Understanding Model Context: A Developer's Guide to AI and LLMs

1def generate_response(model, prompt, conversation_history):
2    # Limited context window example
3    context = conversation_history[-model.context_window_size:]  # Slice to fit window
4    combined_input = "".join(context) + prompt
5    response = model.generate(combined_input)
6    return response
7
8# Assume model has an attribute context_window_size = 2
9# conversation_history = ["User: Hi", "Bot: Hello", "User: What's the weather?", "Bot: Sunny"]
10# prompt = "User: What about tomorrow?"
11# The model will only consider "User: What's the weather?" and "Bot: Sunny" when generating a response.
12

1class Chatbot:
2    def __init__(self):
3        self.memory = {}
4
5    def respond(self, user_id, message):
6        if user_id not in self.memory:
7            self.memory[user_id] = []
8
9        self.memory[user_id].append(message) # Store in the memory
10
11        # Process messages with context (not shown here)
12
13        response = f"Echo: {message}"
14        return response
15
16# Example use:
17# bot = Chatbot()
18# bot.respond("user123", "Hello") # Echo: Hello
19# bot.respond("user123", "How are you?") # Echo: How are you? (memory["user123"] contains both messages)
20

1def truncate_context(text, max_length):
2    if len(text) > max_length:
3        return text[:max_length] # Simple truncation.
4    else:
5        return text
6
7# Example:
8# context = "This is a very long piece of text that exceeds the maximum length allowed."
9# truncated_context = truncate_context(context, 50)
10# print(truncated_context) # Output: This is a very long piece of text that exceeds t
11