2. Agent ReAct Chat

"User: Who was Albert Einstein?"
"User: What time is it?"

def get_current_time(*args, **kwargs):
    """Returns the current time in H:MM AM/PM format."""
    import datetime
    now = datetime.datetime.now()
    return now.strftime("%I:%M %p")

def search_wikipedia(query):
    """Searches Wikipedia and returns the summary of the first result."""
    from wikipedia import summary
    try:
        return summary(query, sentences=2)
    except:
        return "I couldn't find any information on that."

from langchain_core.tools import Tool

tools = [
    Tool(
        name="Time",
        func=get_current_time,
        description="Useful for when you need to know the current time.",
    ),
    Tool(
        name="Wikipedia",
        func=search_wikipedia,
        description="Useful for when you need to know information about a topic.",
    ),
]

from langchain import hub
from langchain.agents import create_structured_chat_agent
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import init_chat_model

prompt = hub.pull("hwchase17/structured-chat-agent")
llm = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)

from langchain.agents import AgentExecutor
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage

agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    verbose=True,
    memory=memory,
    handle_parsing_errors=True,
)

initial_message = "You are an AI assistant that can provide helpful answers using available tools."
memory.chat_memory.add_message(SystemMessage(content=initial_message))

# Interactive chat loop snippet
# user_input = input("User: ")
# memory.chat_memory.add_message(HumanMessage(content=user_input))
# response = agent_executor.invoke({"input": user_input})
# memory.chat_memory.add_message(AIMessage(content=response["output"]))

from dotenv import load_dotenv
from langchain import hub
from langchain.agents import AgentExecutor, create_structured_chat_agent
from langchain.memory import ConversationBufferMemory
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.tools import Tool
from langchain.chat_models import init_chat_model

# Load environment variables from .env file
load_dotenv()


# Define Tools
def get_current_time(*args, **kwargs):
    """Returns the current time in H:MM AM/PM format."""
    import datetime

    now = datetime.datetime.now()
    return now.strftime("%I:%M %p")


def search_wikipedia(query):
    """Searches Wikipedia and returns the summary of the first result."""
    from wikipedia import summary

    try:
        # Limit to two sentences for brevity
        return summary(query, sentences=2)
    except:
        return "I couldn't find any information on that."


# Define the tools that the agent can use
tools = [
    Tool(
        name="Time",
        func=get_current_time,
        description="Useful for when you need to know the current time.",
    ),
    Tool(
        name="Wikipedia",
        func=search_wikipedia,
        description="Useful for when you need to know information about a topic.",
    ),
]

# Load the correct JSON Chat Prompt from the hub
prompt = hub.pull("hwchase17/structured-chat-agent")

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

# Create a structured Chat Agent with Conversation Buffer Memory
memory = ConversationBufferMemory(
    memory_key="chat_history", return_messages=True)

# create_structured_chat_agent initializes a chat agent designed to interact using a structured prompt and tools
agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)

# AgentExecutor is responsible for managing the interaction between the user input, the agent, and the tools
agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    verbose=True,
    memory=memory,  # Use the conversation memory to maintain context
    handle_parsing_errors=True,  # Handle any parsing errors gracefully
)

# Initial system message to set the context for the chat
initial_message = "You are an AI assistant that can provide helpful answers using available tools.\nIf you are unable to answer, you can use the following tools: Time and Wikipedia."
memory.chat_memory.add_message(SystemMessage(content=initial_message))

# Chat Loop to interact with the user
while True:
    user_input = input("User: ")
    if user_input.lower() == "exit":
        break

    # Add the user's message to the conversation memory
    memory.chat_memory.add_message(HumanMessage(content=user_input))

    # Invoke the agent with the user input and the current chat history
    response = agent_executor.invoke({"input": user_input})
    print("Bot:", response["output"])

    # Add the agent's response to the conversation memory
    memory.chat_memory.add_message(AIMessage(content=response["output"]))