heavily based on Stockholm and the walled city of Visby on the island of Gotland in Sweden.. you Guest was flying on your broom down to the town, since it was the closest place to shelter that you had since there was a storm about to come along behind you

import openai import chromadb from chromadb.utils import embedding_functions

1. Initialize the AI Client and Vector Database (The Memory Bank)

openai_client = openai.OpenAI(api_key="YOUR_OPENAI_API_KEY") chroma_client = chromadb.PersistentClient(path="./ai_memory_db")

Use OpenAI embeddings to convert human text into mathematical concepts

embedding_func = embedding_functions.OpenAIEmbeddingFunction( api_key="YOUR_OPENAI_API_KEY", model_name="text-embedding-3-small" )

Create or load a memory collection

memory_collection = chroma_client.get_or_create_collection( name="user_memories", embedding_function=embedding_func )

def save_to_long_term_memory(user_id, interaction_text, memory_id): """Saves any important fact or interaction into the database.""" memory_collection.add( documents=[interaction_text], ids=[f"{user_id}_{memory_id}"] )

def recall_relevant_memories(query_text, num_results=3): """Searches the database to find past contexts matching the current topic.""" results = memory_collection.query( query_texts=[query_text], n_results=num_results ) # Flatten the list of retrieved memories return " ".join(results['documents'][0]) if results['documents'] else ""

def chat_with_memory(user_id, current_user_input, current_memory_count): """The main execution loop that gives the AI a memory."""

# Step A: Query the memory bank for past interactions relevant to the user's input
past_context = recall_relevant_memories(current_user_input)

# Step B: Inject those memories directly into the System Prompt
system_prompt = f"""
You are an advanced AI assistant who never forgets. 
Here is relevant historical information about this user from past chats:
---
{past_context}
---
Answer the user's current prompt using the historical context above if relevant.
"""

# Step C: Call the stateless AI model with the injected memory
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": current_user_input}
    ]
)
ai_reply = response.choices[0].message.content

# Step D: Save this new interaction so it can be recalled in the future
full_log = f"User said: {current_user_input}. AI replied: {ai_reply}"
save_to_long_term_memory(user_id, full_log, current_memory_count)

return ai_reply