Refactor with Personas Integration

help me write a prompt I can give to continue.dev to help refactor a main.py file so that it will load the personas generated by this project: https://github.com/kliewerdaniel/ngpg05-2, this is the main.py file: # main.py import click import os import networkx as nx from langchain import PromptTemplate, LLMChain from langchain.llms import Ollama import json from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["http://localhost:3000"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.post("/api/process-graph") async def process_graph_endpoint(request: dict): iterations = request.get('iterations', 1) # Call your existing process_graph function result = process_graph(G, iterations) # You'll need to modify this to return the graph data return result # Define the Node class class Node: def __init__(self, node_id, prompt_text, persona_name): self.id = node_id self.prompt_text = prompt_text self.response_text = None self.context = "" self.persona_name = persona_name self.persona_attributes = {} # Initialize the graph G = nx.DiGraph() def build_graph(nodes_info, edges_info): G = nx.DiGraph() nodes = {} # Create nodes for node_info in nodes_info: node_id = node_info['id'] prompt_text = node_info['prompt_text'] persona_name = node_info['persona_name'] node = Node(node_id, prompt_text, persona_name) G.add_node(node_id, data=node) nodes[node_id] = node # Add edges for edge in edges_info: G.add_edge(edge['from'], edge['to']) return G def process_graph(G, iterations): """Process the graph for the specified number of iterations.""" for iteration in range(iterations): print(f"\nProcessing iteration {iteration + 1}/{iterations}") # Store previous responses for context previous_responses = {} for node_id in G.nodes(): node = G.nodes[node_id]['data'] if node.response_text: previous_responses[node_id] = node.response_text # Process each node in topological order for node_id in nx.topological_sort(G): node = G.nodes[node_id]['data'] if node.persona_name != "Analyst": node.context = collect_context(node_id, G, iteration, previous_responses) node.response_text = generate_response(node, iteration) update_markdown(node, iteration) else: analyze_responses(node, G, iteration) def load_personas(persona_dir): personas = {} for filename in os.listdir(persona_dir): if filename.endswith('.json'): filepath = os.path.join(persona_dir, filename) with open(filepath, 'r', encoding='utf-8') as f: persona_data = json.load(f) name = persona_data.get('name') if name: personas[name] = persona_data return personas # Load personas persona_dir = 'personas' # Directory where persona JSON files are stored personas = load_personas(persona_dir) # Function to collect context from predecessor nodes def collect_context(node_id, G, iteration, previous_responses): """Collect context including previous iterations.""" predecessors = list(G.predecessors(node_id)) context = "" # Add context from previous iterations if they exist if iteration > 0: context += f"\nPrevious iteration responses:\n" for pred_id in predecessors: if pred_id in previous_responses: pred_node = G.nodes[pred_id]['data'] context += f"From {pred_node.persona_name} (previous round):\n{previous_responses[pred_id]}\n\n" # Add context from current iteration context += f"\nCurrent iteration responses:\n" for pred_id in predecessors: pred_node = G.nodes[pred_id]['data'] if pred_node.response_text: context += f"From {pred_node.persona_name}:\n{pred_node.response_text}\n\n" return context # Function to generate responses using LangChain and Ollama def generate_response(node, iteration): """Generate response with awareness of the current iteration.""" persona = personas.get(node.persona_name) if not persona: raise ValueError(f"Persona '{node.persona_name}' not found.") node.persona_attributes = persona system_prompt = build_system_prompt(persona) # Modify the prompt to include iteration information iteration_prompt = f"This is round {iteration + 1} of the conversation. " if iteration > 0: iteration_prompt += "Please consider the previous responses in your reply. " prompt_template = PromptTemplate( input_variables=["system_prompt", "iteration_prompt", "context", "prompt"], template="{system_prompt}\n\n{iteration_prompt}\n\n{context}\n\n{prompt}" ) llm = Ollama( base_url="http://localhost:11434", model="qwq", ) chain = LLMChain(llm=llm, prompt=prompt_template) response = chain.run( system_prompt=system_prompt, iteration_prompt=iteration_prompt, context=node.context, prompt=node.prompt_text ) return response def build_system_prompt(persona): # Construct descriptive sentences based on persona attributes # We'll focus on key attributes for brevity name = persona.get('name', 'The speaker') tone = persona.get('tone', 'neutral') sentence_structure = persona.get('sentence_structure', 'varied') vocabulary_complexity = persona.get('vocabulary_complexity', 5) formality_level = persona.get('formality_level', 5) pronoun_preference = persona.get('pronoun_preference', 'third-person') language_abstraction = persona.get('language_abstraction', 'mixed') # Create a description description = ( f"You are {name}, writing in a {tone} tone using {sentence_structure} sentences. " f"Your vocabulary complexity is {vocabulary_complexity}/10, and your formality level is {formality_level}/10. " f"You prefer {pronoun_preference} narration and your language abstraction is {language_abstraction}." ) # Include any other attributes as needed # ... return description # Function to log interactions to a markdown file def update_markdown(node, iteration): """Update markdown file with iteration information.""" with open("conversation.md", "a", encoding="utf-8") as f: f.write(f"## Iteration {iteration + 1} - Node {node.id}: {node.persona_name}\n\n") f.write(f"**Prompt:**\n\n{node.prompt_text}\n\n") f.write(f"**Response:**\n\n{node.response_text}\n\n---\n\n") # Function for nodes that perform analysis def analyze_responses(node, G, iteration): """Analyze responses with awareness of iteration context.""" predecessors = list(G.predecessors(node.id)) analysis_input = f"Analysis for Iteration {iteration + 1}:\n\n" for pred_id in predecessors: pred_node = G.nodes[pred_id]['data'] analysis_input += f"{pred_node.persona_name}'s response:\n{pred_node.response_text}\n\n" node.prompt_text = ( f"Provide an analysis comparing the following perspectives from iteration {iteration + 1}:\n\n" f"{analysis_input}\n" f"Consider how the conversation has evolved across iterations." ) node.context = "" node.response_text = generate_response(node, iteration) update_markdown(node, iteration) @click.group() def cli(): pass @cli.command() def list_personas(): """List all available personas.""" for persona_name in personas.keys(): print(persona_name) @cli.command() @click.option('--nodes', '-n', default=2, help='Number of nodes (excluding the analyst node).') @click.option('--iterations', '-i', default=1, help='Number of conversation iterations.') def run(nodes, iterations): """Run the application with the specified number of nodes and iterations.""" # Clear previous conversation file with open("conversation.md", "w", encoding="utf-8") as f: f.write("# Conversation Log\n\n") # Let the user select personas and input prompts for each node nodes_info = [] for i in range(1, nodes + 1): print(f"\nConfiguring Node {i}") persona_name = click.prompt('Enter the persona name', type=str) while persona_name not in personas: print('Persona not found. Available personas:') for name in personas.keys(): print(f" - {name}") persona_name = click.prompt('Enter the persona name', type=str) prompt_text = click.prompt('Enter the prompt text', type=str) node_info = { 'id': i, 'prompt_text': prompt_text, 'persona_name': persona_name } nodes_info.append(node_info) # Add the analyst node analyst_node_id = nodes + 1 analyst_node_info = { 'id': analyst_node_id, 'prompt_text': '', 'persona_name': 'Analyst' } nodes_info.append(analyst_node_info) # Define edges edges_info = [] for i in range(1, nodes + 1): edges_info.append({'from': i, 'to': analyst_node_id}) # Build and process the graph G = build_graph(nodes_info, edges_info) # Process the graph for the specified number of iterations process_graph(G, iterations) print(f"\nConversation with {iterations} iterations has been generated and logged to conversation.md") if __name__ == '__main__': cli()

Here's a concise and structured prompt you can use with continue.dev to help refactor the `main.py` file: --- **Prompt:** I have a Python project where I want to refactor `main.py` to integrate persona files generated by the project [https://github.com/kliewerdaniel/ngpg05-2](https://github.com/kliewerdaniel/ngpg05-2). Here's what I need help with: 1. Modify the current `load_personas` function to seamlessly load personas using the JSON format generated by the referenced project. 2. Ensure each persona is dynamically added to the graph's nodes (`Node` objects) with their respective attributes. 3. Refactor the `generate_response` and `build_system_prompt` functions to handle the extended persona attributes without hardcoding the attribute list. The attributes should be parsed dynamically based on the JSON structure. 4. Enhance code readability and modularity, introducing helper functions or classes as necessary for persona handling. 5. Validate that existing functionality (like CLI commands and FastAPI endpoints) works with the new persona integration. The current `main.py` file is as follows: ```python # <Insert the full code of main.py here> ``` Please provide a refactored version of the code and suggest improvements or any edge cases to address.