Class: DSPy::LM

Inherits:
Object
  • Object
show all
Defined in:
lib/dspy/lm.rb,
lib/dspy/lm/errors.rb,
lib/dspy/lm/adapter.rb,
lib/dspy/lm/response.rb,
lib/dspy/lm/adapter_factory.rb,
lib/dspy/lm/adapters/openai_adapter.rb,
lib/dspy/lm/adapters/anthropic_adapter.rb

Defined Under Namespace

Classes: Adapter, AdapterError, AdapterFactory, AnthropicAdapter, ConfigurationError, Error, OpenAIAdapter, Response, UnsupportedProviderError

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(model_id, api_key: nil) ⇒ LM

Returns a new instance of LM.



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# File 'lib/dspy/lm.rb', line 21

def initialize(model_id, api_key: nil)
  @model_id = model_id
  @api_key = api_key
  
  # Parse provider and model from model_id
  @provider, @model = parse_model_id(model_id)
  
  # Create appropriate adapter
  @adapter = AdapterFactory.create(model_id, api_key: api_key)
end

Instance Attribute Details

#adapter ⇒ Object (readonly)

Returns the value of attribute adapter.



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# File 'lib/dspy/lm.rb', line 19

def adapter
  @adapter
end

#api_key ⇒ Object (readonly)

Returns the value of attribute api_key.



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# File 'lib/dspy/lm.rb', line 19

def api_key
  @api_key
end

#model ⇒ Object (readonly)

Returns the value of attribute model.



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# File 'lib/dspy/lm.rb', line 19

def model
  @model
end

#model_id ⇒ Object (readonly)

Returns the value of attribute model_id.



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# File 'lib/dspy/lm.rb', line 19

def model_id
  @model_id
end

#provider ⇒ Object (readonly)

Returns the value of attribute provider.



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# File 'lib/dspy/lm.rb', line 19

def provider
  @provider
end

Instance Method Details

#chat(inference_module, input_values, &block) ⇒ Object 



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# File 'lib/dspy/lm.rb', line 32

def chat(inference_module, input_values, &block)
  signature_class = inference_module.signature_class
  
  # Build messages from inference module
  messages = build_messages(inference_module, input_values)
  
  # Calculate input size for monitoring
  input_text = messages.map { |m| m[:content] }.join(' ')
  input_size = input_text.length
  
  # Use smart consolidation: emit LM events only when not in nested context
  response = nil
  token_usage = {}
  
  if should_emit_lm_events?
    # Emit all LM events when not in nested context
    response = Instrumentation.instrument('dspy.lm.request', {
      gen_ai_operation_name: 'chat',
      gen_ai_system: provider,
      gen_ai_request_model: model,
      signature_class: signature_class.name,
      provider: provider,
      adapter_class: adapter.class.name,
      input_size: input_size
    }) do
      adapter.chat(messages: messages, &block)
    end
    
    # Extract actual token usage from response (more accurate than estimation)
    token_usage = Instrumentation::TokenTracker.extract_token_usage(response, provider)
    
    # Emit token usage event if available
    if token_usage.any?
      Instrumentation.emit('dspy.lm.tokens', token_usage.merge({
        gen_ai_system: provider,
        gen_ai_request_model: model,
        signature_class: signature_class.name
      }))
    end
    
    # Instrument response parsing
    parsed_result = Instrumentation.instrument('dspy.lm.response.parsed', {
      signature_class: signature_class.name,
      provider: provider,
      response_length: response.content&.length || 0
    }) do
      parse_response(response, input_values, signature_class)
    end
  else
    # Consolidated mode: execute without nested instrumentation
    response = adapter.chat(messages: messages, &block)
    token_usage = Instrumentation::TokenTracker.extract_token_usage(response, provider)
    parsed_result = parse_response(response, input_values, signature_class)
  end
  
  parsed_result
end