SparkLLM 聊天
由 iFlyTek 提供的 SparkLLM 聊天模型 API。有关更多信息，请参见 iFlyTek 开放平台。
基本使用
<!--IMPORTS:[{"imported": "ChatSparkLLM", "source": "langchain_community.chat_models", "docs": "https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sparkllm.ChatSparkLLM.html", "title": "SparkLLM Chat"}, {"imported": "HumanMessage", "source": "langchain_core.messages", "docs": "https://python.langchain.com/api_reference/core/messages/langchain_core.messages.human.HumanMessage.html", "title": "SparkLLM Chat"}]-->
"""For basic init and call"""
from langchain_community.chat_models import ChatSparkLLM
from langchain_core.messages import HumanMessage
chat = ChatSparkLLM(
spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>"
)
message = HumanMessage(content="Hello")
chat([message])
AIMessage(content='Hello! How can I help you today?')
- 从 iFlyTek SparkLLM API 控制台 获取 SparkLLM 的 app_id、api_key 和 api_secret (更多信息，请参见 iFlyTek SparkLLM 介绍 )，然后设置环境变量
IFLYTEK_SPARK_APP_ID、IFLYTEK_SPARK_API_KEY和IFLYTEK_SPARK_API_SECRET，或者在创建ChatSparkLLM时传递参数，如上面的示例所示。
用于 ChatSparkLLM 的流式处理
chat = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>",
streaming=True,
)
for chunk in chat.stream("Hello!"):
print(chunk.content, end="")
Hello! How can I help you today?
用于 v2
<!--IMPORTS:[{"imported": "ChatSparkLLM", "source": "langchain_community.chat_models", "docs": "https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sparkllm.ChatSparkLLM.html", "title": "SparkLLM Chat"}, {"imported": "HumanMessage", "source": "langchain_core.messages", "docs": "https://python.langchain.com/api_reference/core/messages/langchain_core.messages.human.HumanMessage.html", "title": "SparkLLM Chat"}]-->
"""For basic init and call"""
from langchain_community.chat_models import ChatSparkLLM
from langchain_core.messages import HumanMessage
chat = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>",
spark_api_url="wss://spark-api.xf-yun.com/v2.1/chat",
spark_llm_domain="generalv2",
)
message = HumanMessage(content="Hello")
chat([message])
