Rough draft first pass
Browse files
app.py
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from langchain.llms import OpenAI
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from langchain.agents import TextProcessingAgent
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from dspy.agents import Agent # Base class for custom agent
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from dspy.utils import spawn_processes # Distributed computing utility
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# API key **ADD A KEY OR LOCAL LLM PATHWAY**
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openai = OpenAI(api_key="KEY")
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# User prompt intake
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user_prompt = "What are the potential strategies to increase my online sales?"
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# Synthetic data generation (using Langchain's Text-Davinci-003 model for illustration)
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def generate_synthetic_data(prompt):
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response = openai.complete(prompt=prompt, engine="text-davinci-003", max_tokens=100)
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return response.choices[0].text
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# Custom data processing agent (inheriting from DSPy's Agent class) [TONIC PLEASE HELP LOL]
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class DataProcessingAgent(Agent):
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def __init__(self):
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super().__init__()
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def process(self, data):
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# Implement our custom data processing logic here (e.g., feature engineering)
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processed_data = data.lower().strip()
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return processed_data
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# Dynamic team composition (replace with logic for dynamic team creation)
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team = [
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OpenAI(api_key="YOUR_OPENAI_API_KEY", engine="text-davinci-003"), # LLM agent
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DataProcessingAgent(), # Custom data processing agent
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]
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# Prompt and data flow refinement
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combined_data = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"
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# [TONIC PLEASE HELP LOL]
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for agent in team:
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combined_data = agent.process(combined_data)
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# 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]
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def produce_outputs(processed_data):
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# Use Langchain for LLM-based analysis, recommendations, etc. Should this be updated to DSPy too? again:[TONIC PLEASE HELP LOL]
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analysis = openai.complete(prompt=f"Analyze {processed_data}", engine="text-davinci-003", max_tokens=200)
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recommendations = openai.complete(prompt=f"Recommend strategies based on {processed_data}", engine="text-davinci-003", max_tokens=100)
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# Replace with your visualization logic
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visualization = None
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return analysis.choices[0].text, recommendations.choices[0].text, visualization
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# Synth data generation using DSPy's distributed computing capabilities (taken partially from DSPY documentation)
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def generate_synthetic_data_distributed(prompt, num_nodes=3):
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# Spawn synthetic data generation processes across multiple nodes
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processes = [spawn_processes(generate_synthetic_data, [f"Simulate scenarios for {prompt}"]) for _ in range(num_nodes)]
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# Collect the results from each node
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synthetic_data_list = []
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for process in processes:
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synthetic_data_list.extend(process.get())
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# Combine the results and return the synthetic data
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return "\n".join(synthetic_data_list)
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# Generate synthetic data using DSPy's distributed computing capabilities. Again:[TONIC PLEASE HELP LOL]
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synthetic_data = generate_synthetic_data_distributed(user_prompt)
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# Generate outputs
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report, recommendations, visualization = produce_outputs(combined_data)
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# Print the results
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print("Report:")
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print(report)
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print("\nRecommendations:")
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print(recommendations)
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print("\nVisualization:")
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print(visualization) # Currently "None" due to placeholder 'visualization'
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