136 lines
4.0 KiB
Ruby
136 lines
4.0 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Completions
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module Endpoints
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class OpenAi < Base
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def self.can_contact?(model_provider)
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%w[open_ai azure].include?(model_provider)
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end
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def normalize_model_params(model_params)
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model_params = model_params.dup
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# max_tokens, temperature are already supported
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if model_params[:stop_sequences]
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model_params[:stop] = model_params.delete(:stop_sequences)
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end
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model_params
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end
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def default_options
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{ model: llm_model.name }
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end
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def provider_id
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AiApiAuditLog::Provider::OpenAI
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end
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def perform_completion!(
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dialect,
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user,
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model_params = {},
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feature_name: nil,
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feature_context: nil,
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partial_tool_calls: false,
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&blk
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)
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if dialect.respond_to?(:is_gpt_o?) && dialect.is_gpt_o? && block_given?
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# we need to disable streaming and simulate it
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blk.call "", lambda { |*| }
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response = super(dialect, user, model_params, feature_name: feature_name, &nil)
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blk.call response, lambda { |*| }
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else
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super
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end
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end
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private
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def model_uri
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if llm_model.url.to_s.starts_with?("srv://")
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service = DiscourseAi::Utils::DnsSrv.lookup(llm_model.url.sub("srv://", ""))
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api_endpoint = "https://#{service.target}:#{service.port}/v1/chat/completions"
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else
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api_endpoint = llm_model.url
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end
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@uri ||= URI(api_endpoint)
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end
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def prepare_payload(prompt, model_params, dialect)
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payload = default_options.merge(model_params).merge(messages: prompt)
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if @streaming_mode
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payload[:stream] = true
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# Usage is not available in Azure yet.
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# We'll fallback to guess this using the tokenizer.
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payload[:stream_options] = { include_usage: true } if llm_model.provider == "open_ai"
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end
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if dialect.tools.present?
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payload[:tools] = dialect.tools
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if dialect.tool_choice.present?
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payload[:tool_choice] = { type: "function", function: { name: dialect.tool_choice } }
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end
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end
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payload
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end
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def prepare_request(payload)
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headers = { "Content-Type" => "application/json" }
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api_key = llm_model.api_key
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if llm_model.provider == "azure"
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headers["api-key"] = api_key
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else
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headers["Authorization"] = "Bearer #{api_key}"
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org_id = llm_model.lookup_custom_param("organization")
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headers["OpenAI-Organization"] = org_id if org_id.present?
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end
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Net::HTTP::Post.new(model_uri, headers).tap { |r| r.body = payload }
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end
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def final_log_update(log)
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log.request_tokens = processor.prompt_tokens if processor.prompt_tokens
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log.response_tokens = processor.completion_tokens if processor.completion_tokens
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end
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def decode(response_raw)
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processor.process_message(JSON.parse(response_raw, symbolize_names: true))
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end
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def decode_chunk(chunk)
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@decoder ||= JsonStreamDecoder.new
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elements =
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(@decoder << chunk)
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.map { |parsed_json| processor.process_streamed_message(parsed_json) }
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.flatten
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.compact
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# Remove duplicate partial tool calls
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# sometimes we stream weird chunks
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seen_tools = Set.new
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elements.select { |item| !item.is_a?(ToolCall) || seen_tools.add?(item) }
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end
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def decode_chunk_finish
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@processor.finish
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end
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def xml_tools_enabled?
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false
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end
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private
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def processor
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@processor ||= OpenAiMessageProcessor.new(partial_tool_calls: partial_tool_calls)
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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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