Snowflake Cortex
Snowflake Cortex gives you instant access to industry-leading large language models (LLMs) trained by researchers at companies like Mistral, Reka, Meta, and Google, includingSnowflake Arctic, an open enterprise-grade model developed by Snowflake.
This example goes over how to use LangChain to interact with Snowflake Cortex.
Installation and setup
We start by installing thesnowflake-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
# First step is to set up the environment variables, to connect to Snowflake,
# you can also pass these snowflake credentials while instantiating the model
if os.environ.get("SNOWFLAKE_ACCOUNT")isNone:
os.environ["SNOWFLAKE_ACCOUNT"]= getpass.getpass("Account: ")
if os.environ.get("SNOWFLAKE_USERNAME")isNone:
os.environ["SNOWFLAKE_USERNAME"]= getpass.getpass("Username: ")
if os.environ.get("SNOWFLAKE_PASSWORD")isNone:
os.environ["SNOWFLAKE_PASSWORD"]= getpass.getpass("Password: ")
if os.environ.get("SNOWFLAKE_DATABASE")isNone:
os.environ["SNOWFLAKE_DATABASE"]= getpass.getpass("Database: ")
if os.environ.get("SNOWFLAKE_SCHEMA")isNone:
os.environ["SNOWFLAKE_SCHEMA"]= getpass.getpass("Schema: ")
if os.environ.get("SNOWFLAKE_WAREHOUSE")isNone:
os.environ["SNOWFLAKE_WAREHOUSE"]= getpass.getpass("Warehouse: ")
if os.environ.get("SNOWFLAKE_ROLE")isNone:
os.environ["SNOWFLAKE_ROLE"]= getpass.getpass("Role: ")
from langchain_community.chat_modelsimport ChatSnowflakeCortex
from langchain_core.messagesimport HumanMessage, SystemMessage
# By default, we'll be using the cortex provided model: `mistral-large`, with function: `complete`
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(
# Change the default cortex model and function
model="mistral-large",
cortex_function="complete",
# Change the default generation parameters
temperature=0,
max_tokens=10,
top_p=0.95,
# Specify your Snowflake Credentials
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"
)
Calling the chat model
We can now call the chat model using theinvoke orstream methods.
messages = [SystemMessage(content="You are a friendly assistant."),HumanMessage(content="What are large language models?"),]chat.invoke(messages)
Stream
# Sample input prompt
messages=[
SystemMessage(content="You are a friendly assistant."),
HumanMessage(content="What are large language models?"),
]
# Invoke the stream method and print each chunk as it arrives
print("Stream Method Response:")
for chunkin chat._stream(messages):
print(chunk.message.content)
Related
- Chat modelconceptual guide
- Chat modelhow-to guides