160 lines
4.6 KiB
Ruby
160 lines
4.6 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?(llm_model)
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llm_model.provider == "open_router" || llm_model.provider == "open_ai" ||
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llm_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 embed_user_ids?
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return @embed_user_ids if defined?(@embed_user_ids)
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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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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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def disable_native_tools?
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return @disable_native_tools if defined?(@disable_native_tools)
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!!@disable_native_tools = llm_model.lookup_custom_param("disable_native_tools")
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end
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private
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def tools_dialect
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if disable_native_tools?
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super
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else
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@tools_dialect ||= DiscourseAi::Completions::Dialects::OpenAiTools.new(prompt.tools)
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end
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end
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# developer messages are preferred on recent reasoning models
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def supports_developer_messages?
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!legacy_reasoning_model? && llm_model.provider == "open_ai" &&
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(llm_model.name.start_with?("o1") || llm_model.name.start_with?("o3"))
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end
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def legacy_reasoning_model?
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llm_model.provider == "open_ai" &&
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(llm_model.name.start_with?("o1-preview") || llm_model.name.start_with?("o1-mini"))
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end
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def system_msg(msg)
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content = msg[:content]
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if disable_native_tools? && tools_dialect.instructions.present?
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content = content + "\n\n" + tools_dialect.instructions
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end
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if supports_developer_messages?
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{ role: "developer", content: content }
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elsif legacy_reasoning_model?
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{ role: "user", content: content }
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else
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{ role: "system", content: content }
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end
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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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if disable_native_tools?
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super
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else
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tools_dialect.from_raw_tool_call(msg)
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end
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end
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def tool_msg(msg)
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if disable_native_tools?
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super
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else
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tools_dialect.from_raw_tool(msg)
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end
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end
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def user_msg(msg)
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content_array = []
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user_message = { role: "user" }
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if msg[:id]
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if embed_user_ids?
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content_array << "#{msg[:id]}: "
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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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content_array << msg[:content]
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content_array =
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to_encoded_content_array(
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content: content_array.flatten,
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image_encoder: ->(details) { image_node(details) },
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text_encoder: ->(text) { { type: "text", text: text } },
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allow_vision: vision_support?,
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)
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user_message[:content] = no_array_if_only_text(content_array)
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user_message
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end
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def no_array_if_only_text(content_array)
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if content_array.size == 1 && content_array.first[:type] == "text"
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content_array.first[:text]
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else
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content_array
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end
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end
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def image_node(details)
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{
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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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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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