Memory mechanisms play a crucial role in enhancing the capabilities of machine learning models. This article explores 10 Memory AI libraries available on GitHub. Whether looking to improve your AI applications or dive into cutting-edge research, these libraries offer valuable resources to elevate your work.
1. LangChain
Langchain is a framework for building applications with language models. It provides tools to manage the memory in AI workflows, which can be used for chatbots or more complex AI assistants with persistent memory.
2. Haystack
Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search, and more. It offers memory features to support AI models in retrieving information from large document sets.
3. Hugging Face Transformers
Hugging Face’s Transformers library supports various LLMs like GPT, BERT, and more. It can persist information across sessions by integrating retrieval-augmented generation and memory systems.
4. MemN2N (End-to-End Memory Networks)
MemN2N, or End-to-End Memory Networks, is an advanced deep learning model introduced by Facebook AI, designed to store past inputs explicitly in memory. The model uses this memory to reason and answer questions based on previous knowledge.
5. LlamaIndex (formerly GPT Index)
It is a project designed to manage large collections of documents and effectively index them for AI models, there by allowing for memory over large datasets.
6. EleutherAI GPT-NeoX
EleutherAI’s GPT-NeoX library is a GPT-like large language model that can be fine-tuned for memory-based tasks, where retaining context over long conversations is crucial.
7. OpenAI Baselines
Though primarily for reinforcement learning, OpenAI Baselines can be adapted to store past states, enabling memory functions within RL environments.
Read complete article on https://codeymaze.com/memory-ai-libraries-on-github/
Originally published at https://codeymaze.com on September 2, 2024.
