Large Language Model (LLM) Functions (Snowflake Cortex)¶

Required Privileges¶

The CORTEX_USER database role in the SNOWFLAKE database includes the privileges that allow users to call Snowflake Cortex LLM functions. By default, this database role is granted to only the ACCOUNTADMIN role. ACCOUNTADMIN must propagate this role to user roles in order to allow users to access Cortex LLM Functions.

The SNOWFLAKE.CORTEX_USER database role cannot be granted directly to a user. A user with the ACCOUNTADMIN role must first grant it to an account role, and then grant the account role to users. For more information, see Using SNOWFLAKE Database Roles.

In the following example, you assume ACCOUNTADMIN and grant the user some_user the CORTEX_USER database role via the account role cortex_user_role, which you create for this purpose.

USE ROLE ACCOUNTADMIN;

CREATE ROLE cortex_user_role;
GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE cortex_user_role;

GRANT ROLE cortex_user_role TO USER some_user;

You can also grant access to Snowflake Cortex LLM functions through existing roles commonly used by specific groups of users. (See User roles.) For example, if you have created an analyst role that is used as a default role by analysts in your organization, you can easily grant these users access to Snowflake Cortex LLM functions with a single GRANT statement.

GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE analyst;

The PUBLIC pseudo-role is automatically granted to all users and roles, so granting cortex_user_role to PUBLIC allows all users in your account to use the Snowflake Cortex LLM functions.

GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE PUBLIC;

Availability¶

Snowflake Cortex LLM functions are currently available in the following regions.

Cost Considerations¶

Snowflake Cortex LLM functions incur compute cost based on the number of tokens processed. Refer to the consumption table for each function’s cost in credits per million tokens.

A token is the smallest unit of text processed by Snowflake Cortex LLM functions, approximately equal to four characters of text. The equivalence of raw input or output text to tokens can vary by model.

• For functions that generate new text in the response (COMPLETE, SUMMARIZE, and TRANSLATE), both input and output tokens are counted.

• For functions that only extract information from the input (EXTRACT_ANSWER and SENTIMENT), only input tokens are counted.

• For EXTRACT_ANSWER, the number of billable tokens is the sum of the number of tokens in the from_text and question fields.

• SUMMARIZE, TRANSLATE, EXTRACT_ANSWER, and SENTIMENT add a prompt to the input text in order to generate the response. As a result, the input token count is slightly higher than the number of tokens in the text you provide.

Snowflake recommends executing queries that call a Snowflake Cortex LLM Function with a smaller warehouse (no larger than MEDIUM) because larger warehouses do not increase performance. The cost associated with keeping a warehouse active will continue to apply when executing a query that calls a Snowflake Cortex LLM Function. For general information on compute costs, see Understanding compute cost.

Usage Quotas¶

To ensure a high standard of performance for all Snowflake customers, Snowflake Cortex LLM functions are subject to usage quotas beyond which requests may be throttled. Snowflake may adjust these quotas from time to time. The quotas in the table below are applied per account.

Managing Costs and Throttling¶

During this preview, Snowflake recommends using a warehouse size no larger than MEDIUM when calling Snowflake Cortex LLM functions. Using a larger warehouse than necessary does not increase performance, but can result in unnecessary costs and a higher risk of throttling. This recommendation may not apply in the future due to upcoming product updates.

Model Restrictions¶

Models used by Snowflake Cortex have limitations on size as described in the table below. Sizes are given in tokens. Tokens generally correspond to words, but not all tokens are words, so the number of words corresponding to a limit is slightly less than the number of tokens. Inputs that exceed the limit result in an error.

Choosing a Model¶

The Snowflake Cortex COMPLETE function supports multiple models of varying capability, latency, and cost. These models have been carefully chosen to align with common customer use cases. To achieve the best performance per credit, choose a model that’s a good match for the content size and complexity of your task. Here are brief overviews of the available models.

• mistral-large is Mistral AI’s most advanced large language model with top-tear reasoning capabilities. Mistral Large is ideal for complex tasks that require large reasoning capabilities or are highly specialized, such as synthetic text generation, code generation, or agents.

• reka-flash is a highly capable multilingual language model optimized for fast workloads that require high quality, such as writing product descriptions or blog posts, coding, and extracting answers from documents with hundreds of pages.

• mixtral-8x7b is ideal for text generation, classification, and question answering. Mistral models are optimized for low latency with low memory requirements, which translates into higher throughput for enterprise use cases.

• llama2-70b-chat is well-suited to complex, large-scale tasks that require a moderate amount of reasoning, like extracting data or helping you to write job descriptions.

• mistral-7b is ideal for your simplest summarization, structuration, and question answering tasks that need to be done quickly. It offers low latency and high throughput processing for multiple pages of text with its 32K context window.

• gemma-7b is suitable for simple code and text completion tasks. It has a context window of 8,000 tokens but is surprisingly capable within that limit, and quite cost-effective.

If you’re not sure where to start, try the most powerful models first to establish a baseline to evaluate other models. mistral-large is the most capable model offered by Snowflake Cortex, and will give you a good idea what a state-of-the-art model can do.

To help you decide, the following table provides information on how popular models perform on various benchmarks, including the models offered by Snowflake Cortex COMPLETE as well as a few other popular models.

^*Provided for comparison; not available in Snowflake Cortex COMPLETE.

LLM Functions Overview¶

Error Conditions¶

Snowflake Cortex LLM functions can produce the following error messages.

Using Snowflake Cortex LLM Functions with Python¶

Snowflake Cortex LLM functions are available in Snowpark ML version 1.1.2 and later. See Installing Snowpark ML for instructions on setting up Snowpark ML.

If you run your Python script outside of Snowflake, you must create a Snowpark session to use these functions. See Connecting to Snowflake for instructions.

The following Python example illustrates calling Snowflake Cortex LLM functions on single values:

from snowflake.cortex import Complete, ExtractAnswer, Sentiment, Summarize, Translate

text = """
    The Snowflake company was co-founded by Thierry Cruanes, Marcin Zukowski,
    and Benoit Dageville in 2012 and is headquartered in Bozeman, Montana.
"""

print(Complete("llama2-70b-chat", "how do snowflakes get their unique patterns?"))
print(ExtractAnswer(text, "When was snowflake founded?"))
print(Sentiment("I really enjoyed this restaurant. Fantastic service!"))
print(Summarize(text))
print(Translate(text, "en", "fr"))

You can also call an LLM function on a table column, as shown below. This example requires a session object (stored in session) and a table articles containing a text column abstract_text, and creates a new column abstract_summary containing a summary of the abstract.

from snowflake.cortex import Summarize
from snowflake.snowpark.functions import col

article_df = session.table("articles")
article_df = article_df.withColumn(
    "abstract_summary",
    Summarize(col("abstract_text"))
)
article_df.collect()

Legal Notices¶

Snowflake Cortex LLM Functions are powered by machine learning technology, including Meta’s LLaMA 2 and Google’s Gemma 7B models.

The foundation LLaMA 2 model is licensed under the LLaMA 2 Community License and is Copyright (c) Meta Platforms, Inc. All Rights Reserved. Your use of any LLM Functions based on the LLama 2 model is subject to Meta’s Acceptable Use Policy.

The foundation Gemma 7B model is licensed under the Gemma Terms of Use, and use of it is subject to the Gemma Prohibited Use Policy.

Machine learning technology and results provided may be inaccurate, inappropriate, or biased. Decisions based on machine learning outputs, including those built into automatic pipelines, should have human oversight and review processes to ensure model-generated content is accurate.

LLM function queries are treated like any other SQL query and may be considered metadata.

For further information, see Snowflake AI Trust and Safety FAQ.