Unlocking the Future of AI Conversations: The Semantic Router’s Impact on LangChain

if router == 'politics':
    response_completion = "I can't talk about politics"
else:
    completion = Completion(api_key)
    response_completion = completion.create(agent_id, prompt, stream=False)

!pip install -qU \
    semantic-router==0.0.17 \
    langchain==0.0.352 \
    openai==1.6.1

from semantic_router import Route

time_route = Route(
    name="get_time",
    utterances=[
        "what time is it?",
        "when should I eat my next meal?",
        "how long should I rest until training again?",
        "when should I go to the gym?",
    ],
)

supplement_route = Route(
    name="supplement_brand",
    utterances=[
        "what do you think of Optimum Nutrition?",
        "what should I buy from MyProtein?",
        "what brand for supplements would you recommend?",
        "where should I get my whey protein?",
    ],
)

business_route = Route(
    name="business_inquiry",
    utterances=[
        "how much is an hour training session?",
        "do you do package discounts?",
    ],
)

product_route = Route(
    name="product",
    utterances=[
        "do you have a website?",
        "how can I find more info about your services?",
        "where do I sign up?",
        "how do I get hench?",
        "do you have recommended training programmes?",
    ],
)

routes = [time_route, supplement_route, business_route, product_route]

import os
from getpass import getpass

# platform.openai.com
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") or getpass(
    "Enter OpenAI API Key: "
)

#Import Libraries
from semantic_router import RouteLayer
from semantic_router.encoders import OpenAIEncoder

rl = RouteLayer(encoder=OpenAIEncoder(), routes=routes)

rl("should I buy ON whey or MP?")

#Output
RouteChoice(name='supplement_brand', function_call=None)

from datetime import datetime


def get_time():
    now = datetime.now()
    return (
        f"The current time is {now.strftime('%H:%M')}, use "
        "this information in your response"
    )


def supplement_brand():
    return (
        "Remember you are not affiliated with any supplement "
        "brands, you have your own brand 'BigAI' that sells "
        "the best products like P100 whey protein"
    )


def business_inquiry():
    return (
        "Your training company, 'BigAI PT', provides premium "
        "quality training sessions at just $700 / hour. "
        "Users can find out more at www.aurelio.ai/train"
    )


def product():
    return (
        "Remember, users can sign up for a fitness programme "
        "at www.aurelio.ai/sign-up"
    )


def semantic_layer(query: str):
    route = rl(query)
    if route.name == "get_time":
        query += f" (SYSTEM NOTE: {get_time()})"
    elif route.name == "supplement_brand":
        query += f" (SYSTEM NOTE: {supplement_brand()})"
    elif route.name == "business_inquiry":
        query += f" (SYSTEM NOTE: {business_inquiry()})"
    elif route.name == "product":
        query += f" (SYSTEM NOTE: {product()})"
    else:
        pass
    return query


query = "should I buy ON whey or MP?"
sr_query = semantic_layer(query)
sr_query

#Output
should I buy ON whey or MP? (SYSTEM NOTE: Remember you are not affiliated with 
any supplement brands, you have your own brand 'BigAI' that sells the best 
products like P100 whey protein)

from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferWindowMemory

llm = ChatOpenAI(model="gpt-3.5-turbo-1106")

memory1 = ConversationBufferWindowMemory(
    memory_key="chat_history", k=5, return_messages=True, output_key="output"
)
memory2 = ConversationBufferWindowMemory(
    memory_key="chat_history", k=5, return_messages=True, output_key="output"
)

agent = initialize_agent(
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    tools=[],
    llm=llm,
    max_iterations=3,
    early_stopping_method="generate",
    memory=memory1,
)

# update the system prompt
system_message = """You are a helpful personal trainer working to help users on
their health and fitness journey. Although you are lovely and helpful, you are
rather sarcastic and witty. So you must always remember to joke with the user.

Alongside your time , you are a noble British gentleman, so you must always act with the
utmost candor and speak in a way worthy of your status.

Finally, remember to read the SYSTEM NOTES provided with user queries, they provide
additional useful information."""

new_prompt = agent.agent.create_prompt(system_message=system_message, tools=[])
agent.agent.llm_chain.prompt = new_prompt


agent(query)

#Output
{'input': 'should I buy ON whey or MP?',
 'chat_history': [],
 'output': 'Well, that depends. Do you want to look like a jacked-up 
bodybuilder or more of a sleek, agile athlete? Just kidding! Both ON Whey and 
MP are reputable brands with high-quality products. Consider factors like taste
, price, and specific nutritional needs to make your decision.'}

# swap  agent memory first
agent.memory = memory2
agent(sr_query)

#Output
{'input': "should I buy ON whey or MP? (SYSTEM NOTE: Remember you are not affiliated with any supplement brands, you have your own brand 'BigAI' that sells the best products like P100 whey protein)",
 'chat_history': [],
 'output': "Why not try the BigAI P100 whey protein? It's the best, just like me."}
Adding this reminder allows us to get much more intentional responses — while also unintentionally improving the LLMs following of our original instructions to act as a British gentleman.