brain.py

# brain.py

# AI brain implementation
# author_github: benediktbwimmer
# date and time: april 2, 2023 at 13:28
# location: Innsbruck
# repository_url: AI_FILL
# description: This file is part of a human-ai collab project. it should implement the ability for an LLM to access a conversation.md

import os

# Load memory from the file
def load_memory(memory_file):
    if os.path.exists(memory_file):
        with open(memory_file, 'r') as f:
            return f.read()
    else:
        print(f"Memory file {memory_file} not found.")
        return ""

memory = load_memory("conversation.md")
FACTOR = 10

def answer_prompt(context, prompt):
    thinking_effort = FACTOR  # Add a default thinking_effort value based on the FACTOR
    thinking_iterations = estimate_optimal_iterations_for_context_refinement(context, prompt, thinking_effort)
    for _ in range(thinking_iterations):
        context = refine_context(context, prompt, memory)
    return predict_best_response(context, prompt)

def estimate_optimal_iterations_for_context_refinement(context, prompt, thinking_effort=FACTOR):  # Add default value for thinking_effort
    complexity = analyze_complexity(context, prompt)
    thinking_iterations = int(complexity * thinking_effort)
    return thinking_iterations

def analyze_complexity(context, prompt):
    keywords = ['explain', 'describe', 'compare', 'analyze', 'how']
    num_tokens = len(prompt.split())
    num_keywords = sum([1 for word in prompt.split() if word.lower() in keywords])
    complexity = (num_tokens + num_keywords) / 10
    return complexity

def filter_memory_based_on_criteria(memory_lines, criteria):
    filtered_memory = [line for line in memory_lines if line_meets_criteria(line, criteria)]
    return '\n'.join(filtered_memory)

def improve_context_with_respect_to_criteria_(context, prompt, memory, criteria):
    memory_lines = memory.split('\n')
    filtered_memory = filter_memory_based_on_criteria(memory_lines, criteria)
    refined_context = context + ' ' + filtered_memory
    return refined_context


def refine_context(context, prompt, memory):
    criteria = determine_evaluation_criteria(context, prompt, memory)
    refined_context = improve_context_with_respect_to_criteria_(context, prompt, memory, criteria)
    return refined_context

def determine_evaluation_criteria(context, prompt, memory):
    # Placeholder: a more sophisticated method to determine criteria based on context, prompt, and memory
    # This can be improved with a more advanced analysis of the input data
    criteria = {'relevance': 1.0, 'accuracy': 1.0, 'coherence': 1.0}
    return criteria

def line_meets_criteria(line, criteria):
    # Placeholder: a method to determine if a memory line meets the given criteria
    # This can be improved with a more advanced text filtering or information retrieval algorithm
    score = calculate_line_score(line, criteria)
    threshold = 0.5  # Placeholder: adjust the threshold based on the desired level of filtering
    return score >= threshold

def calculate_line_score(line, criteria):
    # Placeholder: a method to calculate the score of a memory line based on the criteria
    # This can be improved with a more advanced scoring algorithm
    score = 0.5  # Placeholder: a fixed score for all lines
    return score

def predict_best_response(context, prompt):
    response = generate_message_for_ai_model(context, prompt)
    return response

def generate_message_for_ai_model(context, prompt):
    message = f"Context: {context}\nPrompt: {prompt}\n\nPlease generate a response based on the given context and prompt."
    return message