הבנת מסמכים

from google import genai
from google.genai import types
import httpx

client = genai.Client()

doc_url = "https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf"

# Retrieve and encode the PDF byte
doc_data = httpx.get(doc_url).content

prompt = "Summarize this document"
response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents=[
      types.Part.from_bytes(
        data=doc_data,
        mime_type='application/pdf',
      ),
      prompt])
print(response.text)

from google import genai
from google.genai import types
import pathlib
import httpx

client = genai.Client()

doc_url = "https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf"

# Retrieve and encode the PDF byte
filepath = pathlib.Path('file.pdf')
filepath.write_bytes(httpx.get(doc_url).content)

prompt = "Summarize this document"
response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents=[
      types.Part.from_bytes(
        data=filepath.read_bytes(),
        mime_type='application/pdf',
      ),
      prompt])
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)

sample_doc = client.files.upload(
  # You can pass a path or a file-like object here
  file=doc_io,
  config=dict(
    mime_type='application/pdf')
)

prompt = "Summarize this document"

response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents=[sample_doc, prompt])
print(response.text)

from google import genai
from google.genai import types
import pathlib
import httpx

client = genai.Client()

long_context_pdf_path = "https://www.nasa.gov/wp-content/uploads/static/history/alsj/a17/A17_FlightPlan.pdf"

# Retrieve the PDF
file_path = pathlib.Path('A17.pdf')
file_path.write_bytes(httpx.get(long_context_pdf_path).content)

# Upload the PDF using the File API
sample_file = client.files.upload(
  file=file_path,
)

prompt="Summarize this document"

response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents=[sample_file, "Summarize this document"])
print(response.text)

from google import genai
import pathlib

client = genai.Client()

fpath = pathlib.Path('example.txt')
fpath.write_text('hello')

file = client.files.upload('example.txt')

file_info = client.files.get(file.name)
print(file_info.model_dump_json(indent=4))

from google import genai
import io
import httpx

client = genai.Client()

doc_url_1 = "https://arxiv.org/pdf/2312.11805"
doc_url_2 = "https://arxiv.org/pdf/2403.05530"

# Retrieve and upload both PDFs using the File API
doc_data_1 = io.BytesIO(httpx.get(doc_url_1).content)
doc_data_2 = io.BytesIO(httpx.get(doc_url_2).content)

sample_pdf_1 = client.files.upload(
  file=doc_data_1,
  config=dict(mime_type='application/pdf')
)
sample_pdf_2 = client.files.upload(
  file=doc_data_2,
  config=dict(mime_type='application/pdf')
)

prompt = "What is the difference between each of the main benchmarks between these two papers? Output these in a table."

response = client.models.generate_content(
  model="gemini-2.0-flash",
  contents=[sample_pdf_1, sample_pdf_2, prompt])
print(response.text)