

Memoryscope
Overview :
MemoryScope is a framework that endows large language model (LLM) chatbots with long-term memory capabilities. By utilizing a memory database and a working library, chatbots can store and retrieve memory fragments, leading to personalized user interactions. Through processes like memory retrieval and integration, the chatbot understands and remembers user habits and preferences, delivering a more personalized and coherent conversational experience. MemoryScope supports various model APIs, including OpenAI and Dashscope, and can be combined with existing agent frameworks such as AutoGen and AgentScope, providing extensive customization and scalability.
Target Users :
MemoryScope is designed for developers and enterprises that need to provide long-term memory capabilities for chatbots. It is particularly well-suited for scenarios aimed at enhancing the personalization of chatbot services and user experience, such as personal assistants and emotional companions. With MemoryScope, developers can create intelligent systems that understand and remember user preferences, offering richer and more coherent interactions.
Use Cases
Use MemoryScope to provide memory capabilities for a personal assistant chatbot, allowing it to remember the user's schedule and preferences.
In an emotional companion application, enable the bot to understand changes in the user's emotions through MemoryScope and provide appropriate emotional support.
Combine with the AutoGen framework to provide long-term memory for a customer service chatbot, enhancing problem-solving efficiency and user satisfaction.
Features
Memory Database: An integrated vector database (default is Elasticsearch) for storing all memory fragments recorded by the system.
Working Library: Atomizes long-term memory capabilities into multiple work units, including information query filtering, observation extraction, and insight updates, with over 20 work units available.
Operation Library: Constructs memory service operations based on a workflow pipeline, implementing key features like memory retrieval and integration.
Memory Retrieval: Returns semantically relevant memory fragments and/or time-limited fragments related to user queries.
Memory Integration: Processes a batch of user queries, extracting significant user information from the queries to store as integrated observation results in the memory database.
Reflection and Reintegration: Regularly reflects on newly recorded observations, forming and updating insights, followed by memory reintegration to ensure consistency among memory fragments.
Supported Model APIs: Supports various generative and embedding model APIs, with plans to support more model interfaces and locally deployed LLMs and embedding services in the future.
How to Use
Visit the MemoryScope GitHub page for project details and installation guidelines.
Follow the instructions in the Installation.md document to install MemoryScope and its dependencies.
Read the Quick Start documentation to quickly begin using MemoryScope.
Interact with MemoryScope through the CLI or API to implement memory retrieval and memory integration features.
If needed, combine with frameworks like AutoGen or AgentScope for advanced customization.
Regularly check for updates to MemoryScope to take advantage of the latest features and improvements.
Engage with the MemoryScope community to provide feedback and contribute code, helping to advance the project.
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