# frozen_string_literal: true # A facade that abstracts multiple LLMs behind a single interface. # # Internally, it consists of the combination of a dialect and an endpoint. # After receiving a prompt using our generic format, it translates it to # the target model and routes the completion request through the correct gateway. # # Use the .proxy method to instantiate an object. # It chooses the correct dialect and endpoint for the model you want to interact with. # # Tests of modules that perform LLM calls can use .with_prepared_responses to return canned responses # instead of relying on WebMock stubs like we did in the past. # module DiscourseAi module Completions class Llm UNKNOWN_MODEL = Class.new(StandardError) class << self def models_by_provider # ChatGPT models are listed under open_ai but they are actually available through OpenAI and Azure. # However, since they use the same URL/key settings, there's no reason to duplicate them. @models_by_provider ||= { aws_bedrock: %w[claude-instant-1 claude-2], anthropic: %w[claude-instant-1 claude-2], vllm: %w[ mistralai/Mixtral-8x7B-Instruct-v0.1 mistralai/Mistral-7B-Instruct-v0.2 StableBeluga2 Upstage-Llama-2-*-instruct-v2 Llama2-*-chat-hf Llama2-chat-hf ], hugging_face: %w[ mistralai/Mixtral-8x7B-Instruct-v0.1 mistralai/Mistral-7B-Instruct-v0.2 StableBeluga2 Upstage-Llama-2-*-instruct-v2 Llama2-*-chat-hf Llama2-chat-hf ], open_ai: %w[gpt-3.5-turbo gpt-4 gpt-3.5-turbo-16k gpt-4-32k gpt-4-turbo], google: %w[gemini-pro], }.tap { |h| h[:fake] = ["fake"] if Rails.env.test? || Rails.env.development? } end def valid_provider_models return @valid_provider_models if defined?(@valid_provider_models) valid_provider_models = [] models_by_provider.each do |provider, models| valid_provider_models.concat(models.map { |model| "#{provider}:#{model}" }) end @valid_provider_models = Set.new(valid_provider_models) end def with_prepared_responses(responses, llm: nil) @canned_response = DiscourseAi::Completions::Endpoints::CannedResponse.new(responses) @canned_llm = llm yield(@canned_response, llm) ensure # Don't leak prepared response if there's an exception. @canned_response = nil @canned_llm = nil end def proxy(model_name) provider_and_model_name = model_name.split(":") provider_name = provider_and_model_name.first model_name_without_prov = provider_and_model_name[1..].join dialect_klass = DiscourseAi::Completions::Dialects::Dialect.dialect_for(model_name_without_prov) if @canned_response if @canned_llm && @canned_llm != model_name raise "Invalid call LLM call, expected #{@canned_llm} but got #{model_name}" end return new(dialect_klass, @canned_response, model_name) end gateway = DiscourseAi::Completions::Endpoints::Base.endpoint_for( provider_name, model_name_without_prov, ).new(model_name_without_prov, dialect_klass.tokenizer) new(dialect_klass, gateway, model_name_without_prov) end end def initialize(dialect_klass, gateway, model_name) @dialect_klass = dialect_klass @gateway = gateway @model_name = model_name end delegate :tokenizer, to: :dialect_klass # @param generic_prompt { DiscourseAi::Completions::Prompt } - Our generic prompt object # @param user { User } - User requesting the summary. # # @param &on_partial_blk { Block - Optional } - The passed block will get called with the LLM partial response alongside a cancel function. # # @returns { String } - Completion result. # # When the model invokes a tool, we'll wait until the endpoint finishes replying and feed you a fully-formed tool, # even if you passed a partial_read_blk block. Invocations are strings that look like this: # # # # get_weather # get_weather # # Sydney # c # # # # def generate( prompt, temperature: nil, top_p: nil, max_tokens: nil, stop_sequences: nil, user:, &partial_read_blk ) model_params = { max_tokens: max_tokens, stop_sequences: stop_sequences } model_params[:temperature] = temperature if temperature model_params[:top_p] = top_p if top_p if prompt.is_a?(String) prompt = DiscourseAi::Completions::Prompt.new( "You are a helpful bot", messages: [{ type: :user, content: prompt }], ) elsif prompt.is_a?(Array) prompt = DiscourseAi::Completions::Prompt.new(messages: prompt) end if !prompt.is_a?(DiscourseAi::Completions::Prompt) raise ArgumentError, "Prompt must be either a string, array, of Prompt object" end model_params.keys.each { |key| model_params.delete(key) if model_params[key].nil? } dialect = dialect_klass.new(prompt, model_name, opts: model_params) gateway.perform_completion!(dialect, user, model_params, &partial_read_blk) end def max_prompt_tokens dialect_klass.new(DiscourseAi::Completions::Prompt.new(""), model_name).max_prompt_tokens end attr_reader :model_name private attr_reader :dialect_klass, :gateway end end end