Exercise#1 Single step agent

from dotenv import load_dotenv
import sys
import json

from langchain.prompts import PromptTemplate

# Load the file that contains the API keys - OPENAI_API_KEY
load_dotenv('C:\\Users\\raj\\.jupyter\\.env')

# setting path
sys.path.append('../')

from utils.create_chat_llm import create_gpt_chat_llm, create_cohere_chat_llm, create_anthropic_chat_llm, create_hugging_face_chat_llm

# Try with GPT
llm = create_gpt_chat_llm({"temperature":0.1})

# Tempplate
template = """
You are a helpful assistant capable of answering questions on various topics. 
You must not use your internal knowledge or information to answer questions.

Instructions:

Think step-by-step to create a plan.
Use only the following available tools to find information.
Tools Available:

{tools}
Guidelines for Responses:

Format 1: If the question cannot be answered with the available tools, use this format:
{{"answer": "No appropriate tool available"}}

Format 2: If you need to run tools to obtain the information, use this format:
{{"actions": [{{ "action" : tool name, "arguments" : dictionary of argument values}}}}]

Format 3: If you can answer the question using the responses from the tools, use this format:
{{"answer": "your response to the question", "explanation": "provide your explanation here"}}


Avoid any preamble; respond directly using one of the specified JSON formats.
Question:

{question}
Tool Responses:

{tool_responses}
Your Response:

"""

prompt = PromptTemplate(
    template = template,
    input_variables = ['tools', 'question', 'tool_responses']
)

## Tool 1 for stocks
def  company_stock_price(stock_symbol: str) -> float:
    if stock_symbol.upper()=='AAPL':
        return  {"price": 192.32}
    elif stock_symbol.upper()=='MSFT':
        return  {"price": 415.60}
    elif stock_symbol.upper()=='AMZN':
        return  {"price": 183.60}
    else:
        return {"price": "unknown"}


stock_tool_description = {
    "name" : "company_stock_price",
    "description": "This tool returns the last known stock price for a company based on its ticker symbol",
    "arguments": [
        {"stock_symbol" : "stock ticker symbol for the company"}
    ],
    "response": "last known stock price"
}

## Tool 2 for city weather
def city_weather(city: str) -> int:
    if city.lower() == "new york":
        return {"temperature": 68, "forecast": "rain"}
    elif city.lower() == "paris":
        return {"temperature": 73, "forecast": "sunny"}
    elif city.lower() == "london":
        return {"temperature": 82, "forecast": "cloudy"}
    else:
        return {"temperature": "unknown"}
        
city_weather_tool_description = {
    "name" : "city_weather",
    "description": "This tool returns the current temperature and forecast for the given city",
    "arguments": [
        {"city" : "name of the city"}
    ],
    "response": "current temperature & forecast"
}

# Maintain the tools in a map for invocation by th eagent
tools = [stock_tool_description, city_weather_tool_description]
tools_map = {
    'company_stock_price': company_stock_price,
    'city_weather' : city_weather
}

def  invoke_agent(question):
    
    # Setup the prompt. Since no tool has been invoked set action_response as blank
    query = prompt.format(tools=tools, question=question, tool_responses="")

    # STEP-1 Invoke LLM for a plan i.e., tools to execute
    # ===================================================
    # Invoke LLM to get the tools to be run
    # The response consist of tools that LLM requires to be executed
    response = llm.invoke(query)

    # Convert response to JSON object. The response is of type AIMessage
    response_json = json.loads(response.content)

    # print the response
    print("STEP-1:", response_json, "\n")

    # STEP-2  Invoke the tool(s) suggested by LLM
    # ===========================================
    # LLM may respond with an answer 
    # It may happen if LLM determines that no tool is available for responding to the question
    action_responses=[]
    if "answer" in response_json:
        # If the answer is already there
        return {"answer" : response_json["answer"]}
    elif "actions" in response_json:
        # If the LLM has suggested tools to be executed, execute the tools
        action_responses = invoke_tools(response_json)

    # Print the tool responses
    print("STEP-2", "   Agent tool invocation responses :", action_responses, "\n")

    # STEP-3  Invoke LLM to generate final response
    # =============================================
    # Now send the action responses to LLM for generating the answer
    query = prompt.format(tools=tools, question=question,tool_responses=action_responses)
    response = llm.invoke(query)

    # print the response
    print("STEP-3:", response_json, "\n")

    # Convert response to JSON object
    response_json = json.loads(response.content)

    # Extract the answer from the response    
    if "answer" in response_json:
        return response_json["answer"]
    else:
        return ("Can't generate as there is no response from the tool!!!")

# Utility function to process the actions receieved from the LLM
# Responses from the tools are expected to be in JSON format 
def   invoke_tools(response):
    action_responses = []
    if len(response["actions"]) == 0:
        print('question cannot be answered as there is no tool to use !!!')
        exit
    else:
        for action in response["actions"]:

            # Get the function pointer from the map
            action_function = tools_map[action["action"]]
            
            # Invoke the tool/function with the arguments as suggested by the LLM
            action_invoke_result = action_function(**action["arguments"])
            action["response"] = action_invoke_result

            # Add the response to the action attribute
            action_responses.append(action)

    # Return the response      
    return action_responses

question = "Which of these cities is hotter, Paris or London"
# question = "I am visting paris, should i carry an umbrella?"

response = invoke_agent(question)

print("Final response::",response)

STEP-1: {'actions': [{'action': 'city_weather', 'arguments': {'city': 'Paris'}}, {'action': 'city_weather', 'arguments': {'city': 'London'}}]} 

STEP-2    Agent tool invocation responses : [{'action': 'city_weather', 'arguments': {'city': 'Paris'}, 'response': {'temperature': 73, 'forecast': 'sunny'}}, {'action': 'city_weather', 'arguments': {'city': 'London'}, 'response': {'temperature': 82, 'forecast': 'cloudy'}}] 

STEP-3: {'actions': [{'action': 'city_weather', 'arguments': {'city': 'Paris'}, 'response': {'temperature': 73, 'forecast': 'sunny'}}, {'action': 'city_weather', 'arguments': {'city': 'London'}, 'response': {'temperature': 82, 'forecast': 'cloudy'}}]} 

Final response:: London is hotter than Paris

question = "search the web for articles on 'large language models'"

response = invoke_agent(question)

print("Final response::",response)

STEP-1: {'answer': 'No appropriate tool available'} 

Final response:: {'answer': 'No appropriate tool available'}

question = """
I am interested in investing in one of these stocks: AAPL, MSFT, or AMZN.

Decision Criteria:

Sunny Weather: Choose the stock with the lowest price.
Raining Weather: Choose the stock with the highest price.
Cloudy Weather: Do not buy any stock.
Location:

I am currently in New York.
Question:

Based on the current weather in New York and the stock prices, which stock should I invest in?
"""

response = invoke_agent(question)

print("Final response:",response)

STEP-1: {'actions': [{'action': 'city_weather', 'arguments': {'city': 'New York'}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AAPL'}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'MSFT'}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AMZN'}}]}

STEP-2    Agent tool invocation responses : [{'action': 'city_weather', 'arguments': {'city': 'New York'}, 'response': {'temperature': 68, 'forecast': 'rain'}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AAPL'}, 'response': {'price': 192.32}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'MSFT'}, 'response': {'price': 415.6}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AMZN'}, 'response': {'price': 183.6}}]

STEP-3: {'actions': [{'action': 'city_weather', 'arguments': {'city': 'New York'}, 'response': {'temperature': 68, 'forecast': 'rain'}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AAPL'}, 'response': {'price': 192.32}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'MSFT'}, 'response': {'price': 415.6}}, {'action': 'company_stock_price', 'arguments': {'stock_symbol': 'AMZN'}, 'response': {'price': 183.6}}]}

Final response: AAPL

question = "I have only $200, can i buy GOOG stock?"

response = invoke_agent(question)

print("Final response:",response)

STEP-1: {'actions': [{'action': 'company_stock_price', 'arguments': {'stock_symbol': 'GOOG'}}]} 

STEP-2    Agent tool invocation responses : [{'action': 'company_stock_price', 'arguments': {'stock_symbol': 'GOOG'}, 'response': {'price': 'unknown'}}] 

STEP-3: {'actions': [{'action': 'company_stock_price', 'arguments': {'stock_symbol': 'GOOG'}, 'response': {'price': 'unknown'}}]} 

Final response: Can't generate as there is no response from the tool!!!