Langchainrb Save Abandoned

💎🔗 LangChain.rb

⚡ Building applications with LLMs through composability ⚡

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

:warning: 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"

Supported vector search databases and features:

Database	Querying	Storage	Schema Management	Backups	Rails Integration
Chroma	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Hnswlib	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Milvus	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Pinecone	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Pgvector	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Qdrant	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP
Weaviate	:white_check_mark:	:white_check_mark:	:white_check_mark:	WIP	WIP

Using Vector Search Databases 🔍

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

Add gem "weaviate-ruby", "~> 0.8.0" to your Gemfile.

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

client = Langchain::Vectorsearch::Weaviate.new(
    url: ENV["WEAVIATE_URL"],
    api_key: ENV["WEAVIATE_API_KEY"],
    index: "",
    llm: Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])
)

# You can instantiate any other supported vector search database:
client = Langchain::Vectorsearch::Chroma.new(...) # `gem "chroma-db", "~> 0.3.0"`
client = Langchain::Vectorsearch::Hnswlib.new(...) # `gem "hnswlib", "~> 0.8.1"`
client = Langchain::Vectorsearch::Milvus.new(...) # `gem "milvus", "~> 0.9.0"`
client = Langchain::Vectorsearch::Pinecone.new(...) # `gem "pinecone", "~> 0.1.6"`
client = Langchain::Vectorsearch::Pgvector.new(...) # `gem "pgvector", "~> 0.2"`
client = Langchain::Vectorsearch::Qdrant.new(...) # `gem"qdrant-ruby", "~> 0.9.0"`

# Creating the default schema
client.create_default_schema

# Store plain texts 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."
    ]
)

# Store the contents of your files in your vector search database
my_pdf = Langchain.root.join("path/to/my.pdf")
my_text = Langchain.root.join("path/to/my.txt")
my_docx = Langchain.root.join("path/to/my.docx")

client.add_data(paths: [my_pdf, my_text, my_docx])

# 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 🗣️

Add gem "ruby-openai", "~> 4.0.0" to your Gemfile.

OpenAI

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

You can pass additional parameters to the constructor, it will be passed to the OpenAI client:

openai = Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"], llm_options: {uri_base: "http://localhost:1234"}) )

openai.embed(text: "foo bar")

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

Cohere

Add gem "cohere-ruby", "~> 0.9.3" to your Gemfile.

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

cohere.embed(text: "foo bar")

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

HuggingFace

Add gem "hugging-face", "~> 0.3.2" to your Gemfile.

hugging_face = Langchain::LLM::HuggingFace.new(api_key: ENV["HUGGING_FACE_API_KEY"])

Replicate

Add gem "replicate-ruby", "~> 0.2.2" to your Gemfile.

replicate = Langchain::LLM::Replicate.new(api_key: ENV["REPLICATE_API_KEY"])

Google PaLM (Pathways Language Model)

Add "google_palm_api", "~> 0.1.1" to your Gemfile.

google_palm = Langchain::LLM::GooglePalm.new(api_key: ENV["GOOGLE_PALM_API_KEY"])

AI21

Add gem "ai21", "~> 0.2.0" to your Gemfile.

ai21 = Langchain::LLM::AI21.new(api_key: ENV["AI21_API_KEY"])

Using Prompts 📋

Prompt Templates

Create a prompt with one input variable:

prompt = Langchain::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 = Langchain::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 = Langchain::Prompt::PromptTemplate.from_template("Tell me a funny joke about chickens.")
prompt.input_variables # []
prompt.format # "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 = Langchain::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 = Langchain::Prompt::FewShotPromptTemplate.new(
  prefix: "Write antonyms for the following words.",
  suffix: "Input: {adjective}\nOutput:",
  example_prompt: Langchain::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 = Langchain::Prompt.load_from_path(file_path: "spec/fixtures/prompt/few_shot_prompt_template.json")
prompt.prefix # "Write antonyms for the following words."

Loading a new prompt template using a YAML file:

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

Using Agents 🤖

Agents are semi-autonomous bots that can respond to user questions and use available to them Tools to provide informed replies. They break down problems into series of steps and define Actions (and Action Inputs) along the way that are executed and fed back to them as additional information. Once an Agent decides that it has the Final Answer it responds with it.

Chain-of-Thought Agent

Add gem "ruby-openai", gem "eqn", and gem "google_search_results" to your Gemfile

search_tool = Langchain::Tool::GoogleSearch.new(api_key: ENV["SERPAPI_API_KEY"])
calculator = Langchain::Tool::Calculator.new

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

agent = Langchain::Agent::ChainOfThoughtAgent.new(
  llm: openai,
  tools: [search_tool, calculator]
)

agent.tools
# => ["google_search", "calculator"]

agent.run(question: "How many full soccer fields would be needed to cover the distance between NYC and DC in a straight line?")
#=> "Approximately 2,945 soccer fields would be needed to cover the distance between NYC and DC in a straight line."

SQL-Query Agent

Add gem "sequel" to your Gemfile

database = Langchain::Tool::Database.new(connection_string: "postgres://user:password@localhost:5432/db_name")

agent = Langchain::Agent::SQLQueryAgent.new(llm: Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"]), db: database)

agent.run(question: "How many users have a name with length greater than 5 in the users table?")
#=> "14 users have a name with length greater than 5 in the users table."

Demo

Available Tools 🛠️

Name	Description	ENV Requirements	Gem Requirements
"calculator"	Useful for getting the result of a math expression		gem "eqn", "~> 1.6.5"
"database"	Useful for querying a SQL database		gem "sequel", "~> 5.68.0"
"ruby_code_interpreter"	Interprets Ruby expressions		gem "safe_ruby", "~> 1.0.4"
"google_search"	A wrapper around Google Search	ENV["SERPAPI_API_KEY"] (https://serpapi.com/manage-api-key)	gem "google_search_results", "~> 2.0.0"
"weather"	Calls Open Weather API to retrieve the current weather	ENV["OPEN_WEATHER_API_KEY] (https://home.openweathermap.org/api_keys)	gem "open-weather-ruby-client", "~> 0.3.0"
"wikipedia"	Calls Wikipedia API to retrieve the summary		gem "wikipedia-client", "~> 1.17.0"

Loaders 🚚

Need to read data from various sources? Load it up.

Usage

Just call Langchan::Loader.load with the path to the file or a URL you want to load.

Langchain::Loader.load('/path/to/file.pdf')

or

Langchain::Loader.load('https://www.example.com/file.pdf')

Supported Formats

Format	Pocessor	Gem Requirements
docx	Langchain::Processors::Docx	gem "docx", "~> 0.8.0"
html	Langchain::Processors::HTML	gem "nokogiri", "~> 1.13"
pdf	Langchain::Processors::PDF	gem "pdf-reader", "~> 1.4"
text	Langchain::Processors::Text
JSON	Langchain::Processors::JSON
JSONL	Langchain::Processors::JSONL
csv	Langchain::Processors::CSV
xlsx	Langchain::Processors::Xlsx	gem "roo", "~> 2.10.0"

Examples

Additional examples available: /examples

Logging

LangChain.rb uses standard logging mechanisms and defaults to :warn level. Most messages are at info level, but we will add debug or warn statements as needed. To show all log messages:

Langchain.logger.level = :info

Development

1. git clone https://github.com/andreibondarev/langchainrb.git
2. cp .env.example .env, then fill out the environment variables in .env
3. bundle exec rake to ensure that the tests pass and to run standardrb
4. bin/console to load the gem in a REPL session. Feel free to add your own instances of LLMs, Tools, Agents, etc. and experiment with them.
5. Optionally, install lefthook git hooks for pre-commit to auto lint: gem install lefthook && lefthook install -f

Discord

Join us in the Langchain.rb Discord server.

Core Contributors

Honorary Contributors

(Criteria for becoming an Honorary Contributor or Core Contributor is pending...)

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.