Gemini 3.8 Flash është tani në dispozicion. Provojeni .

Google përdor teknologjinë e IA-së për të përkthyer përmbajtjet në gjuhën tënde të preferuar. Përkthimet nga IA-ja mund të përmbajnë gabime.

Kuptimi i dokumentit

Modelet e Gemini mund të përpunojnë dokumente në formatin PDF, duke përdorur aftësinë e integruar të shikimit për të kuptuar kontekstet e të gjithë dokumenteve. Kjo shkon përtej nxjerrjes së tekstit, duke e lejuar Gemini që:

Analizo dhe interpreto përmbajtjet, duke përfshirë tekstet, imazhet, diagramet, grafikët dhe tabelat, edhe në dokumente të gjata me deri në 1000 faqe.
Nxirr informacionet në formate të strukturuara të daljes.
Përmblidh dhe përgjigju pyetjeve bazuar si në elementet vizuale ashtu edhe në ato tekstuale në një dokument.
Transkripto përmbajtjen e dokumentit (p.sh. në HTML), duke ruajtur strukturat dhe formatimin, për përdorim në aplikacionet pasuese.

Mund të kalosh gjithashtu dokumente jo PDF në të njëjtën mënyrë, por Gemini do t'i shohë ato si tekst normal, gjë që do të eliminojë kontekstin si grafikët ose formatimin.

Të dhënat e PDF-së po transmetohen në linjë

Mund t'i kalosh të dhënat e PDF-së në linjë në kërkesë. Kjo është më e përshtatshme për dokumente më të vogla ose për përpunim të përkohshëm ku nuk ke nevojë t'i referohesh skedarit në kërkesat vijuese. Ne rekomandojmë përdorimin e API-së së "Skedarëve" për dokumente më të mëdha që duhet t'i referosh në ndërveprimet me shumë kthesa për të përmirësuar vonesën e kërkesës dhe për të zvogëluar përdorimin e gjerësisë së brezit.

Shembulli i mëposhtëm të tregon se si të kalosh të dhënat e PDF-së në linjë:

Python

from google import genai
import base64

client = genai.Client()

with open('path/to/document.pdf', 'rb') as f:
    pdf_bytes = f.read()

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {
            "type": "document",
            "data": base64.b64encode(pdf_bytes).decode('utf-8'),
            "mime_type": "application/pdf"
        },
        {"type": "text", "text": "Summarize this document"}
    ]
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

async function main() {
    const pdfData = fs.readFileSync("path/to/document.pdf", {
        encoding: "base64"
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3.8-flash",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                data: pdfData,
                mime_type: "application/pdf"
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.Base64;
import java.util.List;

Client client = new Client();

byte[] pdfBytes = Files.readAllBytes(Paths.get("path/to/document.pdf"));
String base64Pdf = Base64.getEncoder().encodeToString(pdfBytes);

Content docContent =
    DocumentContent.builder()
        .data(base64Pdf)
        .mimeType(DocumentContentMimeType.APPLICATION_PDF)
        .build();
Content textContent = TextContent.builder().text("Summarize this document").build();

List<Content> contents = Arrays.asList(docContent, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "encoding/base64"
    "fmt"
    "log"
    "os"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    pdfBytes, err := os.ReadFile("path/to/document.pdf")
    if err != nil {
        log.Fatal(err)
    }
    base64Pdf := base64.StdEncoding.EncodeToString(pdfBytes)

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.DocumentContent{
                    Data:     genai.Ptr(base64Pdf),
                    MimeType: interactions.DocumentContentMimeTypeApplicationPdf.ToPointer(),
                }),
                interactions.NewContent(interactions.TextContent{
                    Text: "Summarize this document",
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

QETËSI

PDF_PATH="path/to/document.pdf"

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {
        "type": "document",
        "data": "'$(base64 $B64FLAGS $PDF_PATH)'",
        "mime_type": "application/pdf"
      },
      {"type": "text", "text": "Summarize this document"}
    ]
  }'

Mund të ngarkosh gjithashtu një skedar PDF lokal për përpunim:

Python

from google import genai

client = genai.Client()

uploaded_file = client.files.upload(file="file.pdf")

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "document", "uri": uploaded_file.uri, "mime_type": uploaded_file.mime_type},
        {"type": "text", "text": "Summarize this document"}
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
    const uploadedFile = await ai.files.upload({
        file: "file.pdf",
        config: { mime_type: "application/pdf" }
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3.8-flash",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                uri: uploadedFile.uri,
                mime_type: uploadedFile.mime_type
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        new java.io.File("file.pdf"),
        UploadFileConfig.builder().mimeType("application/pdf").build());

Content docContent =
    DocumentContent.builder()
        .uri(uploadedFile.uri().orElse(""))
        .mimeType(DocumentContentMimeType.of(uploadedFile.mimeType().orElse("application/pdf")))
        .build();
Content textContent = TextContent.builder().text("Summarize this document").build();

List<Content> contents = Arrays.asList(docContent, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "file.pdf", &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.DocumentContent{
                    URI:      genai.Ptr(uploadedFile.URI),
                    MimeType: interactions.DocumentContentMimeType(uploadedFile.MIMEType).ToPointer(),
                }),
                interactions.NewContent(interactions.TextContent{
                    Text: "Summarize this document",
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

QETËSI

PDF_PATH="file.pdf"
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME="file.pdf"
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: application/pdf" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3.8-flash",
      "input": [
        {"type": "document", "uri": "'$file_uri'", "mime_type": "application/pdf"},
        {"type": "text", "text": "Summarize this document"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq -r ".steps[-1].content[0].text" response.json

Ngarkimi i skedarëve PDF duke përdorur API-në e "Skedarëve"

Ne të rekomandojmë të përdorësh API-në e "Skedarëve" për skedarët më të mëdhenj ose kur synon të ripërdorësh një dokument në disa kërkesa. Kjo përmirëson vonesën e kërkesës dhe zvogëlon përdorimin e gjerësisë së brezit duke shkëputur ngarkimin e skedarit nga kërkesat e modelit.

Skedarë të mëdhenj PDF nga URL-të

Përdor File API për të thjeshtuar ngarkimin dhe përpunimin e skedarëve të mëdhenj PDF nga URL-të:

Python

from google import genai
import io
import httpx

client = genai.Client()

long_context_pdf_path = "https://arxiv.org/pdf/2312.11805"

doc_io = io.BytesIO(httpx.get(long_context_pdf_path).content)

sample_doc = client.files.upload(
  file=doc_io,
  config=dict(
    mime_type='application/pdf')
)

prompt = "Summarize this document"

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "document", "uri": sample_doc.uri, "mime_type": sample_doc.mime_type},
        {"type": "text", "text": prompt}
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {

    const pdfBuffer = await fetch("https://arxiv.org/pdf/2312.11805")
        .then((response) => response.arrayBuffer());

    const fileBlob = new Blob([pdfBuffer], { type: 'application/pdf' });

    const file = await ai.files.upload({
        file: fileBlob,
        config: {
            displayName: 'A17_FlightPlan.pdf',
        },
    });

    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    const interaction = await ai.interactions.create({
        model: 'gemini-3.8-flash',
        input: [
            { type: "document", uri: file.uri, mime_type: file.mime_type },
            { type: "text", text: "Summarize this document" }
        ],
    });

    console.log(interaction.output_text);

}

main();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

String longContextPdfPath = "https://arxiv.org/pdf/2312.11805";
HttpClient httpClient = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder().uri(URI.create(longContextPdfPath)).build();
byte[] pdfBytes = httpClient.send(request, HttpResponse.BodyHandlers.ofByteArray()).body();

File sampleDoc =
    client.files.upload(
        pdfBytes, UploadFileConfig.builder().mimeType("application/pdf").build());

String prompt = "Summarize this document";

Content docContent =
    DocumentContent.builder()
        .uri(sampleDoc.uri().orElse(""))
        .mimeType(DocumentContentMimeType.of(sampleDoc.mimeType().orElse("application/pdf")))
        .build();
Content textContent = TextContent.builder().text(prompt).build();

List<Content> contents = Arrays.asList(docContent, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "bytes"
    "context"
    "fmt"
    "io"
    "log"
    "net/http"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    longContextPdfPath := "https://arxiv.org/pdf/2312.11805"
    resp, err := http.Get(longContextPdfPath)
    if err != nil {
        log.Fatal(err)
    }
    defer resp.Body.Close()
    pdfBytes, err := io.ReadAll(resp.Body)
    if err != nil {
        log.Fatal(err)
    }

    sampleDoc, err := client.Files.Upload(ctx, bytes.NewReader(pdfBytes), &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }

    prompt := "Summarize this document"

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.DocumentContent{
                    URI:      genai.Ptr(sampleDoc.URI),
                    MimeType: interactions.DocumentContentMimeType(sampleDoc.MIMEType).ToPointer(),
                }),
                interactions.NewContent(interactions.TextContent{
                    Text: prompt,
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

QETËSI

PDF_PATH="https://arxiv.org/pdf/2312.11805"
DISPLAY_NAME="Gemini_paper"
PROMPT="Summarize this document"

# Download the PDF from the provided URL
wget -O "${DISPLAY_NAME}.pdf" "${PDF_PATH}"

MIME_TYPE=$(file -b --mime-type "${DISPLAY_NAME}.pdf")
NUM_BYTES=$(wc -c < "${DISPLAY_NAME}.pdf")

echo "MIME_TYPE: ${MIME_TYPE}"
echo "NUM_BYTES: ${NUM_BYTES}"

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${DISPLAY_NAME}.pdf" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo "file_uri: ${file_uri}"

# Create payload JSON file for safety
cat << EOF > payload.json
{
  "model": "gemini-3.8-flash",
  "input": [
    {"type": "text", "text": "${PROMPT}"},
    {"type": "document", "uri": "${file_uri}", "mime_type": "application/pdf"}
  ]
}
EOF

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d @payload.json 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

# Clean up
rm "${DISPLAY_NAME}.pdf"
rm payload.json

Skedarët e mëdhenj PDF të ruajtur në nivel lokal

Python

from google import genai
import pathlib

client = genai.Client()

file_path = pathlib.Path('large_file.pdf')
sample_file = client.files.upload(
    file=file_path,
)

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "document", "uri": sample_file.uri, "mime_type": sample_file.mime_type},
        {"type": "text", "text": "Summarize this document"}
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
    const file = await ai.files.upload({
        file: 'large_file.pdf',
        config: {
            displayName: 'A17_FlightPlan.pdf',
        },
    });

    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    const interaction = await ai.interactions.create({
        model: 'gemini-3.8-flash',
        input: [
            { type: "document", uri: file.uri, mime_type: file.mime_type },
            { type: "text", text: "Summarize this document" }
        ],
    });

    console.log(interaction.output_text);

}

main();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File sampleFile =
    client.files.upload(
        new java.io.File("large_file.pdf"),
        UploadFileConfig.builder().mimeType("application/pdf").build());

Content docContent =
    DocumentContent.builder()
        .uri(sampleFile.uri().orElse(""))
        .mimeType(DocumentContentMimeType.of(sampleFile.mimeType().orElse("application/pdf")))
        .build();
Content textContent = TextContent.builder().text("Summarize this document").build();

List<Content> contents = Arrays.asList(docContent, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    sampleFile, err := client.Files.UploadFromPath(ctx, "large_file.pdf", &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.DocumentContent{
                    URI:      genai.Ptr(sampleFile.URI),
                    MimeType: interactions.DocumentContentMimeType(sampleFile.MIMEType).ToPointer(),
                }),
                interactions.NewContent(interactions.TextContent{
                    Text: "Summarize this document",
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

QETËSI

PDF_PATH="large_file.pdf"
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME=TEXT
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: application/pdf" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3.8-flash",
      "input": [
        {"type": "document", "uri": "'$file_uri'", "mime_type": "application/pdf"},
        {"type": "text", "text": "Can you add a few more lines to this poem?"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq -r ".steps[-1].content[0].text" response.json

Mund të verifikosh që API-ja e ruajti me sukses skedarin e ngarkuar dhe të marrësh metadatat e tij duke telefonuar files.get. Vetëm name (dhe si rrjedhim, uri) janë unike.

Python

from google import genai
import pathlib

client = genai.Client()

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

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

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

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

async function main() {
    fs.writeFileSync("example.pdf", "hello");

    const file = await ai.files.upload({
        file: "example.pdf",
        config: { mime_type: "application/pdf" }
    });

    const fileInfo = await ai.files.get({ name: file.name });
    console.log(fileInfo);
}

main();

Java

import com.google.genai.Client;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

Client client = new Client();

Path fpath = Paths.get("example.pdf");
Files.write(fpath, "hello".getBytes(StandardCharsets.UTF_8));

File file =
    client.files.upload(
        fpath.toFile(), UploadFileConfig.builder().mimeType("application/pdf").build());

File fileInfo = client.files.get(file.name().orElse(""), null);
System.out.println(fileInfo.toJson());

Go

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    if err := os.WriteFile("example.pdf", []byte("hello"), 0644); err != nil {
        log.Fatal(err)
    }

    file, err := client.Files.UploadFromPath(ctx, "example.pdf", &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }

    fileInfo, err := client.Files.Get(ctx, file.Name, nil)
    if err != nil {
        log.Fatal(err)
    }
    fmt.Println(fileInfo)
}

QETËSI

name=$(jq -r ".file.name" file_info.json)
# Get the file of interest to check state
curl "https://generativelanguage.googleapis.com/v1beta/$name?key=$GEMINI_API_KEY" > file_info.json
# Print some information about the file you got
name=$(jq -r ".name" file_info.json)
echo name=$name
file_uri=$(jq -r ".uri" file_info.json)
echo file_uri=$file_uri

Po kalojnë disa skedarë PDF

API-ja e Gemini mund të përpunojë disa dokumente PDF (deri në 1000 faqe) në një kërkesë të vetme, për sa kohë që madhësia e kombinuar e dokumenteve dhe e kërkesës me tekst qëndron brenda dritares kontekstuale të modelit.

Python

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"

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."

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "document", "uri": sample_pdf_1.uri, "mime_type": sample_pdf_1.mime_type},
        {"type": "document", "uri": sample_pdf_2.uri, "mime_type": sample_pdf_2.mime_type},
        {"type": "text", "text": prompt}
    ]
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function uploadRemotePDF(url, displayName) {
    const pdfBuffer = await fetch(url)
        .then((response) => response.arrayBuffer());

    const fileBlob = new Blob([pdfBuffer], { type: 'application/pdf' });

    const file = await ai.files.upload({
        file: fileBlob,
        config: {
            displayName: displayName,
        },
    });

    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    return file;
}

async function main() {
    const file1 = await uploadRemotePDF("https://arxiv.org/pdf/2312.11805", "PDF 1");
    const file2 = await uploadRemotePDF("https://arxiv.org/pdf/2403.05530", "PDF 2");

    const interaction = await ai.interactions.create({
        model: 'gemini-3.8-flash',
        input: [
            { type: "document", uri: file1.uri, mime_type: file1.mime_type },
            { type: "document", uri: file2.uri, mime_type: file2.mime_type },
            { type: "text", text: "What is the difference between each of the main benchmarks between these two papers? Output these in a table." }
        ],
    });

    console.log(interaction.output_text);
}

main();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

String docUrl1 = "https://arxiv.org/pdf/2312.11805";
String docUrl2 = "https://arxiv.org/pdf/2403.05530";

HttpClient httpClient = HttpClient.newHttpClient();
byte[] docData1 =
    httpClient
        .send(HttpRequest.newBuilder().uri(URI.create(docUrl1)).build(), HttpResponse.BodyHandlers.ofByteArray())
        .body();
byte[] docData2 =
    httpClient
        .send(HttpRequest.newBuilder().uri(URI.create(docUrl2)).build(), HttpResponse.BodyHandlers.ofByteArray())
        .body();

File samplePdf1 =
    client.files.upload(
        docData1, UploadFileConfig.builder().mimeType("application/pdf").build());
File samplePdf2 =
    client.files.upload(
        docData2, UploadFileConfig.builder().mimeType("application/pdf").build());

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

Content doc1Content =
    DocumentContent.builder()
        .uri(samplePdf1.uri().orElse(""))
        .mimeType(DocumentContentMimeType.of(samplePdf1.mimeType().orElse("application/pdf")))
        .build();
Content doc2Content =
    DocumentContent.builder()
        .uri(samplePdf2.uri().orElse(""))
        .mimeType(DocumentContentMimeType.of(samplePdf2.mimeType().orElse("application/pdf")))
        .build();
Content textContent = TextContent.builder().text(prompt).build();

List<Content> contents = Arrays.asList(doc1Content, doc2Content, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "bytes"
    "context"
    "fmt"
    "io"
    "log"
    "net/http"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    docURL1 := "https://arxiv.org/pdf/2312.11805"
    docURL2 := "https://arxiv.org/pdf/2403.05530"

    resp1, err := http.Get(docURL1)
    if err != nil {
        log.Fatal(err)
    }
    defer resp1.Body.Close()
    docData1, err := io.ReadAll(resp1.Body)
    if err != nil {
        log.Fatal(err)
    }

    resp2, err := http.Get(docURL2)
    if err != nil {
        log.Fatal(err)
    }
    defer resp2.Body.Close()
    docData2, err := io.ReadAll(resp2.Body)
    if err != nil {
        log.Fatal(err)
    }

    samplePdf1, err := client.Files.Upload(ctx, bytes.NewReader(docData1), &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }
    samplePdf2, err := client.Files.Upload(ctx, bytes.NewReader(docData2), &genai.UploadFileConfig{
        MIMEType: "application/pdf",
    })
    if err != nil {
        log.Fatal(err)
    }

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

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.DocumentContent{
                    URI:      genai.Ptr(samplePdf1.URI),
                    MimeType: interactions.DocumentContentMimeType(samplePdf1.MIMEType).ToPointer(),
                }),
                interactions.NewContent(interactions.DocumentContent{
                    URI:      genai.Ptr(samplePdf2.URI),
                    MimeType: interactions.DocumentContentMimeType(samplePdf2.MIMEType).ToPointer(),
                }),
                interactions.NewContent(interactions.TextContent{
                    Text: prompt,
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

QETËSI

DOC_URL_1="https://arxiv.org/pdf/2312.11805"
DOC_URL_2="https://arxiv.org/pdf/2403.05530"
DISPLAY_NAME_1="Gemini_paper"
DISPLAY_NAME_2="Gemini_1.5_paper"
PROMPT="What is the difference between each of the main benchmarks between these two papers? Output these in a table."

# Function to download and upload a PDF
upload_pdf() {
  local doc_url="$1"
  local display_name="$2"

  echo "Downloading ${display_name} from ${doc_url}..." >&2
  # Download the PDF
  wget -O "${display_name}.pdf" "${doc_url}" 2> /dev/null

  local MIME_TYPE=$(file -b --mime-type "${display_name}.pdf")
  local NUM_BYTES=$(wc -c < "${display_name}.pdf")

  echo "MIME_TYPE: ${MIME_TYPE}" >&2
  echo "NUM_BYTES: ${NUM_BYTES}" >&2

  local tmp_header_file="upload-header-${display_name}.tmp"

  # Initial resumable request
  # Using GEMINI_API_KEY instead of GOOGLE_API_KEY
  curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
    -D "${tmp_header_file}" \
    -H "X-Goog-Upload-Protocol: resumable" \
    -H "X-Goog-Upload-Command: start" \
    -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
    -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
    -H "Content-Type: application/json" \
    -d "{'file': {'display_name': '${display_name}'}}" 2> /dev/null

  local upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
  rm "${tmp_header_file}"

  echo "Upload URL for ${display_name}: ${upload_url}" >&2

  # Upload the PDF
  curl "${upload_url}" \
    -H "Content-Length: ${NUM_BYTES}" \
    -H "X-Goog-Upload-Offset: 0" \
    -H "X-Goog-Upload-Command: upload, finalize" \
    --data-binary "@${display_name}.pdf" 2> /dev/null > "file_info_${display_name}.json"

  local file_uri=$(jq -r ".file.uri" "file_info_${display_name}.json")
  echo "file_uri for ${display_name}: ${file_uri}" >&2

  # Clean up the downloaded PDF
  rm "${display_name}.pdf"

  echo "${file_uri}"
}

# Upload the first PDF
file_uri_1=$(upload_pdf "${DOC_URL_1}" "${DISPLAY_NAME_1}")

# Upload the second PDF
file_uri_2=$(upload_pdf "${DOC_URL_2}" "${DISPLAY_NAME_2}")

# Create payload JSON file for safety
cat << EOF > payload_multi.json
{
  "model": "gemini-3.8-flash",
  "input": [
    {"type": "document", "uri": "${file_uri_1}", "mime_type": "application/pdf"},
    {"type": "document", "uri": "${file_uri_2}", "mime_type": "application/pdf"},
    {"type": "text", "text": "${PROMPT}"}
  ]
}
EOF

# Now create an interaction using both files
# Using GEMINI_API_KEY instead of GOOGLE_API_KEY
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d @payload_multi.json 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

# Clean up
rm payload_multi.json
rm "file_info_${DISPLAY_NAME_1}.json"
rm "file_info_${DISPLAY_NAME_2}.json"

Detajet teknike

Gemini mbështet skedarët PDF deri në 50 MB ose 1000 faqe. Ky kufi zbatohet për të dhënat e integruara dhe ngarkimet e API-së së skedarëve. Çdo faqe dokumenti është ekuivalente me 258 kode qasjeje.

Megjithëse nuk ka kufij specifikë për numrin e pikselëve në një dokument përveç dritares së kontekstit të modelit, faqet më të mëdha zvogëlohen në një rezolucion maksimal prej 3072 x 3072 duke ruajtur raportin e tyre origjinal të pamjes, ndërsa faqet më të vogla zmadhohen në 768 x 768 pikselë. Nuk ka ulje të kostos për faqet me madhësi më të vogla, përveç gjerësisë së brezit ose përmirësimit të performancës për faqet me rezolucion më të lartë.

Modelet e Gemini 3

Gemini 3 prezanton kontrollin e detajuar mbi përpunimin multimodal të shikimit me parametrin media_resolution. Tani mund të caktosh rezolucionin në të ulët, mesatar ose të lartë për çdo pjesë individuale të medias. Me këtë shtesë, përpunimi i dokumenteve PDF është përditësuar:

Përfshirja e tekstit origjinal: Teksti i integruar në mënyrë të natyrshme në skedarin PDF nxirret dhe i ofrohet modelit.
Raportimi i faturimit dhe i kodeve të qasjes:
- Nuk tarifohesh për kodet që vijnë nga teksti origjinal i nxjerrë në skedarët PDF.
- Në seksionin usage_metadata të përgjigjes së API-së, kodet e krijuara nga përpunimi i faqeve të PDF-së (si imazhe) tani numërohen nën modalitetin IMAGE, jo në një modalitet të veçantë DOCUMENT si në disa versione të mëparshme.

Për më shumë detaje rreth parametrit të rezolucionit të medias, shiko udhëzuesin e Rezolucionit të medias.

Llojet e dokumenteve

Teknikisht, mund të kalosh lloje të tjera MIME për kuptimin e dokumentit, si TXT, Markdown, HTML, XML etj. Megjithatë, shikimi i dokumentit kupton në mënyrë të kuptimshme vetëm skedarët PDF. Llojet e tjera do të nxirren si tekst i thjeshtë dhe modeli nuk do të mund të interpretojë atë që shohim në paraqitjen e atyre skedarëve. Çdo specifikë e llojit të skedarit, si p.sh. grafikët, diagramet, etiketat HTML, formatimi i "Shënimit të formatit" etj., do të humbasë.

Për të mësuar rreth metodave të tjera të hyrjes së skedarëve, shiko udhëzuesin e Metodave të hyrjes së skedarëve.

Praktikat më të mira

Për rezultate më të mira:

Rrotullo faqet në orientimin e duhur para se t'i ngarkosh.
Shmang faqet e turbullta.
Nëse përdor një faqe të vetme, vendos kërkesën me tekst pas faqes.

Çfarë ka më pas

Për të mësuar më shumë, shiko burimet e mëposhtme:

Strategjitë e kërkesave për skedarët: Gemini API mbështet kërkesat me të dhëna tekstesh, imazhesh, audiosh dhe videosh, të njohura gjithashtu si kërkesa shumëmodale.
Udhëzimet e sistemit: Udhëzimet e sistemit të lejojnë të drejtosh sjelljen e modelit bazuar në nevojat dhe rastet e tua specifike të përdorimit.