DEV Community: Tatsuya

[CKE & Snowflake Intelligence] Smart AI-Powered Search for Snowflake Documentation!

Tatsuya — Mon, 27 Oct 2025 08:15:57 +0000

Introduction

Cortex Knowledge Extensions (CKEs) became Generally Available (GA) on August 12, 2025. This was announced in the official blog.

The Easy Button for Context-Rich AI Agents

Discover how Snowflake Intelligence and Cortex Knowledge Extensions help enterprises unlock insights from data within their organizations as well as third-party expertise.

snowflake.com

This article will provide an overview of CKEs and introduce various ways to utilize them.

Usage Example

After acquiring the CKEs for Snowflake Documentation and configuring Snowflake Intelligence (currently in Public Preview as of October 27, 2025), it can provide answers that reference the CKEs as shown above. The setup is very simple and will be explained later in this article.

What are Cortex Knowledge Extensions (CKEs)?

First, let's explore what Cortex Knowledge Extensions (CKEs), which became GA, actually are. According to the Cortex Knowledge Extensions documentation:

Cortex Knowledge Extensions (CKEs) are Cortex Search Services that can be shared on the Snowflake Marketplace or via private listings or organizational listings. They can be used in a retrieval-augmented generation (RAG) architecture to integrate licensed and proprietary content into Cortex AI applications. For example, CKEs can be used to integrate knowledge from unstructured content, such as articles, market research, books, or forum posts, into Cortex AI applications, such as chatbots and agentic systems.

As stated in the documentation, CKEs are essentially "Cortex Search" services shared via Snowflake Marketplace, private listings, or organizational listings.
CKEs listed on the Snowflake Marketplace can be found here:
Snowflake Marketplace

Various data providers have started offering CKEs, including Snowflake-provided ones like "Snowflake Documentation" and "PubMed Biomedical Research Corpus".

How CKEs Work

As described in the documentation, the mechanism is as follows:

Providers (CKE providers) upload text data to tables in their accounts and create Cortex Search services on those tables. These Cortex Search services are shared on the Snowflake Marketplace. The Cortex Search services shared on Snowflake Marketplace become Cortex Knowledge Extensions (CKEs). When using these CKEs, you can develop applications by combining them with Cortex AI SQL or Cortex Agent API.

As mentioned earlier, CKEs are essentially Cortex Search services, which means they can be used in various ways. Let's explore what forms of utilization are possible.

Use Cases

Here are some examples of how CKEs can be integrated and used.

Snowflake Intelligence

Integration with Snowflake Intelligence is straightforward. Following the procedure outlined in the Quickstart guide below, you can register the CKE's Cortex Search as an "agent" to make it immediately available in Snowflake Intelligence.
Getting Started with Snowflake Intelligence

Snowflake Intelligence is a great option when you want to make Cortex Agent, Cortex Analyst, and Cortex Search widely available within your organization.

Snowflake Cortex AI MCP Server

"Snowflake Cortex AI MCP Server" is available on Snowflake-Labs. By configuring it to use CKE's Cortex Search, you can utilize it with any MCP client (Claude Desktop, Cursor, gemini-cli, etc.).
Snowflake Cortex AI Model Context Protocol (MCP) Server

Snowflake Managed MCP Servers

Snowflake Managed MCP Servers were released as a preview on October 1, 2025. This allows you to host Cortex Analyst / Cortex Search as remote MCP Servers on Snowflake.

Introducing Snowflake Managed MCP Servers for Secure, Governed Data Agents

Snowflake Managed MCP Servers, an open-standards interface for AI agents to access your governed data without custom integrations or separate infrastructure.

snowflake.com

Setting Up for Use with Snowflake Intelligence

In this article, let's configure CKEs for use with Snowflake Intelligence.

Prerequisites

To use CKEs, the easiest way to start is with CKEs published by Snowflake itself, such as their Documentation.
After logging into your Snowflake account, access this URL and acquire it.

Configuring Cortex Agent

To use Cortex Search with Snowflake Intelligence, you need to configure a Cortex Agent. While you can set this up via the GUI in Snowsight, it's also possible to define it using SQL. For documentation on handling Cortex Agent with SQL, please refer to this page.
After acquiring Snowflake Documentation from the Snowflake Marketplace, executing the following SQL will make it available in Snowflake Intelligence.

Note that cross-region inference is enabled in the SQL, so please be aware of this.

details Cortex Agent for CKEs Setup SQL:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';

CREATE DATABASE IF NOT EXISTS snowflake_intelligence;
GRANT USAGE ON DATABASE snowflake_intelligence TO ROLE PUBLIC;

CREATE SCHEMA IF NOT EXISTS snowflake_intelligence.agents;
GRANT USAGE ON SCHEMA snowflake_intelligence.agents TO ROLE PUBLIC;

GRANT CREATE AGENT ON SCHEMA snowflake_intelligence.agents TO ROLE PUBLIC;

CREATE OR REPLACE AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.CKE_SNOWDOC_AGENT
WITH PROFILE='{ "display_name": "Snowflake Document" }'
    COMMENT=$$ This is an agent that can answer questions about Snowflake Documentation. $$
FROM SPECIFICATION $$
{
  "models": {
    "orchestration": ""
  },
  "instructions": {
    "response": "# Persona\nYou are a Snowflake expert consultant. Act as an experienced professional supporting enterprise customers.\n\n# Tone & Manner\n- Always use polite language and provide accurate, concise answers.\n- Add brief explanations for technical terms when necessary, so that even beginners can understand.\n\n# Strict Rules for Response Generation\n1. Strict adherence to sources: All answers must be based on Snowflake's official documentation. Do not include personal opinions or information that cannot be verified in the documentation.\n2. Clear source attribution: At the end of your response, include a section titled [Reference Documentation] and list the URLs of referenced pages in bullet points.\n3. Formatting:\n- Enclose code such as SQL, Python, JSON, etc., in code blocks with language specification (e.g., ```

sql ...

 ```).\n- Organize procedures and lists using bullet points (-) or numbered lists (1.) for readability.\n- Emphasize important keywords such as function names and parameter names with `backticks`.\n4. Handling unclear questions:\n- If an answer to a question cannot be found in the official documentation, honestly respond with \"I was unable to find a direct answer from the official documentation for your question.\"\n- Then, provide related information by saying \"Here are potentially relevant documents\" or suggest next steps like \"You might get answers from other experts by asking in the Snowflake Community.\"\n5. Prohibited actions:\n- Do not answer specific amounts regarding pricing or licenses; always provide a link to the official pricing page.\n- When asked about security-related configurations or personal information, only provide general best practices and never present specific configuration values or prompt for information input.\n\n\n",
    "sample_questions": [
      {
        "question": "Tell me how to set up Snowflake Openflow"
      },
      {
        "question": "What is the current status of Snowflake's Iceberg support?"
      },
      {
        "question": "What LLM models are available in Snowflake?"
      },
      {
        "question": "Which clouds and regions can be used when using Snowflake in Japan?"
      }
    ]
  },
  "tools": [
    {
      "tool_spec": {
        "type": "cortex_search",
        "name": "Search Snowflake Documents",
        "description": "Cortex Search Knowledge Extensions for searching Snowflake documentation"
      }
    }
  ],
  "tool_resources": {
    "Search Snowflake Documents": {
      "id_column": "SOURCE_URL",
      "max_results": 5,
      "name": "SNOWFLAKE_DOCUMENTATION.SHARED.CKE_SNOWFLAKE_DOCS_SERVICE",
      "title_column": "DOCUMENT_TITLE"
    }
  }
}
$$;

Using with Snowflake Intelligence

After executing the above SQL, navigate to "AI & ML" > "Snowflake Intelligence" as shown below to access Snowflake Intelligence.

After launching Snowflake Intelligence, select "Snowflake Document" and ask a question to receive results.

Conclusion

CKE is a service that allows you to easily acquire and utilize knowledge bases provided on the Snowflake Marketplace. When utilizing created Cortex Search services, you can choose different approaches based on your use case: Streamlit for building custom UIs, Snowflake Intelligence for deploying to various users within your organization, or MCP Server for data scientists, data engineers, and application developers. We hope you'll utilize CKE and Cortex Search according to your specific use cases.

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Update History

October 27, 2025: New post

gpt-oss, GPT-5 is here!: A Guide to Snowflake's Cross-Region LLM Inference

Tatsuya — Thu, 14 Aug 2025 10:50:03 +0000

Introduction

Hello everyone. I work as a Partner Solution Engineer at Snowflake, and I plan to use this dev.to platform to share things I've tried and insights I've gained in my daily work. Today, I'd like to organize my thoughts on Snowflake's "Cross-region inference" feature.

Note:
This article represents my personal views and not those of Snowflake.

`gpt-oss` and `GPT-5` Released!

As you may have seen on social media, these models have been making waves. These models are also available for use on Snowflake.
Announcing OpenAI GPT-5 on Snowflake Cortex AI

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa | Sho Tanaka

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa

linkedin.com

Let's Try Using Them on Snowflake Right Away

Let's try the following on a freshly created Snowflake account in the AWS US East (N. Virginia) region:

SELECT AI_COMPLETE('openai-gpt-5', 'Please tell me three advantages of Snowflake');

Note:
The AI_COMPLETE is a function that generates responses from text or images using supported LLMs.

However, the following error message appears and it cannot be executed:

100351 (P0000): Request failed for external function COMPLETE$V6 with remote service error: 400 '"The model you requested is unavailable in your region. To access it, enable cross region inference with AZURE_US, ANY_REGION. For more information, see https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference."

Actually, these gpt-oss and GPT-5 models currently have limited availability in terms of cloud providers and regions on Snowflake. That's why the above error occurs. However, by using the "cross-region inference" feature, it becomes possible to use them across cloud providers and regions.

What is Cross-Region Inference?

On Snowflake, various LLMs (Large Language Models) can be used through Cortex AI, a service that enables the use of generative AI on Snowflake. However, some of these models are available only on specific cloud providers or regions, and there may be cases where they cannot be used directly on the cloud provider or region where your Snowflake account is running.

Therefore, Snowflake provides a feature called cross-region inference, which enables the use of LLMs across cloud providers and regions. "Cross-region inference" is a feature announced on August 9, 2024. The announcement blog can be found here: Announcing Cross-region Inference on Snowflake Cortex AI.

This feature specifies a region that can process inference requests when the inference request cannot be processed by the cloud provider and region where the Snowflake account is running.
This is controlled by a parameter called CORTEX_ENABLED_CROSS_REGION. The default value (i.e., immediately after creating a Snowflake account) is DISABLED. In this state, only models available on the cloud provider and region where the Snowflake account is running can be used.

This parameter can be changed with the following command:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AZURE_US';

With the above setting, in addition to the cloud provider and region where the Snowflake account is running, models available in AZURE_US can also be used.

For example, even if a Snowflake account is running in the AWS US East (N. Virginia) region, this setting makes it possible to use models available in AZURE_US. The documentation explaining the cross-region inference feature can be found here.

OpenAI Model Releases on August 5 and 7, 2025, and Their Use on Snowflake

As mentioned at the beginning of this article, OpenAI released models consecutively on August 5 and 7, 2025, and these became available on Snowflake immediately. Snowflake has also made announcements about this.

August 5, 2025: OpenAI announced gpt-oss
- OpenAI's announcement: Introducing gpt-oss
- Snowflake's announcement: LinkedIn post
August 7, 2025: OpenAI announced GPT-5
- OpenAI's announcement: GPT-5
- Snowflake's announcement: Announcing OpenAI GPT-5 on Snowflake Cortex AI

These models can be easily used with syntax like the one mentioned at the beginning.
However, as of August 14, 2025, these models have limited availability in terms of cloud providers and regions. The regional support status is specifically documented here.
For example, looking at the gpt-oss and GPT-5 mentioned in this article, the support status is as follows:

openai-gpt-5, openai-gpt-5-mini, openai-gpt-5-nano are available in "Cross Cloud (Any Region)" and Azure US (Cross-Region)
openai-gpt-oss-120b, openai-gpt-oss-20b are available in "Cross Cloud (Any Region)"

Note that each is in "In preview" status, and for gpt-oss, as of August 14, 2025, only "Cross Cloud (Any Region)" is available.

Configuration Method for Cross-Region Inference

The configuration method for using cross-region inference is as follows, as mentioned earlier:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AZURE_US';

There are variations for the AZURE_US part, as follows. The documentation is summarized here.

DISABLED: Default value. Only models available on the cloud provider and region where the Snowflake account is running can be used
ANY_REGION: All models available in all regions that Snowflake supports, including the cloud provider and region where the request is made, can be used

It's also possible to specify specific regions without specifying ANY_REGION. In that case, as of August 14, 2025, the following variations are available:

AWS_APJ, AWS_EU, AWS_US, AZURE_EU, AZURE_US: These values can be specified simultaneously with comma separation. For example, they can be set as follows:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AWS_US,AZURE_US';

Which regions become available when each option is specified is documented here.

For example, when specifying AWS_APJ, the following regions may be used:

The region where the request is placed.
AWS Asia Pacific (Tokyo) ap-northeast-1
AWS Asia Pacific (Seoul) ap-northeast-2
AWS Asia Pacific (Osaka) ap-northeast-3
AWS Asia Pacific (Mumbai) ap-south-1
AWS Asia Pacific (Hyderabad) ap-south-2
AWS Asia Pacific (Singapore) ap-southeast-1
AWS Asia Pacific (Sydney) ap-southeast-2
AWS Asia Pacific (Melbourne) ap-southeast-4

The current configuration status of CORTEX_ENABLED_CROSS_REGION can be checked with the following SQL:

SHOW PARAMETERS LIKE 'CORTEX_ENABLED_CROSS_REGION' IN ACCOUNT;

Considerations and Important Points

Cross-region inference has several considerations and important points, which I'd like to organize by extracting from the documentation.

Configuration Scope

First, this parameter can only be set at the Snowflake account level and cannot be set at the user level or session level. Therefore, if you plan to use Cortex AI on that account, you need to decide at the account level whether to perform cross-region inference.

Latency between regions

Latency depends on the cloud provider's infrastructure and network conditions. It's recommended to test in advance whether it can withstand your expected use cases.

Data Handling During Cross-Region Inference

User input, service-generated prompts, and output are not stored or cached during cross-region inference.

Regarding data movement, data required for inference requests is handled as follows:

When both the request source and destination regions are within AWS, data remains within the AWS global network. All data flowing through the AWS global network that interconnects data centers and regions is automatically encrypted at the physical layer
When both the request source and destination regions are within Azure, traffic remains within the Azure global network. It does not enter the public internet
When the source and destination regions are on different cloud providers, data travels over the public internet using Mutual Transport Layer Security (mTLS)

As supplementary information, let's also look at AWS and Azure information regarding data movement.
Regarding AWS communication, the "Amazon VPC FAQs" states the following:
Amazon VPC FAQs

Packets that originate from the AWS network with a destination on the AWS network stay on the AWS global network, except traffic to or from AWS China Regions.In addition, all data flowing across the AWS global network that interconnects our data centers and Regions is automatically encrypted at the physical layer before it leaves our secured facilities. Additional encryption layers exist as well; for example, all VPC cross-region peering traffic, and customer or service-to-service Transport Layer Security (TLS) connections.

Regarding Azure communication, "Microsoft global network" states the following:
Microsoft global network

So, does that mean all traffic when using Microsoft services? Yes, any traffic between data centers, within Microsoft Azure or between Microsoft services such as Virtual Machines, Microsoft 365, XBox, SQL DBs, Storage, and virtual networks routes within our global network and never over the public Internet. This routing ensures optimal performance and integrity.

Pricing

Using LLMs consumes credits. Credits are considered consumed in the requesting region. For example, if you call an LLM function from the us-east-2 region and the request is processed in the us-west-2 region, credits are considered consumed in the us-east-2 region.
For information on how many credits are used for each LLM, please refer to the Snowflake Service Consumption Table.

Also, using cross-region inference does not incur data egress charges.

The original text is as follows, so please also refer to this:
Cross-region inference | Cost considerations

Please Also Refer to the Documentation for Other Points!

The above is an excerpt of perspectives, so please also check this documentation for other points!

Conclusion

This time, I summarized Snowflake's cross-region inference in conjunction with the release of gpt-oss and GPT-5. I hope you will make full use of the power of LLMs after carefully reviewing the considerations and important points.

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Snowflake What's New Update on X (by tsubasa-san)

Tsubasa-san distributes Snowflake What's New update information on X. Please follow for catching up on the latest information.

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Link Summary

Parameter documentation CORTEX_ENABLED_CROSS_REGION
Cross-region inference announcement blog Announcing Cross-region Inference on Snowflake Cortex AI
Cross-region inference feature documentation Cross-region inference
Regional support status for each LLM
Credit consumption for each LLM Snowflake Service Consumption Table

Update History

August 14, 2025: New post

Japnese original version

https://zenn.dev/tatsu_tech/articles/c9a3e09f3964de

DEV Community: Tatsuya

[CKE & Snowflake Intelligence] Smart AI-Powered Search for Snowflake Documentation!

Introduction

The Easy Button for Context-Rich AI Agents

Usage Example

What are Cortex Knowledge Extensions (CKEs)?

How CKEs Work

Use Cases

Snowflake Intelligence

Snowflake Cortex AI MCP Server

Snowflake Managed MCP Servers

Introducing Snowflake Managed MCP Servers for Secure, Governed Data Agents

Setting Up for Use with Snowflake Intelligence

Prerequisites

Configuring Cortex Agent

Using with Snowflake Intelligence

Conclusion

Promotions

Snowflake What's New Updates (by tsubasa)

Japanese Version

English Version

Japanese version

Update History

gpt-oss, GPT-5 is here!: A Guide to Snowflake's Cross-Region LLM Inference

Introduction

gpt-oss and GPT-5 Released!

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa | Sho Tanaka

Let's Try Using Them on Snowflake Right Away

What is Cross-Region Inference?

OpenAI Model Releases on August 5 and 7, 2025, and Their Use on Snowflake

Configuration Method for Cross-Region Inference

Considerations and Important Points

Configuration Scope

Latency between regions

Data Handling During Cross-Region Inference

Pricing

Please Also Refer to the Documentation for Other Points!

Conclusion

Promotion

Snowflake What's New Update on X (by tsubasa-san)

Japanese Version

English Version

Link Summary

Update History

Japnese original version

`gpt-oss` and `GPT-5` Released!