chatbot.py

from langgraph.graph import StateGraph, MessagesState
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq  # Replaced OpenAI with Groq
from langgraph.prebuilt import ToolNode
from tools import query_knowledge_base, search_for_product_reccommendations, data_protection_check, create_new_customer, place_order, retrieve_existing_customer_orders

from dotenv import load_dotenv

import os

load_dotenv()

# Set environment variable for Groq API key
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")

prompt = """#Purpose 

You are a customer service chatbot for a flower shop company. You can help the customer achieve the goals listed below.

#Goals

1. Answer questions the user might have relating to serivces offered
2. Recommend products to the user based on their preferences
3. Help the customer check on an existing order, or place a new order
4. To place and manage orders, you will need a customer profile (with an associated id). If the customer already has a profile, perform a data protection check to retrieve their details. If not, create them a profile.

#Tone

Helpful and friendly. Use gen-z emojis to keep things lighthearted. You MUST always include a funny flower related pun in every response."""

chat_template = ChatPromptTemplate.from_messages(
    [
        ('system', prompt),
        ('placeholder', "{messages}")
    ]
)

#with open('./.env', 'r', encoding='utf-8') as f:
#    for line in f:
#        key, value = line.strip().split('=')
#        os.environ[key] = value

tools = [query_knowledge_base, search_for_product_reccommendations, data_protection_check, create_new_customer, place_order, retrieve_existing_customer_orders]

# Replaced OpenAI llm with Groq llm
llm = ChatGroq(
    model="llama-3.1-70b-versatile",
    temperature=0.5
)

llm_with_prompt = chat_template | llm.bind_tools(tools)

def call_agent(message_state: MessagesState):
    
    response = llm_with_prompt.invoke(message_state)

    return {
        'messages': [response]
    }

def is_there_tool_calls(state: MessagesState):
    last_message = state['messages'][-1]
    if last_message.tool_calls:
        return 'tool_node'
    else:
        return '__end__'

graph = StateGraph(MessagesState)

tool_node = ToolNode(tools)

graph.add_node('agent', call_agent)
graph.add_node('tool_node', tool_node)

graph.add_conditional_edges(
    "agent",
    is_there_tool_calls
)
graph.add_edge('tool_node', 'agent')

graph.set_entry_point('agent')

app = graph.compile()