💎🔗 Langchain.rb

⚡ Building LLM-powered applications in Ruby ⚡

For deep Rails integration see: langchainrb_rails gem.

Available for paid consulting engagements! Email me.

Use Cases

• Retrieval Augmented Generation (RAG) and vector search
• Assistants (chat bots)

Table of Contents

• Installation
• Usage
• Unified Interface for LLMs
• Prompt Management
• Output Parsers
• Building RAG
• Assistants
• Evaluations
• Examples
• Logging
• Problems
• Development
• Discord

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

Additional gems may be required. They're not included by default so you can include only what you need.

Usage

require "langchain"

Unified Interface for LLMs

The Langchain::LLM module provides a unified interface for interacting with various Large Language Model (LLM) providers. This abstraction allows you to easily switch between different LLM backends without changing your application code.

Supported LLM Providers

• AI21
• Anthropic
• AWS Bedrock
• Azure OpenAI
• Cohere
• Google Gemini
• Google PaLM (deprecated)
• Google Vertex AI
• HuggingFace
• LlamaCpp
• Mistral AI
• Ollama
• OpenAI
• Replicate

Usage

All LLM classes inherit from Langchain::LLM::Base and provide a consistent interface for common operations:

1. Generating embeddings
2. Generating prompt completions
3. Generating chat completions

Initialization

Most LLM classes can be initialized with an API key and optional default options:

llm = Langchain::LLM::OpenAI.new(
  api_key: ENV["OPENAI_API_KEY"],
  default_options: { temperature: 0.7, chat_completion_model_name: "gpt-4o" }
)

Generating Embeddings

Use the embed method to generate embeddings for given text:

response = llm.embed(text: "Hello, world!")
embedding = response.embedding

Accepted parameters for embed()

• text: (Required) The input text to embed.
• model: (Optional) The model name to use or default embedding model will be used.

Prompt completions

Use the complete method to generate completions for a given prompt:

response = llm.complete(prompt: "Once upon a time")
completion = response.completion

Accepted parameters for complete()

• prompt: (Required) The input prompt for completion.
• max_tokens: (Optional) The maximum number of tokens to generate.
• temperature: (Optional) Controls randomness in generation. Higher values (e.g., 0.8) make output more random, while lower values (e.g., 0.2) make it more deterministic.
• top_p: (Optional) An alternative to temperature, controls diversity of generated tokens.
• n: (Optional) Number of completions to generate for each prompt.
• stop: (Optional) Sequences where the API will stop generating further tokens.
• presence_penalty: (Optional) Penalizes new tokens based on their presence in the text so far.
• frequency_penalty: (Optional) Penalizes new tokens based on their frequency in the text so far.

Generating Chat Completions

Use the chat method to generate chat completions:

messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "What's the weather like today?" }
]
response = llm.chat(messages: messages)
chat_completion = response.chat_completion

Accepted parameters for chat()

• messages: (Required) An array of message objects representing the conversation history.
• model: (Optional) The specific chat model to use.
• temperature: (Optional) Controls randomness in generation.
• top_p: (Optional) An alternative to temperature, controls diversity of generated tokens.
• n: (Optional) Number of chat completion choices to generate.
• max_tokens: (Optional) The maximum number of tokens to generate in the chat completion.
• stop: (Optional) Sequences where the API will stop generating further tokens.
• presence_penalty: (Optional) Penalizes new tokens based on their presence in the text so far.
• frequency_penalty: (Optional) Penalizes new tokens based on their frequency in the text so far.
• logit_bias: (Optional) Modifies the likelihood of specified tokens appearing in the completion.
• user: (Optional) A unique identifier representing your end-user.
• tools: (Optional) A list of tools the model may call.
• tool_choice: (Optional) Controls how the model calls functions.

Switching LLM Providers

Thanks to the unified interface, you can easily switch between different LLM providers by changing the class you instantiate:

# Using Anthropic
anthropic_llm = Langchain::LLM::Anthropic.new(api_key: ENV["ANTHROPIC_API_KEY"])

# Using Google Gemini
gemini_llm = Langchain::LLM::GoogleGemini.new(api_key: ENV["GOOGLE_GEMINI_API_KEY"])

# Using OpenAI
openai_llm = Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])

Response Objects

Each LLM method returns a response object that provides a consistent interface for accessing the results:

• embedding: Returns the embedding vector
• completion: Returns the generated text completion
• chat_completion: Returns the generated chat completion
• tool_calls: Returns tool calls made by the LLM
• prompt_tokens: Returns the number of tokens in the prompt
• completion_tokens: Returns the number of tokens in the completion
• total_tokens: Returns the total number of tokens used

Note

While the core interface is consistent across providers, some LLMs may offer additional features or parameters. Consult the documentation for each LLM class to learn about provider-specific capabilities and options.

Prompt Management

Prompt Templates

Create a prompt with 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"]

Output Parsers

Parse LLM text responses into structured output, such as JSON.

Structured Output Parser

You can use the StructuredOutputParser to generate a prompt that instructs the LLM to provide a JSON response adhering to a specific JSON schema:

json_schema = {
  type: "object",
  properties: {
    name: {
      type: "string",
      description: "Persons name"
    },
    age: {
      type: "number",
      description: "Persons age"
    },
    interests: {
      type: "array",
      items: {
        type: "object",
        properties: {
          interest: {
            type: "string",
            description: "A topic of interest"
          },
          levelOfInterest: {
            type: "number",
            description: "A value between 0 and 100 of how interested the person is in this interest"
          }
        },
        required: ["interest", "levelOfInterest"],
        additionalProperties: false
      },
      minItems: 1,
      maxItems: 3,
      description: "A list of the person's interests"
    }
  },
  required: ["name", "age", "interests"],
  additionalProperties: false
}
parser = Langchain::OutputParsers::StructuredOutputParser.from_json_schema(json_schema)
prompt = Langchain::Prompt::PromptTemplate.new(template: "Generate details of a fictional character.\n{format_instructions}\nCharacter description: {description}", input_variables: ["description", "format_instructions"])
prompt_text = prompt.format(description: "Korean chemistry student", format_instructions: parser.get_format_instructions)
# Generate details of a fictional character.
# You must format your output as a JSON value that adheres to a given "JSON Schema" instance.
# ...

Then parse the llm response:

llm = Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])
llm_response = llm.chat(messages: [{role: "user", content: prompt_text}]).completion
parser.parse(llm_response)
# {
#   "name" => "Kim Ji-hyun",
#   "age" => 22,
#   "interests" => [
#     {
#       "interest" => "Organic Chemistry",
#       "levelOfInterest" => 85
#     },
#     ...
#   ]
# }

If the parser fails to parse the LLM response, you can use the OutputFixingParser. It sends an error message, prior output, and the original prompt text to the LLM, asking for a "fixed" response:

begin
  parser.parse(llm_response)
rescue Langchain::OutputParsers::OutputParserException => e
  fix_parser = Langchain::OutputParsers::OutputFixingParser.from_llm(
    llm: llm,
    parser: parser
  )
  fix_parser.parse(llm_response)
end

Alternatively, if you don't need to handle the OutputParserException, you can simplify the code:

# we already have the `OutputFixingParser`:
# parser = Langchain::OutputParsers::StructuredOutputParser.from_json_schema(json_schema)
fix_parser = Langchain::OutputParsers::OutputFixingParser.from_llm(
  llm: llm,
  parser: parser
)
fix_parser.parse(llm_response)

See here for a concrete example

Building Retrieval Augment Generation (RAG) system

RAG is a methodology that assists LLMs generate accurate and up-to-date information. A typical RAG workflow follows the 3 steps below:

1. Relevant knowledge (or data) is retrieved from the knowledge base (typically a vector search DB)
2. A prompt, containing retrieved knowledge above, is constructed.
3. LLM receives the prompt above to generate a text completion. Most common use-case for a RAG system is powering Q&A systems where users pose natural language questions and receive answers in natural language.

Vector search databases

Langchain.rb provides a convenient unified interface on top of supported vectorsearch databases that make it easy to configure your index, add data, query and retrieve from it.

Supported vector search databases and features:

Database	Open-source	Cloud offering
Chroma	✅	✅
Epsilla	✅	✅
Hnswlib	✅	❌
Milvus	✅	✅ Zilliz Cloud
Pinecone	❌	✅
Pgvector	✅	✅
Qdrant	✅	✅
Weaviate	✅	✅
Elasticsearch	✅	✅

Using Vector Search Databases 🔍

Pick the vector search database you'll be using, add the gem dependency and instantiate the client:

gem "weaviate-ruby", "~> 0.8.9"

Choose and instantiate the LLM provider you'll be using to generate embeddings

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

client = Langchain::Vectorsearch::Weaviate.new(
    url: ENV["WEAVIATE_URL"],
    api_key: ENV["WEAVIATE_API_KEY"],
    index_name: "Documents",
    llm: llm
)

You can instantiate any other supported vector search database:

client = Langchain::Vectorsearch::Chroma.new(...)   # `gem "chroma-db", "~> 0.6.0"`
client = Langchain::Vectorsearch::Epsilla.new(...)  # `gem "epsilla-ruby", "~> 0.0.3"`
client = Langchain::Vectorsearch::Hnswlib.new(...)  # `gem "hnswlib", "~> 0.8.1"`
client = Langchain::Vectorsearch::Milvus.new(...)   # `gem "milvus", "~> 0.9.3"`
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", "~> 0.9.3"`
client = Langchain::Vectorsearch::Elasticsearch.new(...)   # `gem "elasticsearch", "~> 8.2.0"`

Create the default schema:

client.create_default_schema

Add plain text data to 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."
  ]
)

Or use the file parsers to load, parse and index data into your 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])

Supported file formats: docx, html, pdf, text, json, jsonl, csv, xlsx, eml, pptx.

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 query string passed in via the HyDE technique:

client.similarity_search_with_hyde()

Retrieve similar documents based on the embedding passed in:

client.similarity_search_by_vector(
  embedding:,
  k:       # number of results to be retrieved
)

RAG-based querying

client.ask(question: "...")

Assistants

Langchain::Assistant is a powerful and flexible class that combines Large Language Models (LLMs), tools, and conversation management to create intelligent, interactive assistants. It's designed to handle complex conversations, execute tools, and provide coherent responses based on the context of the interaction.

Features

• Supports multiple LLM providers (OpenAI, Google Gemini, Anthropic, Mistral AI and open-source models via Ollama)
• Integrates with various tools to extend functionality
• Manages conversation threads
• Handles automatic and manual tool execution
• Supports different message formats for various LLM providers

Usage

llm = Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])
assistant = Langchain::Assistant.new(
  llm: llm,
  instructions: "You're a helpful AI assistant",
  tools: [Langchain::Tool::NewsRetriever.new(api_key: ENV["NEWS_API_KEY"])]
)

# Add a user message and run the assistant
assistant.add_message_and_run!(content: "What's the latest news about AI?")

# Supply an image to the assistant
assistant.add_message_and_run!(
  content: "Show me a picture of a cat",
  image: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
)

# Access the conversation thread
messages = assistant.messages

# Run the assistant with automatic tool execution
assistant.run(auto_tool_execution: true)

# If you want to stream the response, you can add a response handler
assistant = Langchain::Assistant.new(
  llm: llm,
  instructions: "You're a helpful AI assistant",
  tools: [Langchain::Tool::NewsRetriever.new(api_key: ENV["NEWS_API_KEY"])]
) do |response_chunk|
  # ...handle the response stream
  # print(response_chunk.inspect)
end
assistant.add_message(content: "Hello")
assistant.run(auto_tool_execution: true)

Configuration

• llm: The LLM instance to use (required)
• tools: An array of tool instances (optional)
• instructions: System instructions for the assistant (optional)
• tool_choice: Specifies how tools should be selected. Default: "auto". A specific tool function name can be passed. This will force the Assistant to always use this function.
• add_message_callback: A callback function (proc, lambda) that is called when any message is added to the conversation (optional)

Key Methods

• add_message: Adds a user message to the messages array
• run!: Processes the conversation and generates responses
• add_message_and_run!: Combines adding a message and running the assistant
• submit_tool_output: Manually submit output to a tool call
• messages: Returns a list of ongoing messages

Built-in Tools 🛠️

• Langchain::Tool::Calculator: Useful for evaluating math expressions. Requires gem "eqn".
• Langchain::Tool::Database: Connect your SQL database. Requires gem "sequel".
• Langchain::Tool::FileSystem: Interact with the file system (read & write).
• Langchain::Tool::RubyCodeInterpreter: Useful for evaluating generated Ruby code. Requires gem "safe_ruby" (In need of a better solution).
• Langchain::Tool::NewsRetriever: A wrapper around NewsApi.org to fetch news articles.
• Langchain::Tool::Tavily: A wrapper around Tavily AI.
• Langchain::Tool::Weather: Calls Open Weather API to retrieve the current weather.
• Langchain::Tool::Wikipedia: Calls Wikipedia API.

Creating custom Tools

The Langchain::Assistant can be easily extended with custom tools by creating classes that extend Langchain::ToolDefinition module and implement required methods.

class MovieInfoTool
  include Langchain::ToolDefinition

  define_function :search_movie, description: "MovieInfoTool: Search for a movie by title" do
    property :query, type: "string", description: "The movie title to search for", required: true
  end

  define_function :get_movie_details, description: "MovieInfoTool: Get detailed information about a specific movie" do
    property :movie_id, type: "integer", description: "The TMDb ID of the movie", required: true
  end

  def initialize(api_key:)
    @api_key = api_key
  end

  def search_movie(query:)
    ...
  end

  def get_movie_details(movie_id:)
    ...
  end
end

Example usage:

movie_tool = MovieInfoTool.new(api_key: "...")

assistant = Langchain::Assistant.new(
  llm: llm,
  instructions: "You're a helpful AI assistant that can provide movie information",
  tools: [movie_tool]
)

assistant.add_message_and_run(content: "Can you tell me about the movie 'Inception'?")
# Check the response in the last message in the conversation
assistant.messages.last

Error Handling

The assistant includes error handling for invalid inputs, unsupported LLM types, and tool execution failures. It uses a state machine to manage the conversation flow and handle different scenarios gracefully.

Demos

1. Building an AI Assistant that operates a simulated E-commerce Store
2. New Langchain.rb Assistants interface
3. Langchain.rb Assistant demo with NewsRetriever and function calling on Gemini - code

Evaluations (Evals)

The Evaluations module is a collection of tools that can be used to evaluate and track the performance of the output products by LLM and your RAG (Retrieval Augmented Generation) pipelines.

RAGAS

Ragas helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. The implementation is based on this paper and the original Python repo. Ragas tracks the following 3 metrics and assigns the 0.0 - 1.0 scores:

• Faithfulness - the answer is grounded in the given context.
• Context Relevance - the retrieved context is focused, containing little to no irrelevant information.
• Answer Relevance - the generated answer addresses the actual question that was provided.

# We recommend using Langchain::LLM::OpenAI as your llm for Ragas
ragas = Langchain::Evals::Ragas::Main.new(llm: llm)

# The answer that the LLM generated
# The question (or the original prompt) that was asked
# The context that was retrieved (usually from a vectorsearch database)
ragas.score(answer: "", question: "", context: "")
# =>
# {
#   ragas_score: 0.6601257446503674,
#   answer_relevance_score: 0.9573145866787608,
#   context_relevance_score: 0.6666666666666666,
#   faithfulness_score: 0.5
# }

Examples

Additional examples available: /examples

Logging

Langchain.rb uses the standard Ruby Logger mechanism and defaults to same level value (currently Logger::DEBUG).

To show all log messages:

Langchain.logger.level = Logger::DEBUG

The logger logs to STDOUT by default. In order to configure the log destination (ie. log to a file) do:

Langchain.logger = Logger.new("path/to/file", **Langchain::LOGGER_OPTIONS)

Problems

If you're having issues installing unicode gem required by pragmatic_segmenter, try running:

gem install unicode -- --with-cflags="-Wno-incompatible-function-pointer-types"

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.

Star History

Contributing

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

License

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