# 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 Any, Optional, cast
from pydantic import ValidationError
from neo4j_graphrag.exceptions import LLMGenerationError
from neo4j_graphrag.llm.base import LLMInterface
from neo4j_graphrag.llm.types import BaseMessage, LLMMessage, LLMResponse, MessageList
try:
from vertexai.generative_models import (
Content,
GenerativeModel,
Part,
ResponseValidationError,
)
except ImportError:
GenerativeModel = None
ResponseValidationError = None
[docs]
class VertexAILLM(LLMInterface):
"""Interface for large language models on Vertex AI
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 VertexAILLM
from vertexai.generative_models import GenerationConfig
generation_config = GenerationConfig(temperature=0.0)
llm = VertexAILLM(
model_name="gemini-1.5-flash-001", generation_config=generation_config
)
llm.invoke("Who is the mother of Paul Atreides?")
"""
def __init__(
self,
model_name: str = "gemini-1.5-flash-001",
model_params: Optional[dict[str, Any]] = None,
system_instruction: Optional[str] = None,
**kwargs: Any,
):
if GenerativeModel is None or ResponseValidationError is None:
raise ImportError(
"""Could not import Vertex AI Python client.
Please install it with `pip install "neo4j-graphrag[google]"`."""
)
super().__init__(model_name, model_params)
self.model_name = model_name
self.system_instruction = system_instruction
self.options = kwargs
[docs]
def get_messages(
self, input: str, message_history: Optional[list[LLMMessage]] = None
) -> list[Content]:
messages = []
if message_history:
try:
MessageList(messages=cast(list[BaseMessage], message_history))
except ValidationError as e:
raise LLMGenerationError(e.errors()) from e
for message in message_history:
if message.get("role") == "user":
messages.append(
Content(
role="user", parts=[Part.from_text(message.get("content"))]
)
)
elif message.get("role") == "assistant":
messages.append(
Content(
role="model", parts=[Part.from_text(message.get("content"))]
)
)
messages.append(Content(role="user", parts=[Part.from_text(input)]))
return messages
[docs]
def invoke(
self,
input: str,
message_history: Optional[list[LLMMessage]] = 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[list]): 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 invokation.
Returns:
LLMResponse: The response from the LLM.
"""
system_message = [system_instruction] if system_instruction is not None else []
self.model = GenerativeModel(
model_name=self.model_name,
system_instruction=system_message,
**self.options,
)
try:
messages = self.get_messages(input, message_history)
response = self.model.generate_content(messages, **self.model_params)
return LLMResponse(content=response.text)
except ResponseValidationError as e:
raise LLMGenerationError(e)
[docs]
async def ainvoke(
self,
input: str,
message_history: Optional[list[LLMMessage]] = 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[list]): 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 invokation.
Returns:
LLMResponse: The response from the LLM.
"""
try:
system_message = (
[system_instruction] if system_instruction is not None else []
)
self.model = GenerativeModel(
model_name=self.model_name,
system_instruction=system_message,
**self.options,
)
messages = self.get_messages(input, message_history)
response = await self.model.generate_content_async(
messages, **self.model_params
)
return LLMResponse(content=response.text)
except ResponseValidationError as e:
raise LLMGenerationError(e)