Gemini API: Quickstart with Pythonに従って、Pythonパッケージを
pip install google-generativeai
でインストールすれば、
import google.generativeai as genai
で使えるようになる。
Google AI StudioのAPI keysで「Create API key in new project」を選んでAPIキーを発行してもらう。キーは
export GOOGLE_API_KEY="..."
のようにして環境変数としておくと勝手に使ってもらえるが、うまくいかなければ
genai.configure(api_key="...")
で指定する。
list(genai.list_models())
と打ち込めば、使えるモデルが一覧できる。例：
[Model(name='models/chat-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 Chat (Legacy)',
description='A legacy text-only model optimized for chat conversations',
input_token_limit=4096,
output_token_limit=1024,
supported_generation_methods=['generateMessage', 'countMessageTokens'],
temperature=0.25,
top_p=0.95,
top_k=40),
Model(name='models/text-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 (Legacy)',
description='A legacy model that understands text and generates text as an output',
input_token_limit=8196,
output_token_limit=1024,
supported_generation_methods=['generateText', 'countTextTokens', 'createTunedTextModel'],
temperature=0.7,
top_p=0.95,
top_k=40),
Model(name='models/embedding-gecko-001',
base_model_id='',
version='001',
display_name='Embedding Gecko',
description='Obtain a distributed representation of a text.',
input_token_limit=1024,
output_token_limit=1,
supported_generation_methods=['embedText', 'countTextTokens'],
temperature=None,
top_p=None,
top_k=None),
Model(name='models/gemini-1.0-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=1),
Model(name='models/gemini-1.0-pro-001',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro 001 (Tuning)',
description=('The best model for scaling across a wide range of tasks. This is a stable '
'model that supports tuning.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens', 'createTunedModel'],
temperature=0.9,
top_p=1.0,
top_k=1),
Model(name='models/gemini-1.0-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Latest',
description=('The best model for scaling across a wide range of tasks. This is the latest '
'model.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=1),
Model(name='models/gemini-1.0-pro-vision-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32),
Model(name='models/gemini-1.5-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=None),
Model(name='models/gemini-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=1),
Model(name='models/gemini-pro-vision',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32),
Model(name='models/embedding-001',
base_model_id='',
version='001',
display_name='Embedding 001',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None),
Model(name='models/text-embedding-004',
base_model_id='',
version='004',
display_name='Text Embedding 004',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None),
Model(name='models/aqa',
base_model_id='',
version='001',
display_name='Model that performs Attributed Question Answering.',
description=('Model trained to return answers to questions that are grounded in provided '
'sources, along with estimating answerable probability.'),
input_token_limit=7168,
output_token_limit=1024,
supported_generation_methods=['generateAnswer'],
temperature=0.2,
top_p=1.0,
top_k=40)]
models/ は省略できるようだ。'gemini-1.5-pro-latest' を使って何か聞いてみよう：
model = genai.GenerativeModel('gemini-1.5-pro-latest')
response = model.generate_content("What is the meaning of life?")
print(response.text)
もし検閲に引っかかるようなら response.prompt_feedback を表示してみれば詳細がわかる。
上の方法では一つしか質問できない。会話は、次のようにする。
model = genai.GenerativeModel('gemini-1.5-pro-latest')
chat = model.start_chat(history=[])
response = chat.send_message("What is your knowledge cutoff?")
print(response.text)
response = chat.send_message("What are reputable news sources?")
print(response.text)
...
会話全体は chat.history で参照できる。
for h in chat.history:
print(f"[{h.role}]\n{h.parts[0].text}")
温度（ランダムさの度合い）などを指定することもできる：
config = genai.GenerationConfig(temperature=0)
model = genai.GenerativeModel('gemini-1.5-pro-latest',
generation_config=config)