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annoy_ltm

This repository contains an extension for the oobabooga-text-generation-webui application, introducing long-term memory to chat bots using the Annoy (Approximate Nearest Neighbors Oh Yeah) nearest neighbor vector database.

Features

The annoy_ltm extension provides chat bots with a form of long-term memory. It leverages the efficient search algorithm of Annoy to retrieve similar vector representations from the history, allowing the bot to reference past interactions.

Installation

This extension can be installed like any other extension to the oobabooga-text-generation-webui, with an additional requirement for the Spacy language model. Follow the instructions below:

  1. Download and install the Spacy en_core_web_sm model. You can do this by running the cmd_windows.bat and then executing the following commands in the resulting cmd shell:

Windows WSL:

pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm

Linux: In the environment you are using for Oobabooga-text-generation-webui, run the folowing command:

python -m spacy download en_core_web_sm

  1. Follow the regular installation process for extensions to the oobabooga-text-generation-webui application.

  2. Navigate to the annoy_ltm extension folder and run the following command to install the dependencies:

pip install -r requirements.txt

Usage

Once the extension is enabled, it works automatically with no additional steps needed. You can configure its behavior by modifying the following parameters in the settings.json of the webui:

Parameter Description Default Value
annoy_output_dir Directory where outputs are stored. "extensions/annoy_ltm/outputs/"
logger_level Logging level, higher number results in more verbose logging. Maximum reasonable value for normal debugging is 3. 1
memory_retention_threshold Retention threshold for memories. Lower values cause memories to retain longer, potentially at the cost of stack overflow and irrelevant memory retention. Ranges from 0-1. 0.68
full_memory_additional_weight Additional weight for the full memory. Smaller values result in higher weight. Ranges from 0-1. 0.5
num_memories_to_retrieve Number of related memories to retrieve for the full message and every keyword group generated from the message. Higher values can cause significant slowdowns. 5
keyword_grouping Number to group keywords into. Higher values make it harder to find an exact match, potentially improving context relevance at the cost of memory retrieval. 4
maximum_memory_stack_size Maximum size for the memory stack, preventing overflow. 50
prompt_memory_ratio The ratio of the prompt after character context is applied that will be dedicated for memories. 0.4
vector_dim_override Override value for the hidden layer dimension of your loaded model, Use if you encounter issues with the generated embeddings not matching the dimensionality of the annoy index. -1 is disabled. -1

These parameters allow you to tune the operation of annoy_ltm to best suit your specific use-case.

Support

For any issues or queries, please use the Issues tab on this repository.

Docker

Hey you! Yeah you about to install some random project extension code into your non-dockerized oobabooga instance! Don't you know that's dangerous? I highly recommend you check out the docker setup for oobabooga-text-generation-webui before randomly installing anything and do your due dilligance by reading through the extension code! You got that kind of time. https://github.com/oobabooga/text-generation-webui#alternative-docker