Class: SentinelRb::Client::OpenAI

Inherits:
Base
  • Object
show all
Defined in:
lib/sentinel_rb/client/openai.rb

Overview

OpenAI client implementation for LLM interactions

Instance Method Summary collapse

Constructor Details

#initialize(config) ⇒ OpenAI

Returns a new instance of OpenAI.



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# File 'lib/sentinel_rb/client/openai.rb', line 10

def initialize(config)
  super
  @client = ::OpenAI::Client.new(
    access_token: ENV[config.api_key_env] || ENV["OPENAI_API_KEY"],
    log_errors: config["log_level"] == "debug"
  )
  @model = config.model
end

Instance Method Details

#analyze_content(prompt) ⇒ Object

Analyze content for relevance using LLM



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# File 'lib/sentinel_rb/client/openai.rb', line 32

def analyze_content(prompt)
  response = @client.chat(
    parameters: {
      model: @model,
      messages: [
        {
          role: "system",
          content: "You are a prompt quality analyzer. Rate the relevance and focus of the given prompt on a scale of 0.0 to 1.0, where 1.0 means highly relevant and focused, and 0.0 means completely irrelevant or unfocused. Respond with just the numeric score."
        },
        {
          role: "user",
          content: "Analyze this prompt for relevance and focus:\n\n#{prompt}"
        }
      ],
      temperature: 0.1,
      max_tokens: 10
    }
  )

  pp response if @config["log_level"] == "debug"
  score_text = response.dig("choices", 0, "message", "content").to_s.strip
  score = extract_score(score_text)

  {
    relevance_score: score,
    raw_response: score_text
  }
rescue StandardError => e
  puts "Warning: Content analysis failed: #{e.message}" if @config["log_level"] == "debug"
  { relevance_score: 0.5, raw_response: "Analysis failed" }
end

#fact_check(statement) ⇒ Object

Basic fact-checking implementation



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# File 'lib/sentinel_rb/client/openai.rb', line 65

def fact_check(statement)
  response = @client.chat(
    parameters: {
      model: @model,
      messages: [
        {
          role: "system",
          content: "You are a fact-checker. Evaluate the accuracy of the given statement. Respond with 'TRUE' if accurate, 'FALSE' if inaccurate, or 'UNKNOWN' if uncertain. Then provide a confidence score from 0.0 to 1.0."
        },
        {
          role: "user",
          content: "Fact-check this statement: #{statement}"
        }
      ],
      temperature: 0.1,
      max_tokens: 50
    }
  )

  result = response.dig("choices", 0, "message", "content").to_s.strip
  parse_fact_check_result(result)
rescue StandardError => e
  puts "Warning: Fact-checking failed: #{e.message}" if @config["log_level"] == "debug"
  { accurate: true, confidence: 0.5, reason: "Fact-checking unavailable" }
end

#similarity(text1, text2) ⇒ Object

Calculate semantic similarity between two texts using embeddings



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# File 'lib/sentinel_rb/client/openai.rb', line 20

def similarity(text1, text2)
  embedding1 = get_embedding(text1)
  embedding2 = get_embedding(text2)

  cosine_similarity(embedding1, embedding2)
rescue StandardError => e
  # Fallback to basic text comparison if embeddings fail
  puts "Warning: Embeddings failed, using fallback similarity: #{e.message}" if @config["log_level"] == "debug"
  fallback_similarity(text1, text2)
end