Source code for neo4j_graphrag.llm.vertexai_llm

#  Neo4j Sweden AB [https://neo4j.com]
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from __future__ import annotations

from typing import Any, Optional

from neo4j_graphrag.exceptions import LLMGenerationError
from neo4j_graphrag.llm.base import LLMInterface
from neo4j_graphrag.llm.types import LLMResponse

try:
    from vertexai.generative_models import GenerativeModel, 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. **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, **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 google-cloud-aiplatform`." ) super().__init__(model_name, model_params) self.model = GenerativeModel(model_name=model_name, **kwargs)
[docs] def invoke(self, input: str) -> LLMResponse: """Sends text to the LLM and returns a response. Args: input (str): The text to send to the LLM. Returns: LLMResponse: The response from the LLM. """ try: response = self.model.generate_content(input, **self.model_params) return LLMResponse(content=response.text) except ResponseValidationError as e: raise LLMGenerationError(e)
[docs] async def ainvoke(self, input: str) -> LLMResponse: """Asynchronously sends text to the LLM and returns a response. Args: input (str): The text to send to the LLM. Returns: LLMResponse: The response from the LLM. """ try: response = await self.model.generate_content_async( input, **self.model_params ) return LLMResponse(content=response.text) except ResponseValidationError as e: raise LLMGenerationError(e)