Module: Legion::LLM::Inference::Prompt

Defined in:
lib/legion/llm/inference/prompt.rb

Class Method Summary collapse

Class Method Details

.decide(question, options:, tools: []) ⇒ Object

Pick from a set of options with reasoning.



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# File 'lib/legion/llm/inference/prompt.rb', line 113

def decide(question, options:, tools: [], **)
  prompt = build_decide_prompt(question, options)
  dispatch(prompt, tools: tools, **)
end

.dispatch(message, intent: nil, tier: nil, exclude: {}, provider: nil, model: nil, schema: nil, tools: nil, escalate: nil, max_escalations: 3, thinking: nil, temperature: nil, max_tokens: nil, tracing: nil, agent: nil, caller: nil, cache: nil, quality_check: nil) ⇒ Object

Auto-routed: Router picks the best provider+model based on intent. Primary entry point for most LLM calls. When provider/model are passed explicitly, they take precedence over routing.



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# File 'lib/legion/llm/inference/prompt.rb', line 12

def dispatch(message, # rubocop:disable Metrics/ParameterLists
             intent: nil,
             tier: nil,
             exclude: {},
             provider: nil,
             model: nil,
             schema: nil,
             tools: nil,
             escalate: nil,
             max_escalations: 3,
             thinking: nil,
             temperature: nil,
             max_tokens: nil,
             tracing: nil,
             agent: nil,
             caller: nil,
             cache: nil,
             quality_check: nil,
             **)
  resolved_provider = provider
  resolved_model = model

  if resolved_provider.nil? && resolved_model.nil? && defined?(Router) && Router.routing_enabled? && (intent || tier)
    resolution = Router.resolve(intent: intent, tier: tier, exclude: exclude)
    resolved_provider = resolution&.provider
    resolved_model = resolution&.model
  end

  resolved_provider ||= Legion::LLM.settings[:default_provider]
  resolved_model ||= Legion::LLM.settings[:default_model]

  request(message,
          provider:        resolved_provider,
          model:           resolved_model,
          intent:          intent,
          tier:            tier,
          schema:          schema,
          tools:           tools,
          escalate:        escalate,
          max_escalations: max_escalations,
          thinking:        thinking,
          temperature:     temperature,
          max_tokens:      max_tokens,
          tracing:         tracing,
          agent:           agent,
          caller:          caller,
          cache:           cache,
          quality_check:   quality_check,
          **)
end

.extract(text, schema:, tools: []) ⇒ Object

Extract structured data from unstructured text.



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# File 'lib/legion/llm/inference/prompt.rb', line 107

def extract(text, schema:, tools: [], **)
  prompt = build_extract_prompt(text)
  dispatch(prompt, schema: schema, tools: tools, **)
end

.request(message, provider:, model:, intent: nil, tier: nil, schema: nil, tools: nil, escalate: nil, max_escalations: 3, thinking: nil, temperature: nil, max_tokens: nil, tracing: nil, agent: nil, caller: nil, cache: nil, quality_check: nil) ⇒ Object

Pinned: caller specifies exact provider+model. Full pipeline runs in-process.



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# File 'lib/legion/llm/inference/prompt.rb', line 64

def request(message, # rubocop:disable Metrics/ParameterLists
            provider:,
            model:,
            intent: nil,
            tier: nil,
            schema: nil,
            tools: nil,
            escalate: nil,
            max_escalations: 3,
            thinking: nil,
            temperature: nil,
            max_tokens: nil,
            tracing: nil,
            agent: nil,
            caller: nil,
            cache: nil,
            quality_check: nil,
            **)
  if provider.nil? || model.nil?
    raise LLMError, "Prompt.request: provider and model must be set (got provider=#{provider.inspect}, model=#{model.inspect}). " \
                    'Configure Legion::Settings[:llm][:default_provider] and [:default_model], or pass them explicitly.'
  end

  pipeline_request = build_pipeline_request(
    message, provider: provider, model: model, intent: intent, tier: tier,
             schema: schema, tools: tools,
             escalate: escalate, max_escalations: max_escalations,
             thinking: thinking, temperature: temperature, max_tokens: max_tokens,
             tracing: tracing, agent: agent, caller: caller, cache: cache,
             quality_check: quality_check, **
  )

  executor = Inference::Executor.new(pipeline_request)
  executor.call
end

.summarize(messages, tools: []) ⇒ Object

Condense a conversation or feedback history into a shorter form.



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# File 'lib/legion/llm/inference/prompt.rb', line 101

def summarize(messages, tools: [], **)
  prompt = build_summarize_prompt(messages)
  dispatch(prompt, tools: tools, **)
end