4. Chat Model Conversations

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Objectives

1. Setup an interactive command-line chat loop.
2. Accumulate user queries and AI responses in a shared history list.

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User Chat Loop

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Goal

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Sample Input

1. User: Hello, I am Satish.
2. User: What is my name?

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Sample Output

1. AI: Hello Satish! How can I help you today?
2. AI: Your name is Satish.

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Plan

1. Initialize model using the core abstraction helper init_chat_model("llama-3.3-70b-versatile", model_provider="groq").
2. Initialize chat_history = [SystemMessage(content="You are a helpful AI assistant.")].
3. Create a while True loop to take user console input, append HumanMessage, invoke model on history, append AIMessage, and print the response.

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Code Implementation

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1. Implement Chat Loop

from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

load_dotenv()

# Initialize model using core abstractions (Groq Llama model)
model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

chat_history = []
chat_history.append(SystemMessage(content="You are a helpful AI assistant."))

while True:
    query = input("You: ")
    if query.lower() == "exit":
        break
    chat_history.append(HumanMessage(content=query))

    result = model.invoke(chat_history)
    response = result.content
    chat_history.append(AIMessage(content=response))

    print(f"AI: {response}")

print("---- Message History ----")
print(chat_history)

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Exercise: Strict Math Helper Bot 🧮

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Goal

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Sample Input

1. User: What is 5 + 5?
2. User: Who is the president of USA?

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Sample Output

1. AI: 5 + 5 is 10.
2. AI: I am sorry, but I can only answer math-related questions.

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Plan

1. Adjust the initial SystemMessage content to instruct the model to only solve math questions and refuse other topics.
2. Run the loop to verify the constraint.

from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain.chat_models import init_chat_model
from dotenv import load_dotenv

load_dotenv()

# Initialize model using core abstractions (Google Gemini)
model = init_chat_model("gemini-2.5-flash", model_provider="google_genai")
chat_history = [SystemMessage(content="You are a math tutor. You must only answer math equations. Politeness refuse any other topic.")]

while True:
    query = input("User: ")
    if query.lower() == "exit":
        break
    chat_history.append(HumanMessage(content=query))
    res = model.invoke(chat_history)
    chat_history.append(AIMessage(content=res.content))
    print(f"AI: {res.content}")

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Practice & Exercises