コンテキストのキャッシュ保存

import os import pathlib import requests import time  from google import genai from google.genai import types  client = genai.Client()  # Download video file url = 'https://storage.googleapis.com/generativeai-downloads/data/SherlockJr._10min.mp4' path_to_video_file = pathlib.Path('SherlockJr._10min.mp4') if not path_to_video_file.exists():   with path_to_video_file.open('wb') as wf:     response = requests.get(url, stream=True)     for chunk in response.iter_content(chunk_size=32768):       wf.write(chunk)  # Upload the video using the Files API video_file = client.files.upload(file=path_to_video_file)  # Wait for the file to finish processing while video_file.state.name == 'PROCESSING':   print('Waiting for video to be processed.')   time.sleep(2)   video_file = client.files.get(name=video_file.name)  print(f'Video processing complete: {video_file.uri}')  # You must use an explicit version suffix: "-flash-001", not just "-flash". model='models/gemini-2.0-flash-001'  # Create a cache with a 5 minute TTL cache = client.caches.create(     model=model,     config=types.CreateCachedContentConfig(       display_name='sherlock jr movie', # used to identify the cache       system_instruction=(           'You are an expert video analyzer, and your job is to answer '           'the user\'s query based on the video file you have access to.'       ),       contents=[video_file],       ttl="300s",   ) )  # Construct a GenerativeModel which uses the created cache. response = client.models.generate_content(   model = model,   contents= (     'Introduce different characters in the movie by describing '     'their personality, looks, and names. Also list the timestamps '     'they were introduced for the first time.'),   config=types.GenerateContentConfig(cached_content=cache.name) )  print(response.usage_metadata)  # The output should look something like this: # # prompt_token_count: 696219 # cached_content_token_count: 696190 # candidates_token_count: 214 # total_token_count: 696433  print(response.text) 

from google import genai from google.genai import types import io import httpx  client = genai.Client()  long_context_pdf_path = "https://www.nasa.gov/wp-content/uploads/static/history/alsj/a17/A17_FlightPlan.pdf"  # Retrieve and upload the PDF using the File API doc_io = io.BytesIO(httpx.get(long_context_pdf_path).content)  document = client.files.upload(   file=doc_io,   config=dict(mime_type='application/pdf') )  model_name = "gemini-2.0-flash-001" system_instruction = "You are an expert analyzing transcripts."  # Create a cached content object cache = client.caches.create(     model=model_name,     config=types.CreateCachedContentConfig(       system_instruction=system_instruction,       contents=[document],     ) )  # Display the cache details print(f'{cache=}')  # Generate content using the cached prompt and document response = client.models.generate_content(   model=model_name,   contents="Please summarize this transcript",   config=types.GenerateContentConfig(     cached_content=cache.name   ))  # (Optional) Print usage metadata for insights into the API call print(f'{response.usage_metadata=}')  # Print the generated text print('\n\n', response.text)