├── .env
├── prompts.txt
├── requirements.txt
└── techx_workshop.py


/.env:
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1 | WATSONX_PROJECTID=
2 | WATSONX_APIKEY=
3 | SERPER_API_KEY=


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/prompts.txt:
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 1 | "Senior Finance Analyst"
 2 | "Research company financials in order to produce insights."
 3 | "You are a veteran finance analyst with a background in banking and analysis."
 4 | 
 5 | "Senior Python Engineer"
 6 | "Build, Test and Execute code for the research team"
 7 | "You are a veteran Python engineer with a background in computer science physics."
 8 | 
 9 | 
10 | find me a list of promising AI companies to invest in. 
11 | a summary list with details of the company. use the yfinance python library to get financial metrics including EPS and P/E ratio over the last financial year. 
12 | 
13 | a summary list with details of the company. use the python tool to calculate financial metrics including EPS and P/E ratio.


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/requirements.txt:
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1 | crewai==0.22.5
2 | crewai-tools==0.0.15
3 | langchain-ibm==0.1.3


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/techx_workshop.py:
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 1 | from dotenv import load_dotenv
 2 | import os
 3 | from colorama import init, Fore
 4 | 
 5 | # Bring in LLM class
 6 | from langchain_ibm import WatsonxLLM
 7 | 
 8 | # CrewAI
 9 | from crewai_tools import SerperDevTool
10 | from crewai import Crew, Agent, Task
11 | 
12 | params = {"decoding_method": "greedy", "max_new_tokens": 1000}
13 | 
14 | llm = WatsonxLLM(
15 |     model_id="mistralai/mistral-large",
16 |     project_id=os.environ["WATSONX_PROJECTID"],
17 |     url="https://us-south.ml.cloud.ibm.com",
18 |     params=params,
19 | )
20 | 
21 | search = SerperDevTool()
22 | researcher = Agent(
23 |     llm=llm,
24 |     function_calling_llm=llm,
25 |     role="Senior Finance Analyst",
26 |     goal="Research company financials in order to produce insights.",
27 |     backstory="You are a veteran finance analyst with a background in banking and analysis.",
28 |     verbose=True,
29 |     allow_delegation=True,
30 |     tools=[search],
31 | )
32 | # Agent 2
33 | developer = Agent(
34 |     llm=llm,
35 |     function_calling_llm=llm,
36 |     role="Senior Python Engineer",
37 |     goal="Build, Test and Execute code for the research team",
38 |     backstory="You are a veteran Python engineer with a background in computer science and physics.",
39 |     verbose=True,
40 |     allow_delegation=True,
41 |     allow_code_execution=True,
42 | )
43 | 
44 | if __name__ == "__main__":
45 |     while True:
46 |         description = input(
47 |             Fore.YELLOW + "Enter your prompt here, type \quit to exit: " + Fore.RESET
48 |         )
49 |         expected_output = input(
50 |             Fore.YELLOW + "Whats your desired output: " + Fore.RESET
51 |         )
52 | 
53 |         if description.lower() == "\quit":
54 |             print(Fore.RED + "Exiting, catch ya later." + Fore.RESET)
55 |             break
56 | 
57 |         task1 = Task(
58 |             description=description, expected_output=expected_output, agent=researcher
59 |         )
60 |         crew = Crew(
61 |             agents=[researcher, developer],
62 |             tasks=[task1],
63 |             verbose=True,
64 |         )
65 | 
66 |         # Send to llm
67 |         response = crew.kickoff()
68 |         print(Fore.LIGHTMAGENTA_EX + response + Fore.RESET)
69 | 


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