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Showing content from https://python.langchain.com/docs/integrations/chat/snowflake/ below:

Snowflake Cortex | 🦜️🔗 LangChain

Snowflake Cortex gives you instant access to industry-leading large language models (LLMs) trained by researchers at companies like Mistral, Reka, Meta, and Google, including Snowflake Arctic, an open enterprise-grade model developed by Snowflake.

This example goes over how to use LangChain to interact with Snowflake Cortex.

We start by installing the snowflake-snowpark-python library, using the command below. Then we configure the credentials for connecting to Snowflake, as environment variables or pass them directly.

%pip install --upgrade --quiet snowflake-snowpark-python

import getpass
import os




if os.environ.get("SNOWFLAKE_ACCOUNT") is None:
    os.environ["SNOWFLAKE_ACCOUNT"] = getpass.getpass("Account: ")

if os.environ.get("SNOWFLAKE_USERNAME") is None:
    os.environ["SNOWFLAKE_USERNAME"] = getpass.getpass("Username: ")

if os.environ.get("SNOWFLAKE_PASSWORD") is None:
    os.environ["SNOWFLAKE_PASSWORD"] = getpass.getpass("Password: ")

if os.environ.get("SNOWFLAKE_DATABASE") is None:
    os.environ["SNOWFLAKE_DATABASE"] = getpass.getpass("Database: ")

if os.environ.get("SNOWFLAKE_SCHEMA") is None:
    os.environ["SNOWFLAKE_SCHEMA"] = getpass.getpass("Schema: ")

if os.environ.get("SNOWFLAKE_WAREHOUSE") is None:
    os.environ["SNOWFLAKE_WAREHOUSE"] = getpass.getpass("Warehouse: ")

if os.environ.get("SNOWFLAKE_ROLE") is None:
    os.environ["SNOWFLAKE_ROLE"] = getpass.getpass("Role: ")

from langchain_community.chat_models import ChatSnowflakeCortex
from langchain_core.messages import HumanMessage, SystemMessage


chat = ChatSnowflakeCortex()

The above cell assumes that your Snowflake credentials are set in your environment variables. If you would rather manually specify them, use the following code:

chat = ChatSnowflakeCortex(
    
    model="mistral-large",
    cortex_function="complete",

    
    temperature=0,
    max_tokens=10,
    top_p=0.95,

    
    account="YOUR_SNOWFLAKE_ACCOUNT",
    username="YOUR_SNOWFLAKE_USERNAME",
    password="YOUR_SNOWFLAKE_PASSWORD",
    database="YOUR_SNOWFLAKE_DATABASE",
    schema="YOUR_SNOWFLAKE_SCHEMA",
    role="YOUR_SNOWFLAKE_ROLE",
    warehouse="YOUR_SNOWFLAKE_WAREHOUSE"
)

We can now call the chat model using the invoke or stream methods.

messages = [ SystemMessage(content="You are a friendly assistant."), HumanMessage(content="What are large language models?"), ] chat.invoke(messages)

messages = [
    SystemMessage(content="You are a friendly assistant."),
    HumanMessage(content="What are large language models?"),
]


print("Stream Method Response:")
for chunk in chat._stream(messages):
    print(chunk.message.content)

• Chat model conceptual guide
• Chat model how-to guides

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