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Bump anthropic from 0.37.1 to 0.39.0 #355

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Nov 15, 2024
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Bump anthropic from 0.37.1 to 0.39.0 #355

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42b015a
Bump anthropic from 0.37.1 to 0.39.0
dependabot[bot] Nov 13, 2024
6c96a82
remove count_tokens api call, it has been removed in anthropic 0.39.0
wenzhe-log10 Nov 14, 2024
0f2a949
skip langchain anthropic test for now
wenzhe-log10 Nov 14, 2024
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19 changes: 9 additions & 10 deletions poetry.lock
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6 changes: 3 additions & 3 deletions src/log10/anthropic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,9 +119,9 @@ def convert_claude_to_messages(prompt: str):

@staticmethod
def create_tokens_usage(prompt: str, completion: str):
client = anthropic.Anthropic()
prompt_tokens = client.count_tokens(prompt)
completion_tokens = client.count_tokens(completion)
# count_tokens is removed from the API in 0.39.0, consider to deprecate anthropic completion
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anthropic 0.39.0 removed "legacy client.count_tokens() method". We only call create_tokens_usage for message using legacy completion model, like claude-2.1. Tried to use a new API beta.messages.count_tokens but got 400 error from the API.
https://github.com/anthropics/anthropic-sdk-python/releases

Consider the rare usage of legacy models, I set the token to 0 and we could discuss if we want to deprecate the support in future.

prompt_tokens = 0
completion_tokens = 0
total_tokens = prompt_tokens + completion_tokens

# Imitate OpenAI usage format.
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5 changes: 3 additions & 2 deletions tests/test_langchain.py
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Expand Up @@ -24,10 +24,11 @@ def test_chat_openai_messages(session, openai_model):
_LogAssertion(completion_id=session.last_completion_id(), message_content=content).assert_chat_response()


@pytest.mark.skip(reason="Anthropic API removed count_tokens in 0.39.0, langchain_community not updated")
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error in langchain-community:

cls = <class 'langchain_community.chat_models.anthropic.ChatAnthropic'>
values = {'AI_PROMPT': '\n\nAssistant:', 'HUMAN_PROMPT': '\n\nHuman:', 'anthropic_api_key': SecretStr('**********'), 'anthropic_api_url': 'https://api.anthropic.com', ...}

    @pre_init
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        values["anthropic_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
        )
        # Get custom api url from environment.
        values["anthropic_api_url"] = get_from_dict_or_env(
            values,
            "anthropic_api_url",
            "ANTHROPIC_API_URL",
            default="https://api.anthropic.com",
        )

        try:
            import anthropic

            check_package_version("anthropic", gte_version="0.3")
            values["client"] = anthropic.Anthropic(
                base_url=values["anthropic_api_url"],
                api_key=values["anthropic_api_key"].get_secret_value(),
                timeout=values["default_request_timeout"],
                max_retries=values["max_retries"],
            )
            values["async_client"] = anthropic.AsyncAnthropic(
                base_url=values["anthropic_api_url"],
                api_key=values["anthropic_api_key"].get_secret_value(),
                timeout=values["default_request_timeout"],
                max_retries=values["max_retries"],
            )
            values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
            values["AI_PROMPT"] = anthropic.AI_PROMPT
>           values["count_tokens"] = values["client"].count_tokens
E           AttributeError: 'Anthropic' object has no attribute 'count_tokens'

venv/lib/python3.12/site-packages/langchain_community/llms/anthropic.py:108: AttributeError

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discussed offline that the test will be enabled with a follow up to upgrade the langchain anthropic 0.3.

@pytest.mark.chat
def test_chat_anthropic_messages(session, anthropic_legacy_model):
def test_chat_anthropic_messages(session, anthropic_model):
log10(anthropic)
llm = ChatAnthropic(model=anthropic_legacy_model, temperature=0.7)
llm = ChatAnthropic(model=anthropic_model, temperature=0.7)
messages = [SystemMessage(content="You are a ping pong machine"), HumanMessage(content="Ping?")]
completion = llm.predict_messages(messages)

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