{
"cells": [
{
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"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"from langchain.utilities import SQLDatabase\n",
"from sqlalchemy import create_engine # Import create_engine\n",
"\n",
"# --- Initialize Core Components ---\n",
"\n",
"# 1. Dialogue Context (Memory)\n",
"memory = ConversationBufferMemory()\n",
"\n",
"# 2. LLM (for routing, service selection, state tracking, and response generation)\n",
"llm = ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo\") # Or another suitable model\n",
"\n",
"# 3. Database (using SQLite in-memory for demonstration)\n",
"engine = create_engine(\"sqlite:///:memory:\") # Create an in-memory SQLite engine\n",
"db = SQLDatabase(engine) # Pass the engine to SQLDatabase\n",
"\n",
"# --- Define Prompts ---\n",
"\n",
"# Router Prompt\n",
"router_template = \"\"\"\n",
"You are a helpful assistant that classifies user input into two categories:\n",
"\n",
"1. open-domain: General conversation, chit-chat, or questions not related to a specific task.\n",
"2. task-oriented: The user wants to perform a specific action or get information related to a predefined service.\n",
"\n",
"Based on the dialogue history, classify the latest user input:\n",
"\n",
"{chat_history}\n",
"\n",
"User: {user_input}\n",
"\n",
"Classification:\n",
"\"\"\"\n",
"router_prompt = PromptTemplate(\n",
" input_variables=[\"chat_history\", \"user_input\"], template=router_template\n",
")\n",
"\n",
"# Service Selection Prompt\n",
"service_selection_template = \"\"\"\n",
"You are a helpful assistant that classifies user input into one of the following predefined services:\n",
"\n",
"Services:\n",
"- book_flight: For booking flight tickets.\n",
"- check_order_status: For checking the status of an order.\n",
"- find_restaurants: For finding restaurants based on criteria.\n",
"\n",
"Based on the dialogue history, which service best matches the user's intent?\n",
"\n",
"{chat_history}\n",
"\n",
"User: {user_input}\n",
"\n",
"Selected Service:\n",
"\"\"\"\n",
"service_selection_prompt = PromptTemplate(\n",
" input_variables=[\"chat_history\", \"user_input\"],\n",
" template=service_selection_template,\n",
")\n",
"\n",
"# Dialogue State Tracking Prompt\n",
"state_tracking_template = \"\"\"\n",
"You are a helpful assistant that extracts information from user input to fill in the slots for a specific service.\n",
"\n",
"Service: {service}\n",
"Slots: {slots}\n",
"\n",
"Based on the dialogue history, extract the values for each slot from the conversation. \n",
"Return the output in JSON format. If a slot is not filled, use null as the value.\n",
"\n",
"{chat_history}\n",
"\n",
"User: {user_input}\n",
"\n",
"Extracted Information (JSON):\n",
"\"\"\"\n",
"state_tracking_prompt = PromptTemplate(\n",
" input_variables=[\"service\", \"slots\", \"chat_history\", \"user_input\"],\n",
" template=state_tracking_template,\n",
")\n",
"\n",
"# Response Generation Prompt\n",
"response_generation_template = \"\"\"\n",
"You are a helpful assistant that generates natural language responses to the user.\n",
"\n",
"Dialogue History:\n",
"{chat_history}\n",
"\n",
"User: {user_input}\n",
"\n",
"{slot_info}\n",
"\n",
"{db_results}\n",
"\n",
"Response:\n",
"\"\"\"\n",
"response_generation_prompt = PromptTemplate(\n",
" input_variables=[\"chat_history\", \"user_input\", \"slot_info\", \"db_results\"],\n",
" template=response_generation_template,\n",
")\n",
"\n",
"# --- Define Chains ---\n",
"\n",
"router_chain = LLMChain(llm=llm, prompt=router_prompt, output_key=\"classification\")\n",
"service_selection_chain = LLMChain(\n",
" llm=llm, prompt=service_selection_prompt, output_key=\"service\"\n",
")\n",
"state_tracking_chain = LLMChain(\n",
" llm=llm, prompt=state_tracking_prompt, output_key=\"slot_json\"\n",
")\n",
"response_generation_chain = LLMChain(\n",
" llm=llm, prompt=response_generation_prompt, output_key=\"response\"\n",
")\n",
"\n",
"# --- Define Service Slots ---\n",
"# (In a real application, this would likely be loaded from a configuration file or database)\n",
"service_slots = {\n",
" \"book_flight\": [\"destination\", \"departure_date\", \"num_passengers\"],\n",
" \"check_order_status\": [\"order_id\"],\n",
" \"find_restaurants\": [\"cuisine\", \"location\", \"price_range\"],\n",
"}\n",
"\n",
"# --- Main Dialogue Loop ---\n",
"\n",
"def process_user_input(user_input):\n",
" # 1. Add user input to memory\n",
" memory.chat_memory.add_user_message(user_input)\n",
"\n",
" # 2. Route the input\n",
" router_output = router_chain(\n",
" {\"chat_history\": memory.load_memory_variables({}), \"user_input\": user_input}\n",
" )\n",
" classification = router_output[\"classification\"].strip()\n",
"\n",
" print(f\"Router Classification: {classification}\")\n",
"\n",
" if classification == \"open-domain\":\n",
" # 3. Handle open-domain conversation\n",
" llm_response = llm(memory.load_memory_variables({})[\"history\"])\n",
" response = llm_response.content\n",
" else:\n",
" # 4. Select the service\n",
" service_output = service_selection_chain(\n",
" {\"chat_history\": memory.load_memory_variables({}), \"user_input\": user_input}\n",
" )\n",
" service = service_output[\"service\"].strip()\n",
"\n",
" print(f\"Selected Service: {service}\")\n",
"\n",
" if service not in service_slots:\n",
" response = \"I'm sorry, I cannot understand that service request yet. We currently support booking flights, checking order status and finding restaurants only.\"\n",
" else:\n",
" # 5. Track the dialogue state (slot filling)\n",
" slots = service_slots[service]\n",
" state_output = state_tracking_chain(\n",
" {\n",
" \"service\": service,\n",
" \"slots\": \", \".join(slots),\n",
" \"chat_history\": memory.load_memory_variables({}),\n",
" \"user_input\": user_input,\n",
" }\n",
" )\n",
" slot_json_str = state_output[\"slot_json\"].strip()\n",
"\n",
" print(f\"Slot Filling Output (JSON): {slot_json_str}\")\n",
"\n",
" try:\n",
" import json\n",
" slot_values = json.loads(slot_json_str)\n",
" except json.JSONDecodeError:\n",
" slot_values = {} # Handle cases where JSON decoding fails\n",
" response = \"I'm sorry, there seems to be a problem understanding your request details.\"\n",
"\n",
" # (Optional) 6. Database interaction (based on service and filled slots)\n",
" db_results = \"\" # Initialize db_results as an empty string\n",
" if service == \"check_order_status\" and \"order_id\" in slot_values:\n",
" try:\n",
" order_id = slot_values[\"order_id\"]\n",
" # Basic query without table information\n",
" db_results = db.run(f\"SELECT * FROM orders WHERE order_id = '{order_id}'\")\n",
" db_results = f\"Database Results: {db_results}\"\n",
" except Exception as e:\n",
" print(f\"Error during database query: {e}\")\n",
" db_results = \"\"\n",
"\n",
" # 7. Generate the response\n",
" response_output = response_generation_chain(\n",
" {\n",
" \"chat_history\": memory.load_memory_variables({}),\n",
" \"user_input\": user_input,\n",
" \"slot_info\": f\"Slots: {slot_json_str}\",\n",
" \"db_results\": db_results,\n",
" }\n",
" )\n",
" response = response_output[\"response\"]\n",
"\n",
" # 8. Add the system response to memory\n",
" memory.chat_memory.add_ai_message(response)\n",
"\n",
" return response\n",
"\n",
"# --- Example Usage ---\n",
"\n",
"while True:\n",
" user_input = input(\"You: \")\n",
" if user_input.lower() == \"exit\":\n",
" break\n",
" response = process_user_input(user_input)\n",
" print(f\"AI: {response}\")"
]
}
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