mirror of
https://github.com/discourse/discourse-ai.git
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e0bf6adb5b
This PR adds tool support to available LLMs. We'll buffer tool invocations and return them instead of making users of this service parse the response. It also adds support for conversation context in the generic prompt. It includes bot messages, user messages, and tool invocations, which we'll trim to make sure it doesn't exceed the prompt limit, then translate them to the correct dialect. Finally, It adds some buffering when reading chunks to handle cases when streaming is extremely slow.:M
78 lines
2.2 KiB
Ruby
78 lines
2.2 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 HuggingFace < Base
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def self.can_contact?(model_name)
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%w[StableBeluga2 Upstage-Llama-2-*-instruct-v2 Llama2-*-chat-hf Llama2-chat-hf].include?(
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model_name,
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)
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end
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def default_options
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{ parameters: { repetition_penalty: 1.1, temperature: 0.7, return_full_text: false } }
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end
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def provider_id
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AiApiAuditLog::Provider::HuggingFaceTextGeneration
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end
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private
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def model_uri
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URI(SiteSetting.ai_hugging_face_api_url)
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end
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def prepare_payload(prompt, model_params, _dialect)
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default_options
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.merge(inputs: prompt)
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.tap do |payload|
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payload[:parameters].merge!(model_params)
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token_limit = SiteSetting.ai_hugging_face_token_limit || 4_000
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payload[:parameters][:max_new_tokens] = token_limit - prompt_size(prompt)
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payload[:stream] = true if @streaming_mode
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end
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end
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def prepare_request(payload)
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headers =
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{ "Content-Type" => "application/json" }.tap do |h|
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if SiteSetting.ai_hugging_face_api_key.present?
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h["Authorization"] = "Bearer #{SiteSetting.ai_hugging_face_api_key}"
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end
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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 extract_completion_from(response_raw)
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parsed = JSON.parse(response_raw, symbolize_names: true)
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if @streaming_mode
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# Last chunk contains full response, which we already yielded.
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return if parsed.dig(:token, :special)
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parsed.dig(:token, :text).to_s
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else
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parsed[0][:generated_text].to_s
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end
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end
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def partials_from(decoded_chunk)
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decoded_chunk
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.split("\n")
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.map do |line|
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data = line.split("data:", 2)[1]
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data&.squish == "[DONE]" ? nil : data
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end
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.compact
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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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