Added LiteLLM to the stack
This commit is contained in:
@@ -0,0 +1,441 @@
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from unittest.mock import MagicMock
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from litellm.llms.anthropic.chat.handler import ModelResponseIterator
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from litellm.types.llms.openai import (
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ChatCompletionToolCallChunk,
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ChatCompletionToolCallFunctionChunk,
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)
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from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
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def test_redacted_thinking_content_block_delta():
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chunk = {
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"type": "content_block_start",
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"index": 58,
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"content_block": {
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"type": "redacted_thinking",
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"data": "EuoBCoYBGAIiQJ/SxkPAgqxhKok29YrpJHRUJ0OT8ahCHKAwyhmRuUhtdmDX9+mn4gDzKNv3fVpQdB01zEPMzNY3QuTCd+1bdtEqQK6JuKHqdndbwpr81oVWb4wxd1GqF/7Jkw74IlQa27oobX+KuRkopr9Dllt/RDe7Se0sI1IkU7tJIAQCoP46OAwSDF51P09q67xhHlQ3ihoM2aOVlkghq/X0w8NlIjBMNvXYNbjhyrOcIg6kPFn2ed/KK7Cm5prYAtXCwkb4Wr5tUSoSHu9T5hKdJRbr6WsqEc7Lle7FULqMLZGkhqXyc3BA",
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},
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}
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=False, json_mode=False
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)
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model_response = model_response_iterator.chunk_parser(chunk=chunk)
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print(f"\n\nmodel_response: {model_response}\n\n")
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assert model_response.choices[0].delta.thinking_blocks is not None
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assert len(model_response.choices[0].delta.thinking_blocks) == 1
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print(
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f"\n\nmodel_response.choices[0].delta.thinking_blocks[0]: {model_response.choices[0].delta.thinking_blocks[0]}\n\n"
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)
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assert (
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model_response.choices[0].delta.thinking_blocks[0]["type"]
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== "redacted_thinking"
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)
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assert model_response.choices[0].delta.provider_specific_fields is not None
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assert "thinking_blocks" in model_response.choices[0].delta.provider_specific_fields
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def test_handle_json_mode_chunk_response_format_tool():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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response_format_tool = ChatCompletionToolCallChunk(
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id="tool_123",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=RESPONSE_FORMAT_TOOL_NAME,
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arguments='{"question": "What is the weather?", "answer": "It is sunny"}',
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),
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index=0,
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)
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text, tool_use = model_response_iterator._handle_json_mode_chunk(
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"", response_format_tool
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)
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print(f"\n\nresponse_format_tool text: {text}\n\n")
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print(f"\n\nresponse_format_tool tool_use: {tool_use}\n\n")
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assert text == '{"question": "What is the weather?", "answer": "It is sunny"}'
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assert tool_use is None
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def test_handle_json_mode_chunk_regular_tool():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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regular_tool = ChatCompletionToolCallChunk(
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id="tool_456",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name="get_weather", arguments='{"location": "San Francisco, CA"}'
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),
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index=0,
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)
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text, tool_use = model_response_iterator._handle_json_mode_chunk("", regular_tool)
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print(f"\n\nregular_tool text: {text}\n\n")
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print(f"\n\nregular_tool tool_use: {tool_use}\n\n")
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assert text == ""
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assert tool_use is not None
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assert tool_use["function"]["name"] == "get_weather"
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def test_handle_json_mode_chunk_streaming_response_format_tool():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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# First chunk: response_format tool with id and name, but no arguments
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first_chunk = ChatCompletionToolCallChunk(
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id="tool_123",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=RESPONSE_FORMAT_TOOL_NAME, arguments=""
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),
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index=0,
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)
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# Second chunk: continuation with arguments delta (no id)
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second_chunk = ChatCompletionToolCallChunk(
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id=None,
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=None, arguments='{"question": "What is the weather?"'
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),
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index=0,
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)
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# Third chunk: more arguments delta (no id)
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third_chunk = ChatCompletionToolCallChunk(
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id=None,
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=None, arguments=', "answer": "It is sunny"}'
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),
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index=0,
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)
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# Process first chunk - should set tracking flag but not convert yet (no args)
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text1, tool_use1 = model_response_iterator._handle_json_mode_chunk("", first_chunk)
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print(f"\n\nfirst_chunk text: {text1}\n\n")
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print(f"\n\nfirst_chunk tool_use: {tool_use1}\n\n")
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# Process second chunk - should convert arguments to text
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text2, tool_use2 = model_response_iterator._handle_json_mode_chunk("", second_chunk)
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print(f"\n\nsecond_chunk text: {text2}\n\n")
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print(f"\n\nsecond_chunk tool_use: {tool_use2}\n\n")
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# Process third chunk - should convert arguments to text
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text3, tool_use3 = model_response_iterator._handle_json_mode_chunk("", third_chunk)
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print(f"\n\nthird_chunk text: {text3}\n\n")
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print(f"\n\nthird_chunk tool_use: {tool_use3}\n\n")
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# Verify response_format tool chunks are converted to content
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assert text1 == "" # First chunk has no arguments
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assert tool_use1 is None # Tool call suppressed
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assert text2 == '{"question": "What is the weather?"' # Second chunk arguments
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assert tool_use2 is None # Tool call suppressed
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assert text3 == ', "answer": "It is sunny"}' # Third chunk arguments
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assert tool_use3 is None # Tool call suppressed
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def test_handle_json_mode_chunk_streaming_regular_tool():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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# First chunk: regular tool with id and name, but no arguments
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first_chunk = ChatCompletionToolCallChunk(
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id="tool_456",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(name="get_weather", arguments=""),
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index=0,
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)
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# Second chunk: continuation with arguments delta (no id)
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second_chunk = ChatCompletionToolCallChunk(
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id=None,
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=None, arguments='{"location": "San Francisco, CA"}'
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),
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index=0,
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)
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# Process first chunk - should pass through as regular tool
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text1, tool_use1 = model_response_iterator._handle_json_mode_chunk("", first_chunk)
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print(f"\n\nregular first_chunk text: {text1}\n\n")
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print(f"\n\nregular first_chunk tool_use: {tool_use1}\n\n")
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# Process second chunk - should pass through as regular tool
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text2, tool_use2 = model_response_iterator._handle_json_mode_chunk("", second_chunk)
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print(f"\n\nregular second_chunk text: {text2}\n\n")
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print(f"\n\nregular second_chunk tool_use: {tool_use2}\n\n")
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# Verify regular tool chunks are passed through unchanged
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assert text1 == "" # Original text unchanged
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assert tool_use1 is not None # Tool call preserved
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assert tool_use1["function"]["name"] == "get_weather"
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assert text2 == "" # Original text unchanged
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assert tool_use2 is not None # Tool call preserved
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assert tool_use2["function"]["arguments"] == '{"location": "San Francisco, CA"}'
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def test_response_format_tool_finish_reason():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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# First chunk: response_format tool
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response_format_tool = ChatCompletionToolCallChunk(
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id="tool_123",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=RESPONSE_FORMAT_TOOL_NAME, arguments='{"answer": "test"}'
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),
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index=0,
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)
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# Process the tool call (should set converted_response_format_tool flag)
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text, tool_use = model_response_iterator._handle_json_mode_chunk(
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"", response_format_tool
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)
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print(
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f"\n\nconverted_response_format_tool flag: {model_response_iterator.converted_response_format_tool}\n\n"
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)
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# Simulate message_delta chunk with tool_use stop_reason
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message_delta_chunk = {
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"type": "message_delta",
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"delta": {"stop_reason": "tool_use", "stop_sequence": None},
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"usage": {"output_tokens": 10},
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}
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# Process the message_delta chunk
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model_response = model_response_iterator.chunk_parser(message_delta_chunk)
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print(f"\n\nfinish_reason: {model_response.choices[0].finish_reason}\n\n")
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# Verify that finish_reason is overridden to "stop" for response_format tools
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assert model_response_iterator.converted_response_format_tool is True
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assert model_response.choices[0].finish_reason == "stop"
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def test_regular_tool_finish_reason():
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model_response_iterator = ModelResponseIterator(
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streaming_response=MagicMock(), sync_stream=True, json_mode=True
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)
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# First chunk: regular tool (not response_format)
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regular_tool = ChatCompletionToolCallChunk(
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id="tool_456",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name="get_weather", arguments='{"location": "San Francisco, CA"}'
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),
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index=0,
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)
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# Process the tool call (should NOT set converted_response_format_tool flag)
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text, tool_use = model_response_iterator._handle_json_mode_chunk("", regular_tool)
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print(
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f"\n\nconverted_response_format_tool flag: {model_response_iterator.converted_response_format_tool}\n\n"
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)
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# Simulate message_delta chunk with tool_use stop_reason
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message_delta_chunk = {
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"type": "message_delta",
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"delta": {"stop_reason": "tool_use", "stop_sequence": None},
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"usage": {"output_tokens": 10},
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}
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# Process the message_delta chunk
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model_response = model_response_iterator.chunk_parser(message_delta_chunk)
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print(f"\n\nfinish_reason: {model_response.choices[0].finish_reason}\n\n")
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# Verify that finish_reason remains "tool_calls" for regular tools
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assert model_response_iterator.converted_response_format_tool is False
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assert model_response.choices[0].finish_reason == "tool_calls"
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def test_text_only_streaming_has_index_zero():
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"""Test that text-only streaming responses have choice index=0"""
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chunks = [
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{
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"type": "message_start",
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"message": {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [],
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"usage": {"input_tokens": 10, "output_tokens": 1},
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},
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},
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "text", "text": ""},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "text_delta", "text": "Hello"},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "text_delta", "text": " world"},
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},
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{"type": "content_block_stop", "index": 0},
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{
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"type": "message_delta",
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"delta": {"stop_reason": "end_turn"},
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"usage": {"output_tokens": 2},
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},
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]
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iterator = ModelResponseIterator(None, sync_stream=True)
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# Check all chunks have choice index=0
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for chunk in chunks:
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parsed = iterator.chunk_parser(chunk)
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if parsed.choices:
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assert (
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parsed.choices[0].index == 0
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), f"Expected index=0, got {parsed.choices[0].index}"
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def test_text_and_tool_streaming_has_index_zero():
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"""Test that mixed text and tool streaming responses have choice index=0"""
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chunks = [
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{
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"type": "message_start",
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"message": {
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"id": "msg_123",
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"type": "message",
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"role": "assistant",
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"content": [],
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"usage": {"input_tokens": 10, "output_tokens": 1},
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},
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},
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# Reasoning content at index 0
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "text", "text": ""},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "text_delta", "text": "I need to search..."},
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},
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{"type": "content_block_stop", "index": 0},
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# Regular content at index 1
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{
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"type": "content_block_start",
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"index": 1,
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"content_block": {"type": "text", "text": ""},
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},
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{
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"type": "content_block_delta",
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"index": 1,
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"delta": {"type": "text_delta", "text": "Let me help you"},
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},
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{"type": "content_block_stop", "index": 1},
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# Tool call at index 2
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{
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"type": "content_block_start",
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"index": 2,
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"content_block": {
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"type": "tool_use",
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"id": "tool_123",
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"name": "search",
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"input": {},
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},
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},
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{
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"type": "content_block_delta",
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"index": 2,
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"delta": {"type": "input_json_delta", "partial_json": '{"query"'},
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},
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{
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"type": "content_block_delta",
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"index": 2,
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"delta": {"type": "input_json_delta", "partial_json": ': "test"}'},
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},
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{"type": "content_block_stop", "index": 2},
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{
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"type": "message_delta",
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"delta": {"stop_reason": "tool_use"},
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"usage": {"output_tokens": 10},
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},
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]
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iterator = ModelResponseIterator(None, sync_stream=True)
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# Check all chunks have choice index=0 despite different Anthropic indices
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for chunk in chunks:
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parsed = iterator.chunk_parser(chunk)
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if parsed.choices:
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assert (
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parsed.choices[0].index == 0
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), f"Expected index=0 for chunk type {chunk.get('type')}, got {parsed.choices[0].index}"
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def test_multiple_tools_streaming_has_index_zero():
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"""Test that multiple tool calls all have choice index=0"""
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chunks = [
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{
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"type": "message_start",
|
||||
"message": {
|
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"id": "msg_123",
|
||||
"type": "message",
|
||||
"role": "assistant",
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"content": [],
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"usage": {"input_tokens": 10, "output_tokens": 1},
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},
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},
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# First tool at index 0
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {
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"type": "tool_use",
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"id": "tool_1",
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"name": "search",
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"input": {},
|
||||
},
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||||
},
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||||
{"type": "content_block_stop", "index": 0},
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# Second tool at index 1
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{
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"type": "content_block_start",
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"index": 1,
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||||
"content_block": {
|
||||
"type": "tool_use",
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||||
"id": "tool_2",
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||||
"name": "get",
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||||
"input": {},
|
||||
},
|
||||
},
|
||||
{"type": "content_block_stop", "index": 1},
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||||
{
|
||||
"type": "message_delta",
|
||||
"delta": {"stop_reason": "tool_use"},
|
||||
"usage": {"output_tokens": 5},
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},
|
||||
]
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||||
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iterator = ModelResponseIterator(None, sync_stream=True)
|
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# All tool chunks should have choice index=0
|
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for chunk in chunks:
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parsed = iterator.chunk_parser(chunk)
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if parsed.choices:
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||||
assert (
|
||||
parsed.choices[0].index == 0
|
||||
), f"Expected index=0, got {parsed.choices[0].index}"
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@@ -0,0 +1,348 @@
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../../../../..")
|
||||
) # Adds the parent directory to the system path
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
|
||||
from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
|
||||
|
||||
|
||||
def test_response_format_transformation_unit_test():
|
||||
config = AnthropicConfig()
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||||
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response_format_json_schema = {
|
||||
"description": 'Progress report for the thinking process\n\nThis model represents a snapshot of the agent\'s current progress during\nthe thinking process, providing a brief description of the current activity.\n\nAttributes:\n agent_doing: Brief description of what the agent is currently doing.\n Should be kept under 10 words. Example: "Learning about home automation"',
|
||||
"properties": {"agent_doing": {"title": "Agent Doing", "type": "string"}},
|
||||
"required": ["agent_doing"],
|
||||
"title": "ThinkingStep",
|
||||
"type": "object",
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
result = config._create_json_tool_call_for_response_format(
|
||||
json_schema=response_format_json_schema
|
||||
)
|
||||
|
||||
assert result["input_schema"]["properties"] == {
|
||||
"agent_doing": {"title": "Agent Doing", "type": "string"}
|
||||
}
|
||||
print(result)
|
||||
|
||||
|
||||
def test_calculate_usage():
|
||||
"""
|
||||
Do not include cache_creation_input_tokens in the prompt_tokens
|
||||
|
||||
Fixes https://github.com/BerriAI/litellm/issues/9812
|
||||
"""
|
||||
config = AnthropicConfig()
|
||||
|
||||
usage_object = {
|
||||
"input_tokens": 3,
|
||||
"cache_creation_input_tokens": 12304,
|
||||
"cache_read_input_tokens": 0,
|
||||
"output_tokens": 550,
|
||||
}
|
||||
usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
|
||||
assert usage.prompt_tokens == 3
|
||||
assert usage.completion_tokens == 550
|
||||
assert usage.total_tokens == 3 + 550
|
||||
assert usage.prompt_tokens_details.cached_tokens == 0
|
||||
assert usage._cache_creation_input_tokens == 12304
|
||||
assert usage._cache_read_input_tokens == 0
|
||||
|
||||
@pytest.mark.parametrize("usage_object,expected_usage", [
|
||||
[
|
||||
{
|
||||
"cache_creation_input_tokens": None,
|
||||
"cache_read_input_tokens": None,
|
||||
"input_tokens": None,
|
||||
"output_tokens": 43,
|
||||
"server_tool_use": None
|
||||
},
|
||||
{
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 43,
|
||||
"total_tokens": 43,
|
||||
"_cache_creation_input_tokens": 0,
|
||||
"_cache_read_input_tokens": 0
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
"cache_creation_input_tokens": 100,
|
||||
"cache_read_input_tokens": 200,
|
||||
"input_tokens": 1,
|
||||
"output_tokens": None,
|
||||
"server_tool_use": None
|
||||
},
|
||||
{
|
||||
"prompt_tokens": 1 + 200,
|
||||
"completion_tokens": 0,
|
||||
"total_tokens": 1 + 200,
|
||||
"_cache_creation_input_tokens": 100,
|
||||
"_cache_read_input_tokens": 200,
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
"server_tool_use": {
|
||||
"web_search_requests": 10
|
||||
}
|
||||
},
|
||||
{
|
||||
"server_tool_use": ServerToolUse(web_search_requests=10)
|
||||
}
|
||||
]
|
||||
])
|
||||
def test_calculate_usage_nulls(usage_object, expected_usage):
|
||||
"""
|
||||
Correctly deal with null values in usage object
|
||||
|
||||
Fixes https://github.com/BerriAI/litellm/issues/11920
|
||||
"""
|
||||
config = AnthropicConfig()
|
||||
|
||||
usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
|
||||
for k, v in expected_usage.items():
|
||||
assert hasattr(usage, k)
|
||||
assert getattr(usage, k) == v
|
||||
|
||||
@pytest.mark.parametrize("usage_object", [
|
||||
{
|
||||
"server_tool_use": {
|
||||
"web_search_requests": None
|
||||
}
|
||||
},
|
||||
{
|
||||
"server_tool_use": None
|
||||
}
|
||||
])
|
||||
def test_calculate_usage_server_tool_null(usage_object):
|
||||
"""
|
||||
Correctly deal with null values in usage object
|
||||
|
||||
Fixes https://github.com/BerriAI/litellm/issues/11920
|
||||
"""
|
||||
config = AnthropicConfig()
|
||||
|
||||
usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
|
||||
assert not hasattr(usage, "server_tool_use")
|
||||
|
||||
def test_extract_response_content_with_citations():
|
||||
config = AnthropicConfig()
|
||||
|
||||
completion_response = {
|
||||
"id": "msg_01XrAv7gc5tQNDuoADra7vB4",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": "claude-3-5-sonnet-20241022",
|
||||
"content": [
|
||||
{"type": "text", "text": "According to the documents, "},
|
||||
{
|
||||
"citations": [
|
||||
{
|
||||
"type": "char_location",
|
||||
"cited_text": "The grass is green. ",
|
||||
"document_index": 0,
|
||||
"document_title": "My Document",
|
||||
"start_char_index": 0,
|
||||
"end_char_index": 20,
|
||||
}
|
||||
],
|
||||
"type": "text",
|
||||
"text": "the grass is green",
|
||||
},
|
||||
{"type": "text", "text": " and "},
|
||||
{
|
||||
"citations": [
|
||||
{
|
||||
"type": "char_location",
|
||||
"cited_text": "The sky is blue.",
|
||||
"document_index": 0,
|
||||
"document_title": "My Document",
|
||||
"start_char_index": 20,
|
||||
"end_char_index": 36,
|
||||
}
|
||||
],
|
||||
"type": "text",
|
||||
"text": "the sky is blue",
|
||||
},
|
||||
{"type": "text", "text": "."},
|
||||
],
|
||||
"stop_reason": "end_turn",
|
||||
"stop_sequence": None,
|
||||
"usage": {
|
||||
"input_tokens": 610,
|
||||
"cache_creation_input_tokens": 0,
|
||||
"cache_read_input_tokens": 0,
|
||||
"output_tokens": 51,
|
||||
},
|
||||
}
|
||||
|
||||
_, citations, _, _, _ = config.extract_response_content(completion_response)
|
||||
assert citations is not None
|
||||
|
||||
|
||||
def test_map_tool_helper():
|
||||
config = AnthropicConfig()
|
||||
|
||||
tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 5}
|
||||
|
||||
result, _ = config._map_tool_helper(tool)
|
||||
assert result is not None
|
||||
assert result["name"] == "web_search"
|
||||
assert result["max_uses"] == 5
|
||||
|
||||
|
||||
def test_server_tool_use_usage():
|
||||
config = AnthropicConfig()
|
||||
|
||||
usage_object = {
|
||||
"input_tokens": 15956,
|
||||
"cache_creation_input_tokens": 0,
|
||||
"cache_read_input_tokens": 0,
|
||||
"output_tokens": 567,
|
||||
"server_tool_use": {"web_search_requests": 1},
|
||||
}
|
||||
usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
|
||||
assert usage.server_tool_use.web_search_requests == 1
|
||||
|
||||
|
||||
def test_web_search_tool_transformation():
|
||||
from litellm.types.llms.openai import OpenAIWebSearchOptions
|
||||
|
||||
config = AnthropicConfig()
|
||||
|
||||
openai_web_search_options = OpenAIWebSearchOptions(
|
||||
user_location={
|
||||
"type": "approximate",
|
||||
"approximate": {
|
||||
"city": "San Francisco",
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
anthropic_web_search_tool = config.map_web_search_tool(openai_web_search_options)
|
||||
assert anthropic_web_search_tool is not None
|
||||
assert anthropic_web_search_tool["user_location"] is not None
|
||||
assert anthropic_web_search_tool["user_location"]["type"] == "approximate"
|
||||
assert anthropic_web_search_tool["user_location"]["city"] == "San Francisco"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"search_context_size, expected_max_uses", [("low", 1), ("medium", 5), ("high", 10)]
|
||||
)
|
||||
def test_web_search_tool_transformation_with_search_context_size(
|
||||
search_context_size, expected_max_uses
|
||||
):
|
||||
from litellm.types.llms.openai import OpenAIWebSearchOptions
|
||||
|
||||
config = AnthropicConfig()
|
||||
|
||||
openai_web_search_options = OpenAIWebSearchOptions(
|
||||
user_location={
|
||||
"type": "approximate",
|
||||
"approximate": {
|
||||
"city": "San Francisco",
|
||||
},
|
||||
},
|
||||
search_context_size=search_context_size,
|
||||
)
|
||||
|
||||
anthropic_web_search_tool = config.map_web_search_tool(openai_web_search_options)
|
||||
assert anthropic_web_search_tool is not None
|
||||
assert anthropic_web_search_tool["user_location"] is not None
|
||||
assert anthropic_web_search_tool["user_location"]["type"] == "approximate"
|
||||
assert anthropic_web_search_tool["user_location"]["city"] == "San Francisco"
|
||||
assert anthropic_web_search_tool["max_uses"] == expected_max_uses
|
||||
|
||||
|
||||
def test_add_code_execution_tool():
|
||||
config = AnthropicConfig()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What is in this sheet?"},
|
||||
{
|
||||
"type": "container_upload",
|
||||
"file_id": "file_011CPd1KVEsbD8MjfZSwBd1u",
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
tools = []
|
||||
tools = config.add_code_execution_tool(messages=messages, tools=tools)
|
||||
assert tools is not None
|
||||
assert len(tools) == 1
|
||||
assert tools[0]["type"] == "code_execution_20250522"
|
||||
|
||||
|
||||
def test_map_tool_choice():
|
||||
config = AnthropicConfig()
|
||||
|
||||
tool_choice = "none"
|
||||
result = config._map_tool_choice(tool_choice=tool_choice, parallel_tool_use=True)
|
||||
assert result is not None
|
||||
assert result["type"] == "none"
|
||||
print(result)
|
||||
|
||||
|
||||
def test_transform_response_with_prefix_prompt():
|
||||
import httpx
|
||||
|
||||
from litellm.types.utils import ModelResponse
|
||||
|
||||
config = AnthropicConfig()
|
||||
|
||||
completion_response = {
|
||||
"id": "msg_01XrAv7gc5tQNDuoADra7vB4",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": "claude-3-5-sonnet-20241022",
|
||||
"content": [{"type": "text", "text": " The grass is green."}],
|
||||
"stop_reason": "end_turn",
|
||||
"stop_sequence": None,
|
||||
"usage": {
|
||||
"input_tokens": 610,
|
||||
"cache_creation_input_tokens": 0,
|
||||
"cache_read_input_tokens": 0,
|
||||
"output_tokens": 51,
|
||||
},
|
||||
}
|
||||
|
||||
raw_response = httpx.Response(
|
||||
status_code=200,
|
||||
headers={},
|
||||
)
|
||||
|
||||
model_response = ModelResponse()
|
||||
|
||||
result = config.transform_parsed_response(
|
||||
completion_response=completion_response,
|
||||
raw_response=raw_response,
|
||||
model_response=model_response,
|
||||
json_mode=False,
|
||||
prefix_prompt="You are a helpful assistant.",
|
||||
)
|
||||
|
||||
assert result is not None
|
||||
assert (
|
||||
result.choices[0].message.content
|
||||
== "You are a helpful assistant. The grass is green."
|
||||
)
|
||||
|
||||
|
||||
def test_get_supported_params_thinking():
|
||||
config = AnthropicConfig()
|
||||
params = config.get_supported_openai_params(model="claude-sonnet-4-20250514")
|
||||
assert "thinking" in params
|
||||
Reference in New Issue
Block a user