Ollama Crewai Promp Example for stock and product analysis using llama38b model

by jamied_uk 30th July 2024, 11:30

start_quest.sh

Code:: #!/bin/bash # Print a blank line echo "" # Create a Python virtual environment in the "venv" directory if not already created if [ ! -d "venv" ]; then python -m venv venv fi # Activate the virtual environment source venv/bin/activate # Upgrade pip to the latest version (optional) #pip install --upgrade pip # Install required packages from requirements.txt (if needed) #pip install -r requirements.txt # Run the Python script with story.txt as an argument if provided if [ $# -eq 1 ]; then python questV3.py "$1" else # Run the Python script without any argument (interactive mode) python questV3.py fi # Deactivate the virtual environment deactivate

read products.txt

the file contents of that file is the prompt

Code:: Analyze product and stock data to identify profitable opportunities. Compare current data with historical data to find the most profitable new products and stocks. Allow for optional focus on specific categories or sectors, if provided. try using the internet without using appi keys or 3rd arty scrapers only use brave browser and copy and paste the text emulating ctrl c and ctrl v and paste into file in files folder inside the projects folder and record as much data as you can manually grabbing results from searches and closing any cookie boxes that may appear for google search page for example and others like it! when you have the top results start analysis and comparisons and find the next product to invest in (find the top 3 best investments / stocks aswell as an extra

Code:: # # (c)J~Net 2024 # # ./start_quest.sh # # https://jnet.forumotion.com/t2016-rag-with-recovery-project-builder-crewai-ollama#3108 import sys import os import time from crewai import Agent, Task from dotenv import load_dotenv from langchain_openai import ChatOpenAI import logging load_dotenv() llm=ChatOpenAI( model="crewai-llama3:8b", # model="crewai-dolphin-llama3", base_url="http://localhost:11434/v1" ) # Configure logging logging.basicConfig(filename='script.log', level=logging.ERROR) # Ensure the 'files' directory exists os.makedirs('files', exist_ok=True) def read_existing_steps(): steps=[] for filename in sorted(os.listdir('files')): if filename.startswith('step') and filename.endswith('.txt'): with open(f'files/{filename}', 'r') as file: steps.append(file.read()) latest_output="" if os.path.exists('files/latest_output.txt'): with open('files/latest_output.txt', 'r') as file: latest_output=file.read() return steps, latest_output def save_step_result(step_number, result): step_filename=f"files/step{step_number}.txt" with open(step_filename, 'w') as step_file: step_file.write(result) def execute_task_chain(content, agent, start_step=0): tasks=content.splitlines() for index, task_description in enumerate(tasks[start_step:], start=start_step + 1): if task_description.strip(): try: task_config={ 'description': task_description.strip(), 'expected_output': 'No specific output expected', 'agent': agent } task=Task(**task_config) result=task.execute() print(result) # Save result to latest_output.txt with open('files/latest_output.txt', 'a') as f: f.write(result + '\n') # Save step result save_step_result(index, result) except Exception as e: logging.error(f"Error occurred during task execution: {e}", exc_info=True) print(f"Error occurred during task execution: {e}") def generate_filename(base_name): count=1 while True: filename=f"files/{base_name}-{count}" if not os.path.exists(filename): return filename count += 1 def create_file(filename, content): try: with open(filename, 'w') as file: file.write(content) return f"File '{filename}' created successfully." except Exception as e: logging.error(f"Error occurred while creating the file: {e}", exc_info=True) return f"An error occurred while creating the file: {e}" def create_agent(role, goal, backstory): if os.path.exists('goal.txt'): with open('goal.txt', 'r') as goal_file: goal=goal_file.read().strip() return Agent(role=role, goal=goal, backstory=backstory, allow_delegation=False, verbose=True, llm=llm) def main(): existing_steps, latest_output=read_existing_steps() start_step=len(existing_steps) while True: prompt=input("Enter your command (type 'exit' to quit): ").strip() if prompt.lower() == 'exit': break general_agent=create_agent( role='Requirements Manager', goal="""Handle any user input commands including reading a file and creating files for apps based on user input. For file reading, read the contents and display them. For file creation, generate a filename based on user input or a pattern and create the file with the specified content. Respond as if you were an expert developer providing file creation services on demand.""", backstory="""You are an expert developer with a strong background in software engineering. You provide high quality, thorough, and efficient file creation services based on user requirements.""" ) if prompt.lower().startswith('read '): start_time=time.time() filename=prompt[len('read '):].strip() content=read_file(filename) # Set the new goal description to the content read from the file general_agent.goal=content.strip() # Write the updated goal to goal.txt create_file('goal.txt', general_agent.goal) execute_task_chain(content, general_agent, start_step) end_time=time.time() elapsed_time=end_time - start_time elapsed_hours, rem=divmod(elapsed_time, 3600) elapsed_minutes, elapsed_seconds=divmod(rem, 60) print(f"Execution time: {int(elapsed_hours)}h {int(elapsed_minutes)}m {int(elapsed_seconds)}s") elif prompt.lower().startswith('create file '): parts=prompt[len('create file '):].split(' with content ', 1) if len(parts) == 2: filename, content=parts filename=generate_filename(filename.strip()) result=create_file(filename, content.strip()) print(result) # Save result to latest_output.txt with open('files/latest_output.txt', 'a') as f: f.write(result + '\n') else: print("Invalid command format. Use: create file <filename> with content <content>") else: # Execute dynamic task based on user input task_description=prompt try: task=Task(description=task_description.strip(), agent=general_agent) result=task.execute() print(result) # Save result to latest_output.txt with open('files/latest_output.txt', 'a') as f: f.write(result + '\n') except Exception as e: logging.error(f"Error occurred during task execution: {e}", exc_info=True) print(f"Error occurred during task execution: {e}") def read_file(filename): try: with open(filename, 'r') as file: content=file.read() return content except FileNotFoundError: logging.error(f"File '{filename}' not found.") return f"File '{filename}' not found." if __name__ == "__main__": if len(sys.argv) > 1: start_time=time.time() filename=sys.argv[1] content=read_file(filename) existing_steps, latest_output=read_existing_steps() start_step=len(existing_steps) general_agent=create_agent( role='Requirements Manager', goal="""Handle any user input commands including reading a file and creating files for apps based on user input. For file reading, read the contents and display them. For file creation, generate a filename based on user input or a pattern and create the file with the specified content. Respond as if you were an expert developer providing file creation services on demand.""", backstory="""You are an expert developer with a strong background in software engineering. You provide high quality, thorough, and efficient file creation services based on user requirements.""" ) execute_task_chain(content, general_agent, start_step) end_time=time.time() elapsed_time=end_time - start_time elapsed_hours, rem=divmod(elapsed_time, 3600) elapsed_minutes, elapsed_seconds=divmod(rem, 60) print(f"Execution time: {int(elapsed_hours)}h {int(elapsed_minutes)}m {int(elapsed_seconds)}s") else: main()

by jamied_uk 30th July 2024, 15:57

Updated and model file setups

app.box.com/s/756gkfy7hbhq0vfrt3ovz82tedlehjdq

Ollama Crewai Promp Example for stock and product analysis using llama38b model

Ollama Crewai Promp Example for stock and product analysis using llama38b model

Re: Ollama Crewai Promp Example for stock and product analysis using llama38b model