Tìm kiếm tệp

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

client = genai.Client()

# File name will be visible in citations
file_search_store = client.file_search_stores.create(config={'display_name': 'your-fileSearchStore-name'})

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)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="""Can you tell me about [insert question]""",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                file_search=types.FileSearch(
                    file_search_store_names=[file_search_store.name]
                )
            )
        ]
    )
)

print(response.text)

const { GoogleGenAI } = require('@google/genai');

const ai = new GoogleGenAI({});

async function run() {
  // File name will be visible in citations
  const fileSearchStore = await ai.fileSearchStores.create({
    config: { displayName: 'your-fileSearchStore-name' }
  });

  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 response = await ai.models.generateContent({
    model: "gemini-2.5-flash",
    contents: "Can you tell me about [insert question]",
    config: {
      tools: [
        {
          fileSearch: {
            fileSearchStoreNames: [fileSearchStore.name]
          }
        }
      ]
    }
  });

  console.log(response.text);
}

run();

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

client = genai.Client()

# File name will be visible in citations
sample_file = client.files.upload(file='sample.txt', config={'name': 'display_file_name'})

file_search_store = client.file_search_stores.create(config={'display_name': 'your-fileSearchStore-name'})

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)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="""Can you tell me about [insert question]""",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                file_search=types.FileSearch(
                    file_search_store_names=[file_search_store.name]
                )
            )
        ]
    )
)

print(response.text)

const { GoogleGenAI } = require('@google/genai');

const ai = new GoogleGenAI({});

async function run() {
  // File name will be visible in citations
  const sampleFile = await ai.files.upload({
    file: 'sample.txt',
    config: { name: 'file-name' }
  });

  const fileSearchStore = await ai.fileSearchStores.create({
    config: { displayName: 'your-fileSearchStore-name' }
  });

  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 response = await ai.models.generateContent({
    model: "gemini-2.5-flash",
    contents: "Can you tell me about [insert question]",
    config: {
      tools: [
        {
          fileSearch: {
            fileSearchStoreNames: [fileSearchStore.name]
          }
        }
      ]
    }
  });

  console.log(response.text);
}

run();

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

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

file_search_store = client.file_search_stores.create(config={'display_name': 'my-file_search-store-123'})

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

my_file_search_store = client.file_search_stores.get(name='fileSearchStores/my-file_search-store-123')

client.file_search_stores.delete(name='fileSearchStores/my-file_search-store-123', config={'force': True})

const fileSearchStore = await ai.fileSearchStores.create({
  config: { displayName: 'my-file_search-store-123' }
});

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

const myFileSearchStore = await ai.fileSearchStores.get({
  name: 'fileSearchStores/my-file_search-store-123'
});

await ai.fileSearchStores.delete({
  name: 'fileSearchStores/my-file_search-store-123',
  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" }'

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

curl "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/my-file_search-store-123?key=${GEMINI_API_KEY}"

curl -X DELETE "https://generativelanguage.googleapis.com/v1beta/fileSearchStores/my-file_search-store-123?key=${GEMINI_API_KEY}"

op = client.file_search_stores.import_file(
    file_search_store_name=file_search_store.name,
    file_name=sample_file.name,
    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 }
    ]
  }
});

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Tell me about the book 'I, Claudius'",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                file_search=types.FileSearch(
                    file_search_store_names=[file_search_store.name],
                    metadata_filter="author=Robert Graves",
                )
            )
        ]
    )
)

print(response.text)

const response = await ai.models.generateContent({
  model: "gemini-2.5-flash",
  contents: "Tell me about the book 'I, Claudius'",
  config: {
    tools: [
      {
        fileSearch: {
          fileSearchStoreNames: [fileSearchStore.name],
          metadataFilter: 'author="Robert Graves"',
        }
      }
    ]
  }
});

console.log(response.text);

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=${GEMINI_API_KEY}" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
            "contents": [{
                "parts":[{"text": "Tell me about the book I, Claudius"}]          
            }],
            "tools": [{
                "file_search": { 
                    "file_search_store_names":["'$STORE_NAME'"],
                    "metadata_filter": "author = \"Robert Graves\""
                }
            }]
        }' 2> /dev/null > response.json

cat response.json

print(response.candidates[0].grounding_metadata)

console.log(JSON.stringify(response.candidates?.[0]?.groundingMetadata, null, 2));