Creating an Agent

Initializing an Agent

Langchain provides functions for creating a new Agent. There are different types of agents that you can create. The create_react_agent() function creates a ReAct - Reasoning and Acting) agent type.

Agents are run using an AgentExecutor object, which is responsible for executing the actions returned by the Agent.

To create a new agent, create a new agent.py file, and copy and paste the code below.

from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub

# Include the LLM from a previous lesson
from llm import llm

agent_prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True
    )

The code is still missing some variables used in the create_react_agent call, so we’ll cover them one by one.

from llm import llm

from langchain.tools import Tool

tools = [
    Tool.from_function(
        name="General Chat",
        description="For general chat not covered by other tools",
        func=llm.invoke,
        return_direct=True
    )
]

from langchain.chains.conversation.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(
    memory_key='chat_history',
    k=5,
    return_messages=True,
)

Verbose Output

When the verbose argument is set to True, you can view the reasoning the agent has used in the console.

> Entering new AgentExecutor chain...
{
    "action": "Cypher QA",
    "action_input": "What are some films starring Leonardo DiCaprio?"
}

> Entering new GraphCypherQAChain chain...
Generated Cypher:
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = "Leonardo DiCaprio"
RETURN m.title AS film_starring_Leonardo_DiCaprio
Full Context:
[{'film_starring_Leonardo_DiCaprio': 'Great Gatsby, The'}, {'film_starring_Leonardo_DiCaprio': 'Gangs of New York'}, {'film_starring_Leonardo_DiCaprio': 'Aviator, The'}, {'film_starring_Leonardo_DiCaprio': 'Departed, The'}, {'film_starring_Leonardo_DiCaprio': 'Total Eclipse'}, {'film_starring_Leonardo_DiCaprio': 'Basketball Diaries, The'}, {'film_starring_Leonardo_DiCaprio': 'Titanic'}, {'film_starring_Leonardo_DiCaprio': 'Man in the Iron Mask, The'}, {'film_starring_Leonardo_DiCaprio': "William Shakespeare's Romeo + Juliet"}, {'film_starring_Leonardo_DiCaprio': 'Blood Diamond'}]

> Finished chain.

Observation: {'query': 'What are some films starring Leonardo DiCaprio?', 'result': 'Some films starring Leonardo DiCaprio include "Great Gatsby, The", "Gangs of New York", "Aviator, The", "Departed, The", "Total Eclipse", "Basketball Diaries, The", "Titanic", "Man in the Iron Mask, The", "William Shakespeare\'s Romeo + Juliet", and "Blood Diamond".'}
Thought:{
    "action": "Final Answer",
    "action_input": "Some films starring Leonardo DiCaprio include 'Great Gatsby, The', 'Gangs of New York', 'Aviator, The', 'Departed, The', 'Total Eclipse', 'Basketball Diaries, The', 'Titanic', 'Man in the Iron Mask, The', 'William Shakespeare's Romeo + Juliet', and 'Blood Diamond'."
}

Add a Handler Function

The agent_executor object is callable and expects a single input, the user’s input. The function returns a dict that will contain an output key containing the final response generated by the LLM.

At the bottom of the file, create a new generate_response() function to replicate the value. The role of the function should be to take a single string input, call the agent object and return the answer generated by the LLM.

def generate_response(prompt):
    """
    Create a handler that calls the Conversational agent
    and returns a response to be rendered in the UI
    """

    response = agent_executor.invoke({"input": prompt})

    return response['output']

Calling the new Handler function

You can now update the bot to call the new generate_response() function by modifying the handle_submit() function in bot.py.

Start by importing the generate_response() from the agent.py file.

from agent import generate_response

Now modify the handle_submit() button to instead call the handle_submit() method. You can use the write_message() function to display the message on screen.

# Submit handler
def handle_submit(message):
    # Handle the response
    with st.spinner('Thinking...'):

        response = generate_response(message)
        write_message('assistant', response)

Receving a Response

You should now have the makings of an intelligent LLM-integrated chatbot. If you ask a question, you should get a generated response from the LLM appended to the list of messages.

Once you have received a response from the LLM, click the button below to mark the challenge as completed.