# 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, List, Optional, Union, cast, Sequence
from pydantic import ValidationError
from neo4j_graphrag.exceptions import LLMGenerationError
from neo4j_graphrag.llm.base import LLMInterface
from neo4j_graphrag.llm.types import (
BaseMessage,
LLMResponse,
MessageList,
ToolCall,
ToolCallResponse,
)
from neo4j_graphrag.message_history import MessageHistory
from neo4j_graphrag.tool import Tool
from neo4j_graphrag.types import LLMMessage
try:
from vertexai.generative_models import (
Content,
FunctionCall,
FunctionDeclaration,
GenerationResponse,
GenerativeModel,
Part,
ResponseValidationError,
Tool as VertexAITool,
ToolConfig,
)
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[Union[List[LLMMessage], MessageHistory]] = None,
) -> list[Content]:
messages = []
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
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[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.
"""
model = self._get_model(
system_instruction=system_instruction,
)
try:
if isinstance(message_history, MessageHistory):
message_history = message_history.messages
options = self._get_call_params(input, message_history, tools=None)
response = model.generate_content(**options)
return self._parse_content_response(response)
except ResponseValidationError as e:
raise LLMGenerationError("Error calling VertexAILLM") from e
[docs]
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
model = self._get_model(
system_instruction=system_instruction,
)
options = self._get_call_params(input, message_history, tools=None)
response = await model.generate_content_async(**options)
return self._parse_content_response(response)
except ResponseValidationError as e:
raise LLMGenerationError("Error calling VertexAILLM") from e
def _to_vertexai_function_declaration(self, tool: Tool) -> FunctionDeclaration:
return FunctionDeclaration(
name=tool.get_name(),
description=tool.get_description(),
parameters=tool.get_parameters(exclude=["additional_properties"]),
)
def _get_llm_tools(
self, tools: Optional[Sequence[Tool]]
) -> Optional[list[VertexAITool]]:
if not tools:
return None
return [
VertexAITool(
function_declarations=[
self._to_vertexai_function_declaration(tool) for tool in tools
]
)
]
def _get_model(
self,
system_instruction: Optional[str] = None,
) -> GenerativeModel:
system_message = [system_instruction] if system_instruction is not None else []
model = GenerativeModel(
model_name=self.model_name,
system_instruction=system_message,
)
return model
def _get_call_params(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]],
tools: Optional[Sequence[Tool]],
) -> dict[str, Any]:
options = dict(self.options)
if tools:
# we want a tool back, remove generation_config if defined
options.pop("generation_config", None)
options["tools"] = self._get_llm_tools(tools)
if "tool_config" not in options:
options["tool_config"] = ToolConfig(
function_calling_config=ToolConfig.FunctionCallingConfig(
mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
)
)
else:
# no tools, remove tool_config if defined
options.pop("tool_config", None)
messages = self.get_messages(input, message_history)
options["contents"] = messages
return options
async def _acall_llm(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
system_instruction: Optional[str] = None,
tools: Optional[Sequence[Tool]] = None,
) -> GenerationResponse:
model = self._get_model(system_instruction=system_instruction)
options = self._get_call_params(input, message_history, tools)
response = await model.generate_content_async(**options)
return response
def _call_llm(
self,
input: str,
message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
system_instruction: Optional[str] = None,
tools: Optional[Sequence[Tool]] = None,
) -> GenerationResponse:
model = self._get_model(system_instruction=system_instruction)
options = self._get_call_params(input, message_history, tools)
response = model.generate_content(**options)
return response
def _to_tool_call(self, function_call: FunctionCall) -> ToolCall:
return ToolCall(
name=function_call.name,
arguments=function_call.args,
)
def _parse_tool_response(self, response: GenerationResponse) -> ToolCallResponse:
function_calls = response.candidates[0].function_calls
return ToolCallResponse(
tool_calls=[self._to_tool_call(f) for f in function_calls],
content=None,
)
def _parse_content_response(self, response: GenerationResponse) -> LLMResponse:
return LLMResponse(
content=response.text,
)