🦜️🔗 LangChain.rb

⚡ Building applications with LLMs through composability ⚡

👨‍💻👩‍💻 CURRENTLY SEEKING PEOPLE TO FORM THE CORE GROUP OF MAINTAINERS WITH

⚠️ UNDER ACTIVE AND RAPID DEVELOPMENT (MAY BE BUGGY AND UNTESTED)

Langchain.rb is a library that's an abstraction layer on top many emergent AI, ML and other DS tools. The goal is to abstract complexity and difficult concepts to make building AI/ML-supercharged applications approachable for traditional software engineers.

Installation

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

$ bundle add langchainrb

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

$ gem install langchainrb

Usage

require "langchain"

List of currently supported vector search databases and features:

Database	Querying	Storage	Schema Management	Backups	Rails Integration	???
Weaviate	✅	WIP	WIP	WIP
Qdrant	✅	WIP	WIP	WIP
Milvus	✅	WIP	WIP	WIP
Pinecone	✅	WIP	WIP	WIP

Using Vector Search Databases

Choose the LLM provider you'll be using (OpenAI or Cohere) and retrieve the API key.

Pick the vector search database you'll be using and instantiate the client:

client = Vectorsearch::Weaviate.new(
    url: ENV["WEAVIATE_URL"],
    api_key: ENV["WEAVIATE_API_KEY"],
    llm: :openai, # or :cohere
    llm_api_key: ENV["OPENAI_API_KEY"]
)

# You can instantiate any other supported vector search database:
client = Vectorsearch::Milvus.new(...)
client = Vectorsearch::Qdrant.new(...)
client = Vectorsearch::Pinecone.new(...)

# Creating the default schema
client.create_default_schema

# Store your documents in your vector search database
client.add_texts(
    texts: [
        "Begin by preheating your oven to 375°F (190°C). Prepare four boneless, skinless chicken breasts by cutting a pocket into the side of each breast, being careful not to cut all the way through. Season the chicken with salt and pepper to taste. In a large skillet, melt 2 tablespoons of unsalted butter over medium heat. Add 1 small diced onion and 2 minced garlic cloves, and cook until softened, about 3-4 minutes. Add 8 ounces of fresh spinach and cook until wilted, about 3 minutes. Remove the skillet from heat and let the mixture cool slightly.",
        "In a bowl, combine the spinach mixture with 4 ounces of softened cream cheese, 1/4 cup of grated Parmesan cheese, 1/4 cup of shredded mozzarella cheese, and 1/4 teaspoon of red pepper flakes. Mix until well combined. Stuff each chicken breast pocket with an equal amount of the spinach mixture. Seal the pocket with a toothpick if necessary. In the same skillet, heat 1 tablespoon of olive oil over medium-high heat. Add the stuffed chicken breasts and sear on each side for 3-4 minutes, or until golden brown."
    ]
)

# Retrieve similar documents based on the query string passed in
client.similarity_search(
    query:,
    k:       # number of results to be retrieved
)

# Retrieve similar documents based on the embedding passed in
client.similarity_search_by_vector(
    embedding:,
    k:       # number of results to be retrieved
)

# Q&A-style querying based on the question passed in
client.ask(
    question:
)

Using Standalone LLMs

OpenAI

openai = LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])

openai.embed(text: "foo bar")

openai.complete(prompt: "What is the meaning of life?")

Cohere

cohere = LLM::Cohere.new(api_key: ENV["COHERE_API_KEY"])

cohere.embed(text: "foo bar")

cohere.complete(prompt: "What is the meaning of life?")

Using Prompts

Prompt Templates

Create a prompt with one input variable:

prompt = Prompt::PromptTemplate.new(template: "Tell me a {adjective} joke.", input_variables: ["adjective"])
prompt.format(adjective: "funny") # "Tell me a funny joke."

Create a prompt with multiple input variables:

prompt = Prompt::PromptTemplate.new(template: "Tell me a {adjective} joke about {content}.", input_variables: ["adjective", "content"])
prompt.format(adjective: "funny", content: "chickens") # "Tell me a funny joke about chickens."

Creating a PromptTemplate using just a prompt and no input_variables:

prompt = Prompt::PromptTemplate.from_template("Tell me a {adjective} joke about {content}.")
prompt.input_variables # ["adjective", "content"]
prompt.format(adjective: "funny", content: "chickens") # "Tell me a funny joke about chickens."

Save prompt template to JSON file:

prompt.save(file_path: "spec/fixtures/prompt/prompt_template.json")

Loading a new prompt template using a JSON file:

prompt = Prompt.load_from_path(file_path: "spec/fixtures/prompt/prompt_template.json")
prompt.input_variables # ["adjective", "content"]

Few Shot Prompt Templates

Create a prompt with a few shot examples:

prompt = Prompt::FewShotPromptTemplate.new(
  prefix: "Write antonyms for the following words.",
  suffix: "Input: {adjective}\nOutput:",
  example_prompt: Prompt::PromptTemplate.new(
    input_variables: ["input", "output"],
    template: "Input: {input}\nOutput: {output}"
  ),
  examples: [
    { "input": "happy", "output": "sad" },
    { "input": "tall", "output": "short" }
  ],
   input_variables: ["adjective"]
)

prompt.format(adjective: "good")

# Write antonyms for the following words.
#
# Input: happy
# Output: sad
#
# Input: tall
# Output: short
#
# Input: good
# Output:

Save prompt template to JSON file:

prompt.save(file_path: "spec/fixtures/prompt/few_shot_prompt_template.json")

Loading a new prompt template using a JSON file:

prompt = Prompt.load_from_path(file_path: "spec/fixtures/prompt/few_shot_prompt_template.json")
prompt.prefix # "Write antonyms for the following words."

Development

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.

Honorary Contributors

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/andreibondarev/langchain.

License

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