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gemini_grounding_example.py
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gemini_grounding_example.py
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# Copyright 2024 Google LLC
#
# 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 vertexai.generative_models import GenerationResponse
def generate_text_with_grounding_web(project_id: str) -> GenerationResponse:
# [START generativeaionvertexai_gemini_grounding_with_web]
import vertexai
from vertexai.generative_models import (
GenerationConfig,
GenerativeModel,
Tool,
grounding,
)
# TODO(developer): Update and un-comment below line
# project_id = "PROJECT_ID"
vertexai.init(project=project_id, location="us-central1")
model = GenerativeModel(model_name="gemini-1.5-flash-001")
# Use Google Search for grounding
tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval())
prompt = "When is the next total solar eclipse in US?"
response = model.generate_content(
prompt,
tools=[tool],
generation_config=GenerationConfig(
temperature=0.0,
),
)
print(response)
# [END generativeaionvertexai_gemini_grounding_with_web]
return response
def generate_text_with_grounding_vertex_ai_search(
project_id: str, data_store_path: str
) -> GenerationResponse:
# [START generativeaionvertexai_gemini_grounding_with_vais]
import vertexai
from vertexai.preview.generative_models import grounding
from vertexai.generative_models import GenerationConfig, GenerativeModel, Tool
# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# data_store_path = "projects/{project_id}/locations/{location}/collections/default_collection/dataStores/{data_store_id}"
vertexai.init(project=project_id, location="us-central1")
model = GenerativeModel(model_name="gemini-1.5-flash-001")
tool = Tool.from_retrieval(
grounding.Retrieval(grounding.VertexAISearch(datastore=data_store_path))
)
prompt = "How do I make an appointment to renew my driver's license?"
response = model.generate_content(
prompt,
tools=[tool],
generation_config=GenerationConfig(
temperature=0.0,
),
)
print(response)
# [END generativeaionvertexai_gemini_grounding_with_vais]
return response