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雪花皮

Snowflake Cortex 让您可以即时访问由 Mistral、Reka、Meta 和 Google 等公司的研究人员训练的行业领先的大型语言模型 （LLM），包括 Snowflake Arctic，这是由 Snowflake 开发的开放式企业级模型。

此示例介绍了如何使用 LangChain 与 Snowflake Cortex 交互。

安装和设置

我们首先安装snowflake-snowpark-python库，使用以下命令。然后，我们将用于连接到 Snowflake 的凭证配置为环境变量或直接传递它们。

%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()

上面的单元格假定您的 Snowflake 凭证是在环境变量中设置的。如果您更愿意手动指定它们，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

现在，我们可以使用invoke或stream方法。

消息 = [ SystemMessage（content=“您是一位友好的助手。 HumanMessage（content=“什么是大型语言模型？”）， ] chat.invoke（消息）

流

# 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)