Memor is a library designed to help users manage the memory of their interactions with Large Language Models (LLMs). It enables users to seamlessly access and utilize the history of their conversations when prompting LLMs. That would create a more personalized and context-aware experience. Memor stands out by allowing users to transfer conversational history across different LLMs, eliminating cold starts where models don't have information about user and their preferences. Users can select specific parts of past interactions with one LLM and share them with another. By bridging the gap between isolated LLM instances, Memor revolutionizes the way users interact with AI by making transitions between models smoother.
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- Check Python Packaging User Guide
- Run
pip install memor==0.1
- Download Version 0.1 or Latest Source
- Run
pip install .
Define your prompt and the response(s) to that; Memor will wrap it into a object with a templated representation.
>>> from memor import Prompt, Response, Role
>>> from memor import PresetPromptTemplate, PromptRenderFormat
>>> response = Response(message="I am fine.", role=Role.ASSISTANT, temperature=0.9, score=0.9)
>>> prompt = Prompt(message="Hello, how are you?",
responses=[response],
role=Role.USER,
template=PresetPromptTemplate.INSTRUCTION1.PROMPT_RESPONSE_STANDARD)
>>> prompt.render(render_format=PromptRenderFormat.OPENAI)
[{'role': 'user', 'content': "I'm providing you with a history of a previous conversation. Please consider this context when responding to my new question.\nPrompt: Hello, how are you?\nResponse: I am fine."}]Just fill an issue and describe it. We'll check it ASAP! or send an email to memor@openscilab.com.
- Please complete the issue template
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