# Copyright (c) "Neo4j"
# 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,
SystemMessage,
UserMessage,
)
from neo4j_graphrag.message_history import MessageHistory
from neo4j_graphrag.types import LLMMessage
if TYPE_CHECKING:
from cohere import ChatMessages
[docs]
class CohereLLM(LLMInterface):
"""Interface for large language models on the Cohere platform
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 CohereLLM
llm = CohereLLM(api_key="...")
llm.invoke("Say something")
"""
def __init__(
self,
model_name: str = "",
model_params: Optional[dict[str, Any]] = None,
rate_limit_handler: Optional[RateLimitHandler] = None,
**kwargs: Any,
) -> None:
try:
import cohere
except ImportError:
raise ImportError(
"""Could not import cohere python client.
Please install it with `pip install "neo4j-graphrag[cohere]"`."""
)
super().__init__(model_name, model_params, rate_limit_handler)
self.cohere = cohere
self.cohere_api_error = cohere.core.api_error.ApiError
self.client = cohere.ClientV2(**kwargs)
self.async_client = cohere.AsyncClientV2(**kwargs)
[docs]
def get_messages(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
system_instruction: Optional[str] = None,
) -> ChatMessages:
messages = []
if system_instruction:
messages.append(SystemMessage(content=system_instruction).model_dump())
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, system_instruction)
res = self.client.chat(
messages=messages,
model=self.model_name,
)
except self.cohere_api_error as e:
raise LLMGenerationError(e)
return LLMResponse(
content=res.message.content[0].text if res.message.content else "",
)
[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, system_instruction)
res = await self.async_client.chat(
messages=messages,
model=self.model_name,
)
except self.cohere_api_error as e:
raise LLMGenerationError(e)
return LLMResponse(
content=res.message.content[0].text if res.message.content else "",
)