ফাইল অনুসন্ধান

from google import genai
from google.genai import types
import time

client = genai.Client()

file_search_store = client.file_search_stores.create(
    config={
        'display_name': 'your-fileSearchStore-name',
        'embedding_model': 'models/gemini-embedding-2'
    }
)

operation = client.file_search_stores.upload_to_file_search_store(
  file='sample.txt',
  file_search_store_name=file_search_store.name,
  config={
      'display_name' : 'display-file-name',
  }
)

while not operation.done:
    time.sleep(5)
    operation = client.operations.get(operation)

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Can you tell me about [insert question]",
    tools=[{
        "type": "file_search",
        "file_search_store_names": [file_search_store.name]
    }]
)

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.type == "text":
                print(content_block.text)
                if content_block.annotations:
                    print("\nSources:")
                    for annotation in content_block.annotations:
                        if annotation.type == "file_citation":
                            print(f"  - {annotation.file_name}: {annotation.source}")

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

const ai = new GoogleGenAI({});

async function run() {
  const fileSearchStore = await ai.fileSearchStores.create({
    config: {
      displayName: 'your-fileSearchStore-name',
      embeddingModel: 'models/gemini-embedding-2'
    }
  });

  let operation = await ai.fileSearchStores.uploadToFileSearchStore({
    file: 'file.txt',
    fileSearchStoreName: fileSearchStore.name,
    config: {
      displayName: 'file-name',
    }
  });

  while (!operation.done) {
    await new Promise(resolve => setTimeout(resolve, 5000));
    operation = await ai.operations.get({ operation });
  }

  const interaction = await ai.interactions.create({
    model: "gemini-3.5-flash",
    input: "Can you tell me about [insert question]",
    tools: [{
      type: "file_search",
      file_search_store_names: [fileSearchStore.name]
    }]
  });

  for (const step of interaction.steps) {
    if (step.type === 'model_output') {
      for (const contentBlock of step.content) {
        if (contentBlock.type === 'text') {
          console.log(contentBlock.text);
          if (contentBlock.annotations) {
            console.log("\nSources:");
            for (const annotation of contentBlock.annotations) {
              if (annotation.type === 'file_citation') {
                console.log(`  - ${annotation.file_name}: ${annotation.source}`);
              }
            }
          }
        }
      }
    }
  }
}

run();

# 1. Create a File Search store
curl -X POST "https://generativelanguage.googleapis.com/v1beta/fileSearchStores?key=$GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "displayName": "your-file-search-store-name",
      "embeddingModel": "models/gemini-embedding-2"
    }' > store_res.json

FILE_SEARCH_STORE_NAME=$(jq -r ".name" store_res.json)

# 2. Upload directly to File Search store using resumable upload
NUM_BYTES=$(wc -c < "sample.txt")
curl "https://generativelanguage.googleapis.com/upload/v1beta/fileSearchStores/$FILE_SEARCH_STORE_NAME:uploadToFileSearchStore?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: text/plain" \
    -H "Content-Type: application/json" \
    -d '{"displayName": "sample.txt"}' 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " upload-header.tmp | cut -d" " -f2 | tr -d "\r")
rm upload-header.tmp

curl "${upload_url}" \
    -H "Content-Length: $NUM_BYTES" \
    -H "X-Goog-Upload-Offset: 0" \
    -H "X-Goog-Upload-Command: upload, finalize" \
    --data-binary "@sample.txt" 2> /dev/null > upload_response.json

cat upload_response.json

# 3. Query using the File Search store
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemini-3.5-flash",
      "input": "Can you tell me about [insert question]",
      "tools": [{
        "type": "file_search",
        "file_search_store_names": ["'"$FILE_SEARCH_STORE_NAME"'"]
      }]
    }'

from google import genai
from google.genai import types
import time

client = genai.Client()

sample_file = client.files.upload(file='sample.txt', config={'display_name': 'display_file_name'})

file_search_store = client.file_search_stores.create(
    config={
        'display_name': 'your-fileSearchStore-name',
        'embedding_model': 'models/gemini-embedding-2'
    }
)

operation = client.file_search_stores.import_file(
    file_search_store_name=file_search_store.name,
    file_name=sample_file.name
)

while not operation.done:
    time.sleep(5)
    operation = client.operations.get(operation)

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Can you tell me about [insert question]",
    tools=[{
        "type": "file_search",
        "file_search_store_names": [file_search_store.name]
    }]
)

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.type == "text":
                print(content_block.text)

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

const ai = new GoogleGenAI({});

async function run() {
  const sampleFile = await ai.files.upload({
    file: 'sample.txt',
    config: { displayName: 'file-name' }
  });

  const fileSearchStore = await ai.fileSearchStores.create({
    config: {
      displayName: 'your-fileSearchStore-name',
      embeddingModel: 'models/gemini-embedding-2'
    }
  });

  let operation = await ai.fileSearchStores.importFile({
    fileSearchStoreName: fileSearchStore.name,
    fileName: sampleFile.name
  });

  while (!operation.done) {
    await new Promise(resolve => setTimeout(resolve, 5000));
    operation = await ai.operations.get({ operation: operation });
  }

  const interaction = await ai.interactions.create({
    model: "gemini-3.5-flash",
    input: "Can you tell me about [insert question]",
    tools: [{
      type: "file_search",
      file_search_store_names: [fileSearchStore.name]
    }]
  });

  for (const step of interaction.steps) {
    if (step.type === 'model_output') {
      for (const contentBlock of step.content) {
        if (contentBlock.type === 'text') {
          console.log(contentBlock.text);
        }
      }
    }
  }
}

run();

# 1. Upload file using the Files API
NUM_BYTES=$(wc -c < "sample.txt")
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=$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: text/plain" \
    -H "Content-Type: application/json" \
    -d '{"file": {"displayName": "sample.txt"}}' 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " upload-header.tmp | cut -d" " -f2 | tr -d "\r")
rm upload-header.tmp

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

FILE_NAME=$(jq -r ".file.name" file_info.json)

# 2. Create a File Search store
curl -X POST "https://generativelanguage.googleapis.com/v1beta/fileSearchStores?key=$GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "displayName": "your-file-search-store-name",
      "embeddingModel": "models/gemini-embedding-2"
    }' > store_res.json

FILE_SEARCH_STORE_NAME=$(jq -r ".name" store_res.json)

# 3. Import the file into the File Search store
curl -X POST "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/$FILE_SEARCH_STORE_NAME:importFile?key=$GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"fileName": "'"$FILE_NAME"'"}'

# 4. Query using the File Search store
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemini-3.5-flash",
      "input": "Can you tell me about [insert question]",
      "tools": [{
        "type": "file_search",
        "file_search_store_names": ["'"$FILE_SEARCH_STORE_NAME"'"]
      }]
    }'

from google import genai
from google.genai import types
import time

client = genai.Client()

operation = client.file_search_stores.upload_to_file_search_store(
    file_search_store_name=file_search_store.name,
    file='sample.txt',
    config={
        'chunking_config': {
          'white_space_config': {
            'max_tokens_per_chunk': 200,
            'max_overlap_tokens': 20
          }
        }
    }
)

while not operation.done:
    time.sleep(5)
    operation = client.operations.get(operation)

print("Custom chunking complete.")

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

const ai = new GoogleGenAI({});

let operation = await ai.fileSearchStores.uploadToFileSearchStore({
  file: 'file.txt',
  fileSearchStoreName: fileSearchStore.name,
  config: {
    displayName: 'file-name',
    chunkingConfig: {
      whiteSpaceConfig: {
        maxTokensPerChunk: 200,
        maxOverlapTokens: 20
      }
    }
  }
});

while (!operation.done) {
  await new Promise(resolve => setTimeout(resolve, 5000));
  operation = await ai.operations.get({ operation });
}
console.log("Custom chunking complete.");

NUM_BYTES=$(wc -c < "sample.txt")
curl "https://generativelanguage.googleapis.com/upload/v1beta/fileSearchStores/$FILE_SEARCH_STORE_NAME:uploadToFileSearchStore?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: text/plain" \
    -H "Content-Type: application/json" \
    -d '{
      "displayName": "sample.txt",
      "chunkingConfig": {
        "whiteSpaceConfig": {
          "maxTokensPerChunk": 200,
          "maxOverlapTokens": 20
        }
      }
    }' 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " upload-header.tmp | cut -d" " -f2 | tr -d "\r")
rm upload-header.tmp

curl "${upload_url}" \
    -H "Content-Length: $NUM_BYTES" \
    -H "X-Goog-Upload-Offset: 0" \
    -H "X-Goog-Upload-Command: upload, finalize" \
    --data-binary "@sample.txt" 2> /dev/null > upload_response.json

cat upload_response.json

file_search_store = client.file_search_stores.create(
    config={
        'display_name': 'myfilesearchstore123',
        'embedding_model': 'models/gemini-embedding-2'
    }
)

for store in client.file_search_stores.list():
    print(store)

my_file_search_store = client.file_search_stores.get(name=file_search_store.name)

client.file_search_stores.delete(name=file_search_store.name, config={'force': True})

const fileSearchStore = await ai.fileSearchStores.create({
  config: {
    displayName: 'myfilesearchstore123',
    embeddingModel: 'models/gemini-embedding-2'
  }
});

const fileSearchStores = await ai.fileSearchStores.list();
for await (const store of fileSearchStores) {
  console.log(store);
}

const myFileSearchStore = await ai.fileSearchStores.get({
  name: fileSearchStore.name
});

await ai.fileSearchStores.delete({
  name: fileSearchStore.name,
  config: { force: true }
});

curl -X POST "https://generativelanguage.googleapis.com/v1beta/fileSearchStores?key=${GEMINI_API_KEY}" \
    -H "Content-Type: application/json" \
    -d '{ "displayName": "My Store", "embedding_model": "models/gemini-embedding-2" }'

curl "https://generativelanguage.googleapis.com/v1beta/fileSearchStores?key=${GEMINI_API_KEY}"

curl "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/myfilesearchstore123?key=${GEMINI_API_KEY}"

curl -X DELETE "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/myfilesearchstore123?key=${GEMINI_API_KEY}"

for document_in_store in client.file_search_stores.documents.list(parent='fileSearchStores/myfilesearchstore123'):
  print(document_in_store)

file_search_document = client.file_search_stores.documents.get(name='fileSearchStores/myfilesearchstore123/documents/sampletxt123')
print(file_search_document)

client.file_search_stores.documents.delete(name='fileSearchStores/myfilesearchstore123/documents/sampletxt123', config={'force': True})

const documents = await ai.fileSearchStores.documents.list({
  parent: 'fileSearchStores/myfilesearchstore123'
});
for await (const doc of documents) {
  console.log(doc);
}

const fileSearchDocument = await ai.fileSearchStores.documents.get({
  name: 'fileSearchStores/myfilesearchstore123/documents/sampletxt123'
});

await ai.fileSearchStores.documents.delete({
  name: 'fileSearchStores/myfilesearchstore123/documents/sampletxt123',
  config: { force: true }
});

curl "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/myfilesearchstore123/documents?key=${GEMINI_API_KEY}"

curl "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/myfilesearchstore123/documents/sampletxt123?key=${GEMINI_API_KEY}"

curl -X DELETE "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/myfilesearchstore123/documents/sampletxt123?key=${GEMINI_API_KEY}&force=true"

op = client.file_search_stores.import_file(
    file_search_store_name=file_search_store.name,
    file_name=sample_file.name,
    config={
        'custom_metadata': [
            {"key": "author", "string_value": "Robert Graves"},
            {"key": "year", "numeric_value": 1934}
        ]
    }
)

let operation = await ai.fileSearchStores.importFile({
  fileSearchStoreName: fileSearchStore.name,
  fileName: sampleFile.name,
  config: {
    customMetadata: [
      { key: "author", stringValue: "Robert Graves" },
      { key: "year", numericValue: 1934 }
    ]
  }
});

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Tell me about the book 'I, Claudius'",
    tools=[{
        "type": "file_search",
        "file_search_store_names": [file_search_store.name],
        "metadata_filter": 'author="Robert Graves"',
    }]
)

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.type == "text":
                print(content_block.text)

const interaction = await ai.interactions.create({
  model: "gemini-3.5-flash",
  input: "Tell me about the book 'I, Claudius'",
  tools: [{
    type: "file_search",
    file_search_store_names: [fileSearchStore.name],
    metadata_filter: 'author="Robert Graves"',
  }]
});

for (const step of interaction.steps) {
  if (step.type === 'model_output') {
    for (const contentBlock of step.content) {
      if (contentBlock.type === 'text') {
        console.log(contentBlock.text);
      }
    }
  }
}

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
            "model": "gemini-3.5-flash",
            "input": [{"type": "text", "text": "Tell me about the book I, Claudius"}],
            "tools": [{
                "type": "file_search",
                "file_search_store_names": ["'$STORE_NAME'"],
                "metadata_filter": "author = \"Robert Graves\""
            }]
        }' 2> /dev/null > response.json

cat response.json

store = client.file_search_stores.create(
    config={
        "display_name": "Multimodal Catalog",
        "embedding_model": "models/gemini-embedding-2",
    }
)

const fileSearchStore = await ai.fileSearchStores.create({
  config: {
    displayName: "Multimodal Catalog",
    embeddingModel: "models/gemini-embedding-2",
  },
});

curl -X POST "https://generativelanguage.googleapis.com/v1beta/fileSearchStores?key=$GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "display_name": "Multimodal Catalog",
      "embedding_model": "models/gemini-embedding-2"
    }'

for step in interaction.steps:
    if step.type == 'model_output':
        for content in step.content:
            if content.type == 'text' and content.annotations:
                print(content.annotations)

for (const step of interaction.steps) {
  if (step.type === 'model_output') {
    for (const contentBlock of step.content) {
      if (contentBlock.type === 'text' && contentBlock.annotations) {
        console.log(JSON.stringify(contentBlock.annotations, null, 2));
      }
    }
  }
}

{
  "steps": [
    {
      "type": "model_output",
      "content": [
        {
          "type": "text",
          "text": "...",
          "annotations": [
            {
              "type": "file_citation",
              "file_name": "sample.txt",
              "source": "..."
            }
          ]
        }
      ]
    }
  ]
}

for step in interaction.steps:
    if step.type == "model_output":
        for content in step.content:
            if content.type == "text" and content.annotations:
                for annotation in content.annotations:
                    if annotation.type == "file_citation" and annotation.page_number:
                        print(f"Cited Page: {annotation.page_number}")

for (const step of interaction.steps) {
  if (step.type === 'model_output') {
    for (const block of step.content) {
      if (block.type === 'text' && block.annotations) {
        for (const annotation of block.annotations) {
          if (annotation.type === 'file_citation' && annotation.pageNumber) {
            console.log(`Cited Page: ${annotation.pageNumber}`);
          }
        }
      }
    }
  }
}

{
  "steps": [
    {
      "type": "model_output",
      "content": [
        {
          "type": "text",
          "text": "...",
          "annotations": [
            {
              "type": "file_citation",
              "file_name": "document.pdf",
              "page_number": 1,
              "source": "..."
            }
          ]
        }
      ]
    }
  ]
}

for step in interaction.steps:
    if step.type == "model_output":
        for content in step.content:
            if content.type == "text" and content.annotations:
                for annotation in content.annotations:
                    if annotation.type == "file_citation" and annotation.media_id:
                        print(f"Cited Media ID: {annotation.media_id}")
                        blob_content = client.file_search_stores.download_media(
                            media_id=annotation.media_id
                        )

for (const step of interaction.steps) {
  if (step.type === 'model_output') {
    for (const block of step.content) {
      if (block.type === 'text' && block.annotations) {
        for (const annotation of block.annotations) {
          if (annotation.type === 'file_citation' && annotation.mediaId) {
            console.log(`Cited Media ID: ${annotation.mediaId}`);
            const blobContent = await ai.fileSearchStores.downloadMedia(annotation.mediaId);
          }
        }
      }
    }
  }
}

curl -X GET "https://generativelanguage.googleapis.com/v1/fileSearchStores/my-store-123/media/BlobId-456" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Tell me about [insert question]",
    tools=[{
        "type": "file_search",
        "file_search_store_names": [file_search_store.name]
    }]
)

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.annotations:
                for annotation in content_block.annotations:
                    print(annotation)

const interaction = await ai.interactions.create({
  model: "gemini-3.5-flash",
  input: "Tell me about [insert question]",
  tools: [{
    type: "file_search",
    file_search_store_names: [fileSearchStore.name]
  }]
});

for (const step of interaction.steps) {
  if (step.type === 'model_output') {
    for (const contentBlock of step.content) {
      if (contentBlock.annotations) {
        contentBlock.annotations.forEach((annotation) => {
          console.log(annotation);
        });
      }
    }
  }
}

{
  "steps": [
    {
      "type": "model_output",
      "content": [
        {
          "type": "text",
          "text": "...",
          "annotations": [
            {
              "file_name": "...",
              "source": "...",
              "custom_metadata": [
                {
                  "key": "author",
                  "string_value": "Robert Graves"
                },
                {
                  "key": "year",
                  "numeric_value": 1934
                }
              ]
            }
          ]
        }
      ]
    }
  ]
}

from pydantic import BaseModel, Field

class Money(BaseModel):
    amount: str = Field(description="The numerical part of the amount.")
    currency: str = Field(description="The currency of amount.")

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="What is the minimum hourly wage in Tokyo right now?",
    tools=[{
        "type": "file_search",
        "file_search_store_names": [file_search_store.name]
    }],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Money.model_json_schema()
    },
)
result = Money.model_validate_json(interaction.output_text)
print(result)

import { z } from "zod";

const moneyJsonSchema = {
  type: "object",
  properties: {
    amount: { type: "string", description: "The numerical part of the amount." },
    currency: { type: "string", description: "The currency of amount." }
  },
  required: ["amount", "currency"]
};

const moneySchema = z.fromJSONSchema(moneyJsonSchema);

async function run() {
  const interaction = await ai.interactions.create({
    model: "gemini-3.5-flash",
    input: "What is the minimum hourly wage in Tokyo right now?",
    tools: [{
      type: "file_search",
      file_search_store_names: [fileSearchStore.name],
    }],
    response_format: {
      type: 'text',
      mime_type: 'application/json',
      schema: moneyJsonSchema
    },
  });

  const result = moneySchema.parse(JSON.parse(interaction.output_text));
  console.log(result);
}

run();

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "model": "gemini-3.5-flash",
    "input": "What is the minimum hourly wage in Tokyo right now?",
    "tools": [{
      "type": "file_search",
      "file_search_store_names": ["$FILE_SEARCH_STORE_NAME"]
    }],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
          "amount": {"type": "string", "description": "The numerical part of the amount."},
          "currency": {"type": "string", "description": "The currency of amount."}
        },
        "required": ["amount", "currency"]
      }
    }
  }'