🧠 LLM Memory 🌊🐴

LLM Memory is a Ruby gem designed to provide large language models (LLMs) like ChatGPT with memory using in-context learning. This enables better integration with systems such as Rails and web services while providing a more user-friendly and abstract interface based on brain terms.

• In-context learning through input prompt context insertion
• Data connectors for various data sources
• Inspired by the Python library, LlamaIndex
• Focus on integration with existing systems
• Easy-to-understand abstraction using brain-related terms
• Plugin architecture for custom loader creation and extending LLM support

1. LlmMemory::Wernicke: Responsible for loading external data (currently from files). More loader types are planned for future development.

Wernicke's area in brain is involved in the comprehension of written and spoken language

2. LlmMemory::Hippocampus: Handles interaction with vector databases to retrieve relevant information based on the query. Currently, Redis with the Redisearch module is used as the vector database. (Note that Redisearch is a proprietary modules and available on RedisCloud, which offers a free plan). The reason for choosing this is that Redis is commonly used and easy to integrate with web services.

Hippocampus in brain plays important roles in the consolidation of information from short-term memory to long-term memory

3. LlmMemory::Broca: Responds to queries using memories provided by the Hippocampus component. ERB is used for prompt templates, and a variety of templates can be found online (e.g., in LangChain Hub).

Broca's area in brain is also known as the motor speech area.

Install the gem and add to the application's Gemfile by executing:

$ bundle add llm_memory

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install llm_memory

Set environment variable OPENAI_ACCESS_TOKEN and REDISCLOUD_URL or set in initializer.

LlmMemory.configure do |c|
  c.openai_access_token = "xxxxx"
  c.redis_url = "redis://xxxx:6379"
end

To use LLM Memory, follow these steps:

If you want to use pgvector instead of Redisearch. You can use the plugin. Please check the doc and change the setup steps(2&3)

1. Install the gem: gem install llm_memory
2. Set up Redis with Redisearch module enabled - Go to Reids Cloud and get the redis url.
3. Configure LLM Memory to connect to your Redis instance
4. Use LlmMemory::Wernicke to load data from your external sources
5. Use LlmMemory::Hippocampus to search for relevant information based on user queries
6. Create and use ERB templates with LlmMemory::Broca to generate responses based on the information retrieved

For the details of each class, please refer to API reference document.

docs = LlmMemory::Wernicke.load(:file, "/tmp/a_directory")
# docs is just an array of hash.
# You don't have to use load method but
# create own hash with having content and metadata(optional)
# docs = [{
#   content: "Hi there",
#   metadata: {
#     file_name: "a.txt",
#     timestamp: "20201231235959"
#   }
# },,,]

hippocampus = LlmMemory::Hippocampus.new
res = hippocampus.memorize(docs)

query_str = "What is my name?"
related_docs = hippocampus.query(query_str, limit: 3)
#[{
#   vector_score: "0.192698478699",
#   content: "My name is Mike",
#   metadata: { ... }
#},,,]

# ERB
prompt = <<-TEMPLATE
Context information is below.
---------------------
<% related_docs.each do |doc| %>
<%= doc[:content] %>
file: <%= doc[:metadata][:file_name] %>

<% end %>
---------------------
Given the context information and not prior knowledge,
answer the question: <%= query_str %>
TEMPLATE

broca = LlmMemory::Broca.new(prompt: prompt, model: 'gpt-3.5-turbo')
message = broca.respond(query_str: query_str, related_docs: related_docs)

...
query_str2 = "How are you?"
related_docs = hippocampus.query(query_str2, limit: 3)
message2 = broca.respond(query_str: query_str2, related_docs: related_docs)

The table below provides a list of plugins utilized by llm_memory. The aim is to keep the core llm_memory lightweight while allowing for easy extensibility through the use of plugins.

Install the plugin and update the method.

For example, if you wan to use pgvector. then,

$ bundle add llm_memory_pgvector

Then, load it instead of :redis (default is redis).

# may need to have require depending on the project
# require llm_memory_pgvector
hippocamups = LlmMemory::Hippocampus.new(store: :pgvector)`

Please refer to the links for the details.

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Bug reports and pull requests are welcome on GitHub at https://github.com/shohey1226/llm_memory. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

The gem is available as open source under the terms of the MIT License.

Everyone interacting in the LlmMemory project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.