This notebook shows how to use ZHIPU AI API in LangChain with the langchain.chat_models.ChatZhipuAI.
GLM-4 is a multi-lingual large language model aligned with human intent, featuring capabilities in Q&A, multi-turn dialogue, and code generation. The overall performance of the new generation base model GLM-4 has been significantly improved compared to the previous generation, supporting longer contexts; Stronger multimodality; Support faster inference speed, more concurrency, greatly reducing inference costs; Meanwhile, GLM-4 enhances the capabilities of intelligent agents.

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Getting started

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Installation

First, ensure the zhipuai package is installed in your Python environment. Run the following command: 
#!pip install --upgrade httpx httpx-sse PyJWT

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Importing the Required Modules

After installation, import the necessary modules to your Python script: 
from langchain_community.chat_models import ChatZhipuAI
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

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Setting Up Your API Key

Sign in to ZHIPU AI for an API Key to access our models. 
import os

os.environ["ZHIPUAI_API_KEY"] = "zhipuai_api_key"

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Initialize the ZHIPU AI Chat Model

Here’s how to initialize the chat model: 
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

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Basic Usage

Invoke the model with system and human messages like this: 
messages = [
    AIMessage(content="Hi."),
    SystemMessage(content="Your role is a poet."),
    HumanMessage(content="Write a short poem about AI in four lines."),
]

response = chat.invoke(messages)
print(response.content)  # Displays the AI-generated poem

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Advanced Features

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Streaming Support

For continuous interaction, use the streaming feature: 
from langchain_core.callbacks.manager import CallbackManager
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

streaming_chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
    streaming=True,
    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
)

streaming_chat(messages)

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Asynchronous Calls

For non-blocking calls, use the asynchronous approach: 
async_chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

response = await async_chat.agenerate([messages])
print(response)

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Using With Functions Call

GLM-4 Model can be used with the function call as well，use the following code to run a simple LangChain json_chat_agent. 
os.environ["TAVILY_API_KEY"] = "tavily_api_key"

from langchain import hub
from langchain.agents import AgentExecutor, create_json_chat_agent
from langchain_community.tools.tavily_search import TavilySearchResults

tools = [TavilySearchResults(max_results=1)]
prompt = hub.pull("hwchase17/react-chat-json")
llm = ChatZhipuAI(temperature=0.01, model="glm-4")

agent = create_json_chat_agent(llm, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)

agent_executor.invoke({"input": "what is LangChain?"})