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# Neo4j Sweden AB [https://neo4j.com]
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# #
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# #
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# https://www.apache.org/licenses/LICENSE-2.0
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# #
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Any,
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Iterable,
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List,
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Optional,
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Type,
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Union,
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cast,
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)
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from pydantic import BaseModel, ValidationError
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from neo4j_graphrag.exceptions import LLMGenerationError
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from neo4j_graphrag.llm.base import LLMBase
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from neo4j_graphrag.llm.types import (
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BaseMessage,
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LLMResponse,
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LLMUsage,
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MessageList,
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UserMessage,
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)
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from neo4j_graphrag.message_history import MessageHistory
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from neo4j_graphrag.types import LLMMessage
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from neo4j_graphrag.utils.rate_limit import (
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RateLimitHandler,
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)
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from neo4j_graphrag.utils.rate_limit import (
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async_rate_limit_handler as async_rate_limit_handler_decorator,
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)
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from neo4j_graphrag.utils.rate_limit import (
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rate_limit_handler as rate_limit_handler_decorator,
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)
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if TYPE_CHECKING:
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from anthropic import NotGiven
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from anthropic.types.message_param import MessageParam
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# pylint: disable=redefined-builtin, arguments-differ, raise-missing-from, no-else-return, import-outside-toplevel
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class AnthropicLLM(LLMBase):
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"""Interface for large language models on Anthropic
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Args:
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model_name (str): Name of the LLM to use.
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model_params (Optional[dict], optional): Additional parameters for LLMInterface(V1) passed to the model when text is sent to it. Defaults to None.
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system_instruction: Optional[str], optional): Additional instructions for setting the behavior and context for the model in a conversation. Defaults to None.
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rate_limit_handler (Optional[RateLimitHandler], optional): Handler for managing rate limits for LLMInterface(V1). Defaults to None.
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**kwargs (Any): Arguments passed to the model when for the class is initialised. Defaults to None.
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Raises:
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LLMGenerationError: If there's an error generating the response from the model.
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Example:
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.. code-block:: python
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from neo4j_graphrag.llm import AnthropicLLM
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llm = AnthropicLLM(
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model_name="claude-3-opus-20240229",
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model_params={"max_tokens": 1000},
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api_key="sk...", # can also be read from env vars
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)
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llm.invoke("Who is the mother of Paul Atreides?")
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"""
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def __init__(
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self,
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model_name: str,
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model_params: Optional[dict[str, Any]] = None,
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rate_limit_handler: Optional[RateLimitHandler] = None,
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**kwargs: Any,
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):
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try:
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import anthropic
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except ImportError:
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raise ImportError(
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"""Could not import Anthropic Python client.
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Please install it with `pip install "neo4j-graphrag[anthropic]"`."""
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)
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LLMBase.__init__(
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self,
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model_name=model_name,
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model_params=model_params or {},
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rate_limit_handler=rate_limit_handler,
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**kwargs,
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)
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self.anthropic = anthropic
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self.client = anthropic.Anthropic(**kwargs)
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self.async_client = anthropic.AsyncAnthropic(**kwargs)
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def invoke(
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self,
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input: Union[str, List[LLMMessage]],
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message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
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system_instruction: Optional[str] = None,
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response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
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**kwargs: Any,
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) -> LLMResponse:
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if isinstance(input, str):
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return self.__invoke_v1(input, message_history, system_instruction)
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elif isinstance(input, list):
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return self.__invoke_v2(input, response_format=response_format, **kwargs)
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else:
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raise ValueError(f"Invalid input type for invoke method - {type(input)}")
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async def ainvoke(
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self,
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input: Union[str, List[LLMMessage]],
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message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
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system_instruction: Optional[str] = None,
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response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
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**kwargs: Any,
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) -> LLMResponse:
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if isinstance(input, str):
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return await self.__ainvoke_v1(input, message_history, system_instruction)
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elif isinstance(input, list):
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return await self.__ainvoke_v2(
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input, response_format=response_format, **kwargs
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)
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else:
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raise ValueError(f"Invalid input type for ainvoke method - {type(input)}")
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# implementaions
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@rate_limit_handler_decorator
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def __invoke_v1(
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self,
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input: str,
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message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
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system_instruction: Optional[str] = None,
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) -> LLMResponse:
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"""Sends text to the LLM and returns a response.
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Args:
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input (str): The text to send to the LLM.
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message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages,
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with each message having a specific role assigned.
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system_instruction (Optional[str]): An option to override the llm system message for this invocation.
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Returns:
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LLMResponse: The response from the LLM.
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"""
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try:
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if isinstance(message_history, MessageHistory):
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message_history = message_history.messages
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messages = self.get_messages(input, message_history)
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response = self.client.messages.create(
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model=self.model_name,
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system=system_instruction or self.anthropic.NOT_GIVEN,
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messages=messages,
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**self.model_params,
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)
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response_content = response.content
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if response_content and len(response_content) > 0:
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text = response_content[0].text
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else:
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raise LLMGenerationError("LLM returned empty response.")
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usage = LLMUsage(
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request_tokens=response.usage.input_tokens,
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response_tokens=response.usage.output_tokens,
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total_tokens=response.usage.input_tokens + response.usage.output_tokens,
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)
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return LLMResponse(content=text, usage=usage)
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except self.anthropic.APIError as e:
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raise LLMGenerationError(e)
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@rate_limit_handler_decorator
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def __invoke_v2(
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self,
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input: List[LLMMessage],
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response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
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**kwargs: Any,
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) -> LLMResponse:
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if response_format is not None:
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raise NotImplementedError(
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"AnthropicLLM does not currently support structured output"
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)
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try:
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system_instruction, messages = self.get_messages_v2(input)
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response = self.client.messages.create(
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model=self.model_name,
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system=system_instruction,
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messages=messages,
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**self.model_params,
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**kwargs,
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)
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response_content = response.content
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if response_content and len(response_content) > 0:
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text = response_content[0].text
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else:
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raise LLMGenerationError("LLM returned empty response.")
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usage = LLMUsage(
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request_tokens=response.usage.input_tokens,
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response_tokens=response.usage.output_tokens,
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total_tokens=response.usage.input_tokens + response.usage.output_tokens,
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)
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return LLMResponse(content=text, usage=usage)
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except self.anthropic.APIError as e:
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raise LLMGenerationError(e)
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@async_rate_limit_handler_decorator
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async def __ainvoke_v1(
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self,
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input: str,
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message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
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system_instruction: Optional[str] = None,
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) -> LLMResponse:
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"""Asynchronously sends text to the LLM and returns a response.
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Args:
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input (str): The text to send to the LLM.
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message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages,
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with each message having a specific role assigned.
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system_instruction (Optional[str]): An option to override the llm system message for this invocation.
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Returns:
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LLMResponse: The response from the LLM.
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"""
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try:
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if isinstance(message_history, MessageHistory):
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message_history = message_history.messages
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messages = self.get_messages(input, message_history)
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response = await self.async_client.messages.create(
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model=self.model_name,
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system=system_instruction or self.anthropic.NOT_GIVEN,
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messages=messages,
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**self.model_params,
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)
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response_content = response.content
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if response_content and len(response_content) > 0:
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text = response_content[0].text
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else:
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raise LLMGenerationError("LLM returned empty response.")
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usage = LLMUsage(
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request_tokens=response.usage.input_tokens,
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response_tokens=response.usage.output_tokens,
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total_tokens=response.usage.input_tokens + response.usage.output_tokens,
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)
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return LLMResponse(content=text, usage=usage)
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except self.anthropic.APIError as e:
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raise LLMGenerationError(e)
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@async_rate_limit_handler_decorator
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async def __ainvoke_v2(
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self,
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input: List[LLMMessage],
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response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
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**kwargs: Any,
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) -> LLMResponse:
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"""Asynchronously sends text to the LLM and returns a response.
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Args:
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input (List[LLMMessage]): The messages to send to the LLM.
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response_format: Not supported by AnthropicLLM.
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Returns:
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LLMResponse: The response from the LLM.
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"""
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if response_format is not None:
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raise NotImplementedError(
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"AnthropicLLM does not currently support structured output"
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)
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try:
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system_instruction, messages = self.get_messages_v2(input)
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response = await self.async_client.messages.create(
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model=self.model_name,
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system=system_instruction,
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messages=messages,
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**self.model_params,
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**kwargs,
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)
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response_content = response.content
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if response_content and len(response_content) > 0:
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text = response_content[0].text
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else:
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raise LLMGenerationError("LLM returned empty response.")
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usage = LLMUsage(
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request_tokens=response.usage.input_tokens,
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response_tokens=response.usage.output_tokens,
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total_tokens=response.usage.input_tokens + response.usage.output_tokens,
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)
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return LLMResponse(content=text, usage=usage)
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except self.anthropic.APIError as e:
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raise LLMGenerationError(e)
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async def aclose(self) -> None:
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self.client.close()
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await self.async_client.close()
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# subsidiary methods
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def get_messages(
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self,
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input: str,
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message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
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) -> Iterable[MessageParam]:
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"""Constructs the message list for the LLM from the input and message history."""
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messages: list[dict[str, str]] = []
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if message_history:
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if isinstance(message_history, MessageHistory):
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message_history = message_history.messages
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try:
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MessageList(messages=cast(list[BaseMessage], message_history))
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except ValidationError as e:
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raise LLMGenerationError(e.errors()) from e
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messages.extend(cast(Iterable[dict[str, Any]], message_history))
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messages.append(UserMessage(content=input).model_dump())
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return messages # type: ignore
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def get_messages_v2(
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self,
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input: list[LLMMessage],
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) -> tuple[Union[str, NotGiven], Iterable[MessageParam]]:
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"""Constructs the message list for the LLM from the input."""
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messages: list[MessageParam] = []
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system_instruction: Union[str, NotGiven] = self.anthropic.NOT_GIVEN
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for i in input:
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if i["role"] == "system":
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system_instruction = i["content"]
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else:
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if i["role"] not in ("user", "assistant"):
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raise ValueError(f"Unknown role: {i['role']}")
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messages.append(
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self.anthropic.types.MessageParam(
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role=i["role"],
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content=i["content"],
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)
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)
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return system_instruction, messages
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