Creates cached content, this call will initialize the cached content in the data storage, and users need to pay for the cache data storage.
Endpoint
post https:Where {service-endpoint}
is one of the supported service endpoints
.
Path parameters
parent
string
Required. The parent resource where the cached content will be created
Request body
The request body contains an instance of CachedContent
.
Example request
C#
using
Google.Cloud.AIPlatform.V1Beta1
;
using
Google.Protobuf.WellKnownTypes
;
using
System
;
using
System.Threading.Tasks
;
public
class
CreateContextCache
{
public
async
Task<CachedContentName>
Create
(
string
projectId
)
{
var
client
=
await
new
GenAiCacheServiceClientBuilder
{
Endpoint
=
"us-central1-aiplatform.googleapis.com"
}.
BuildAsync
();
var
request
=
new
CreateCachedContentRequest
{
Parent
=
$"projects/{projectId}/locations/us-central1"
,
CachedContent
=
new
CachedContent
{
Model
=
$"projects/{projectId}/locations/us-central1/publishers/google/models/gemini-1.5-pro-001"
,
SystemInstruction
=
new
Content
{
Parts
=
{
new
Part
{
Text
=
"You are an expert researcher. You always stick to the facts in the sources provided and"
+
" never make up new facts. Now look at these research papers, and answer the following questions."
}
}
},
Contents
=
{
new
Content
{
Role
=
"USER"
,
Parts
=
{
new
Part
{
FileData
=
new
()
{
MimeType
=
"application/pdf"
,
FileUri
=
"gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf"
}
},
new
Part
{
FileData
=
new
()
{
MimeType
=
"application/pdf"
,
FileUri
=
"gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
}
}
}
}
},
Ttl
=
Duration
.
FromTimeSpan
(
TimeSpan
.
FromMinutes
(
60
))
}
};
var
cachedContent
=
await
client
.
CreateCachedContentAsync
(
request
);
Console
.
WriteLine
(
$"Created cache: {cachedContent.CachedContentName}"
);
return
cachedContent
.
CachedContentName
;
}
}
Go
import
(
"context"
"fmt"
"io"
"time"
"cloud.google.com/go/vertexai/genai"
)
// createContextCache shows how to create a cached content, and returns its name.
func
createContextCache
(
w
io
.
Writer
,
projectID
,
location
,
modelName
string
)
(
string
,
error
)
{
// location := "us-central1"
// modelName := "gemini-1.5-pro-001"
ctx
:=
context
.
Background
()
systemInstruction
:=
`
You are an expert researcher. You always stick to the facts in the sources provided, and never make up new facts.
Now look at these research papers, and answer the following questions.
`
client
,
err
:=
genai
.
NewClient
(
ctx
,
projectID
,
location
)
if
err
!=
nil
{
return
""
,
fmt
.
Errorf
(
"unable to create client: %w"
,
err
)
}
defer
client
.
Close
()
// These PDF are viewable at
// https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf
// https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf
part1
:=
genai
.
FileData
{
MIMEType
:
"application/pdf"
,
FileURI
:
"gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf"
,
}
part2
:=
genai
.
FileData
{
MIMEType
:
"application/pdf"
,
FileURI
:
"gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
,
}
content
:=
& genai
.
CachedContent
{
Model
:
modelName
,
SystemInstruction
:
& genai
.
Content
{
Parts
:
[]
genai
.
Part
{
genai
.
Text
(
systemInstruction
)},
},
Expiration
:
genai
.
ExpireTimeOrTTL
{
TTL
:
60
*
time
.
Minute
},
Contents
:
[]
*
genai
.
Content
{
{
Role
:
"user"
,
Parts
:
[]
genai
.
Part
{
part1
,
part2
},
},
},
}
result
,
err
:=
client
.
CreateCachedContent
(
ctx
,
content
)
if
err
!=
nil
{
return
""
,
fmt
.
Errorf
(
"CreateCachedContent: %w"
,
err
)
}
fmt
.
Fprint
(
w
,
result
.
Name
)
return
result
.
Name
,
nil
}
Python
import
vertexai
import
datetime
from
vertexai.generative_models
import
Part
from
vertexai.preview
import
caching
# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"
vertexai
.
init
(
project
=
PROJECT_ID
,
location
=
"us-central1"
)
system_instruction
=
"""
You are an expert researcher. You always stick to the facts in the sources provided, and never make up new facts.
Now look at these research papers, and answer the following questions.
"""
contents
=
[
Part
.
from_uri
(
"gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf"
,
mime_type
=
"application/pdf"
,
),
Part
.
from_uri
(
"gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
,
mime_type
=
"application/pdf"
,
),
]
cached_content
=
caching
.
CachedContent
.
create
(
model_name
=
"gemini-1.5-pro-002"
,
system_instruction
=
system_instruction
,
contents
=
contents
,
ttl
=
datetime
.
timedelta
(
minutes
=
60
),
display_name
=
"example-cache"
,
)
print
(
cached_content
.
name
)
# Example response:
# 1234567890
