memor 0.5

Memor: A Python Library for Managing and Transferring Conversational Memory Across LLMs

Memor: A Python Library for Managing and Transferring Conversational Memory Across LLMs

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Overview

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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Installation

PyPI

• Check Python Packaging User Guide
• Run pip install memor==0.5

Source code

• Download Version 0.5 or Latest Source
• Run pip install .

Usage

Define your prompt and the response(s) to that; Memor will wrap it into a object with a templated representation. You can create a session by combining multiple prompts and responses, gradually building it up:

>>> from memor import Session, Prompt, Response, Role
>>> from memor import PresetPromptTemplate, RenderFormat
>>> 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)
>>> system_prompt = Prompt(message="You are a friendly and informative AI assistant designed to answer questions on a wide range of topics.",
                    role=Role.SYSTEM)
>>> session = Session(messages=[system_prompt, prompt])
>>> session.render(RenderFormat.OPENAI)

The rendered output will be a list of messages formatted for compatibility with the OpenAI API.

[{"content": "You are a friendly and informative AI assistant designed to answer questions on a wide range of topics.", "role": "system"},
 {"content": "I'm providing you with a history of a previous conversation. Please consider this context when responding to my new question.\n"
             "Prompt: Hello, how are you?\n"
             "Response: I am fine.",
  "role": "user"}]

Prompt Templates

Preset Templates

Memor provides a variety of pre-defined prompt templates to control how prompts and responses are rendered. Each template is prefixed by an optional instruction string and includes variations for different formatting styles. Following are different variants of parameters:

Instruction Name	Description
INSTRUCTION1	"I'm providing you with a history of a previous conversation. Please consider this context when responding to my new question."
INSTRUCTION2	"Here is the context from a prior conversation. Please learn from this information and use it to provide a thoughtful and context-aware response to my next questions."
INSTRUCTION3	"I am sharing a record of a previous discussion. Use this information to provide a consistent and relevant answer to my next query."

Template Title	Description
PROMPT	Only includes the prompt message.
RESPONSE	Only includes the response message.
RESPONSE0 to RESPONSE3	Include specific responses from a list of multiple responses.
PROMPT_WITH_LABEL	Prompt with a "Prompt: " prefix.
RESPONSE_WITH_LABEL	Response with a "Response: " prefix.
RESPONSE0_WITH_LABEL to RESPONSE3_WITH_LABEL	Labeled response for the i-th response.
PROMPT_RESPONSE_STANDARD	Includes both labeled prompt and response on a single line.
PROMPT_RESPONSE_FULL	A detailed multi-line representation including role, date, model, etc.

You can access them like this:

>>> from memor import PresetPromptTemplate
>>> template = PresetPromptTemplate.INSTRUCTION1.PROMPT_RESPONSE_STANDARD

Custom Templates

You can define custom templates for your prompts using the PromptTemplate class. This class provides two key parameters that control its functionality:

• content: A string that defines the template structure, following Python string formatting conventions. You can include dynamic fields using placeholders like {field_name}, which will be automatically populated using attributes from the prompt object. Some common examples of auto-filled fields are shown below:

Prompt Object Attribute	Placeholder Syntax	Description
prompt.message	{prompt[message]}	The main prompt message
prompt.selected_response	{prompt[response]}	The selected response for the prompt
prompt.date_modified	{prompt[date_modified]}	Timestamp of the last modification
prompt.responses[2].message	{responses[2][message]}	Message from the response at index 2
prompt.responses[0].inference_time	{responses[0][inference_time]}	Inference time for the response at index 0

• custom_map: In addition to the attributes listed above, you can define and insert custom placeholders (e.g., {field_name}) and provide their values through a dictionary. When rendering the template, each placeholder will be replaced with its corresponding value from custom_map.

Suppose you want to prepend an instruction to every prompt message. You can define and use a template as follows:

>>> template = PromptTemplate(content="{instruction}, {prompt[message]}", custom_map={"instruction": "Hi"})
>>> prompt = Prompt(message="How are you?", template=template)
>>> prompt.render()
Hi, How are you?

By using this dynamic structure, you can create flexible and sophisticated prompt templates with Memor. You can design specific schemas for your conversational or instructional formats when interacting with LLM.

Issues & bug reports

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• Please complete the issue template

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

0.5 - 2025-04-16

Added

• Session class check_render method
• Session class clear_messages method
• Prompt class check_render method
• Session class estimate_tokens method
• Prompt class estimate_tokens method
• Response class estimate_tokens method
• universal_tokens_estimator function
• openai_tokens_estimator_gpt_3_5 function
• openai_tokens_estimator_gpt_4 function

Changed

• init_check parameter added to Prompt class
• init_check parameter added to Session class
• Test system modified
• Python 3.6 support dropped
• README.md updated

0.4 - 2025-03-17

Added

• Session class __contains__ method
• Session class __getitem__ method
• Session class mask_message method
• Session class unmask_message method
• Session class masks attribute
• Response class __len__ method
• Prompt class __len__ method

Changed

• inference_time parameter added to Response class
• README.md updated
• Test system modified
• Python typing features added to all modules
• Prompt class default values updated
• Response class default values updated

0.3 - 2025-03-08

Added

• Session class __len__ method
• Session class __iter__ method
• Session class __add__ and __radd__ methods

Changed

• tokens parameter added to Prompt class
• tokens parameter added to Response class
• tokens parameter added to preset templates
• Prompt class modified
• Response class modified
• PromptTemplate class modified

0.2 - 2025-03-01

Added

• Session class

Changed

• Prompt class modified
• Response class modified
• PromptTemplate class modified
• README.md updated
• Test system modified

0.1 - 2025-02-12

Added

• Prompt class
• Response class
• PromptTemplate class
• PresetPromptTemplate class