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from langchain.llms import OpenAI
from langchain.agents import TextProcessingAgent
from dspy.agents import Agent # Base class for custom agent
from dspy.utils import spawn_processes # Distributed computing utility
# API key **ADD A KEY OR LOCAL LLM PATHWAY**
openai = OpenAI(api_key="KEY")
# User prompt intake
user_prompt = "What are the potential strategies to increase my online sales?"
# Synthetic data generation (using Langchain's Text-Davinci-003 model for illustration)
def generate_synthetic_data(prompt):
response = openai.complete(prompt=prompt, engine="text-davinci-003", max_tokens=100)
return response.choices[0].text
# Custom data processing agent (inheriting from DSPy's Agent class) [TONIC PLEASE HELP LOL]
class DataProcessingAgent(Agent):
def __init__(self):
super().__init__()
def process(self, data):
# Implement our custom data processing logic here (e.g., feature engineering)
processed_data = data.lower().strip()
return processed_data
# Dynamic team composition (replace with logic for dynamic team creation)
team = [
OpenAI(api_key="YOUR_OPENAI_API_KEY", engine="text-davinci-003"), # LLM agent
DataProcessingAgent(), # Custom data processing agent
]
# Prompt and data flow refinement
combined_data = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"
# [TONIC PLEASE HELP LOL]
for agent in team:
combined_data = agent.process(combined_data)
# Multimedia output production (using Langchain's Text-Davinci-003 as default) because I don't know how to implement DSPy properly yet [TONIC PLEASE HELP LOL]
def produce_outputs(processed_data):
# Use Langchain for LLM-based analysis, recommendations, etc. Should this be updated to DSPy too? again:[TONIC PLEASE HELP LOL]
analysis = openai.complete(prompt=f"Analyze {processed_data}", engine="text-davinci-003", max_tokens=200)
recommendations = openai.complete(prompt=f"Recommend strategies based on {processed_data}", engine="text-davinci-003", max_tokens=100)
# Replace with your visualization logic
visualization = None
return analysis.choices[0].text, recommendations.choices[0].text, visualization
# Synth data generation using DSPy's distributed computing capabilities (taken partially from DSPY documentation)
def generate_synthetic_data_distributed(prompt, num_nodes=3):
# Spawn synthetic data generation processes across multiple nodes
processes = [spawn_processes(generate_synthetic_data, [f"Simulate scenarios for {prompt}"]) for _ in range(num_nodes)]
# Collect the results from each node
synthetic_data_list = []
for process in processes:
synthetic_data_list.extend(process.get())
# Combine the results and return the synthetic data
return "\n".join(synthetic_data_list)
# Generate synthetic data using DSPy's distributed computing capabilities. Again:[TONIC PLEASE HELP LOL]
synthetic_data = generate_synthetic_data_distributed(user_prompt)
# Generate outputs
report, recommendations, visualization = produce_outputs(combined_data)
# Print the results
print("Report:")
print(report)
print("\nRecommendations:")
print(recommendations)
print("\nVisualization:")
print(visualization) # Currently "None" due to placeholder 'visualization' |