File size: 5,203 Bytes
bf63887 911fa42 302bb07 a88c1e5 302bb07 e9ea4f8 302bb07 2590bac 302bb07 6799d77 4cf81ca a88c1e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
import gradio as gr
# This may be in the wrong spot
def generate_outputs(user_prompt):
report, recommendations, visualization = produce_outputs(combined_data)
return report, recommendations, visualization
from langchain_openai 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 was here but was folded into Gradio interface lines
# 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
message = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"
# Agents Group Chat Finite State Machine
class GroupChatFSM:
def __init__(self, teams_config):
"""
Initialize with configurations for teams.
"""
self.teams = {team_name: self.Team(team_agents) for team_name, team_agents in teams_config.items()}
self.states = ["waiting", "interacting", "finalizing"]
self.current_state = "waiting"
def transition(self, to_state):
"""
Transition the state of the group chat based on FSM rules.
"""
if to_state in self.states:
self.current_state = to_state
else:
raise ValueError("Invalid state transition attempted.")
def broadcast(self, message):
"""
Broadcast a message to all teams based on the current FSM state.
"""
if self.current_state == "interacting":
responses = {team_name: team.broadcast(message) for team_name, team in self.teams.items()}
return responses
else:
return "The group chat is not in an interacting state."
class Team:
def __init__(self, agents_config):
self.agents = [self.Agent(agent_config) for agent_config in agents_config]
def broadcast(self, message):
responses = [agent.respond(message) for agent in self.agents]
return responses
class Agent:
def __init__(self, config):
self.agent_name = config['agent_name']
self.api_key = config['api_key']
self.model = config['model']
def respond(self, message):
return f"{self.agent_name} responding with {self.model}"
# 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 for testing pyrposes
print("Report:")
print(report)
print("\nRecommendations:")
print(recommendations)
print("\nVisualization:")
print(visualization) # Currently "None" due to placeholder 'visualization'
# Moved from other gradio chunk near the top
gr.Interface(fn=generate_outputs, inputs=user_prompt, outputs=["text", "text", "image"]).launch()
|