timrwilliams / langchainrb Goto Github PK

require "langchain"

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(...) # `gem "milvus", "~> 0.9.0"`
client = Vectorsearch::Qdrant.new(...) # `gem"qdrant-ruby", "~> 0.9.0"`
client = Vectorsearch::Pinecone.new(...) # `gem "pinecone", "~> 0.1.6"`
client = Vectorsearch::Chroma.new(...) # `gem "chroma-db", "~> 0.3.0"`

# 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:
)

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

cohere.embed(text: "foo bar")

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

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

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

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

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."

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."

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

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

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:

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

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

agent = Agent::ChainOfThoughtAgent.new(llm: :openai, llm_api_key: ENV["OPENAI_API_KEY"], tools: ['search', 'calculator'])

agent.tools
# => ["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."

Langchain.logger.level = :info