Anthropic

Anthropic

Bases: FunctionCallingLLM

Anthropic LLM.

Examples:

pip install llama-index-llms-anthropic

from llama_index.llms.anthropic import Anthropic

llm = Anthropic(model="claude-instant-1")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
    print(r.delta, end="")

Source code in llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py

class Anthropic(FunctionCallingLLM):
    """Anthropic LLM.

    Examples:
        `pip install llama-index-llms-anthropic`

        ```python
        from llama_index.llms.anthropic import Anthropic

        llm = Anthropic(model="claude-instant-1")
        resp = llm.stream_complete("Paul Graham is ")
        for r in resp:
            print(r.delta, end="")
        ```
    """

    model: str = Field(
        default=DEFAULT_ANTHROPIC_MODEL, description="The anthropic model to use."
    )
    temperature: float = Field(
        default=DEFAULT_TEMPERATURE,
        description="The temperature to use for sampling.",
        gte=0.0,
        lte=1.0,
    )
    max_tokens: int = Field(
        default=DEFAULT_ANTHROPIC_MAX_TOKENS,
        description="The maximum number of tokens to generate.",
        gt=0,
    )

    base_url: Optional[str] = Field(default=None, description="The base URL to use.")
    timeout: Optional[float] = Field(
        default=None, description="The timeout to use in seconds.", gte=0
    )
    max_retries: int = Field(
        default=10, description="The maximum number of API retries.", gte=0
    )
    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict, description="Additional kwargs for the anthropic API."
    )

    _client: Any = PrivateAttr()
    _aclient: Any = PrivateAttr()

    def __init__(
        self,
        model: str = DEFAULT_ANTHROPIC_MODEL,
        temperature: float = DEFAULT_TEMPERATURE,
        max_tokens: int = DEFAULT_ANTHROPIC_MAX_TOKENS,
        base_url: Optional[str] = None,
        timeout: Optional[float] = None,
        max_retries: int = 10,
        api_key: Optional[str] = None,
        additional_kwargs: Optional[Dict[str, Any]] = None,
        callback_manager: Optional[CallbackManager] = None,
        default_headers: Optional[Dict[str, str]] = None,
        system_prompt: Optional[str] = None,
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
    ) -> None:
        additional_kwargs = additional_kwargs or {}
        callback_manager = callback_manager or CallbackManager([])

        self._client = anthropic.Anthropic(
            api_key=api_key,
            base_url=base_url,
            timeout=timeout,
            max_retries=max_retries,
            default_headers=default_headers,
        )
        self._aclient = anthropic.AsyncAnthropic(
            api_key=api_key,
            base_url=base_url,
            timeout=timeout,
            max_retries=max_retries,
            default_headers=default_headers,
        )

        super().__init__(
            temperature=temperature,
            max_tokens=max_tokens,
            additional_kwargs=additional_kwargs,
            base_url=base_url,
            timeout=timeout,
            max_retries=max_retries,
            model=model,
            callback_manager=callback_manager,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
        )

    @classmethod
    def class_name(cls) -> str:
        return "Anthropic_LLM"

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=anthropic_modelname_to_contextsize(self.model),
            num_output=self.max_tokens,
            is_chat_model=True,
            model_name=self.model,
            is_function_calling_model=is_function_calling_model(self.model),
        )

    @property
    def tokenizer(self) -> Tokenizer:
        return self._client.get_tokenizer()

    @property
    def _model_kwargs(self) -> Dict[str, Any]:
        base_kwargs = {
            "model": self.model,
            "temperature": self.temperature,
            "max_tokens": self.max_tokens,
        }
        return {
            **base_kwargs,
            **self.additional_kwargs,
        }

    def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
        return {
            **self._model_kwargs,
            **kwargs,
        }

    def _get_content_and_tool_calls(
        self, response: Any
    ) -> Tuple[str, List[ToolUseBlock]]:
        tool_calls = []
        content = ""
        for content_block in response.content:
            if isinstance(content_block, TextBlock):
                content += content_block.text
            elif isinstance(content_block, ToolUseBlock):
                tool_calls.append(content_block.dict())

        return content, tool_calls

    @llm_chat_callback()
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
        all_kwargs = self._get_all_kwargs(**kwargs)

        response = self._client.beta.tools.messages.create(
            messages=anthropic_messages,
            stream=False,
            system=system_prompt,
            **all_kwargs,
        )

        content, tool_calls = self._get_content_and_tool_calls(response)

        return ChatResponse(
            message=ChatMessage(
                role=MessageRole.ASSISTANT,
                content=content,
                additional_kwargs={"tool_calls": tool_calls},
            ),
            raw=dict(response),
        )

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        complete_fn = chat_to_completion_decorator(self.chat)
        return complete_fn(prompt, **kwargs)

    @llm_chat_callback()
    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
        all_kwargs = self._get_all_kwargs(**kwargs)

        response = self._client.messages.create(
            messages=anthropic_messages, system=system_prompt, stream=True, **all_kwargs
        )

        def gen() -> ChatResponseGen:
            content = ""
            role = MessageRole.ASSISTANT
            for r in response:
                if isinstance(r, ContentBlockDeltaEvent):
                    content_delta = r.delta.text
                    content += content_delta
                    yield ChatResponse(
                        message=ChatMessage(role=role, content=content),
                        delta=content_delta,
                        raw=r,
                    )

        return gen()

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
        return stream_complete_fn(prompt, **kwargs)

    @llm_chat_callback()
    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
        all_kwargs = self._get_all_kwargs(**kwargs)

        response = await self._aclient.beta.tools.messages.create(
            messages=anthropic_messages,
            system=system_prompt,
            stream=False,
            **all_kwargs,
        )

        content, tool_calls = self._get_content_and_tool_calls(response)

        return ChatResponse(
            message=ChatMessage(
                role=MessageRole.ASSISTANT,
                content=content,
                additional_kwargs={"tool_calls": tool_calls},
            ),
            raw=dict(response),
        )

    @llm_completion_callback()
    async def acomplete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        acomplete_fn = achat_to_completion_decorator(self.achat)
        return await acomplete_fn(prompt, **kwargs)

    @llm_chat_callback()
    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        anthropic_messages, system_prompt = messages_to_anthropic_messages(messages)
        all_kwargs = self._get_all_kwargs(**kwargs)

        response = await self._aclient.messages.create(
            messages=anthropic_messages, system=system_prompt, stream=True, **all_kwargs
        )

        async def gen() -> ChatResponseAsyncGen:
            content = ""
            role = MessageRole.ASSISTANT
            async for r in response:
                if isinstance(r, ContentBlockDeltaEvent):
                    content_delta = r.delta.text
                    content += content_delta
                    yield ChatResponse(
                        message=ChatMessage(role=role, content=content),
                        delta=content_delta,
                        raw=r,
                    )

        return gen()

    @llm_completion_callback()
    async def astream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
        return await astream_complete_fn(prompt, **kwargs)

    def chat_with_tools(
        self,
        tools: List["BaseTool"],
        user_msg: Optional[Union[str, ChatMessage]] = None,
        chat_history: Optional[List[ChatMessage]] = None,
        verbose: bool = False,
        allow_parallel_tool_calls: bool = False,
        **kwargs: Any,
    ) -> ChatResponse:
        """Predict and call the tool."""
        chat_history = chat_history or []

        if isinstance(user_msg, str):
            user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
            chat_history.append(user_msg)

        tool_dicts = []
        for tool in tools:
            tool_dicts.append(
                {
                    "name": tool.metadata.name,
                    "description": tool.metadata.description,
                    "input_schema": tool.metadata.get_parameters_dict(),
                }
            )

        response = self.chat(chat_history, tools=tool_dicts, **kwargs)

        if not allow_parallel_tool_calls:
            force_single_tool_call(response)

        return response

    async def achat_with_tools(
        self,
        tools: List["BaseTool"],
        user_msg: Optional[Union[str, ChatMessage]] = None,
        chat_history: Optional[List[ChatMessage]] = None,
        verbose: bool = False,
        allow_parallel_tool_calls: bool = False,
        **kwargs: Any,
    ) -> ChatResponse:
        """Predict and call the tool."""
        chat_history = chat_history or []

        if isinstance(user_msg, str):
            user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
            chat_history.append(user_msg)

        tool_dicts = []
        for tool in tools:
            tool_dicts.append(
                {
                    "name": tool.metadata.name,
                    "description": tool.metadata.description,
                    "input_schema": tool.metadata.get_parameters_dict(),
                }
            )

        response = await self.achat(chat_history, tools=tool_dicts, **kwargs)

        if not allow_parallel_tool_calls:
            force_single_tool_call(response)

        return response

    def get_tool_calls_from_response(
        self,
        response: "AgentChatResponse",
        error_on_no_tool_call: bool = True,
        **kwargs: Any,
    ) -> List[ToolSelection]:
        """Predict and call the tool."""
        tool_calls = response.message.additional_kwargs.get("tool_calls", [])

        if len(tool_calls) < 1:
            if error_on_no_tool_call:
                raise ValueError(
                    f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
                )
            else:
                return []

        tool_selections = []
        for tool_call in tool_calls:
            if (
                "input" not in tool_call
                or "id" not in tool_call
                or "name" not in tool_call
            ):
                raise ValueError("Invalid tool call.")
            if tool_call["type"] != "tool_use":
                raise ValueError("Invalid tool type. Unsupported by Anthropic")
            argument_dict = (
                json.loads(tool_call["input"])
                if isinstance(tool_call["input"], str)
                else tool_call["input"]
            )

            tool_selections.append(
                ToolSelection(
                    tool_id=tool_call["id"],
                    tool_name=tool_call["name"],
                    tool_kwargs=argument_dict,
                )
            )

        return tool_selections

chat_with_tools

chat_with_tools(tools: List[BaseTool], user_msg: Optional[Union[str, ChatMessage]] = None, chat_history: Optional[List[ChatMessage]] = None, verbose: bool = False, allow_parallel_tool_calls: bool = False, **kwargs: Any) -> ChatResponse

Predict and call the tool.

Source code in llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py

def chat_with_tools(
    self,
    tools: List["BaseTool"],
    user_msg: Optional[Union[str, ChatMessage]] = None,
    chat_history: Optional[List[ChatMessage]] = None,
    verbose: bool = False,
    allow_parallel_tool_calls: bool = False,
    **kwargs: Any,
) -> ChatResponse:
    """Predict and call the tool."""
    chat_history = chat_history or []

    if isinstance(user_msg, str):
        user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
        chat_history.append(user_msg)

    tool_dicts = []
    for tool in tools:
        tool_dicts.append(
            {
                "name": tool.metadata.name,
                "description": tool.metadata.description,
                "input_schema": tool.metadata.get_parameters_dict(),
            }
        )

    response = self.chat(chat_history, tools=tool_dicts, **kwargs)

    if not allow_parallel_tool_calls:
        force_single_tool_call(response)

    return response

achat_with_tools async

achat_with_tools(tools: List[BaseTool], user_msg: Optional[Union[str, ChatMessage]] = None, chat_history: Optional[List[ChatMessage]] = None, verbose: bool = False, allow_parallel_tool_calls: bool = False, **kwargs: Any) -> ChatResponse

Predict and call the tool.

Source code in llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py

async def achat_with_tools(
    self,
    tools: List["BaseTool"],
    user_msg: Optional[Union[str, ChatMessage]] = None,
    chat_history: Optional[List[ChatMessage]] = None,
    verbose: bool = False,
    allow_parallel_tool_calls: bool = False,
    **kwargs: Any,
) -> ChatResponse:
    """Predict and call the tool."""
    chat_history = chat_history or []

    if isinstance(user_msg, str):
        user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
        chat_history.append(user_msg)

    tool_dicts = []
    for tool in tools:
        tool_dicts.append(
            {
                "name": tool.metadata.name,
                "description": tool.metadata.description,
                "input_schema": tool.metadata.get_parameters_dict(),
            }
        )

    response = await self.achat(chat_history, tools=tool_dicts, **kwargs)

    if not allow_parallel_tool_calls:
        force_single_tool_call(response)

    return response

get_tool_calls_from_response

get_tool_calls_from_response(response: AgentChatResponse, error_on_no_tool_call: bool = True, **kwargs: Any) -> List[ToolSelection]

Predict and call the tool.

Source code in llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py

def get_tool_calls_from_response(
    self,
    response: "AgentChatResponse",
    error_on_no_tool_call: bool = True,
    **kwargs: Any,
) -> List[ToolSelection]:
    """Predict and call the tool."""
    tool_calls = response.message.additional_kwargs.get("tool_calls", [])

    if len(tool_calls) < 1:
        if error_on_no_tool_call:
            raise ValueError(
                f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
            )
        else:
            return []

    tool_selections = []
    for tool_call in tool_calls:
        if (
            "input" not in tool_call
            or "id" not in tool_call
            or "name" not in tool_call
        ):
            raise ValueError("Invalid tool call.")
        if tool_call["type"] != "tool_use":
            raise ValueError("Invalid tool type. Unsupported by Anthropic")
        argument_dict = (
            json.loads(tool_call["input"])
            if isinstance(tool_call["input"], str)
            else tool_call["input"]
        )

        tool_selections.append(
            ToolSelection(
                tool_id=tool_call["id"],
                tool_name=tool_call["name"],
                tool_kwargs=argument_dict,
            )
        )

    return tool_selections