2575 Caching Spec 1eaff16b

Context caching allows you to save and reuse precomputed input tokens that you wish to use repeatedly, for example when asking different questions about the same media file. This can lead to cost and speed savings, depending on the usage. For a detailed introduction, see theContext cachingguide.

Method: cachedContents.create

• Endpoint
• Request body
• Response body
• Example request
  • Basic
  • From name
  • From chat

Creates CachedContent resource.

Endpoint

posthttps:``/``/generativelanguage.googleapis.com``/v1beta``/cachedContents

Request body

The request body contains an instance ofCachedContent. Fieldscontents[]``object (Content)
Optional. Input only. Immutable. The content to cache. tools[]``object (Tool)
Optional. Input only. Immutable. A list ofToolsthe model may use to generate the next response
expiration``Union type
Specifies when this resource will expire.expirationcan be only one of the following: expireTime``string (Timestamp format)
Timestamp in UTC of when this resource is considered expired. This isalwaysprovided on output, regardless of what was sent on input.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z"or"2014-10-02T15:01:23+05:30". ttl``string (Duration format)
Input only. New TTL for this resource, input only.

A duration in seconds with up to nine fractional digits, ending with 's'. Example:"3.5s". displayName``string
Optional. Immutable. The user-generated meaningful display name of the cached content. Maximum 128 Unicode characters. model``string
Required. Immutable. The name of theModelto use for cached content Format:models/{model} systemInstruction``object (Content)
Optional. Input only. Immutable. Developer set system instruction. Currently text only. toolConfig``object (ToolConfig)
Optional. Input only. Immutable. Tool config. This config is shared for all tools.

Example request

Basic

Python

from google import genai
from google.genai import types

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config=types.CreateCachedContentConfig(
        contents=[document],
        system_instruction="You are an expert analyzing transcripts.",
    ),
)
print(cache)

response = client.models.generate_content(
    model=model_name,
    contents="Please summarize this transcript",
    config=types.GenerateContentConfig(cached_content=cache.name),
)
print(response.text)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L25-L46

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
console.log("Cache created:", cache);

const response = await ai.models.generateContent({
  model: modelName,
  contents: "Please summarize this transcript",
  config: { cachedContent: cache.name },
});
console.log("Response text:", response.text);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L33-L62

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"), 
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromURI(document.URI, document.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}
cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents: contents,
	SystemInstruction: genai.NewContentFromText(
		"You are an expert analyzing transcripts.", genai.RoleUser,
	),
})
if err != nil {
	log.Fatal(err)
}
fmt.Println("Cache created:")
fmt.Println(cache)

// Use the cache for generating content.
response, err := client.Models.GenerateContent(
	ctx,
	modelName,
	genai.Text("Please summarize this transcript"),
	&genai.GenerateContentConfig{
		CachedContent: cache.Name,
	},
)
if err != nil {
	log.Fatal(err)
}
printResponse(response)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L16-L66

Shell

wget https://storage.googleapis.com/generativeai-downloads/data/a11.txt
echo '{
  "model": "models/gemini-1.5-flash-001",
  "contents":[
    {
      "parts":[
        {
          "inline_data": {
            "mime_type":"text/plain",
            "data": "'$(base64 $B64FLAGS a11.txt)'"
          }
        }
      ],
    "role": "user"
    }
  ],
  "systemInstruction": {
    "parts": [
      {
        "text": "You are an expert at analyzing transcripts."
      }
    ]
  },
  "ttl": "300s"
}' > request.json

curl -X POST "https://generativelanguage.googleapis.com/v1beta/cachedContents?key=$GEMINI_API_KEY" \
 -H 'Content-Type: application/json' \
 -d @request.json \
 > cache.json

CACHE_NAME=$(cat cache.json | grep '"name":' | cut -d '"' -f 4 | head -n 1)

curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-001:generateContent?key=$GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
      "contents": [
        {
          "parts":[{
            "text": "Please summarize this transcript"
          }],
          "role": "user"
        },
      ],
      "cachedContent": "'$CACHE_NAME'"
    }'  
https://github.com/google-gemini/deprecated-generative-ai-python/blob/7a7cc5474ddaa0255a4410e05361028a24400abd/samples/rest/cache.sh#L10-L59

From name

Python

from google import genai
from google.genai import types

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config=types.CreateCachedContentConfig(
        contents=[document],
        system_instruction="You are an expert analyzing transcripts.",
    ),
)
cache_name = cache.name  # Save the name for later

# Later retrieve the cache
cache = client.caches.get(name=cache_name)
response = client.models.generate_content(
    model=model_name,
    contents="Find a lighthearted moment from this transcript",
    config=types.GenerateContentConfig(cached_content=cache.name),
)
print(response.text)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L52-L75

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
const cacheName = cache.name; // Save the name for later

// Later retrieve the cache
const retrievedCache = await ai.caches.get({ name: cacheName });
const response = await ai.models.generateContent({
  model: modelName,
  contents: "Find a lighthearted moment from this transcript",
  config: { cachedContent: retrievedCache.name },
});
console.log("Response text:", response.text);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L71-L102

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromURI(document.URI, document.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}
cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents:          contents,
	SystemInstruction: genai.NewContentFromText(
		"You are an expert analyzing transcripts.", genai.RoleUser,
	),
})
if err != nil {
	log.Fatal(err)
}
cacheName := cache.Name

// Later retrieve the cache.
cache, err = client.Caches.Get(ctx, cacheName, &genai.GetCachedContentConfig{})
if err != nil {
	log.Fatal(err)
}

response, err := client.Models.GenerateContent(
	ctx,
	modelName,
	genai.Text("Find a lighthearted moment from this transcript"),
	&genai.GenerateContentConfig{
		CachedContent: cache.Name,
	},
)
if err != nil {
	log.Fatal(err)
}
fmt.Println("Response from cache (create from name):")
printResponse(response)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L76-L131

From chat

Python

from google import genai
from google.genai import types

client = genai.Client()
model_name = "gemini-1.5-flash-001"
system_instruction = "You are an expert analyzing transcripts."

# Create a chat session with the given system instruction.
chat = client.chats.create(
    model=model_name,
    config=types.GenerateContentConfig(system_instruction=system_instruction),
)
document = client.files.upload(file=media / "a11.txt")

response = chat.send_message(
    message=["Hi, could you summarize this transcript?", document]
)
print("\n\nmodel:  ", response.text)
response = chat.send_message(
    message=["Okay, could you tell me more about the trans-lunar injection"]
)
print("\n\nmodel:  ", response.text)

# To cache the conversation so far, pass the chat history as the list of contents.
cache = client.caches.create(
    model=model_name,
    config={
        "contents": chat.get_history(),
        "system_instruction": system_instruction,
    },
)
# Continue the conversation using the cached content.
chat = client.chats.create(
    model=model_name,
    config=types.GenerateContentConfig(cached_content=cache.name),
)
response = chat.send_message(
    message="I didn't understand that last part, could you explain it in simpler language?"
)
print("\n\nmodel:  ", response.text)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L81-L120

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const modelName = "gemini-1.5-flash-001";
const systemInstruction = "You are an expert analyzing transcripts.";

// Create a chat session with the system instruction.
const chat = ai.chats.create({
  model: modelName,
  config: { systemInstruction: systemInstruction },
});
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);

let response = await chat.sendMessage({
  message: createUserContent([
    "Hi, could you summarize this transcript?",
    createPartFromUri(document.uri, document.mimeType),
  ]),
});
console.log("\n\nmodel:", response.text);

response = await chat.sendMessage({
  message: "Okay, could you tell me more about the trans-lunar injection",
});
console.log("\n\nmodel:", response.text);

// To cache the conversation so far, pass the chat history as the list of contents.
const chatHistory = chat.getHistory();
const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: chatHistory,
    systemInstruction: systemInstruction,
  },
});

// Continue the conversation using the cached content.
const chatWithCache = ai.chats.create({
  model: modelName,
  config: { cachedContent: cache.name },
});
response = await chatWithCache.sendMessage({
  message:
    "I didn't understand that last part, could you explain it in simpler language?",
});
console.log("\n\nmodel:", response.text);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L111-L161

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
systemInstruction := "You are an expert analyzing transcripts."

// Create initial chat with a system instruction.
chat, err := client.Chats.Create(ctx, modelName, &genai.GenerateContentConfig{
	SystemInstruction: genai.NewContentFromText(systemInstruction, genai.RoleUser),
}, nil)
if err != nil {
	log.Fatal(err)
}

document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}

// Send first message with the transcript.
parts := make([]genai.Part, 2)
parts[0] = genai.Part{Text: "Hi, could you summarize this transcript?"}
parts[1] = genai.Part{
	FileData: &genai.FileData{
		FileURI :      document.URI,
		MIMEType: document.MIMEType,
	},
}

// Send chat message.
resp, err := chat.SendMessage(ctx, parts...)
if err != nil {
	log.Fatal(err)
}
fmt.Println("\n\nmodel: ", resp.Text())

resp, err = chat.SendMessage(
	ctx, 
	genai.Part{
		Text: "Okay, could you tell me more about the trans-lunar injection",
	},
)
if err != nil {
	log.Fatal(err)
}
fmt.Println("\n\nmodel: ", resp.Text())

// To cache the conversation so far, pass the chat history as the list of contents.
cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents:          chat.History(false),
	SystemInstruction: genai.NewContentFromText(systemInstruction, genai.RoleUser),
})
if err != nil {
	log.Fatal(err)
}

// Continue the conversation using the cached history.
chat, err = client.Chats.Create(ctx, modelName, &genai.GenerateContentConfig{
	CachedContent: cache.Name,
}, nil)
if err != nil {
	log.Fatal(err)
}

resp, err = chat.SendMessage(
	ctx, 
	genai.Part{
		Text: "I didn't understand that last part, could you explain it in simpler language?",
	},
)
if err != nil {
	log.Fatal(err)
}
fmt.Println("\n\nmodel: ", resp.Text())  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L140-L225

Response body

If successful, the response body contains a newly created instance ofCachedContent.

Method: cachedContents.list

• Endpoint
• Query parameters
• Request body
• Response body
  • JSON representation

Lists CachedContents.

Endpoint

gethttps:``/``/generativelanguage.googleapis.com``/v1beta``/cachedContents

Query parameters

pageSize``integer
Optional. The maximum number of cached contents to return. The service may return fewer than this value. If unspecified, some default (under maximum) number of items will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000. pageToken``string
Optional. A page token, received from a previouscachedContents.listcall. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided tocachedContents.listmust match the call that provided the page token.

Request body

The request body must be empty.

Response body

Response with CachedContents list.

If successful, the response body contains data with the following structure: FieldscachedContents[]``object (CachedContent)
List of cached contents. nextPageToken``string
A token, which can be sent aspageTokento retrieve the next page. If this field is omitted, there are no subsequent pages.

Method: cachedContents.get

• Endpoint
• Path parameters
• Request body
• Response body
• Example request
  • Basic

Reads CachedContent resource.

Endpoint

gethttps:``/``/generativelanguage.googleapis.com``/v1beta``/{name=cachedContents``/*}

Path parameters

name``string
Required. The resource name referring to the content cache entry. Format:cachedContents/{id}It takes the formcachedContents/{cachedcontent}.

Request body

The request body must be empty.

Example request

Python

from google import genai

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config={
        "contents": [document],
        "system_instruction": "You are an expert analyzing transcripts.",
    },
)
print(client.caches.get(name=cache.name))  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L144-L157

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
const retrievedCache = await ai.caches.get({ name: cache.name });
console.log("Retrieved Cache:", retrievedCache);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L199-L222

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromURI(document.URI, document.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}

cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents:          contents,
	SystemInstruction: genai.NewContentFromText(
		"You are an expert analyzing transcripts.", genai.RoleUser,
	),
})
if err != nil {
	log.Fatal(err)
}

cache, err = client.Caches.Get(ctx, cache.Name, &genai.GetCachedContentConfig{})
if err != nil {
	log.Fatal(err)
}
fmt.Println("Retrieved cache:")
fmt.Println(cache)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L285-L327

Shell

curl "https://generativelanguage.googleapis.com/v1beta/$CACHE_NAME?key=$GEMINI_API_KEY"  
https://github.com/google-gemini/deprecated-generative-ai-python/blob/7a7cc5474ddaa0255a4410e05361028a24400abd/samples/rest/cache.sh#L69-L70

Response body

If successful, the response body contains an instance ofCachedContent.

Method: cachedContents.patch

• Endpoint
• Path parameters
• Query parameters
• Request body
• Response body
• Example request
  • Basic

Updates CachedContent resource (only expiration is updatable).

Endpoint

patchhttps:``/``/generativelanguage.googleapis.com``/v1beta``/{cachedContent.name=cachedContents``/*}
PATCH https://generativelanguage.googleapis.com/v1beta/{cachedContent.name=cachedContents/*}

Path parameters

cachedContent.name``string
Output only. Identifier. The resource name referring to the cached content. Format:cachedContents/{id}It takes the formcachedContents/{cachedcontent}.

Query parameters

updateMask``string (FieldMask format)
The list of fields to update.

This is a comma-separated list of fully qualified names of fields. Example:"user.displayName,photo".

Request body

The request body contains an instance ofCachedContent. Fields
expiration``Union type
Specifies when this resource will expire.expirationcan be only one of the following: expireTime``string (Timestamp format)
Timestamp in UTC of when this resource is considered expired. This isalwaysprovided on output, regardless of what was sent on input.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z"or"2014-10-02T15:01:23+05:30". ttl``string (Duration format)
Input only. New TTL for this resource, input only.

A duration in seconds with up to nine fractional digits, ending with 's'. Example:"3.5s".

Example request

Python

from google import genai
from google.genai import types
import datetime

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config={
        "contents": [document],
        "system_instruction": "You are an expert analyzing transcripts.",
    },
)

# Update the cache's time-to-live (ttl)
ttl = f"{int(datetime.timedelta(hours=2).total_seconds())}s"
client.caches.update(
    name=cache.name, config=types.UpdateCachedContentConfig(ttl=ttl)
)
print(f"After update:\n {cache}")

# Alternatively, update the expire_time directly
# Update the expire_time directly in valid RFC 3339 format (UTC with a "Z" suffix)
expire_time = (
    (
        datetime.datetime.now(datetime.timezone.utc)
        + datetime.timedelta(minutes=15)
    )
    .isoformat()
    .replace("+00:00", "Z")
)
client.caches.update(
    name=cache.name,
    config=types.UpdateCachedContentConfig(expire_time=expire_time),
)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L184-L220

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

let cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});

// Update the cache's time-to-live (ttl)
const ttl = `${2 * 3600}s`; // 2 hours in seconds
cache = await ai.caches.update({
  name: cache.name,
  config: { ttl },
});
console.log("After update (TTL):", cache);

// Alternatively, update the expire_time directly (in RFC 3339 format with a "Z" suffix)
const expireTime = new Date(Date.now() + 15 * 60000)
  .toISOString()
  .replace(/\.\d{3}Z$/, "Z");
cache = await ai.caches.update({
  name: cache.name,
  config: { expireTime: expireTime },
});
console.log("After update (expire_time):", cache);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L272-L311

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromURI(document.URI, document.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}

cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents:          contents,
	SystemInstruction: genai.NewContentFromText(
		"You are an expert analyzing transcripts.", genai.RoleUser,
	),
})
if err != nil {
	log.Fatal(err)
}

_, err = client.Caches.Delete(ctx, cache.Name, &genai.DeleteCachedContentConfig{})
if err != nil {
	log.Fatal(err)
}
fmt.Println("Cache deleted:", cache.Name)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L237-L278

Shell

curl -X PATCH "https://generativelanguage.googleapis.com/v1beta/$CACHE_NAME?key=$GEMINI_API_KEY" \
 -H 'Content-Type: application/json' \
 -d '{"ttl": "600s"}'  
https://github.com/google-gemini/deprecated-generative-ai-python/blob/7a7cc5474ddaa0255a4410e05361028a24400abd/samples/rest/cache.sh#L74-L77

Response body

If successful, the response body contains an instance ofCachedContent.

Method: cachedContents.delete

• Endpoint
• Path parameters
• Request body
• Response body
• Example request
  • Basic

Deletes CachedContent resource.

Endpoint

deletehttps:``/``/generativelanguage.googleapis.com``/v1beta``/{name=cachedContents``/*}

Path parameters

name``string
Required. The resource name referring to the content cache entry Format:cachedContents/{id}It takes the formcachedContents/{cachedcontent}.

Request body

The request body must be empty.

Example request

Python

from google import genai

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config={
        "contents": [document],
        "system_instruction": "You are an expert analyzing transcripts.",
    },
)
client.caches.delete(name=cache.name)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/python/cache.py#L126-L139

Node.js

// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
await ai.caches.delete({ name: cache.name });
console.log("Cache deleted:", cache.name);  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/javascript/cache.js#L170-L193

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

modelName := "gemini-1.5-flash-001"
document, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "a11.txt"), 
	&genai.UploadFileConfig{
		MIMEType : "text/plain",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromURI(document.URI, document.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, genai.RoleUser),
}

cache, err := client.Caches.Create(ctx, modelName, &genai.CreateCachedContentConfig{
	Contents:          contents,
	SystemInstruction: genai.NewContentFromText(
		"You are an expert analyzing transcripts.", genai.RoleUser,
	),
})
if err != nil {
	log.Fatal(err)
}

_, err = client.Caches.Delete(ctx, cache.Name, &genai.DeleteCachedContentConfig{})
if err != nil {
	log.Fatal(err)
}
fmt.Println("Cache deleted:", cache.Name)  
https://github.com/google-gemini/api-examples/blob/856e8a0f566a2810625cecabba6e2ab1fe97e496/go/cache.go#L237-L278

Shell

curl -X DELETE "https://generativelanguage.googleapis.com/v1beta/$CACHE_NAME?key=$GEMINI_API_KEY"  
https://github.com/google-gemini/deprecated-generative-ai-python/blob/7a7cc5474ddaa0255a4410e05361028a24400abd/samples/rest/cache.sh#L81-L82

Response body

If successful, the response body is an empty JSON object.

REST Resource: cachedContents

• Resource: CachedContent
  • JSON representation
• Content
  • JSON representation
• Part
  • JSON representation
• Blob
  • JSON representation
• FunctionCall
  • JSON representation
• FunctionResponse
  • JSON representation
• FunctionResponsePart
  • JSON representation
• FunctionResponseBlob
  • JSON representation
• Scheduling
• FileData
  • JSON representation
• ExecutableCode
  • JSON representation
• Language
• CodeExecutionResult
  • JSON representation
• Outcome
• VideoMetadata
  • JSON representation
• Tool
  • JSON representation
• FunctionDeclaration
  • JSON representation
• Schema
  • JSON representation
• Type
• Behavior
• GoogleSearchRetrieval
  • JSON representation
• DynamicRetrievalConfig
  • JSON representation
• Mode
• CodeExecution
• GoogleSearch
  • JSON representation
• Interval
  • JSON representation
• ComputerUse
  • JSON representation
• Environment
• UrlContext
• FileSearch
  • JSON representation
• GoogleMaps
  • JSON representation
• ToolConfig
  • JSON representation
• FunctionCallingConfig
  • JSON representation
• Mode
• RetrievalConfig
  • JSON representation
• LatLng
  • JSON representation
• UsageMetadata
  • [JSON representation](https://ai.google.d

…(truncated)