檔案搜尋

Gemini API 可透過檔案搜尋工具啟用檢索增強生成 (RAG) 功能。檔案搜尋會匯入、分塊及建立資料索引，以便根據提供的提示詞快速檢索相關資訊。接著，模型會將這項資訊做為情境，提供更準確且相關的答案。

為了讓開發人員能以簡單實惠的方式使用檔案搜尋功能，我們將在查詢時免費提供檔案儲存空間和嵌入生成功能。您只需在首次為檔案建立索引時支付嵌入費用 (以適用的嵌入模型費用為準)，以及正常的 Gemini 模型輸入 / 輸出權杖費用。這個新計費模式可讓您更輕鬆地建構及擴大規模，且成本效益更高。

直接上傳至檔案搜尋儲存庫

以下範例說明如何直接將檔案上傳至檔案搜尋儲存庫：

Python

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',
        '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-flash-preview",
    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.get("type") == "file_citation":
                            print(f"  - {annotation['file_name']}: {annotation['source']}")

JavaScript

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',
      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-flash-preview",
    input: "Can you tell me about [insert question]",
    tools: [{
      type: "file_search",
      fileSearchStoreNames: [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.fileName}: ${annotation.source}`);
              }
            }
          }
        }
      }
    }
  }
}

run();

詳情請參閱 uploadToFileSearchStore 的 API 參考資料。

匯入檔案

或者，你也可以上傳現有檔案，然後匯入檔案搜尋儲存庫：

Python

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',
        '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-flash-preview",
    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

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',
      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-flash-preview",
    input: "Can you tell me about [insert question]",
    tools: [{
      type: "file_search",
      fileSearchStoreNames: [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();

詳情請參閱 importFile 的 API 參考資料。

分塊設定

將檔案匯入檔案搜尋儲存庫時，系統會自動將檔案分成多個區塊、嵌入、建立索引，然後上傳至檔案搜尋儲存庫。如要進一步控管分塊策略，可以指定 chunking_config 設定，為每個分塊設定最多詞元數，以及最多重疊詞元數。

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_name=sample_file.name,
    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

const { GoogleGenAI } = require('@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.");

如要使用檔案搜尋儲存庫，請將其做為工具傳遞至 interactions.create 方法，如「上傳」和「匯入」範例所示。

運作方式

檔案搜尋功能會使用語意搜尋技術，找出與使用者提示相關的資訊。與標準關鍵字搜尋不同，語意搜尋可解讀查詢的意義和上下文。

匯入檔案時，系統會將檔案轉換為稱為「嵌入」的數值表示法，擷取文字的語意。這些嵌入內容會儲存在專用的檔案搜尋資料庫中。 查詢時，系統也會將查詢內容轉換為嵌入。接著，系統會執行檔案搜尋，從檔案搜尋儲存庫找出最相似且相關的文件區塊。

嵌入內容和檔案沒有存留時間 (TTL)，除非手動刪除或模型遭到淘汰，否則會一直存在。

以下說明使用 File Search uploadToFileSearchStore API 的程序：

  1. 建立檔案搜尋儲存庫：檔案搜尋儲存庫包含檔案中經過處理的資料。這是語意搜尋運作時使用的嵌入內容持續性容器。

  2. 上傳檔案並匯入檔案搜尋儲存庫：同時上傳檔案並將結果匯入檔案搜尋儲存庫。這會建立暫時的 File 物件，也就是原始文件的參照。然後將資料分塊、轉換為檔案搜尋嵌入，並建立索引。File 物件會在 48 小時後刪除，而匯入檔案搜尋儲存區的資料則會無限期保留，直到您選擇刪除為止。

  3. 使用檔案搜尋查詢：最後，您會在 generateContent 呼叫中使用 FileSearch 工具。在工具設定中，您會指定 FileSearchRetrievalResource，指向要搜尋的 FileSearchStore。這會指示模型對該特定「檔案搜尋」儲存庫執行語意搜尋，找出相關資訊做為回覆內容的依據。

檔案搜尋的索引和查詢程序
檔案搜尋的索引和查詢程序

在這張圖表中，從「文件」到「嵌入模型」(使用 gemini-embedding-001) 的虛線代表 uploadToFileSearchStore API (略過「檔案儲存空間」)。否則，使用 Files API 分別建立及匯入檔案，會將索引程序從「文件」移至「檔案儲存空間」，然後移至「嵌入模型」。

檔案搜尋商店

檔案搜尋儲存庫是文件嵌入的容器。透過 File API 上傳的原始檔案會在 48 小時後刪除，但匯入檔案搜尋儲存庫的資料會無限期儲存，直到您手動刪除為止。你可以建立多個檔案搜尋商店，整理文件。您可以使用 FileSearchStore API 建立、列出、取得及刪除檔案，藉此管理檔案搜尋商店。檔案搜尋商店名稱的範圍為全球。

以下列舉幾個管理檔案搜尋商店的例子：

Python

file_search_store = client.file_search_stores.create(
    config={
        'display_name': 'my-file_search-store-123',
        'embedding_model': 'models/gemini-embedding-2'
    }
)

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})

JavaScript

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

await ai.fileSearchStores.delete({
  name: 'fileSearchStores/my-file_search-store-123',
  config: { force: true }
});

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/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}"

檔案搜尋文件

您可以使用 File Search Documents API 管理檔案儲存庫中的個別文件，list 檔案搜尋儲存庫中的每份文件、get 文件的相關資訊，以及 delete 依名稱搜尋文件。

Python

for document_in_store in client.file_search_stores.documents.list(parent='fileSearchStores/my-file_search-store-123'):
  print(document_in_store)

file_search_document = client.file_search_stores.documents.get(name='fileSearchStores/my-file_search-store-123/documents/my_doc')
print(file_search_document)

client.file_search_stores.documents.delete(name='fileSearchStores/my-file_search-store-123/documents/my_doc')

JavaScript

const documents = await ai.fileSearchStores.documents.list({
  parent: 'fileSearchStores/my-file_search-store-123'
});
for await (const doc of documents) {
  console.log(doc);
}

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

await ai.fileSearchStores.documents.delete({
  name: 'fileSearchStores/my-file_search-store-123/documents/my_doc'
});

REST

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

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

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

檔案中繼資料

你可以為檔案新增自訂中繼資料，以便篩選檔案或提供額外背景資訊。中繼資料是一組鍵/值組合。

Python

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}
    ]
)

JavaScript

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

如果檔案搜尋商店中有多份文件，而您只想搜尋其中一部分，這項功能就非常實用。

Python

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    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-flash-preview",
  input: "Tell me about the book 'I, Claudius'",
  tools: [{
    type: "file_search",
    fileSearchStoreNames: [fileSearchStore.name],
    metadataFilter: '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);
      }
    }
  }
}

REST

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
            "model": "gemini-3-flash-preview",
            "input": [{"type": "text", "text": "Tell me about the book I, Claudius"}],
            "tools": [{
                "type": "file_search",
                "file_search_store_names": ["'$STORE_NAME'"],
                "metadata_filter": "author = \"Robert Graves\""
            }]
        }' 2> /dev/null > response.json

cat response.json

如需實作 metadata_filter 清單篩選器語法的指引，請參閱 google.aip.dev/160。

參考資料

使用檔案搜尋功能時，模型的回覆可能會包含引文，指出生成答案時參考了上傳文件的哪些部分。以利進行事實查核和驗證。

您可以在「model_output」步驟的內容區塊中，透過「annotations」欄位存取引用資訊。

Python

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

JavaScript

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

如要進一步瞭解基礎中繼資料的結構，請參閱檔案搜尋教戰手冊中的範例，或 以 Google 搜尋強化事實基礎文件中的建立基準部分。

基礎資料中的自訂中繼資料

如果您已在檔案中加入自訂中繼資料，可以在模型回覆的基礎中繼資料中存取。這項功能可將來源文件中的其他內容 (例如網址、頁碼或作者) 傳遞至應用程式邏輯，每個 grounding_chunk 都包含這項自訂中繼資料。retrieved_context

Python

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    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-flash-preview",
    input: "Tell me about [insert question]",
    tools: [{
      type: "file_search",
      fileSearchStoreNames: [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);
          });
        }
      }
    }
  }

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
                }
              ]
            }
          ]
        }
      ]
    }
  ]
}

結構化輸出內容

從 Gemini 3 模型開始，您可以將檔案搜尋工具與結構化輸出結合使用。

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-flash-preview",
    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.steps[-1].content[0].text)
print(result)

JavaScript

import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const moneySchema = z.object({
  amount: z.string().describe("The numerical part of the amount."),
  currency: z.string().describe("The currency of amount."),
});

async function run() {
  const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is the minimum hourly wage in Tokyo right now?",
    tools: [{
      type: "file_search",
      fileSearchStoreNames: [fileSearchStore.name],
    }],
    response_format: {
      type: 'text',
      mime_type: 'application/json',
      schema: zodToJsonSchema(moneySchema)
    },
  });

  const result = moneySchema.parse(JSON.parse(interaction.steps.at(-1).content[0].text));
  console.log(result);
}

run();

REST

curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": "What is the minimum hourly wage in Tokyo right now?",
    "tools": [{
      "type": "file_search",
      "fileSearchStoreNames": ["$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"]
      }
    }
  }'

支援的模型

下列模型支援檔案搜尋：

型號 檔案搜尋
Gemini 3.1 Pro 預先發布版 ✔️
Gemini 3.1 Flash-Lite ✔️
Gemini 3.1 Flash-Lite 預先發布版 ✔️
Gemini 3 Flash 預先發布版 ✔️
Gemini 2.5 Pro ✔️
Gemini 2.5 Flash-Lite ✔️

支援的工具組合

Gemini 3 模型支援結合內建工具 (例如檔案搜尋) 和自訂工具 (函式呼叫)。詳情請參閱「工具組合」頁面。

支援的檔案類型

檔案搜尋支援多種檔案格式，詳列於下列各節。

應用程式檔案類型

  • 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

文字檔案類型

  • 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

頻率限制

為確保服務穩定性，File Search API 有下列限制：

  • 檔案大小上限 / 每份文件限制：100 MB
  • 專案檔案搜尋儲存空間總大小 (依使用者層級而定)：
    • 免費：1 GB
    • 第 1 級：10 GB
    • 第 2 級：100 GB
    • 第 3 級：1 TB
  • 建議：將每個檔案搜尋商店的大小限制在 20 GB 以下，確保最佳的擷取延遲時間。

定價

  • 系統會在建立索引時，根據現有的嵌入定價 (每 100 萬個權杖 $0.15 美元) 向開發人員收取嵌入費用。
  • 儲存空間免費。
  • 查詢時嵌入功能不會產生費用。
  • 系統會將擷取的文件權杖視為一般背景資訊權杖計費。

後續步驟