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d0f54443ae
Introduce a Discourse Automation based periodical report. Depends on Discourse Automation. Report works best with very large context language models such as GPT-4-Turbo and Claude 2. - Introduces final_insts to generic llm format, for claude to work best it is better to guide the last assistant message (we should add this to other spots as well) - Adds GPT-4 turbo support to generic llm interface
80 lines
2.1 KiB
Ruby
80 lines
2.1 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 Claude < Dialect
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class << self
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def can_translate?(model_name)
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%w[claude-instant-1 claude-2].include?(model_name)
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end
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def tokenizer
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DiscourseAi::Tokenizer::AnthropicTokenizer
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end
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end
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def translate
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claude_prompt = +"Human: #{prompt[:insts]}\n"
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claude_prompt << build_tools_prompt if prompt[:tools]
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claude_prompt << build_examples(prompt[:examples]) if prompt[:examples]
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claude_prompt << conversation_context if prompt[:conversation_context]
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claude_prompt << "#{prompt[:input]}\n"
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claude_prompt << "#{prompt[:post_insts]}\n" if prompt[:post_insts]
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claude_prompt << "Assistant:"
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claude_prompt << " #{prompt[:final_insts]}:" if prompt[:final_insts]
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claude_prompt << "\n"
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end
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def max_prompt_tokens
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50_000
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end
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def conversation_context
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return "" if prompt[:conversation_context].blank?
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trimmed_context = trim_context(prompt[:conversation_context])
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trimmed_context
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.reverse
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.reduce(+"") do |memo, context|
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memo << (context[:type] == "user" ? "Human:" : "Assistant:")
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if context[:type] == "tool"
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memo << <<~TEXT
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<function_results>
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<result>
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<tool_name>#{context[:name]}</tool_name>
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<json>
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#{context[:content]}
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</json>
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</result>
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</function_results>
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TEXT
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else
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memo << " " << context[:content] << "\n"
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end
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memo
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end
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end
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private
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def build_examples(examples_arr)
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examples_arr.reduce("") do |memo, example|
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memo += "<example>\nH: #{example[0]}\nA: #{example[1]}\n</example>\n"
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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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end
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