Meet Memoripy: A Python Library That Brings Real Memory Capabilities to AI Applications

ai systems often struggle to retain meaningful context during prolonged interactions. This limitation poses challenges for applications such as chatbots and virtual assistants, where maintaining a coherent conversation thread is essential. Most traditional ai models operate stateless, focusing solely on immediate inputs without considering the continuity of previous exchanges. This lack of effective memory leads to fragmented and inconsistent interactions, hampering the ability to build truly engaging and context-sensitive ai systems.

Meet Memoripy: a Python library that brings real memory capabilities to ai applications. Memoripy addresses the problem of maintaining conversational context by equipping ai systems with structured memory, allowing them to effectively store, remember, and build on previous interactions. Memoripy provides short- and long-term memory storage, allowing ai systems to retain the context of recent interactions while preserving important information over the long term. By structuring memory in a way that mimics human cognition, prioritizing recent events and retaining key details, Memoripy ensures that interactions remain relevant and consistent over time.

Memoripy organizes memory into short- and long-term groups, allowing you to prioritize recent interactions for immediate recall while preserving meaningful historical interactions for future use. This prevents the ai ​​from being overwhelmed with excess data while ensuring that relevant information is accessible. Memoripy also implements semantic clustering, grouping similar memories together to facilitate efficient context retrieval. This capability allows ai systems to quickly identify and link related memories, thereby improving response quality. Additionally, Memoripy incorporates memory decay and reinforcement mechanisms, whereby less useful memories gradually fade and frequently accessed memories are reinforced, reflecting the principles of human memory. Memoripy's design emphasizes local storage, allowing developers to handle memory operations entirely on local infrastructure. This approach mitigates privacy concerns and provides flexibility for integration with locally hosted language models as well as external services such as OpenAI and Ollama.

To illustrate how Memoripy can be integrated into an ai application, consider the following example:

from memoripy import MemoryManager, JSONStorage

def main():
    # Replace 'your-api-key' with your actual OpenAI API key
    api_key = "your-key"
    if not api_key:
        raise ValueError("Please set your OpenAI API key.")

    # Define chat and embedding models
    chat_model = "openai"  # Choose 'openai' or 'ollama' for chat
    chat_model_name = "gpt-4o-mini"  # Specific chat model name
    embedding_model = "ollama"  # Choose 'openai' or 'ollama' for embeddings
    embedding_model_name = "mxbai-embed-large"  # Specific embedding model name

    # Choose your storage option
    storage_option = JSONStorage("interaction_history.json")

    # Initialize the MemoryManager with the selected models and storage
    memory_manager = MemoryManager(
        api_key=api_key,
        chat_model=chat_model,
        chat_model_name=chat_model_name,
        embedding_model=embedding_model,
        embedding_model_name=embedding_model_name,
        storage=storage_option
    )

    # New user prompt
    new_prompt = "My name is Khazar"

    # Load the last 5 interactions from history (for context)
    short_term, _ = memory_manager.load_history()
    last_interactions = short_term(-5:) if len(short_term) >= 5 else short_term

    # Retrieve relevant past interactions, excluding the last 5
    relevant_interactions = memory_manager.retrieve_relevant_interactions(new_prompt, exclude_last_n=5)

    # Generate a response using the last interactions and retrieved interactions
    response = memory_manager.generate_response(new_prompt, last_interactions, relevant_interactions)

    # Display the response
    print(f"Generated response:\n{response}")

    # Extract concepts for the new interaction
    combined_text = f"{new_prompt} {response}"
    concepts = memory_manager.extract_concepts(combined_text)

    # Store this new interaction along with its embedding and concepts
    new_embedding = memory_manager.get_embedding(combined_text)
    memory_manager.add_interaction(new_prompt, response, new_embedding, concepts)

if __name__ == "__main__":
    main()

In this script, the MemoryManager It is initialized with specified chat and embed models, along with a storage option. A new user message is processed and the system retrieves relevant past interactions to generate a contextually appropriate response. The interaction is then stored with its embedded and extracted concepts for future reference.

Memoripy provides an essential advance in creating ai systems that are more context-aware. The ability to retain and remember relevant information enables the development of virtual assistants, conversational agents and customer service systems that offer more consistent and personalized interactions. For example, a virtual assistant using Memoripy could remember user preferences or details of previous requests, thus offering a more personalized response. Preliminary evaluations indicate that artificial intelligence systems incorporating Memoripy show greater user satisfaction, producing more coherent and contextually appropriate responses. Additionally, Memoripy's emphasis on local storage is crucial for privacy-conscious applications, allowing data to be handled securely without relying on external servers.

In conclusion, Memoripy represents a significant step towards more sophisticated ai interactions by providing true memory capabilities that improve context retention and consistency. By structuring memory in a way that closely mimics human cognitive processes, Memoripy paves the way for artificial intelligence systems that can adapt based on cumulative user interactions and deliver more personalized and contextually aware experiences. This library provides developers with the tools necessary to create ai that not only processes inputs but also learns from interactions in meaningful ways.

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