
tools = [
|
|
{
|
|
"type": "computer_20241022",
|
|
"function": {
|
|
"name": "computer",
|
|
"parameters": {
|
|
"display_height_px": 100,
|
|
"display_width_px": 100,
|
|
"display_number": 1,
|
|
},
|
|
},
|
|
}
|
|
]
|
|
|
|
optional_params = get_optional_params(
|
|
model="claude-3-5-sonnet-v250@20241022",
|
|
custom_llm_provider="anthropic",
|
|
tools=tools,
|
|
)
|
|
assert optional_params["tools"][0]["type"] == "computer_20241022"
|
|
assert optional_params["tools"][0]["display_height_px"] == 100
|
|
assert optional_params["tools"][0]["display_width_px"] == 100
|
|
assert optional_params["tools"][0]["display_number"] == 1
|
|
|
|
|
|
def test_vertex_schema_field():
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "json",
|
|
"description": "Respond with a JSON object.",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"thinking": {
|
|
"type": "string",
|
|
"description": "Your internal thoughts on different problem details given the guidance.",
|
|
},
|
|
"problems": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"icon": {
|
|
"type": "string",
|
|
"enum": [
|
|
"BarChart2",
|
|
"Bell",
|
|
],
|
|
"description": "The name of a Lucide icon to display",
|
|
},
|
|
"color": {
|
|
"type": "string",
|
|
"description": "A Tailwind color class for the icon, e.g., 'text-red-500'",
|
|
},
|
|
"problem": {
|
|
"type": "string",
|
|
"description": "The title of the problem being addressed, approximately 3-5 words.",
|
|
},
|
|
"description": {
|
|
"type": "string",
|
|
"description": "A brief explanation of the problem, approximately 20 words.",
|
|
},
|
|
"impacts": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"description": "A list of potential impacts or consequences of the problem, approximately 3 words each.",
|
|
},
|
|
"automations": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"description": "A list of potential automations to address the problem, approximately 3-5 words each.",
|
|
},
|
|
},
|
|
"required": [
|
|
"icon",
|
|
"color",
|
|
"problem",
|
|
"description",
|
|
"impacts",
|
|
"automations",
|
|
],
|
|
"additionalProperties": False,
|
|
},
|
|
"description": "Please generate problem cards that match this guidance.",
|
|
},
|
|
},
|
|
"required": ["thinking", "problems"],
|
|
"additionalProperties": False,
|
|
"$schema": "http://json-schema.org/draft-07/schema#",
|
|
},
|
|
},
|
|
}
|
|
]
|
|
|
|
optional_params = get_optional_params(
|
|
model="gemini-1.5-flash",
|
|
custom_llm_provider="vertex_ai",
|
|
tools=tools,
|
|
)
|
|
print(optional_params)
|
|
print(optional_params["tools"][0]["function_declarations"][0])
|
|
assert (
|
|
"$schema"
|
|
not in optional_params["tools"][0]["function_declarations"][0]["parameters"]
|
|
)
|
|
|
|
|
|
def test_watsonx_tool_choice():
|
|
optional_params = get_optional_params(
|
|
model="gemini-1.5-pro", custom_llm_provider="watsonx", tool_choice="auto"
|
|
)
|
|
print(optional_params)
|
|
assert optional_params["tool_choice_options"] == "auto"
|
|
|
|
|
|
def test_watsonx_text_top_k():
|
|
optional_params = get_optional_params(
|
|
model="gemini-1.5-pro", custom_llm_provider="watsonx_text", top_k=10
|
|
)
|
|
print(optional_params)
|
|
assert optional_params["top_k"] == 10
|
|
|
|
|
|
def test_together_ai_model_params():
|
|
optional_params = get_optional_params(
|
|
model="together_ai", custom_llm_provider="together_ai", logprobs=1
|
|
)
|
|
print(optional_params)
|
|
assert optional_params["logprobs"] == 1
|
|
|
|
|
|
def test_forward_user_param():
|
|
from litellm.utils import get_supported_openai_params, get_optional_params
|
|
|
|
model = "claude-3-5-sonnet-20240620"
|
|
optional_params = get_optional_params(
|
|
model=model,
|
|
user="test_user",
|
|
custom_llm_provider="anthropic",
|
|
)
|
|
|
|
assert optional_params["metadata"]["user_id"] == "test_user"
|
|
|
|
|
|
def test_lm_studio_embedding_params():
|
|
optional_params = get_optional_params_embeddings(
|
|
model="lm_studio/gemma2-9b-it",
|
|
custom_llm_provider="lm_studio",
|
|
dimensions=1024,
|
|
drop_params=True,
|
|
)
|
|
assert len(optional_params) == 0
|
|
|
|
|
|
def test_ollama_pydantic_obj():
|
|
from pydantic import BaseModel
|
|
|
|
class ResponseFormat(BaseModel):
|
|
x: str
|
|
y: str
|
|
|
|
get_optional_params(
|
|
model="qwen2:0.5b",
|
|
custom_llm_provider="ollama",
|
|
response_format=ResponseFormat,
|
|
)
|
|
|
|
|
|
def test_gemini_frequency_penalty():
|
|
from litellm.utils import get_supported_openai_params
|
|
|
|
optional_params = get_supported_openai_params(
|
|
model="gemini-1.5-flash",
|
|
custom_llm_provider="vertex_ai",
|
|
request_type="chat_completion",
|
|
)
|
|
assert optional_params is not None
|
|
assert "frequency_penalty" in optional_params
|
|
|
|
|
|
def test_litellm_proxy_claude_3_5_sonnet():
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_current_weather",
|
|
"description": "Get the current weather in a given location",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"location": {
|
|
"type": "string",
|
|
"description": "The city and state, e.g. San Francisco, CA",
|
|
},
|
|
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
|
|
},
|
|
"required": ["location"],
|
|
},
|
|
},
|
|
}
|
|
]
|
|
|
|
tool_choice = "auto"
|
|
|
|
optional_params = get_optional_params(
|
|
model="claude-3-5-sonnet",
|
|
custom_llm_provider="litellm_proxy",
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
)
|
|
assert optional_params["tools"] == tools
|
|
assert optional_params["tool_choice"] == tool_choice
|
|
|
|
|
|
def test_is_vertex_anthropic_model():
|
|
assert (
|
|
litellm.VertexAIAnthropicConfig().is_supported_model(
|
|
model="claude-3-5-sonnet", custom_llm_provider="litellm_proxy"
|
|
)
|
|
is False
|
|
)
|
|
|
|
|
|
def test_groq_response_format_json_schema():
|
|
optional_params = get_optional_params(
|
|
model="llama-3.1-70b-versatile",
|
|
custom_llm_provider="groq",
|
|
response_format={"type": "json_object"},
|
|
)
|
|
assert optional_params is not None
|
|
assert "response_format" in optional_params
|
|
assert optional_params["response_format"]["type"] == "json_object"
|
|
|
|
|
|
def test_gemini_frequency_penalty():
|
|
optional_params = get_optional_params(
|
|
model="gemini-1.5-flash", custom_llm_provider="gemini", frequency_penalty=0.5
|
|
)
|
|
assert optional_params["frequency_penalty"] == 0.5
|
|
|
|
|
|
def test_azure_prediction_param():
|
|
optional_params = get_optional_params(
|
|
model="chatgpt-v2",
|
|
custom_llm_provider="azure",
|
|
prediction={
|
|
"type": "content",
|
|
"content": "LiteLLM is a very useful way to connect to a variety of LLMs.",
|
|
},
|
|
)
|
|
assert optional_params["prediction"] == {
|
|
"type": "content",
|
|
"content": "LiteLLM is a very useful way to connect to a variety of LLMs.",
|
|
}
|
|
|
|
|
|
def test_vertex_ai_ft_llama():
|
|
optional_params = get_optional_params(
|
|
model="1984786713414729728",
|
|
custom_llm_provider="vertex_ai",
|
|
frequency_penalty=0.5,
|
|
max_retries=10,
|
|
)
|
|
assert optional_params["frequency_penalty"] == 0.5
|
|
assert "max_retries" not in optional_params
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, expected_thinking",
|
|
[
|
|
("claude-3-5-sonnet", False),
|
|
("claude-3-7-sonnet", True),
|
|
("gpt-3.5-turbo", False),
|
|
],
|
|
)
|
|
def test_anthropic_thinking_param(model, expected_thinking):
|
|
optional_params = get_optional_params(
|
|
model=model,
|
|
custom_llm_provider="anthropic",
|
|
thinking={"type": "enabled", "budget_tokens": 1024},
|
|
drop_params=True,
|
|
)
|
|
if expected_thinking:
|
|
assert "thinking" in optional_params
|
|
else:
|
|
assert "thinking" not in optional_params
|
|
|
|
|
|
def test_bedrock_invoke_anthropic_max_tokens():
|
|
passed_params = {
|
|
"model": "invoke/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
|
|
"functions": None,
|
|
"function_call": None,
|
|
"temperature": 0.8,
|
|
"top_p": None,
|
|
"n": 1,
|
|
"stream": False,
|
|
"stream_options": None,
|
|
"stop": None,
|
|
"max_tokens": None,
|
|
"max_completion_tokens": 1024,
|
|
"modalities": None,
|
|
"prediction": None,
|
|
"audio": None,
|
|
"presence_penalty": None,
|
|
"frequency_penalty": None,
|
|
"logit_bias": None,
|
|
"user": None,
|
|
"custom_llm_provider": "bedrock",
|
|
"response_format": {"type": "text"},
|
|
"seed": None,
|
|
"tools": [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "generate_plan",
|
|
"description": "Generate a plan to execute the task using only the tools outlined in your context.",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"steps": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"type": {
|
|
"type": "string",
|
|
"description": "The type of step to execute",
|
|
},
|
|
"tool_name": {
|
|
"type": "string",
|
|
"description": "The name of the tool to use for this step",
|
|
},
|
|
"tool_input": {
|
|
"type": "object",
|
|
"description": "The input to pass to the tool. Make sure this complies with the schema for the tool.",
|
|
},
|
|
"tool_output": {
|
|
"type": "object",
|
|
"description": "(Optional) The output from the tool if needed for future steps. Make sure this complies with the schema for the tool.",
|
|
},
|
|
},
|
|
"required": ["type"],
|
|
},
|
|
}
|
|
},
|
|
},
|
|
},
|
|
},
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "generate_wire_tool",
|
|
"description": "Create a wire transfer with complete wire instructions",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"company_id": {
|
|
"type": "integer",
|
|
"description": "The ID of the company receiving the investment",
|
|
},
|
|
"investment_id": {
|
|
"type": "integer",
|
|
"description": "The ID of the investment memo",
|
|
},
|
|
"dollar_amount": {
|
|
"type": "number",
|
|
"description": "The amount to wire in USD",
|
|
},
|
|
"wiring_instructions": {
|
|
"type": "object",
|
|
"description": "Complete bank account and routing information for the wire",
|
|
"properties": {
|
|
"account_name": {
|
|
"type": "string",
|
|
"description": "Name on the bank account",
|
|
},
|
|
"address_1": {
|
|
"type": "string",
|
|
"description": "Primary address line",
|
|
},
|
|
"address_2": {
|
|
"type": "string",
|
|
"description": "Secondary address line (optional)",
|
|
},
|
|
"city": {"type": "string"},
|
|
"state": {"type": "string"},
|
|
"zip": {"type": "string"},
|
|
"country": {"type": "string", "default": "US"},
|
|
"bank_name": {"type": "string"},
|
|
"account_number": {"type": "string"},
|
|
"routing_number": {"type": "string"},
|
|
"account_type": {
|
|
"type": "string",
|
|
"enum": ["checking", "savings"],
|
|
"default": "checking",
|
|
},
|
|
"swift_code": {
|
|
"type": "string",
|
|
"description": "Required for international wires",
|
|
},
|
|
"iban": {
|
|
"type": "string",
|
|
"description": "Required for some international wires",
|
|
},
|
|
"bank_city": {"type": "string"},
|
|
"bank_state": {"type": "string"},
|
|
"bank_country": {"type": "string", "default": "US"},
|
|
"bank_to_bank_instructions": {
|
|
"type": "string",
|
|
"description": "Additional instructions for the bank (optional)",
|
|
},
|
|
"intermediary_bank_name": {
|
|
"type": "string",
|
|
"description": "Name of intermediary bank if required (optional)",
|
|
},
|
|
},
|
|
"required": [
|
|
"account_name",
|
|
"address_1",
|
|
"country",
|
|
"bank_name",
|
|
"account_number",
|
|
"routing_number",
|
|
"account_type",
|
|
"bank_country",
|
|
],
|
|
},
|
|
},
|
|
"required": [
|
|
"company_id",
|
|
"investment_id",
|
|
"dollar_amount",
|
|
"wiring_instructions",
|
|
],
|
|
},
|
|
},
|
|
},
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "search_companies",
|
|
"description": "Search for companies by name or other criteria to get their IDs",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {
|
|
"type": "string",
|
|
"description": "Name or part of name to search for",
|
|
},
|
|
"batch": {
|
|
"type": "string",
|
|
"description": 'Optional batch filter (e.g., "W21", "S22")',
|
|
},
|
|
"status": {
|
|
"type": "string",
|
|
"enum": [
|
|
"live",
|
|
"dead",
|
|
"adrift",
|
|
"exited",
|
|
"went_public",
|
|
"all",
|
|
],
|
|
"description": "Filter by company status",
|
|
"default": "live",
|
|
},
|
|
"limit": {
|
|
"type": "integer",
|
|
"description": "Maximum number of results to return",
|
|
"default": 10,
|
|
},
|
|
},
|
|
"required": ["query"],
|
|
},
|
|
"output_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"status": {
|
|
"type": "string",
|
|
"description": "Success or error status",
|
|
},
|
|
"results": {
|
|
"type": "array",
|
|
"description": "List of companies matching the search criteria",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"id": {
|
|
"type": "integer",
|
|
"description": "Company ID to use in other API calls",
|
|
},
|
|
"name": {"type": "string"},
|
|
"batch": {"type": "string"},
|
|
"status": {"type": "string"},
|
|
"valuation": {"type": "string"},
|
|
"url": {"type": "string"},
|
|
"description": {"type": "string"},
|
|
"founders": {"type": "string"},
|
|
},
|
|
},
|
|
},
|
|
"results_count": {
|
|
"type": "integer",
|
|
"description": "Number of companies returned",
|
|
},
|
|
"total_matches": {
|
|
"type": "integer",
|
|
"description": "Total number of matches found",
|
|
},
|
|
},
|
|
},
|
|
},
|
|
},
|
|
],
|
|
"tool_choice": None,
|
|
"max_retries": 0,
|
|
"logprobs": None,
|
|
"top_logprobs": None,
|
|
"extra_headers": None,
|
|
"api_version": None,
|
|
"parallel_tool_calls": None,
|
|
"drop_params": True,
|
|
"reasoning_effort": None,
|
|
"additional_drop_params": None,
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "You are an AI assistant that helps prepare a wire for a pro rata investment.",
|
|
},
|
|
{"role": "user", "content": [{"type": "text", "text": "hi"}]},
|
|
],
|
|
"thinking": None,
|
|
"kwargs": {},
|
|
}
|
|
optional_params = get_optional_params(**passed_params)
|
|
print(f"optional_params: {optional_params}")
|
|
|
|
assert "max_tokens_to_sample" not in optional_params
|
|
assert optional_params["max_tokens"] == 1024
|