108 lines
2.9 KiB
Ruby
108 lines
2.9 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 ChatGpt < Dialect
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class << self
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def can_translate?(model_provider)
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model_provider == "open_ai" || model_provider == "azure"
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end
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end
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VALID_ID_REGEX = /\A[a-zA-Z0-9_]+\z/
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def native_tool_support?
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llm_model.provider == "open_ai" || llm_model.provider == "azure"
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end
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def translate
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@embed_user_ids =
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prompt.messages.any? do |m|
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m[:id] && m[:type] == :user && !m[:id].to_s.match?(VALID_ID_REGEX)
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end
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super
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end
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def max_prompt_tokens
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# provide a buffer of 120 tokens - our function counting is not
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# 100% accurate and getting numbers to align exactly is very hard
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buffer = (opts[:max_tokens] || 2500) + 50
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if tools.present?
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# note this is about 100 tokens over, OpenAI have a more optimal representation
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@function_size ||= llm_model.tokenizer_class.size(tools.to_json.to_s)
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buffer += @function_size
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end
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llm_model.max_prompt_tokens - buffer
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end
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private
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def tools_dialect
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@tools_dialect ||= DiscourseAi::Completions::Dialects::OpenAiTools.new(prompt.tools)
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end
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def system_msg(msg)
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{ role: "system", content: msg[:content] }
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end
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def model_msg(msg)
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{ role: "assistant", content: msg[:content] }
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end
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def tool_call_msg(msg)
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tools_dialect.from_raw_tool_call(msg)
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end
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def tool_msg(msg)
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tools_dialect.from_raw_tool(msg)
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end
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def user_msg(msg)
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user_message = { role: "user", content: msg[:content] }
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if msg[:id]
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if @embed_user_ids
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user_message[:content] = "#{msg[:id]}: #{msg[:content]}"
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else
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user_message[:name] = msg[:id]
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end
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end
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user_message[:content] = inline_images(user_message[:content], msg) if vision_support?
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user_message
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end
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def inline_images(content, message)
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encoded_uploads = prompt.encoded_uploads(message)
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return content if encoded_uploads.blank?
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content_w_imgs =
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encoded_uploads.reduce([]) do |memo, details|
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memo << {
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type: "image_url",
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image_url: {
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url: "data:#{details[:mime_type]};base64,#{details[:base64]}",
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},
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}
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end
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content_w_imgs << { type: "text", text: message[:content] }
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end
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def per_message_overhead
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# open ai defines about 4 tokens per message of overhead
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4
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end
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def calculate_message_token(context)
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llm_model.tokenizer_class.size(context[:content].to_s + context[:name].to_s)
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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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