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test - remove anthropic_adapter tests. no longer used
This commit is contained in:
Ishaan Jaff
parent
29dc67a2aa
commit
1d2a9e423c
1 changed files with 0 additions and 201 deletions
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@ -30,7 +30,6 @@ from litellm import (
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Router,
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adapter_completion,
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)
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from litellm.adapters.anthropic_adapter import anthropic_adapter
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from litellm.types.llms.anthropic import AnthropicResponse
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from litellm.types.utils import GenericStreamingChunk, ChatCompletionToolCallChunk
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from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk
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@ -40,65 +39,6 @@ from httpx import Headers
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from base_llm_unit_tests import BaseLLMChatTest
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def test_anthropic_completion_messages_translation():
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messages = [{"role": "user", "content": "Hey, how's it going?"}]
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translated_messages = AnthropicExperimentalPassThroughConfig().translate_anthropic_messages_to_openai(messages=messages) # type: ignore
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assert translated_messages == [{"role": "user", "content": "Hey, how's it going?"}]
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def test_anthropic_completion_input_translation():
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data = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hey, how's it going?"}],
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}
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translated_input = anthropic_adapter.translate_completion_input_params(kwargs=data)
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assert translated_input is not None
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assert translated_input["model"] == "gpt-3.5-turbo"
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assert translated_input["messages"] == [
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{"role": "user", "content": "Hey, how's it going?"}
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]
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def test_anthropic_completion_input_translation_with_metadata():
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"""
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Tests that cost tracking works as expected with LiteLLM Proxy
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LiteLLM Proxy will insert litellm_metadata for anthropic endpoints to track user_api_key and user_api_key_team_id
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This test ensures that the `litellm_metadata` is not present in the translated input
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It ensures that `litellm.acompletion()` will receieve metadata which is a litellm specific param
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"""
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data = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hey, how's it going?"}],
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"litellm_metadata": {
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"user_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
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"user_api_key_alias": None,
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"user_api_end_user_max_budget": None,
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"litellm_api_version": "1.40.19",
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"global_max_parallel_requests": None,
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"user_api_key_user_id": "default_user_id",
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"user_api_key_org_id": None,
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"user_api_key_team_id": None,
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"user_api_key_team_alias": None,
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"user_api_key_team_max_budget": None,
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"user_api_key_team_spend": None,
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"user_api_key_spend": 0.0,
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"user_api_key_max_budget": None,
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"user_api_key_metadata": {},
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},
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}
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translated_input = anthropic_adapter.translate_completion_input_params(kwargs=data)
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assert "litellm_metadata" not in translated_input
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assert "metadata" in translated_input
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assert translated_input["metadata"] == data["litellm_metadata"]
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def streaming_format_tests(chunk: dict, idx: int):
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"""
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1st chunk - chunk.get("type") == "message_start"
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@ -113,54 +53,6 @@ def streaming_format_tests(chunk: dict, idx: int):
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assert chunk.get("type") == "content_block_delta"
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@pytest.mark.parametrize("stream", [True]) # False
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def test_anthropic_completion_e2e(stream):
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litellm.set_verbose = True
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litellm.adapters = [{"id": "anthropic", "adapter": anthropic_adapter}]
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messages = [{"role": "user", "content": "Hey, how's it going?"}]
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response = adapter_completion(
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model="gpt-3.5-turbo",
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messages=messages,
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adapter_id="anthropic",
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mock_response="This is a fake call",
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stream=stream,
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)
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print("Response: {}".format(response))
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assert response is not None
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if stream is False:
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assert isinstance(response, AnthropicResponse)
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else:
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"""
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- ensure finish reason is returned
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- assert content block is started and stopped
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- ensure last chunk is 'message_stop'
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"""
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assert isinstance(response, litellm.types.utils.AdapterCompletionStreamWrapper)
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finish_reason: Optional[str] = None
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message_stop_received = False
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content_block_started = False
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content_block_finished = False
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for idx, chunk in enumerate(response):
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print(chunk)
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streaming_format_tests(chunk=chunk, idx=idx)
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if chunk.get("delta", {}).get("stop_reason") is not None:
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finish_reason = chunk.get("delta", {}).get("stop_reason")
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if chunk.get("type") == "message_stop":
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message_stop_received = True
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if chunk.get("type") == "content_block_stop":
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content_block_finished = True
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if chunk.get("type") == "content_block_start":
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content_block_started = True
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assert content_block_started and content_block_finished
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assert finish_reason is not None
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assert message_stop_received is True
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anthropic_chunk_list = [
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{
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"type": "content_block_start",
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@ -371,99 +263,6 @@ def test_anthropic_tool_streaming():
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assert tool_use["index"] == correct_tool_index
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def test_anthropic_tool_calling_translation():
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kwargs = {
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"model": "claude-3-5-sonnet-20240620",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Would development of a software platform be under ASC 350-40 or ASC 985?",
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}
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],
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},
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{
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"role": "assistant",
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"content": [
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{
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"type": "tool_use",
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"id": "37d6f703-cbcc-497d-95a1-2aa24a114adc",
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"name": "TaskPlanningTool",
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"input": {
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"completed_steps": [],
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"next_steps": [
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{
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"tool_name": "AccountingResearchTool",
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"description": "Research ASC 350-40 to understand its scope and applicability to software development.",
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},
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{
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"tool_name": "AccountingResearchTool",
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"description": "Research ASC 985 to understand its scope and applicability to software development.",
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},
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{
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"tool_name": "AccountingResearchTool",
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"description": "Compare the scopes of ASC 350-40 and ASC 985 to determine which is more applicable to software platform development.",
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},
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],
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"learnings": [],
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"potential_issues": [
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"The distinction between the two standards might not be clear-cut for all types of software development.",
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"There might be specific circumstances or details about the software platform that could affect which standard applies.",
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],
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"missing_info": [
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"Specific details about the type of software platform being developed (e.g., for internal use or for sale).",
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"Whether the entity developing the software is also the end-user or if it's being developed for external customers.",
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],
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"done": False,
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"required_formatting": None,
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},
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}
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],
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},
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{
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": "eb7023b1-5ee8-43b8-b90f-ac5a23d37c31",
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"content": {
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"completed_steps": [],
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"next_steps": [
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{
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"tool_name": "AccountingResearchTool",
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"description": "Research ASC 350-40 to understand its scope and applicability to software development.",
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},
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{
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"tool_name": "AccountingResearchTool",
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"description": "Research ASC 985 to understand its scope and applicability to software development.",
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},
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{
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"tool_name": "AccountingResearchTool",
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"description": "Compare the scopes of ASC 350-40 and ASC 985 to determine which is more applicable to software platform development.",
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},
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],
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"formatting_step": None,
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},
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}
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],
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},
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],
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}
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from litellm.adapters.anthropic_adapter import anthropic_adapter
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translated_params = anthropic_adapter.translate_completion_input_params(
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kwargs=kwargs
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)
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print(translated_params["messages"])
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assert len(translated_params["messages"]) > 0
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assert translated_params["messages"][0]["role"] == "user"
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def test_process_anthropic_headers_empty():
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result = process_anthropic_headers({})
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assert result == {}, "Expected empty dictionary for no input"
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