LLAMA-style LLMs and LangChain: A Solution to Long-Term Memory Problem

LLAMA-style Long-Form Memory (LLM) models are gaining popularity in solving long-term memory (LTM) problems. However, the creation of LLMs requires a fully manual process. Users may wonder whether any existing GPT-powered applications perform similar tasks. A project called gpt-llama.cpp, which uses llama.cpp and mocks an OpenAI endpoint, has been proposed to support GPT-powered applications with llama.cpp, which supports Vicuna.

LangChain, a framework for building agents, provides a solution to the LTM problem by combining LLMs, tools, and memory. The framework offers different memory types, and users can wrap local LLaMA models into a pipeline for it. LangChain supports vector databases as well for memory, and they have integrations for many models besides OpenAI. The framework is highly recommended to anyone looking to build an agent project.

If users want to integrate LangChain into Vicuna, they only need a wrapper around the API call and return the response. Users can combine LangChain with Vicuna by creating a summary of the prior conversation, along with the last X tokens of conversation history. Conversation chains can be built by using LangChain's ConversationChain class.

If users are interested in creating LLMs with extremely long context windows, they can refer to this paper, which offers a solution: https://arxiv.org/pdf/2302.10866.pdf. Additionally, users can explore memory networks like the ones used by Chatterbot or ParlAI to create a database based on chat logs and pick out relevant sections to parrot back when asked about something in the dataset. This could be used as a perfect complement to LangChain-style LLMs, which are excellent at creativity but cannot remember anything that happened more than a few thousand tokens ago.

Tags: LLAMA-style LLMs, LangChain, GPT-powered applications, long-term memory, vector databases, memory networks, conversation chains.

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