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Snowflake Cortex

Snowflake Cortex 为您即时提供由 Mistral、Reka、Meta 和 Google 等公司研究人员训练的行业领先大型语言模型(LLM),包括由 Snowflake 开发的开放式企业级模型 Snowflake Arctic

本示例将介绍如何使用 LangChain 与 Snowflake Cortex 进行交互。

安装和设置

我们首先使用以下命令安装 snowflake-snowpark-python 库。然后配置连接 Snowflake 的凭据,可以通过环境变量或直接传入。

%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") 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

# By default, we'll be using the cortex provided model: `mistral-large`, with function: `complete`
chat = ChatSnowflakeCortex()

以上单元格假设您的 Snowflake 凭据已在环境变量中设置。如果您想手动指定,请使用以下代码:

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"
)

调用聊天模型

我们现在可以使用 invokestream 方法调用聊天模型。

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

流式传输

# 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 chunk in chat._stream(messages):
print(chunk.message.content)