BxBuilderChain

This gem / building block allows builder apps to use open ai. The following features are included: - OpenAi completion with GPT 3.5 and GPT 4 - Q&A / additional prompt context with a users private documents - Documents can be stored on a global and user/group level

Installation

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

$ bundle add bx_builder_chain

followed by

$ bundle install

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

$ gem install bx_builder_chain

Setup

generate the endpoints & Active admin contollers for Builder Chain along with the DB migrations

$ rails generate builder_chain:install

this will add the following endpoint controllers - File upload - List files - Delete file - OpenAi ask / completion - ActiveAdmin documents controller

It will also add a test view / controller for testing the endpoints. ensure this is removed prior to production release

Usage

“by require “bx_builder_chain”

“

edit the initializer config ‘bx_builder_chain.rb’ setup the api keys and db credentials

“xBuilderChain.configure do |config| config.openai_api_key = ENV[OPENAI_API_KEY]

# for db use this config.pg_url = ENV[DB_URL] || nil # eg ‘postgres://postgres:password@localhost:5432/my_db’

# or this - pg_url with take preference config.database_host = ENV[DB_HOSTNAME] || ‘localhost’ config.database_name = ENV[DB_NAME] config.database_user = ENV[DB_USER] config.database_password = ENV[DB_PASSWORD] config.database_port = ENV[DB_PORT] || ‘5432’ # Defaulting to 5432 if not set

config.public_namespace = “public” config.threshold = 0.25

config.default_prompt_template = “Context information is below

%

Given the context information and not prior knowledge answer the question: %question“ end

“

create the llm and client

“by client = BxBuilderChain::Vectorsearch::Pgvector.new( llm: BxBuilderChain::Llm::OpenAi.new, namespace: user_id # default is nil, nil is used for global public documents )

“

Add documents to the Vector Store

“by

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.] )

“

“by

Store the contents of your files in your vector search database

my_pdf = “path/to/my.pdf” my_text = “path/to/my.txt” my_docx = “path/to/my.docx”

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

“

Or via the service object

“by my_pdf = “path/to/my.pdf” my_text = “path/to/my.txt” my_docx = “path/to/my.docx”

service = DocumentUploadService.new( files: [my_pdf, my_text, my_docx], user_groups: current_user_document_groups, # optional defaults to [public] an uses the first value to store the docs client_class_name: CLIENT_CLASS_NAME, # optional defaults to ‘BxBuilderChain::Vectorsearch::Pgvector’ llm_class_name: LLM_CLASS_NAME # optional defaults to ‘BxBuilderChain::Llm::OpenAi’ )

result = service.upload_and_process

“

Then ask the question

“by client.ask(question: “What is Frogger?”)

“