Module: CPEE::LLM::RubyLLM_Requests

Included in:
Functions
Defined in:
lib/cpee/llm/rubyllm_requests.rb

Overview

}}}

Instance Method Summary collapse

Instance Method Details

#adapt_mermaid_model(llm, user_input, process_model, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 103

def adapt_mermaid_model(llm, user_input, process_model, llms={}) #{{{
  max_tokens = 4000
  temperature = 0
  system_prompt = File.read(File.join(__dir__,"prompts/apply.txt"))
  user_prompt = "Consider following process model: #{process_model}. Update this process model according to provided changes #{user_input}."
  new_mermaid = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return new_mermaid
end

#adapt_xml_model(llm, user_input, process_model, api_specification, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 112

def adapt_xml_model(llm, user_input, process_model, api_specification, llms={}) #{{{
  max_tokens = 20000
  temperature = 0
  system_prompt = File.read(File.join(__dir__,"prompts/adapt_xml.txt"))
  user_prompt = "Consider following process model: #{process_model.to_s} and task specification #{api_specification} with endpoint data. Update this process model according to provided changes #{user_input}."
  new_cpee = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return new_cpee
end

#connect_llm(myllm, llms) ⇒ Object

{{{



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 36

def connect_llm(myllm,llms) #{{{
  chat = nil
  RubyLLM.configure do |config|
    config.request_timeout = llms[:request_timeout]
    config.max_retries = llms[:max_retries]

    llms[:connectors].each do |k,v|
      if myllm =~ /#{k}/ && chat.nil?
        chat = eval(v)
      end
    end

    if chat.nil?
      raise LLMError.new("Selected LLM model does not exist or is not supported. Please, select another LLM model.",  400)
    end
  end
  return chat
end

#generate_content(myllm, system_prompt, user_prompt, max_tokens, temperature, llms) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 55

def generate_content(myllm, system_prompt, user_prompt, max_tokens, temperature, llms) #{{{
 chat = connect_llm(myllm,llms)
 chat.with_instructions system_prompt
 chat.with_temperature(temperature)
 if max_tokens != 0
   if myllm.include?("gemini")
     chat.with_params(generationConfig:{maxOutputTokens: max_tokens})
   elsif myllm.include?("gpt")
     chat.with_params(max_completion_tokens: max_tokens)
   else
     chat.with_params(max_tokens: max_tokens)
   end
 end
 response = chat.ask user_prompt
 return response.content
      rescue Faraday::TimeoutError => e
 raise LLMError.new(e.message, 504)
      rescue Exception => e
 raise LLMError.new(e.message, 500)
end

#generate_dataflow_content(llm, mermaid_model, api_specification, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 141

def generate_dataflow_content(llm, mermaid_model, api_specification, llms={}) #{{{
  max_tokens = 10000
  temperature = 0.1
  system_prompt = File.read(File.join(__dir__,"prompts/dataflow.txt"))
  user_prompt = "Given process mode #{mermaid_model} and task specification #{api_specification} with endpoint data, define the execution context and return a JSON specification."
  dataflow = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return dataflow
end

#generate_endpoint_mermaid_model(llm, user_input, endpoints, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 150

def generate_endpoint_mermaid_model(llm, user_input, endpoints, llms={}) #{{{
  max_tokens = 4000
  temperature = 0.1
  system_prompt = File.read(File.join(__dir__,"prompts/generate_enpoints.txt"))
  user_prompt = "Consider the following process description: #{user_input} and the provided endpoint list: #{endpoints}. Interpret the process description as business intent and generate an executable BPMN model in Mermaid.js format using only the available endpoint capabilities."
  new_mermaid = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return new_mermaid
end

#generate_generic_content(llm, user_input, system_prompt, json, temperature, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 130

def generate_generic_content(llm, user_input, system_prompt, json, temperature, llms={}) #{{{
  max_tokens = 20000
  temperature = temperature.nil? ? 0 : temperature.to_f
  if json == 'true'
    process_description = generate_json_content(llm,system_prompt,user_input,max_tokens,temperature,llms)
  else
    process_description = generate_content(llm,system_prompt,user_input,max_tokens,temperature,llms)
  end
  return process_description
end

#generate_json_content(myllm, system_prompt, user_prompt, max_tokens, temperature, llms) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 76

def generate_json_content(myllm,system_prompt,user_prompt,max_tokens,temperature,llms) #{{{
 chat = connect_llm(myllm,llms)

 #set parameters
 chat.with_params(max_tokens: max_tokens,response_format:{type:'json_object'})
 chat.with_instructions system_prompt
 chat.with_temperature(temperature)
 response = chat.ask user_prompt
 #puts JSON.parse(response.content)
 return response.content
      rescue Faraday::TimeoutError => e
 raise LLMError.new(e.message, 504)
      rescue Exception => e
 raise LLMError.new(e.message, 500)
end

#generate_mermaid_model(llm, user_input, temperature, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 92

def generate_mermaid_model(llm, user_input, temperature, llms={}) #{{{
  max_tokens = 4000
  temperature = temperature.nil? ? 0.1 : temperature.to_f
  pp "here"
  pp temperature
  system_prompt = File.read(File.join(__dir__,"prompts/generate1.txt"))
  user_prompt = "Consider following process description: #{user_input}. Generate a BPMN model in Mermaid.js format."
  new_mermaid = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return new_mermaid
end

#generate_plain_text(llm, user_input, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 121

def generate_plain_text(llm, user_input, llms={}) #{{{
  max_tokens = 4000
  temperature = 0
  system_prompt = File.read(File.join(__dir__,"prompts/describe.txt"))
  user_prompt = "Consider following process process model: #{user_input}. Generate a text describing provided process description."
  process_description = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return process_description
end

#validate_xml_model(llm, cpee_model, llms = {}) ⇒ Object

}}}



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# File 'lib/cpee/llm/rubyllm_requests.rb', line 159

def validate_xml_model(llm, cpee_model, llms={}) #{{{
  max_tokens = 0
  temperature = 0.1
  system_prompt = File.read(File.join(__dir__,"prompts/validate_xml.txt"))
  user_prompt = "Consider following CPEE XML promcess model created by autobpmn.ai: #{cpee_model}. Repair the model so that it becomes executable. Return only the repaired XML without any comments or markdown formatting."
  repaired_cpee = generate_content(llm,system_prompt,user_prompt,max_tokens,temperature,llms)
  return repaired_cpee
end