Gemini 3.8 Flash 现已推出。试试看。

Google 会使用 AI 技术将内容翻译成您偏好的语言。AI 翻译可能包含错误。

文件搜索

Gemini API 通过文件搜索工具实现检索增强生成 ("RAG")。文件搜索功能会导入、分块和索引您的数据，以便根据您提供的提示快速检索相关信息。然后，将检索到的信息用作模型的上下文，以便模型提供更准确且更相关的回答。文件搜索还能够提供多模态功能，该功能支持 gemini-embedding-001 提供的文本嵌入，以及 gemini-embedding-2 提供的图片/多模态嵌入。

在查询时进行文件存储和生成嵌入内容是免费的，您只需支付首次为文件编制索引时创建嵌入内容的费用，以及正常的 Gemini 模型输入 / 输出 token 费用。这种新的结算模式使得文件搜索工具的构建和扩展更加简单且更具成本效益。如需了解详情，请参阅价格部分。

直接上传到文件搜索商店

此示例展示了如何直接将文件上传到文件搜索存储区：

Python

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-3.8-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}")

JavaScript

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-3.8-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();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Annotation;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.FileCitation;
import com.google.genai.gaos.models.interactions.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.CreateFileSearchStoreConfig;
import com.google.genai.types.FileSearchStore;
import com.google.genai.types.UploadToFileSearchStoreConfig;
import com.google.genai.types.UploadToFileSearchStoreOperation;
import java.util.Arrays;

Client client = new Client();

FileSearchStore fileSearchStore =
    client.fileSearchStores.create(
        CreateFileSearchStoreConfig.builder()
            .displayName("your-fileSearchStore-name")
            .embeddingModel("models/gemini-embedding-2")
            .build());

UploadToFileSearchStoreOperation operation =
    client.fileSearchStores.uploadToFileSearchStore(
        fileSearchStore.name().get(),
        "sample.txt",
        UploadToFileSearchStoreConfig.builder().displayName("display-file-name").build());

while (!operation.done().orElse(false)) {
  Thread.sleep(5000);
  operation = client.operations.get(operation, null);
}

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Can you tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList(fileSearchStore.name().get()))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content contentBlock : outputStep.content().get()) {
          if (contentBlock instanceof TextContent) {
            TextContent textContent = (TextContent) contentBlock;
            System.out.println(textContent.text().orElse(""));
            if (textContent.annotations().isPresent()
                && !textContent.annotations().get().isEmpty()) {
              System.out.println("\nSources:");
              for (Annotation annotation : textContent.annotations().get()) {
                if (annotation instanceof FileCitation) {
                  FileCitation citation = (FileCitation) annotation;
                  System.out.printf(
                      "  - %s: %s%n",
                      citation.fileName().orElse(""), citation.source().orElse(""));
                }
              }
            }
          }
        }
      }
    }
  }
}

REST

# 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: $GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "Can you tell me about [insert question]",
      "tools": [{
        "type": "file_search",
        "file_search_store_names": ["'"$FILE_SEARCH_STORE_NAME"'"]
      }]
    }'

如需了解详情，请参阅 uploadToFileSearchStore 的 API 参考文档。

导入文件

或者，您也可以上传现有文件，然后将其导入文件搜索存储区：

Python

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-3.8-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)

JavaScript

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-3.8-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();

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.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.CreateFileSearchStoreConfig;
import com.google.genai.types.File;
import com.google.genai.types.FileSearchStore;
import com.google.genai.types.ImportFileOperation;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;

Client client = new Client();

File sampleFile =
    client.files.upload(
        "sample.txt", UploadFileConfig.builder().displayName("display_file_name").build());

FileSearchStore fileSearchStore =
    client.fileSearchStores.create(
        CreateFileSearchStoreConfig.builder()
            .displayName("your-fileSearchStore-name")
            .embeddingModel("models/gemini-embedding-2")
            .build());

ImportFileOperation operation =
    client.fileSearchStores.importFile(
        fileSearchStore.name().get(), sampleFile.name().get(), null);

while (!operation.done().orElse(false)) {
  Thread.sleep(5000);
  operation = client.operations.get(operation, null);
}

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Can you tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList(fileSearchStore.name().get()))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content contentBlock : outputStep.content().get()) {
          if (contentBlock instanceof TextContent) {
            TextContent textContent = (TextContent) contentBlock;
            System.out.println(textContent.text().orElse(""));
          }
        }
      }
    }
  }
}

REST

# 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: $GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "gemini-3.8-flash",
      "input": "Can you tell me about [insert question]",
      "tools": [{
        "type": "file_search",
        "file_search_store_names": ["'"$FILE_SEARCH_STORE_NAME"'"]
      }]
    }'

如需了解详情，请参阅 importFile 的 API 参考文档。

分块配置

将文件导入到文件搜索存储区后，系统会自动将其分解为多个块，然后进行嵌入、编入索引并上传到您的文件搜索存储区。如果您需要更精细地控制分块策略，可以指定 chunking_config 设置，以设置每个块的 token 数上限和重叠 token 数上限。

Python

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.")

JavaScript

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.");

Java

import com.google.genai.Client;
import com.google.genai.types.ChunkingConfig;
import com.google.genai.types.UploadToFileSearchStoreConfig;
import com.google.genai.types.UploadToFileSearchStoreOperation;
import com.google.genai.types.WhiteSpaceConfig;

Client client = new Client();

UploadToFileSearchStoreOperation operation =
    client.fileSearchStores.uploadToFileSearchStore(
        "fileSearchStores/my-file-search-store",
        "sample.txt",
        UploadToFileSearchStoreConfig.builder()
            .displayName("file-name")
            .chunkingConfig(
                ChunkingConfig.builder()
                    .whiteSpaceConfig(
                        WhiteSpaceConfig.builder()
                            .maxTokensPerChunk(200)
                            .maxOverlapTokens(20)
                            .build())
                    .build())
            .build());

while (!operation.done().orElse(false)) {
  Thread.sleep(5000);
  operation = client.operations.get(operation, null);
}

System.out.println("Custom chunking complete.");

REST

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

如需使用文件搜索存储区，请将其作为工具传递给 interactions.create 方法，如上传和导入示例所示。

运作方式

文件搜索功能使用一种称为语义搜索的技术来查找与用户提示相关的信息。与基于标准关键字的搜索不同，语义搜索能够理解查询的含义和上下文。

导入文件时，系统会将其转换为称为嵌入的数值表示形式，以捕捉上传内容的语义。这些嵌入会存储在专门的文件搜索数据库中。当您提出查询时，系统也会将其转换为嵌入。然后，系统会执行文件搜索，从文件搜索存储区中找到最相似且最相关的文档块。

嵌入向量没有存留时间 (TTL)；它们会一直留存，直到被手动删除或模型被弃用为止。不过，文件会在 48 小时后被删除。

下面详细介绍了使用文件搜索 uploadToFileSearchStore API 的流程：

创建文件搜索存储区：文件搜索存储区包含来自文件的处理后数据。它是语义搜索将使用的嵌入的持久性容器。
上传文件并导入到文件搜索存储区：同时上传文件并将结果导入到文件搜索存储区。这会创建一个临时 File 对象，该对象是对原始文档的引用。然后，该数据会被分块、转换为 File Search 嵌入并编入索引。File对象会在 48 小时后被删除，而导入到文件搜索存储区的数据会无限期存储，直到您选择删除为止。
使用文件搜索进行查询：最后，您可以在 generateContent 调用中使用 FileSearch 工具。在工具配置中，您需要指定一个 FileSearchRetrievalResource，该 FileSearchStore 指向您要搜索的。这会指示模型对该特定文件搜索存储区执行语义搜索，以查找相关信息来为回答提供依据。

在此图中，从文档到嵌入模型（使用 gemini-embedding-001）的虚线表示 uploadToFileSearchStore API（绕过文件存储）。否则，使用 Files API 分别创建文件并导入文件会将索引编制流程从文档移至文件存储，然后再移至嵌入模型。

文件搜索存储区

文件搜索存储区是文档嵌入的容器。虽然通过 File API 上传的原始文件会在 48 小时后被删除，但导入到文件搜索存储区的数据会无限期存储，直到您手动将其删除。您可以创建多个文件搜索存储区来整理文档。借助 FileSearchStore API，您可以创建、列出、获取和删除文件搜索存储区，从而管理这些存储区。文件搜索存储区名称的范围是全局的。

以下是一些有关如何管理文件搜索商店的示例：

Python

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})

JavaScript

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 }
});

Java

import com.google.genai.Client;
import com.google.genai.types.CreateFileSearchStoreConfig;
import com.google.genai.types.DeleteFileSearchStoreConfig;
import com.google.genai.types.FileSearchStore;

Client client = new Client();

FileSearchStore fileSearchStore =
    client.fileSearchStores.create(
        CreateFileSearchStoreConfig.builder()
            .displayName("myfilesearchstore123")
            .embeddingModel("models/gemini-embedding-2")
            .build());

for (FileSearchStore store : client.fileSearchStores.list(null)) {
  System.out.println(store);
}

FileSearchStore myFileSearchStore =
    client.fileSearchStores.get(fileSearchStore.name().get(), null);

client.fileSearchStores.delete(
    fileSearchStore.name().get(), DeleteFileSearchStoreConfig.builder().force(true).build());

REST

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}"

文件搜索文档

您可以使用 File Search Documents API 管理文件存储区中的各个文档，以list 文件搜索存储区中的每个文档、get 文档的相关信息，以及按名称delete 文档。

Python

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})

JavaScript

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 }
});

Java

import com.google.genai.Client;
import com.google.genai.types.DeleteDocumentConfig;
import com.google.genai.types.Document;

Client client = new Client();

for (Document documentInStore :
    client.fileSearchStores.documents.list("fileSearchStores/myfilesearchstore123", null)) {
  System.out.println(documentInStore);
}

Document fileSearchDocument =
    client.fileSearchStores.documents.get(
        "fileSearchStores/myfilesearchstore123/documents/sampletxt123", null);
System.out.println(fileSearchDocument);

client.fileSearchStores.documents.delete(
    "fileSearchStores/myfilesearchstore123/documents/sampletxt123",
    DeleteDocumentConfig.builder().force(true).build());

REST

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"

文件元数据

您可以为文件添加自定义元数据，以便过滤文件或提供更多背景信息。元数据是一组键值对。

Python

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}
        ]
    }
)

JavaScript

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

Java

import com.google.genai.Client;
import com.google.genai.types.CustomMetadata;
import com.google.genai.types.ImportFileConfig;
import com.google.genai.types.ImportFileOperation;
import java.util.Arrays;

Client client = new Client();

ImportFileOperation op =
    client.fileSearchStores.importFile(
        "fileSearchStores/myfilesearchstore123",
        "files/samplefile123",
        ImportFileConfig.builder()
            .customMetadata(
                Arrays.asList(
                    CustomMetadata.builder().key("author").stringValue("Robert Graves").build(),
                    CustomMetadata.builder().key("year").numericValue(1934f).build()))
            .build());

如果您在文件搜索存储区中有多个文档，并且只想搜索其中的一部分，此参数会非常有用。

Python

interaction = client.interactions.create(
    model="gemini-3.8-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)

JavaScript

const interaction = await ai.interactions.create({
  model: "gemini-3.8-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);
      }
    }
  }
}

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.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Tell me about the book 'I, Claudius'"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .metadataFilter("author=\"Robert Graves\"")
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content contentBlock : outputStep.content().get()) {
          if (contentBlock instanceof TextContent) {
            TextContent textContent = (TextContent) contentBlock;
            System.out.println(textContent.text().orElse(""));
          }
        }
      }
    }
  }
}

REST

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
            "model": "gemini-3.8-flash",
            "input": [{"type": "text", "text": "Tell me about the book I, Claudius"}],
            "tools": [{
                "type": "file_search",
                "file_search_store_names": ["'$STORE_NAME'"],
                "metadata_filter": "author = \"Robert Graves\""
            }]
        }' 2> /dev/null > response.json

cat response.json

有关为 metadata_filter 实现列表过滤条件语法的指南，请访问 google.aip.dev/160

多模态文件搜索

借助多模态文件搜索，您可以原生嵌入和搜索图片，从而实现丰富的多模态 RAG 应用。

配置嵌入模型

创建 FileSearchStore 时，您必须替换默认的纯文本嵌入模型，才能使用多模态模型。使用 models/gemini-embedding-2 处理文本和图片。

Python

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

JavaScript

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

Java

import com.google.genai.Client;
import com.google.genai.types.CreateFileSearchStoreConfig;
import com.google.genai.types.FileSearchStore;

Client client = new Client();

FileSearchStore store =
    client.fileSearchStores.create(
        CreateFileSearchStoreConfig.builder()
            .displayName("Multimodal Catalog")
            .embeddingModel("models/gemini-embedding-2")
            .build());

REST

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"
    }'

上传图片

使用多模态嵌入模型创建存储区后，您可以直接使用直接上传到文件搜索存储区或导入文件中所述的相同上传 API 来上传图片文件。

图片文件要求：

图片文件的分辨率不得超过 4K x 4K 像素。
支持的格式包括 PNG、JPEG。

引用

使用文件搜索功能时，模型的回答可能包含引用，指明上传的文档中的哪些部分用于生成回答。这有助于进行事实核查和验证。

您可以通过回答中 model_output 步骤的 content 代码块内的 annotations 属性访问引用信息。

Python

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)

JavaScript

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));
      }
    }
  }
}

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.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Can you tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content content : outputStep.content().get()) {
          if (content instanceof TextContent) {
            TextContent textContent = (TextContent) content;
            if (textContent.annotations().isPresent()) {
              System.out.println(textContent.annotations().get());
            }
          }
        }
      }
    }
  }
}

REST

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

如需详细了解引用的结构，请参阅 Interactions API 参考文档。

页码

如果您使用文件搜索功能搜索包含页面的文档（例如 PDF），模型给出的回答可能会包含找到相关信息的页码。您可以通过 file_citation 注释的 page_number 属性访问此信息。

Python

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}")

JavaScript

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}`);
          }
        }
      }
    }
  }
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Annotation;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.FileCitation;
import com.google.genai.gaos.models.interactions.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Can you tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content content : outputStep.content().get()) {
          if (content instanceof TextContent) {
            TextContent textContent = (TextContent) content;
            if (textContent.annotations().isPresent()) {
              for (Annotation annotation : textContent.annotations().get()) {
                if (annotation instanceof FileCitation) {
                  FileCitation citation = (FileCitation) annotation;
                  if (citation.pageNumber().isPresent()) {
                    System.out.println("Cited Page: " + citation.pageNumber().get());
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

REST

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

媒体引用

当模型在生成过程中引用图片块时，API 会在注解中返回类型为 file_citation 的注解，其中包含 media_id。您可以使用此 ID 下载模型引用的确切图片块。此 media_id 在多次搜索调用中保持不变，因此您可以可靠地检索同一图片或使用该 ID 缓存图片。

以下代码段是一个 REST 响应步骤示例：

{
  "type": "model_output",
  "content": [
    {
      "type": "text",
      "text": "...",
      "annotations": [
        {
          "type": "file_citation",
          "file_name": "product_image",
          "media_id": "fileSearchStores/my-store-123/media/BlobId-456"
        }
      ]
    }
  ]
}

以下代码段演示了如何检索 media_id 和下载媒体：

Python

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
                        )

JavaScript

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);
          }
        }
      }
    }
  }
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Annotation;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.FileCitation;
import com.google.genai.gaos.models.interactions.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Can you tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content content : outputStep.content().get()) {
          if (content instanceof TextContent) {
            TextContent textContent = (TextContent) content;
            if (textContent.annotations().isPresent()) {
              for (Annotation annotation : textContent.annotations().get()) {
                if (annotation instanceof FileCitation) {
                  FileCitation citation = (FileCitation) annotation;
                  if (citation.mediaId().isPresent()) {
                    System.out.println("Cited Media ID: " + citation.mediaId().get());
                    byte[] blobContent =
                        client.fileSearchStores.downloadMedia(citation.mediaId().get(), null);
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

REST

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

自定义元数据

如果您已向文件添加自定义元数据，则可以在模型回答的注释中访问该元数据。这有助于将其他上下文（例如网址、页码或作者）从源文档传递到应用逻辑。类型为 file_citation 的每个引用注释都包含此自定义元数据。

Python

interaction = client.interactions.create(
    model="gemini-3.8-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)

JavaScript

const interaction = await ai.interactions.create({
  model: "gemini-3.8-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);
        });
      }
    }
  }
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Annotation;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.FileSearch;
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.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("Tell me about [insert question]"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .build()))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.steps().isPresent()) {
  for (Step step : interaction.steps().get()) {
    if (step instanceof ModelOutputStep) {
      ModelOutputStep outputStep = (ModelOutputStep) step;
      if (outputStep.content().isPresent()) {
        for (Content contentBlock : outputStep.content().get()) {
          if (contentBlock instanceof TextContent) {
            TextContent textContent = (TextContent) contentBlock;
            if (textContent.annotations().isPresent()) {
              for (Annotation annotation : textContent.annotations().get()) {
                System.out.println(annotation);
              }
            }
          }
        }
      }
    }
  }
}

REST

{
  "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
                }
              ]
            }
          ]
        }
      ]
    }
  ]
}

结构化输出

从 Gemini 3 模型开始，您可以将文件搜索工具与结构化输出相结合。

Python

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-3.8-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)

JavaScript

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-3.8-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();

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.FileSearch;
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.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

Map<String, Object> properties = new HashMap<>();

Map<String, Object> amountProp = new HashMap<>();
amountProp.put("type", "string");
amountProp.put("description", "The numerical part of the amount.");
properties.put("amount", amountProp);

Map<String, Object> currencyProp = new HashMap<>();
currencyProp.put("type", "string");
currencyProp.put("description", "The currency of amount.");
properties.put("currency", currencyProp);

Map<String, Object> moneyJsonSchema = new HashMap<>();
moneyJsonSchema.put("type", "object");
moneyJsonSchema.put("properties", properties);
moneyJsonSchema.put("required", Arrays.asList("amount", "currency"));

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(moneyJsonSchema)
                .build()));

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.of("What is the minimum hourly wage in Tokyo right now?"))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/myfilesearchstore123"))
                    .build()))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

REST

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "model": "gemini-3.8-flash",
    "input": "What is the minimum hourly wage in Tokyo right now?",
    "tools": [{
      "type": "file_search",
      "file_search_store_names": ["$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"]
      }
    }
  }'

支持的模型

以下模型支持文件搜索：

模型	文件搜索
Gemini 3.8 Flash	✔️
Gemini 3.7 Flash	✔️
Gemini 3.6 Flash	✔️
Gemini 3.5 Flash-Lite	✔️
Gemini 3.5 Flash	✔️
Gemini 3.1 Pro 预览版	✔️
Gemini 3.1 Flash-Lite	✔️
Gemini 3 Flash 预览版	✔️

支持的文件类型

文件搜索支持多种文件格式，详见以下各部分。

应用文件类型

application/dart
application/ecmascript
application/json
application/ms-java
application/msword
application/pdf
application/sql
application/typescript
application/vnd.curl
application/vnd.dart
application/vnd.ibm.secure-container
application/vnd.jupyter
application/vnd.ms-excel
application/vnd.oasis.opendocument.text
application/vnd.openxmlformats-officedocument.presentationml.presentation
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/vnd.openxmlformats-officedocument.wordprocessingml.template
application/x-csh
application/x-hwp
application/x-hwp-v5
application/x-latex
application/x-php
application/x-powershell
application/x-sh
application/x-shellscript
application/x-tex
application/x-zsh
application/xml
application/zip

文本文件类型

text/1d-interleaved-parityfec
text/RED
text/SGML
text/cache-manifest
text/calendar
text/cql
text/cql-extension
text/cql-identifier
text/css
text/csv
text/csv-schema
text/dns
text/encaprtp
text/enriched
text/example
text/fhirpath
text/flexfec
text/fwdred
text/gff3
text/grammar-ref-list
text/hl7v2
text/html
text/javascript
text/jcr-cnd
text/jsx
text/markdown
text/mizar
text/n3
text/parameters
text/parityfec
text/php
text/plain
text/provenance-notation
text/prs.fallenstein.rst
text/prs.lines.tag
text/prs.prop.logic
text/raptorfec
text/rfc822-headers
text/rtf
text/rtp-enc-aescm128
text/rtploopback
text/rtx
text/sgml
text/shaclc
text/shex
text/spdx
text/strings
text/t140
text/tab-separated-values
text/texmacs
text/troff
text/tsv
text/tsx
text/turtle
text/ulpfec
text/uri-list
text/vcard
text/vnd.DMClientScript
text/vnd.IPTC.NITF
text/vnd.IPTC.NewsML
text/vnd.a
text/vnd.abc
text/vnd.ascii-art
text/vnd.curl
text/vnd.debian.copyright
text/vnd.dvb.subtitle
text/vnd.esmertec.theme-descriptor
text/vnd.exchangeable
text/vnd.familysearch.gedcom
text/vnd.ficlab.flt
text/vnd.fly
text/vnd.fmi.flexstor
text/vnd.gml
text/vnd.graphviz
text/vnd.hans
text/vnd.hgl
text/vnd.in3d.3dml
text/vnd.in3d.spot
text/vnd.latex-z
text/vnd.motorola.reflex
text/vnd.ms-mediapackage
text/vnd.net2phone.commcenter.command
text/vnd.radisys.msml-basic-layout
text/vnd.senx.warpscript
text/vnd.sosi
text/vnd.sun.j2me.app-descriptor
text/vnd.trolltech.linguist
text/vnd.wap.si
text/vnd.wap.sl
text/vnd.wap.wml
text/vnd.wap.wmlscript
text/vtt
text/wgsl
text/x-asm
text/x-bibtex
text/x-boo
text/x-c
text/x-c++hdr
text/x-c++src
text/x-cassandra
text/x-chdr
text/x-coffeescript
text/x-component
text/x-csh
text/x-csharp
text/x-csrc
text/x-cuda
text/x-d
text/x-diff
text/x-dsrc
text/x-emacs-lisp
text/x-erlang
text/x-gff3
text/x-go
text/x-haskell
text/x-java
text/x-java-properties
text/x-java-source
text/x-kotlin
text/x-lilypond
text/x-lisp
text/x-literate-haskell
text/x-lua
text/x-moc
text/x-objcsrc
text/x-pascal
text/x-pcs-gcd
text/x-perl
text/x-perl-script
text/x-python
text/x-python-script
text/x-r-markdown
text/x-rsrc
text/x-rst
text/x-ruby-script
text/x-rust
text/x-sass
text/x-scala
text/x-scheme
text/x-script.python
text/x-scss
text/x-setext
text/x-sfv
text/x-sh
text/x-siesta
text/x-sos
text/x-sql
text/x-swift
text/x-tcl
text/x-tex
text/x-vbasic
text/x-vcalendar
text/xml
text/xml-dtd
text/xml-external-parsed-entity
text/yaml

限制

Live API：Live API 不支持文件搜索。
工具不兼容：内置接地工具无法相互结合使用；例如，在同一请求中，文件搜索无法同时与依托 Google 搜索进行接地或网址上下文搭配使用。

速率限制

为了确保服务稳定性，文件搜索 API 具有以下限制：

文件大小上限 / 每个文档的限制：100 MB
项目文件搜索存储空间总大小（取决于用户层级）：
- 免费：1 GB
- 1 级：10 GB
- 第 2 级：100 GB
- 第 3 级：1 TB
建议：将每个文件搜索存储区的大小限制在 20 GB 以下，以确保最佳检索延迟时间。

价格

系统会在建立索引时根据现有的嵌入价格向您收取嵌入费用。
存储空间免费。
查询时嵌入是免费的。
检索到的文档 token 会按常规上下文 token 计费。

后续步骤

访问 File Search Stores 和 File Search Documents 的 API 参考文档。