feat(ee): Add Captain features (#10665)

Migration Guide: https://chwt.app/v4/migration

This PR imports all the work related to Captain into the EE codebase. Captain represents the AI-based features in Chatwoot and includes the following key components:

- Assistant: An assistant has a persona, the product it would be trained on. At the moment, the data at which it is trained is from websites. Future integrations on Notion documents, PDF etc. This PR enables connecting an assistant to an inbox. The assistant would run the conversation every time before transferring it to an agent.
- Copilot for Agents: When an agent is supporting a customer, we will be able to offer additional help to lookup some data or fetch information from integrations etc via copilot.
- Conversation FAQ generator: When a conversation is resolved, the Captain integration would identify questions which were not in the knowledge base.
- CRM memory: Learns from the conversations and identifies important information about the contact.

---------

Co-authored-by: Vishnu Narayanan <vishnu@chatwoot.com>
Co-authored-by: Sojan <sojan@pepalo.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>

enterprise/lib/captain/agent.rb

@@ -0,0 +1,132 @@
+require 'openai'
+class Captain::Agent
+  attr_reader :name, :tools, :prompt, :persona, :goal, :secrets
+
+  def initialize(name:, config:)
+    @name = name
+    @prompt = construct_prompt(config)
+    @tools = prepare_tools(config[:tools] || [])
+    @messages = config[:messages] || []
+    @max_iterations = config[:max_iterations] || 10
+    @llm = Captain::LlmService.new(api_key: config[:secrets][:OPENAI_API_KEY])
+    @logger = Rails.logger
+
+    @logger.info(@prompt)
+  end
+
+  def execute(input, context)
+    setup_messages(input, context)
+    result = {}
+    @max_iterations.times do |iteration|
+      push_to_messages(role: 'system', content: 'Provide a final answer') if iteration == @max_iterations - 1
+
+      result = @llm.call(@messages, functions)
+      handle_llm_result(result)
+
+      break if result[:stop]
+    end
+
+    result[:output]
+  end
+
+  def register_tool(tool)
+    @tools << tool
+  end
+
+  private
+
+  def setup_messages(input, context)
+    if @messages.empty?
+      push_to_messages({ role: 'system', content: @prompt })
+      push_to_messages({ role: 'assistant', content: context }) if context.present?
+    end
+    push_to_messages({ role: 'user', content: input })
+  end
+
+  def handle_llm_result(result)
+    if result[:tool_call]
+      tool_result = execute_tool(result[:tool_call])
+      push_to_messages({ role: 'assistant', content: tool_result })
+    else
+      push_to_messages({ role: 'assistant', content: result[:output] })
+    end
+    result[:output]
+  end
+
+  def execute_tool(tool_call)
+    function_name = tool_call['function']['name']
+    arguments = JSON.parse(tool_call['function']['arguments'])
+
+    tool = @tools.find { |t| t.name == function_name }
+    tool.execute(arguments, {})
+  rescue StandardError => e
+    "Tool execution failed: #{e.message}"
+  end
+
+  def construct_prompt(config)
+    return config[:prompt] if config[:prompt]
+
+    "
+      Persona: #{config[:persona]}
+      Objective: #{config[:goal]}
+
+      Guidelines:
+      - Work diligently until the stated objective is achieved.
+      - Utilize only the provided tools for solving the task. Do not make up names of the functions
+      - Set 'stop: true' when the objective is complete.
+      - DO NOT provide tool_call as final answer
+      - If you have enough information to provide the details to the user, prepare a final result collecting all the information you have.
+
+      Output Structure:
+
+      If you find a function, that can be used, directly call the function.
+
+      When providing the final answer, use the JSON format:
+      {
+        'thought_process': 'Describe the reasoning and steps that led to the final result.',
+        'result': 'The complete answer in text form.',
+        'stop': true
+      }
+      "
+  end
+
+  def prepare_tools(tools = [])
+    tools.map do |_, tool|
+      Captain::Tool.new(
+        name: tool['name'],
+        config: {
+          description: tool['description'],
+          properties: tool['properties'],
+          secrets: tool['secrets'],
+          implementation: tool['implementation']
+        }
+      )
+    end
+  end
+
+  def functions
+    @tools.map do |tool|
+      properties = {}
+      tool.properties.each do |property_name, property_details|
+        properties[property_name] = {
+          type: property_details[:type],
+          description: property_details[:description]
+        }
+      end
+      required = tool.properties.select { |_, details| details[:required] == true }.keys
+      {
+        type: 'function',
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: { type: 'object', properties: properties, required: required }
+        }
+      }
+    end
+  end
+
+  def push_to_messages(message)
+    @logger.info("Message: #{message}")
+    @messages << message
+  end
+end

enterprise/lib/captain/llm_service.rb

@@ -0,0 +1,63 @@
+require 'openai'
+
+class Captain::LlmService
+  def initialize(config)
+    @client = OpenAI::Client.new(access_token: config[:api_key]) do |f|
+      f.response :logger, Logger.new($stdout), bodies: true
+    end
+    @logger = Rails.logger
+  end
+
+  def call(messages, functions = [])
+    openai_params = {
+      model: 'gpt-4o-mini',
+      response_format: { type: 'json_object' },
+      messages: messages
+    }
+    openai_params[:tools] = functions if functions.any?
+
+    response = @client.chat(parameters: openai_params)
+    handle_response(response)
+  rescue StandardError => e
+    handle_error(e)
+  end
+
+  private
+
+  def handle_response(response)
+    if response['choices'][0]['message']['tool_calls']
+      handle_tool_calls(response)
+    else
+      handle_direct_response(response)
+    end
+  end
+
+  def handle_tool_calls(response)
+    tool_call = response['choices'][0]['message']['tool_calls'][0]
+    {
+      tool_call: tool_call,
+      output: nil,
+      stop: false
+    }
+  end
+
+  def handle_direct_response(response)
+    content = response.dig('choices', 0, 'message', 'content').strip
+    parsed = JSON.parse(content)
+
+    {
+      output: parsed['result'] || parsed['thought_process'],
+      stop: parsed['stop'] || false
+    }
+  rescue JSON::ParserError => e
+    handle_error(e, content)
+  end
+
+  def handle_error(error, content = nil)
+    @logger.error("LLM call failed: #{error.message}")
+    @logger.error(error.backtrace.join("\n"))
+    @logger.error("Content: #{content}") if content
+
+    { output: 'Error occurred, retrying', stop: false }
+  end
+end

enterprise/lib/captain/tool.rb

@@ -0,0 +1,66 @@
+class Captain::Tool
+  class InvalidImplementationError < StandardError; end
+  class InvalidSecretsError < StandardError; end
+  class ExecutionError < StandardError; end
+
+  REQUIRED_PROPERTIES = %w[name description properties secrets].freeze
+
+  attr_reader :name, :description, :properties, :secrets, :implementation, :memory
+
+  def initialize(name:, config:)
+    @name = name
+    @description = config[:description]
+    @properties =  config[:properties]
+    @secrets =  config[:secrets] || []
+    @implementation = config[:implementation]
+    @memory = config[:memory] || {}
+  end
+
+  def register_method(&block)
+    @implementation = block
+  end
+
+  def execute(input, provided_secrets = {})
+    validate_secrets!(provided_secrets)
+    validate_input!(input)
+
+    raise ExecutionError, 'No implementation registered' unless @implementation
+
+    instance_exec(input, provided_secrets, memory, &@implementation)
+  rescue StandardError => e
+    raise ExecutionError, "Execution failed: #{e.message}"
+  end
+
+  private
+
+  def validate_config!(config)
+    missing_keys = REQUIRED_PROPERTIES - config.keys
+    return if missing_keys.empty?
+
+    raise InvalidImplementationError,
+          "Missing required properties: #{missing_keys.join(', ')}"
+  end
+
+  def validate_secrets!(provided_secrets)
+    required_secrets = secrets.map!(&:to_sym)
+    missing_secrets = required_secrets - provided_secrets.keys
+
+    return if missing_secrets.empty?
+
+    raise InvalidSecretsError, "Missing required secrets: #{missing_secrets.join(', ')}"
+  end
+
+  def validate_input!(input)
+    properties.each do |property, constraints|
+      validate_property!(input, property, constraints)
+    end
+  end
+
+  def validate_property!(input, property, constraints)
+    value = input[property.to_sym]
+
+    raise ArgumentError, "Missing required property: #{property}" if constraints['required'] && value.nil?
+
+    true
+  end
+end

enterprise/lib/enterprise/integrations/openai_processor_service.rb

@@ -3,24 +3,6 @@ module Enterprise::Integrations::OpenaiProcessorService
                            make_friendly make_formal simplify].freeze
   CACHEABLE_EVENTS = %w[label_suggestion].freeze
 
-  def reply_suggestion_message
-    return super unless conversation.inbox.response_bot_enabled?
-
-    messages = conversation_messages(in_array_format: true)
-    last_message = messages.pop
-
-    robin_response = ChatGpt.new(
-      Enterprise::MessageTemplates::ResponseBotService.response_sections(last_message[:content], conversation.inbox)
-    ).generate_response(
-      last_message[:content], messages, last_message[:role]
-    )
-    message_content = robin_response['response']
-    if robin_response['context_ids'].present?
-      message_content += Enterprise::MessageTemplates::ResponseBotService.generate_sources_section(robin_response['context_ids'])
-    end
-    message_content
-  end
-
   def label_suggestion_message
     payload = label_suggestion_body
     return nil if payload.blank?