# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union, cast
from pydantic import ValidationError
from neo4j_graphrag.exceptions import LLMGenerationError
from neo4j_graphrag.llm.base import LLMInterface
from neo4j_graphrag.utils.rate_limit import (
RateLimitHandler,
rate_limit_handler,
async_rate_limit_handler,
)
from neo4j_graphrag.llm.types import (
BaseMessage,
LLMResponse,
MessageList,
UserMessage,
)
from neo4j_graphrag.message_history import MessageHistory
from neo4j_graphrag.types import LLMMessage
if TYPE_CHECKING:
from anthropic.types.message_param import MessageParam
[docs]
class AnthropicLLM(LLMInterface):
"""Interface for large language models on Anthropic
Args:
model_name (str, optional): Name of the LLM to use. Defaults to "gemini-1.5-flash-001".
model_params (Optional[dict], optional): Additional parameters passed to the model when text is sent to it. Defaults to None.
system_instruction: Optional[str], optional): Additional instructions for setting the behavior and context for the model in a conversation. Defaults to None.
**kwargs (Any): Arguments passed to the model when for the class is initialised. Defaults to None.
Raises:
LLMGenerationError: If there's an error generating the response from the model.
Example:
.. code-block:: python
from neo4j_graphrag.llm import AnthropicLLM
llm = AnthropicLLM(
model_name="claude-3-opus-20240229",
model_params={"max_tokens": 1000},
api_key="sk...", # can also be read from env vars
)
llm.invoke("Who is the mother of Paul Atreides?")
"""
def __init__(
self,
model_name: str,
model_params: Optional[dict[str, Any]] = None,
rate_limit_handler: Optional[RateLimitHandler] = None,
**kwargs: Any,
):
try:
import anthropic
except ImportError:
raise ImportError(
"""Could not import Anthropic Python client.
Please install it with `pip install "neo4j-graphrag[anthropic]"`."""
)
super().__init__(model_name, model_params, rate_limit_handler)
self.anthropic = anthropic
self.client = anthropic.Anthropic(**kwargs)
self.async_client = anthropic.AsyncAnthropic(**kwargs)
[docs]
def get_messages(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
) -> Iterable[MessageParam]:
messages: list[dict[str, str]] = []
if message_history:
if isinstance(message_history, MessageHistory):
message_history = message_history.messages
try:
MessageList(messages=cast(list[BaseMessage], message_history))
except ValidationError as e:
raise LLMGenerationError(e.errors()) from e
messages.extend(cast(Iterable[dict[str, Any]], message_history))
messages.append(UserMessage(content=input).model_dump())
return messages # type: ignore
[docs]
@rate_limit_handler
def invoke(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
system_instruction: Optional[str] = None,
) -> LLMResponse:
"""Sends text to the LLM and returns a response.
Args:
input (str): The text to send to the LLM.
message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages,
with each message having a specific role assigned.
system_instruction (Optional[str]): An option to override the llm system message for this invocation.
Returns:
LLMResponse: The response from the LLM.
"""
try:
if isinstance(message_history, MessageHistory):
message_history = message_history.messages
messages = self.get_messages(input, message_history)
response = self.client.messages.create(
model=self.model_name,
system=system_instruction or self.anthropic.NOT_GIVEN,
messages=messages,
**self.model_params,
)
response_content = response.content
if response_content and len(response_content) > 0:
text = response_content[0].text
else:
raise LLMGenerationError("LLM returned empty response.")
return LLMResponse(content=text)
except self.anthropic.APIError as e:
raise LLMGenerationError(e)
[docs]
@async_rate_limit_handler
async def ainvoke(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
system_instruction: Optional[str] = None,
) -> LLMResponse:
"""Asynchronously sends text to the LLM and returns a response.
Args:
input (str): The text to send to the LLM.
message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages,
with each message having a specific role assigned.
system_instruction (Optional[str]): An option to override the llm system message for this invocation.
Returns:
LLMResponse: The response from the LLM.
"""
try:
if isinstance(message_history, MessageHistory):
message_history = message_history.messages
messages = self.get_messages(input, message_history)
response = await self.async_client.messages.create(
model=self.model_name,
system=system_instruction or self.anthropic.NOT_GIVEN,
messages=messages,
**self.model_params,
)
response_content = response.content
if response_content and len(response_content) > 0:
text = response_content[0].text
else:
raise LLMGenerationError("LLM returned empty response.")
return LLMResponse(content=text)
except self.anthropic.APIError as e:
raise LLMGenerationError(e)