⚡ Building applications with LLMs through composability ⚡
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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.
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
require "langchain"| Database | Querying | Storage | Schema Management | Backups | Rails Integration |
|---|---|---|---|---|---|
| Chroma | ✅ | ✅ | ✅ | WIP | ✅ |
| Hnswlib | ✅ | ✅ | ✅ | WIP | WIP |
| Milvus | ✅ | ✅ | ✅ | WIP | WIP |
| Pinecone | ✅ | ✅ | ✅ | WIP | ✅ |
| Pgvector | ✅ | ✅ | ✅ | WIP | ✅ |
| Qdrant | ✅ | ✅ | ✅ | WIP | ✅ |
| Weaviate | ✅ | ✅ | ✅ | WIP | ✅ |
Choose the LLM provider you'll be using (OpenAI or Cohere) and retrieve the API key.
Add gem "weaviate-ruby", "~> 0.8.3" 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_name: "",
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", "~> 0.9.2"`
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"`# 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:
)class Product < ActiveRecord::Base
vectorsearch provider: Langchain::Vectorsearch::Qdrant.new(
api_key: ENV["QDRANT_API_KEY"],
url: ENV["QDRANT_URL"],
index_name: "Products",
llm: Langchain::LLM::GooglePalm.new(api_key: ENV["GOOGLE_PALM_API_KEY"])
)
after_save :upsert_to_vectorsearch
endAdditional info here.
Add gem "ruby-openai", "~> 4.0.0" to your Gemfile.
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?")Conversation support
chat = Langchain::Conversation.new(llm: openai)chat.set_context("You are the climate bot")
chat.set_functions(functions)qdrant:
client.llm.functions = functionsAdd gem "cohere-ruby", "~> 0.9.6" 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?")Add gem "hugging-face", "~> 0.3.2" to your Gemfile.
hugging_face = Langchain::LLM::HuggingFace.new(api_key: ENV["HUGGING_FACE_API_KEY"])Add gem "replicate-ruby", "~> 0.2.2" to your Gemfile.
replicate = Langchain::LLM::Replicate.new(api_key: ENV["REPLICATE_API_KEY"])Add "google_palm_api", "~> 0.1.3" to your Gemfile.
google_palm = Langchain::LLM::GooglePalm.new(api_key: ENV["GOOGLE_PALM_API_KEY"])Add gem "ai21", "~> 0.2.1" to your Gemfile.
ai21 = Langchain::LLM::AI21.new(api_key: ENV["AI21_API_KEY"])Add gem "anthropic", "~> 0.1.0" to your Gemfile.
anthropic = Langchain::LLM::Anthropic.new(api_key: ENV["ANTHROPIC_API_KEY"])anthropic.complete(prompt: "What is the meaning of life?")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"]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"]Parse LLM text responses into structured output, such as JSON.
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(prompt: prompt_text)
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)
endAlternatively, 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
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.
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::ReActAgent.new(
llm: openai,
tools: [search_tool, 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."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."
| 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" |
Need to read data from various sources? Load it up.
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')| Format | Pocessor | Gem Requirements |
|---|---|---|
| docx | Langchain::Processors::Docx | gem "docx", "~> 0.8.0" |
| html | Langchain::Processors::HTML | gem "nokogiri", "~> 1.13" |
| 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" |
Additional examples available: /examples
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 = :infogit clone https://github.com/andreibondarev/langchainrb.gitcp .env.example .env, then fill out the environment variables in.envbundle exec raketo ensure that the tests pass and to run standardrbbin/consoleto load the gem in a REPL session. Feel free to add your own instances of LLMs, Tools, Agents, etc. and experiment with them.- Optionally, install lefthook git hooks for pre-commit to auto lint:
gem install lefthook && lefthook install -f
Join us in the Langchain.rb Discord server.
Bug reports and pull requests are welcome on GitHub at https://github.com/andreibondarev/langchainrb.
The gem is available as open source under the terms of the MIT License.