Building AI Agents with LangChain and LangGraph: Part 1 › Initializing the Language Model

Michael BrenndoerferJuly 21, 20251 min read
Building AI Agents with LangChain and LangGraph: Part 1 › Initializing the Language ModelOne section · Sections in this article3 / 8 ↓
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The init_chat_model function provides a unified interface for working with different language model providers. Here we're using Google's Vertex AI with the Gemini 2.0 Flash model, which offers a good balance of speed and capability for agentic tasks.

The temperature=0 setting ensures deterministic outputs, which is important for reliable tool usage.

Note: While temperature=0 aims for more deterministic outputs, LLMs often end up being non-deterministic due to various factors. For a deeper understanding of this topic, see Why LLMs Are Not Deterministic.

In[66]:
Code
result = llm.invoke("Hello, are you there?")
print(type(result))
Markdown(result.content)
Out[66]:
Console
<class 'langchain_core.messages.ai.AIMessage'>
Yes, I am here. How can I help you today?

Let's test our model with a simple interaction to ensure it's working correctly. The response comes back as an AIMessage object, which is LangChain's standard format for model outputs.

In[67]:
Code
@tool
def draft_customer_reply(customer_name: str, request_category: str, reply: str) -> str:
    """Draft a reply to a customer."""
    return f"I am drafting a reply to {customer_name} in the category {request_category}.\n Content: {reply}"


type(draft_customer_reply)
Out[67]:
Console
langchain_core.tools.structured.StructuredTool