Snowflake Cortex
Snowflake Cortex 使您可以即时访问由 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，这是一个由 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 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，我们配置连接到 Snowflake 的凭据，作为环境变量或直接传递。
%pip install --upgrade --quiet snowflake-snowpark-python
Note: you may need to restart the kernel to use updated packages.
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: `snowflake-arctic`, with function: `complete`
chat = ChatSnowflakeCortex()
以上单元格假设您的 Snowflake 凭据已设置在您的环境变量中。如果您希望手动指定它们，请使用以下代码
chat = ChatSnowflakeCortex(
# change default cortex model and function
model="snowflake-arctic",
cortex_function="complete",
# change default generation parameters
temperature=0,
max_tokens=10,
top_p=0.95,
# specify 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"
)
调用模型
我们现在可以使用 invoke 或 generate 方法调用模型。
生成
messages = [
SystemMessage(content="You are a friendly assistant."),
HumanMessage(content="What are large language models?"),
]
chat.invoke(messages)
AIMessage(content=" Large language models are artificial intelligence systems designed to understand, generate, and manipulate human language. These models are typically based on deep learning techniques and are trained on vast amounts of text data to learn patterns and structures in language. They can perform a wide range of language-related tasks, such as language translation, text generation, sentiment analysis, and answering questions. Some well-known large language models include Google's BERT, OpenAI's GPT series, and Facebook's RoBERTa. These models have shown remarkable performance in various natural language processing tasks, and their applications continue to expand as research in AI progresses.", response_metadata={'completion_tokens': 131, 'prompt_tokens': 29, 'total_tokens': 160}, id='run-5435bd0a-83fd-4295-b237-66cbd1b5c0f3-0')
流式传输
ChatSnowflakeCortex 目前不支持流式传输。流式传输支持将在以后的版本中提供！