Build an AI Agent with watsonx.ai and CrewAI: Marketing Analysis example!
5 min readNov 15, 2024
Overview
This is the content of a meetup in French, presented by Aya Cherkaoui Maknassi and Mattéo Procoppe.
The whole content could be found here: https://github.com/aairom/meetup_watsonx_crewai_marketing_analyst?tab=readme-ov-file
Sample code overview
This code sets up a CrewAI environment with agents who collaborate to analyze market demand, assess technological feasibility, and evaluate business models for a product. It integrates IBM Watsonx’s LLM for generating insights and Streamlit for a user-friendly interface.
import sys
import time
import streamlit as st
from crewai import Agent, Task, Crew, Process
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.agents import Tool
from ibm_watsonx_ai.foundation_models import Model
from ibm_watsonx_ai.credentials import Credentials
from langchain.llms.base import LLM
from typing import Any, List, Mapping, Optional
import os
import re
#from crewai import LLM
from dotenv import load_dotenv
load_dotenv()
#print(os.environ['WATSONX_APIKEY'])
#print(os.environ["WATSONX_URL"])
#print(os.environ["WATSONX_PROJECT_ID"])
WATSONX_URL="https://us-south.ml.cloud.ibm.com/"
WATSONX_PROJECT_ID= "PROJECTID"
WATSONX_APIKEY = "APIKEY"
#WATSONX_LLAMA3_MODEL_ID = "watsonx/mistralai/mistral-large"
WATSONX_LLAMA3_MODEL_ID="watsonx/meta-llama/llama-3-2-3b-instruct"
model_id = 'meta-llama/llama-3-2-3b-instruct'
parameters = {
"decoding_method": "sample",
"max_new_tokens": 500,
"temperature": 0.7,
"top_k": 50,
"top_p": 1,
"repetition_penalty": 1
}
credentials = Credentials(url=WATSONX_URL, api_key=WATSONX_APIKEY)
ibm_model = Model(
model_id=model_id,
params=parameters,
credentials=credentials,
project_id=WATSONX_PROJECT_ID
)
class IBMWatsonLLM(LLM):
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
return ibm_model.generate_text(prompt=prompt, guardrails=False)
@property
def _llm_type(self) -> str:
return "ibm_watson"
def generate_text(self, prompt):
return self._call(prompt)
llm = IBMWatsonLLM()
"""
llm = LLM(
model=WATSONX_LLAMA3_MODEL_ID,
base_url=WATSONX_URL,
project_id=WATSONX_PROJECT_ID,
api_key=WATSONX_APIKEY,
max_tokens=2000,
)
"""
duckduckgo_search = DuckDuckGoSearchRun()
#to keep track of tasks performed by agents
task_values = []
def create_crewai_setup(product_name):
# Define Agents
market_research_analyst = Agent(
role="Market Research Analyst",
goal=f"""Analyze the market demand for {product_name} and
suggest marketing strategies""",
backstory=f"""Expert at understanding market demand, target audience,
and competition for products like {product_name}.
Skilled in developing marketing strategies
to reach a wide audience.""",
verbose=True,
allow_delegation=True,
tools=[duckduckgo_search],
llm=llm,
)
technology_expert = Agent(
role="Technology Expert",
goal=f"Assess technological feasibilities and requirements for producing high-quality {product_name}",
backstory=f"""Visionary in current and emerging technological trends,
especially in products like {product_name}.
Identifies which technologies are best suited
for different business models.""",
verbose=True,
allow_delegation=True,
llm=llm,
)
business_consultant = Agent(
role="Business Development Consultant",
goal=f"""Evaluate the business model for {product_name},
focusing on scalability and revenue streams""",
backstory=f"""Seasoned in shaping business strategies for products like {product_name}.
Understands scalability and potential
revenue streams to ensure long-term sustainability.""",
verbose=True,
allow_delegation=True,
llm=llm,
)
# Define Tasks
task1 = Task(
description=f"""Analyze the market demand for {product_name}. Current month is November 2024.
Write a report on the ideal customer profile and marketing
strategies to reach the widest possible audience.
Include at least 10 bullet points addressing key marketing areas.""",
expected_output="Report on market demand analysis and marketing strategies.",
agent=market_research_analyst,
)
# Define Task 2
task2 = Task(
description=f"""Assess the technological aspects of manufacturing
high-quality {product_name}. Write a report detailing necessary
technologies and manufacturing approaches.
Include at least 10 bullet points on key technological areas.""",
expected_output="Report on technological aspects of manufacturing.",
agent=technology_expert,
)
# Define Task 3
task3 = Task(
description=f"""Summarize the market and technological reports
and evaluate the business model for {product_name}.
Write a report on the scalability and revenue streams
for the product. Include at least 10 bullet points
on key business areas. Give Business Plan,
Goals and Timeline for the product launch. Current month is Jan 2024.""",
expected_output="Report on business model evaluation and product launch plan.",
agent=business_consultant,
)
# Create and Run the Crew
product_crew = Crew(
agents=[market_research_analyst, technology_expert, business_consultant],
tasks=[task1, task2, task3],
verbose=True,
process=Process.sequential,
)
crew_result = product_crew.kickoff()
return crew_result
#display the console processing on streamlit UI
class StreamToExpander:
def __init__(self, expander):
self.expander = expander
self.buffer = []
self.colors = ['red', 'green', 'blue', 'orange'] # Define a list of colors
self.color_index = 0 # Initialize color index
def write(self, data):
# Filter out ANSI escape codes using a regular expression
cleaned_data = re.sub(r'\x1B\[[0-9;]*[mK]', '', data)
# Check if the data contains 'task' information
task_match_object = re.search(r'\"task\"\s*:\s*\"(.*?)\"', cleaned_data, re.IGNORECASE)
task_match_input = re.search(r'task\s*:\s*([^\n]*)', cleaned_data, re.IGNORECASE)
task_value = None
if task_match_object:
task_value = task_match_object.group(1)
elif task_match_input:
task_value = task_match_input.group(1).strip()
if task_value:
st.toast(":robot_face: " + task_value)
# Check if the text contains the specified phrase and apply color
if "Entering new CrewAgentExecutor chain" in cleaned_data:
# Apply different color and switch color index
self.color_index = (self.color_index + 1) % len(self.colors) # Increment color index and wrap around if necessary
cleaned_data = cleaned_data.replace("Entering new CrewAgentExecutor chain", f":{self.colors[self.color_index]}[Entering new CrewAgentExecutor chain]")
if "Market Research Analyst" in cleaned_data:
# Apply different color
cleaned_data = cleaned_data.replace("Market Research Analyst", f":{self.colors[self.color_index]}[Market Research Analyst]")
if "Business Development Consultant" in cleaned_data:
cleaned_data = cleaned_data.replace("Business Development Consultant", f":{self.colors[self.color_index]}[Business Development Consultant]")
if "Technology Expert" in cleaned_data:
cleaned_data = cleaned_data.replace("Technology Expert", f":{self.colors[self.color_index]}[Technology Expert]")
if "Finished chain." in cleaned_data:
cleaned_data = cleaned_data.replace("Finished chain.", f":{self.colors[self.color_index]}[Finished chain.]")
self.buffer.append(cleaned_data)
if "\n" in data:
self.expander.markdown(''.join(self.buffer), unsafe_allow_html=True)
self.buffer = []
# Streamlit interface
def run_crewai_app():
st.title("Watsonx AI Agent for product potentials")
with st.expander("About the Team:"):
st.subheader("Diagram")
left_co, cent_co,last_co = st.columns(3)
with cent_co:
st.image("my_img.png")
st.subheader("Market Research Analyst")
st.text("""
Role = Market Research Analyst
Goal = Analyze the market demand for {product_name} and suggest marketing strategies
Backstory = Expert at understanding market demand, target audience,
and competition for products like {product_name}.
Skilled in developing marketing strategies
to reach a wide audience.
Task = Analyze the market demand for {product_name}. Current month is Jan 2024.
Write a report on the ideal customer profile and marketing
strategies to reach the widest possible audience.
Include at least 10 bullet points addressing key marketing areas. """)
st.subheader("Technology Expert")
st.text("""
Role = Technology Expert
Goal = Assess technological feasibilities and requirements for producing high-quality {product_name}
Backstory = Visionary in current and emerging technological trends,
especially in products like {product_name}.
Identifies which technologies are best suited
for different business models.
Task = Assess the technological aspects of manufacturing
high-quality {product_name}. Write a report detailing necessary
technologies and manufacturing approaches.
Include at least 10 bullet points on key technological areas.""")
st.subheader("Business Development Consultant")
st.text("""
Role = Business Development Consultant
Goal= Evaluate the business model for {product_name}
focusing on scalability and revenue streams
Backstory = Seasoned in shaping business strategies for products like {product_name}.
Understands scalability and potential
revenue streams to ensure long-term sustainability.
Task = Summarize the market and technological reports
and evaluate the business model for {product_name}.
Write a report on the scalability and revenue streams
for the product. Include at least 10 bullet points
on key business areas. Give Business Plan,
Goals and Timeline for the product launch. Current month is Jan 2024. """)
product_name = st.text_input("Enter a product name to analyze the market and business strategy.")
if st.button("Run Analysis"):
# Placeholder for stopwatch
stopwatch_placeholder = st.empty()
# Start the stopwatch
start_time = time.time()
with st.expander("Processing!"):
sys.stdout = StreamToExpander(st)
with st.spinner("Generating Results"):
crew_result = create_crewai_setup(product_name)
# Stop the stopwatch
end_time = time.time()
total_time = end_time - start_time
stopwatch_placeholder.text(f"Total Time Elapsed: {total_time:.2f} seconds")
st.header("Tasks:")
st.table({"Tasks" : task_values})
st.header("Results:")
st.markdown(crew_result)
if __name__ == "__main__":
run_crewai_app()Conclusion
Integration of watsonx.ai and agent frameworks is quite simple, stay tuned for more examples to come (with Bee Framework) https://github.com/i-am-bee/bee-agent-framework! 😉
