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Both endpoints provide OpenAI-compatible servers. The only difference is that Vllm doesn't support passing tools as a separate parameter. Even if the tool param is supported, it ultimately relies on the model's ability to handle native functions, which is not the case with the models we have today. As a part of this change, we are dropping support for StableBeluga/Llama2 models. They don't have a chat_template, meaning the new API can translate them. These changes let us remove some of our existing dialects and are a first step in our plan to support any LLM by defining them as data-driven concepts. I rewrote the "translate" method to use a template method and extracted the tool support strategies into its classes to simplify the code. Finally, these changes bring support for Ollama when running in dev mode. It only works with Mistral for now, but it will change soon..
168 lines
4.5 KiB
Ruby
168 lines
4.5 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Completions
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module Dialects
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class Dialect
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class << self
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def can_translate?(_model_name)
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raise NotImplemented
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end
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def dialect_for(model_name)
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dialects = [
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DiscourseAi::Completions::Dialects::ChatGpt,
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DiscourseAi::Completions::Dialects::Gemini,
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DiscourseAi::Completions::Dialects::Mistral,
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DiscourseAi::Completions::Dialects::Claude,
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DiscourseAi::Completions::Dialects::Command,
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]
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if Rails.env.test? || Rails.env.development?
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dialects << DiscourseAi::Completions::Dialects::Fake
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end
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dialect = dialects.find { |d| d.can_translate?(model_name) }
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raise DiscourseAi::Completions::Llm::UNKNOWN_MODEL if !dialect
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dialect
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end
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def tokenizer
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raise NotImplemented
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end
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end
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def initialize(generic_prompt, model_name, opts: {})
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@prompt = generic_prompt
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@model_name = model_name
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@opts = opts
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end
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VALID_ID_REGEX = /\A[a-zA-Z0-9_]+\z/
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def can_end_with_assistant_msg?
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false
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end
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def native_tool_support?
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false
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end
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def tools
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@tools ||= tools_dialect.translated_tools
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end
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def translate
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messages = prompt.messages
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# Some models use an assistant msg to improve long-context responses.
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if messages.last[:type] == :model && can_end_with_assistant_msg?
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messages = messages.dup
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messages.pop
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end
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trim_messages(messages).map { |msg| send("#{msg[:type]}_msg", msg) }.compact
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end
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def conversation_context
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raise NotImplemented
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end
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def max_prompt_tokens
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raise NotImplemented
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end
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attr_reader :prompt
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private
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attr_reader :model_name, :opts
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def trim_messages(messages)
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prompt_limit = max_prompt_tokens
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current_token_count = 0
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message_step_size = (max_prompt_tokens / 25).to_i * -1
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trimmed_messages = []
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range = (0..-1)
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if messages.dig(0, :type) == :system
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system_message = messages[0]
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trimmed_messages << system_message
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current_token_count += calculate_message_token(system_message)
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range = (1..-1)
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end
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reversed_trimmed_msgs = []
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messages[range].reverse.each do |msg|
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break if current_token_count >= prompt_limit
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message_tokens = calculate_message_token(msg)
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dupped_msg = msg.dup
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# Don't trim tool call metadata.
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if msg[:type] == :tool_call
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break if current_token_count + message_tokens + per_message_overhead > prompt_limit
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current_token_count += message_tokens + per_message_overhead
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reversed_trimmed_msgs << dupped_msg
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next
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end
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# Trimming content to make sure we respect token limit.
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while dupped_msg[:content].present? &&
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message_tokens + current_token_count + per_message_overhead > prompt_limit
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dupped_msg[:content] = dupped_msg[:content][0..message_step_size] || ""
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message_tokens = calculate_message_token(dupped_msg)
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end
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next if dupped_msg[:content].blank?
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current_token_count += message_tokens + per_message_overhead
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reversed_trimmed_msgs << dupped_msg
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end
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reversed_trimmed_msgs.pop if reversed_trimmed_msgs.last&.dig(:type) == :tool
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trimmed_messages.concat(reversed_trimmed_msgs.reverse)
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end
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def per_message_overhead
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0
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end
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def calculate_message_token(msg)
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self.class.tokenizer.size(msg[:content].to_s)
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end
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def tools_dialect
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@tools_dialect ||= DiscourseAi::Completions::Dialects::XmlTools.new(prompt.tools)
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end
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def system_msg(msg)
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raise NotImplemented
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end
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def assistant_msg(msg)
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raise NotImplemented
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end
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def user_msg(msg)
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raise NotImplemented
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end
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def tool_call_msg(msg)
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{ role: "assistant", content: tools_dialect.from_raw_tool_call(msg) }
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end
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def tool_msg(msg)
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{ role: "user", content: tools_dialect.from_raw_tool(msg) }
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end
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end
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end
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end
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end
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