improvements(ai): Improve AI streaming UI/UX interactions + better separation of AI provider responsibilities (#2039)

* Start refactor

* Interface updates

* Rework Assistant, Provider, and tests for better domain boundaries

* Consolidate and simplify OpenAI provider and provider concepts

* Clean up assistant streaming

* Improve assistant message orchestration logic

* Clean up "thinking" UI interactions

* Remove stale class

* Regenerate VCR test responses

app/models/assistant.rb

@@ -1,184 +1,75 @@
-# Orchestrates LLM interactions for chat conversations by:
-# - Streaming generic provider responses
-# - Persisting messages and tool calls
-# - Broadcasting updates to chat UI
-# - Handling provider errors
 class Assistant
-  include Provided
+  include Provided, Configurable, Broadcastable
 
-  attr_reader :chat
+  attr_reader :chat, :instructions
 
   class << self
     def for_chat(chat)
-      new(chat)
+      config = config_for(chat)
+      new(chat, instructions: config[:instructions], functions: config[:functions])
     end
   end
 
-  def initialize(chat)
+  def initialize(chat, instructions: nil, functions: [])
     @chat = chat
-  end
-
-  def streamer(model)
-    assistant_message = AssistantMessage.new(
-      chat: chat,
-      content: "",
-      ai_model: model
-    )
-
-    proc do |chunk|
-      case chunk.type
-      when "output_text"
-        stop_thinking
-        assistant_message.content += chunk.data
-        assistant_message.save!
-      when "function_request"
-        update_thinking("Analyzing your data to assist you with your question...")
-      when "response"
-        stop_thinking
-        assistant_message.ai_model = chunk.data.model
-        combined_tool_calls = chunk.data.functions.map do |tc|
-          ToolCall::Function.new(
-            provider_id: tc.id,
-            provider_call_id: tc.call_id,
-            function_name: tc.name,
-            function_arguments: tc.arguments,
-            function_result: tc.result
-          )
-        end
-
-        assistant_message.tool_calls = combined_tool_calls
-        assistant_message.save!
-        chat.update!(latest_assistant_response_id: chunk.data.id)
-      end
-    end
+    @instructions = instructions
+    @functions = functions
   end
 
   def respond_to(message)
-    chat.clear_error
-    sleep artificial_thinking_delay
-
-    provider = get_model_provider(message.ai_model)
-
-    provider.chat_response(
-      message,
-      instructions: instructions,
-      available_functions: functions,
-      streamer: streamer(message.ai_model)
+    assistant_message = AssistantMessage.new(
+      chat: chat,
+      content: "",
+      ai_model: message.ai_model
     )
+
+    responder = Assistant::Responder.new(
+      message: message,
+      instructions: instructions,
+      function_tool_caller: function_tool_caller,
+      llm: get_model_provider(message.ai_model)
+    )
+
+    latest_response_id = chat.latest_assistant_response_id
+
+    responder.on(:output_text) do |text|
+      if assistant_message.content.blank?
+        stop_thinking
+
+        Chat.transaction do
+          assistant_message.append_text!(text)
+          chat.update_latest_response!(latest_response_id)
+        end
+      else
+        assistant_message.append_text!(text)
+      end
+    end
+
+    responder.on(:response) do |data|
+      update_thinking("Analyzing your data...")
+
+      if data[:function_tool_calls].present?
+        assistant_message.tool_calls = data[:function_tool_calls]
+        latest_response_id = data[:id]
+      else
+        chat.update_latest_response!(data[:id])
+      end
+    end
+
+    responder.respond(previous_response_id: latest_response_id)
   rescue => e
+    stop_thinking
     chat.add_error(e)
   end
 
   private
-    def update_thinking(thought)
-      chat.broadcast_update target: "thinking-indicator", partial: "chats/thinking_indicator", locals: { chat: chat, message: thought }
-    end
+    attr_reader :functions
 
-    def stop_thinking
-      chat.broadcast_remove target: "thinking-indicator"
-    end
-
-    def process_response_artifacts(data)
-      messages = data.messages.map do |message|
-        AssistantMessage.new(
-          chat: chat,
-          content: message.content,
-          provider_id: message.id,
-          ai_model: data.model,
-          tool_calls: data.functions.map do |fn|
-            ToolCall::Function.new(
-              provider_id: fn.id,
-              provider_call_id: fn.call_id,
-              function_name: fn.name,
-              function_arguments: fn.arguments,
-              function_result: fn.result
-            )
-          end
-        )
+    def function_tool_caller
+      function_instances = functions.map do |fn|
+        fn.new(chat.user)
       end
 
-      messages.each(&:save!)
-    end
-
-    def instructions
-      <<~PROMPT
-        ## Your identity
-
-        You are a friendly financial assistant for an open source personal finance application called "Maybe", which is short for "Maybe Finance".
-
-        ## Your purpose
-
-        You help users understand their financial data by answering questions about their accounts,
-        transactions, income, expenses, net worth, and more.
-
-        ## Your rules
-
-        Follow all rules below at all times.
-
-        ### General rules
-
-        - Provide ONLY the most important numbers and insights
-        - Eliminate all unnecessary words and context
-        - Ask follow-up questions to keep the conversation going. Help educate the user about their own data and entice them to ask more questions.
-        - Do NOT add introductions or conclusions
-        - Do NOT apologize or explain limitations
-
-        ### Formatting rules
-
-        - Format all responses in markdown
-        - Format all monetary values according to the user's preferred currency
-        - Format dates in the user's preferred format
-
-        #### User's preferred currency
-
-        Maybe is a multi-currency app where each user has a "preferred currency" setting.
-
-        When no currency is specified, use the user's preferred currency for formatting and displaying monetary values.
-
-        - Symbol: #{preferred_currency.symbol}
-        - ISO code: #{preferred_currency.iso_code}
-        - Default precision: #{preferred_currency.default_precision}
-        - Default format: #{preferred_currency.default_format}
-          - Separator: #{preferred_currency.separator}
-          - Delimiter: #{preferred_currency.delimiter}
-        - Date format: #{preferred_date_format}
-
-        ### Rules about financial advice
-
-        You are NOT a licensed financial advisor and therefore, you should not provide any specific investment advice (such as "buy this stock", "sell that bond", "invest in crypto", etc.).
-
-        Instead, you should focus on educating the user about personal finance using their own data so they can make informed decisions.
-
-        - Do not suggest investments or financial products
-        - Do not make assumptions about the user's financial situation. Use the functions available to get the data you need.
-
-        ### Function calling rules
-
-        - Use the functions available to you to get user financial data and enhance your responses
-        - For functions that require dates, use the current date as your reference point: #{Date.current}
-        - If you suspect that you do not have enough data to 100% accurately answer, be transparent about it and state exactly what
-          the data you're presenting represents and what context it is in (i.e. date range, account, etc.)
-      PROMPT
-    end
-
-    def functions
-      [
-        Assistant::Function::GetTransactions.new(chat.user),
-        Assistant::Function::GetAccounts.new(chat.user),
-        Assistant::Function::GetBalanceSheet.new(chat.user),
-        Assistant::Function::GetIncomeStatement.new(chat.user)
-      ]
-    end
-
-    def preferred_currency
-      Money::Currency.new(chat.user.family.currency)
-    end
-
-    def preferred_date_format
-      chat.user.family.date_format
-    end
-
-    def artificial_thinking_delay
-      1
+      @function_tool_caller ||= FunctionToolCaller.new(function_instances)
     end
 end

app/models/assistant/broadcastable.rb

@@ -0,0 +1,12 @@
+module Assistant::Broadcastable
+  extend ActiveSupport::Concern
+
+  private
+    def update_thinking(thought)
+      chat.broadcast_update target: "thinking-indicator", partial: "chats/thinking_indicator", locals: { chat: chat, message: thought }
+    end
+
+    def stop_thinking
+      chat.broadcast_remove target: "thinking-indicator"
+    end
+end

app/models/assistant/configurable.rb

@@ -0,0 +1,85 @@
+module Assistant::Configurable
+  extend ActiveSupport::Concern
+
+  class_methods do
+    def config_for(chat)
+      preferred_currency = Money::Currency.new(chat.user.family.currency)
+      preferred_date_format = chat.user.family.date_format
+
+      {
+        instructions: default_instructions(preferred_currency, preferred_date_format),
+        functions: default_functions
+      }
+    end
+
+    private
+      def default_functions
+        [
+          Assistant::Function::GetTransactions,
+          Assistant::Function::GetAccounts,
+          Assistant::Function::GetBalanceSheet,
+          Assistant::Function::GetIncomeStatement
+        ]
+      end
+
+      def default_instructions(preferred_currency, preferred_date_format)
+        <<~PROMPT
+          ## Your identity
+
+          You are a friendly financial assistant for an open source personal finance application called "Maybe", which is short for "Maybe Finance".
+
+          ## Your purpose
+
+          You help users understand their financial data by answering questions about their accounts,
+          transactions, income, expenses, net worth, and more.
+
+          ## Your rules
+
+          Follow all rules below at all times.
+
+          ### General rules
+
+          - Provide ONLY the most important numbers and insights
+          - Eliminate all unnecessary words and context
+          - Ask follow-up questions to keep the conversation going. Help educate the user about their own data and entice them to ask more questions.
+          - Do NOT add introductions or conclusions
+          - Do NOT apologize or explain limitations
+
+          ### Formatting rules
+
+          - Format all responses in markdown
+          - Format all monetary values according to the user's preferred currency
+          - Format dates in the user's preferred format: #{preferred_date_format}
+
+          #### User's preferred currency
+
+          Maybe is a multi-currency app where each user has a "preferred currency" setting.
+
+          When no currency is specified, use the user's preferred currency for formatting and displaying monetary values.
+
+          - Symbol: #{preferred_currency.symbol}
+          - ISO code: #{preferred_currency.iso_code}
+          - Default precision: #{preferred_currency.default_precision}
+          - Default format: #{preferred_currency.default_format}
+            - Separator: #{preferred_currency.separator}
+            - Delimiter: #{preferred_currency.delimiter}
+
+          ### Rules about financial advice
+
+          You are NOT a licensed financial advisor and therefore, you should not provide any specific investment advice (such as "buy this stock", "sell that bond", "invest in crypto", etc.).
+
+          Instead, you should focus on educating the user about personal finance using their own data so they can make informed decisions.
+
+          - Do not suggest investments or financial products
+          - Do not make assumptions about the user's financial situation. Use the functions available to get the data you need.
+
+          ### Function calling rules
+
+          - Use the functions available to you to get user financial data and enhance your responses
+          - For functions that require dates, use the current date as your reference point: #{Date.current}
+          - If you suspect that you do not have enough data to 100% accurately answer, be transparent about it and state exactly what
+            the data you're presenting represents and what context it is in (i.e. date range, account, etc.)
+        PROMPT
+      end
+  end
+end

app/models/assistant/function.rb

@@ -34,6 +34,15 @@ class Assistant::Function
     true
   end
 
+  def to_definition
+    {
+      name: name,
+      description: description,
+      params_schema: params_schema,
+      strict: strict_mode?
+    }
+  end
+
   private
     attr_reader :user
 

app/models/assistant/function_tool_caller.rb

@@ -0,0 +1,37 @@
+class Assistant::FunctionToolCaller
+  Error = Class.new(StandardError)
+  FunctionExecutionError = Class.new(Error)
+
+  attr_reader :functions
+
+  def initialize(functions = [])
+    @functions = functions
+  end
+
+  def fulfill_requests(function_requests)
+    function_requests.map do |function_request|
+      result = execute(function_request)
+
+      ToolCall::Function.from_function_request(function_request, result)
+    end
+  end
+
+  def function_definitions
+    functions.map(&:to_definition)
+  end
+
+  private
+    def execute(function_request)
+      fn = find_function(function_request)
+      fn_args = JSON.parse(function_request.function_args)
+      fn.call(fn_args)
+    rescue => e
+      raise FunctionExecutionError.new(
+        "Error calling function #{fn.name} with arguments #{fn_args}: #{e.message}"
+      )
+    end
+
+    def find_function(function_request)
+      functions.find { |f| f.name == function_request.function_name }
+    end
+end

app/models/assistant/responder.rb

@@ -0,0 +1,87 @@
+class Assistant::Responder
+  def initialize(message:, instructions:, function_tool_caller:, llm:)
+    @message = message
+    @instructions = instructions
+    @function_tool_caller = function_tool_caller
+    @llm = llm
+  end
+
+  def on(event_name, &block)
+    listeners[event_name.to_sym] << block
+  end
+
+  def respond(previous_response_id: nil)
+    # For the first response
+    streamer = proc do |chunk|
+      case chunk.type
+      when "output_text"
+        emit(:output_text, chunk.data)
+      when "response"
+        response = chunk.data
+
+        if response.function_requests.any?
+          handle_follow_up_response(response)
+        else
+          emit(:response, { id: response.id })
+        end
+      end
+    end
+
+    get_llm_response(streamer: streamer, previous_response_id: previous_response_id)
+  end
+
+  private
+    attr_reader :message, :instructions, :function_tool_caller, :llm
+
+    def handle_follow_up_response(response)
+      streamer = proc do |chunk|
+        case chunk.type
+        when "output_text"
+          emit(:output_text, chunk.data)
+        when "response"
+          # We do not currently support function executions for a follow-up response (avoid recursive LLM calls that could lead to high spend)
+          emit(:response, { id: chunk.data.id })
+        end
+      end
+
+      function_tool_calls = function_tool_caller.fulfill_requests(response.function_requests)
+
+      emit(:response, {
+        id: response.id,
+        function_tool_calls: function_tool_calls
+      })
+
+      # Get follow-up response with tool call results
+      get_llm_response(
+        streamer: streamer,
+        function_results: function_tool_calls.map(&:to_result),
+        previous_response_id: response.id
+      )
+    end
+
+    def get_llm_response(streamer:, function_results: [], previous_response_id: nil)
+      response = llm.chat_response(
+        message.content,
+        model: message.ai_model,
+        instructions: instructions,
+        functions: function_tool_caller.function_definitions,
+        function_results: function_results,
+        streamer: streamer,
+        previous_response_id: previous_response_id
+      )
+
+      unless response.success?
+        raise response.error
+      end
+
+      response.data
+    end
+
+    def emit(event_name, payload = nil)
+      listeners[event_name.to_sym].each { |block| block.call(payload) }
+    end
+
+    def listeners
+      @listeners ||= Hash.new { |h, k| h[k] = [] }
+    end
+end

app/models/assistant_message.rb

@@ -5,7 +5,8 @@ class AssistantMessage < Message
     "assistant"
   end
 
-  def broadcast?
-    true
+  def append_text!(text)
+    self.content += text
+    save!
   end
 end

app/models/chat.rb

@@ -23,15 +23,25 @@ class Chat < ApplicationRecord
     end
   end
 
+  def needs_assistant_response?
+    conversation_messages.ordered.last.role != "assistant"
+  end
+
   def retry_last_message!
+    update!(error: nil)
+
     last_message = conversation_messages.ordered.last
 
     if last_message.present? && last_message.role == "user"
-      update!(error: nil)
+
       ask_assistant_later(last_message)
     end
   end
 
+  def update_latest_response!(provider_response_id)
+    update!(latest_assistant_response_id: provider_response_id)
+  end
+
   def add_error(e)
     update! error: e.to_json
     broadcast_append target: "messages", partial: "chats/error", locals: { chat: self }
@@ -47,6 +57,7 @@ class Chat < ApplicationRecord
   end
 
   def ask_assistant_later(message)
+    clear_error
     AssistantResponseJob.perform_later(message)
   end
 

app/models/developer_message.rb

@@ -3,7 +3,8 @@ class DeveloperMessage < Message
     "developer"
   end
 
-  def broadcast?
-    chat.debug_mode?
-  end
+  private
+    def broadcast?
+      chat.debug_mode?
+    end
 end

app/models/message.rb

@@ -8,7 +8,7 @@ class Message < ApplicationRecord
     failed: "failed"
   }
 
-  validates :content, presence: true, allow_blank: true
+  validates :content, presence: true
 
   after_create_commit -> { broadcast_append_to chat, target: "messages" }, if: :broadcast?
   after_update_commit -> { broadcast_update_to chat }, if: :broadcast?
@@ -17,6 +17,6 @@ class Message < ApplicationRecord
 
   private
     def broadcast?
-      raise NotImplementedError, "subclasses must set #broadcast?"
+      true
     end
 end

app/models/provider.rb

@@ -4,17 +4,15 @@ class Provider
   Response = Data.define(:success?, :data, :error)
 
   class Error < StandardError
-    attr_reader :details, :provider
+    attr_reader :details
 
-    def initialize(message, details: nil, provider: nil)
+    def initialize(message, details: nil)
       super(message)
       @details = details
-      @provider = provider
     end
 
     def as_json
       {
-        provider: provider,
         message: message,
         details: details
       }

app/models/provider/exchange_rate_concept.rb

@@ -1,6 +1,8 @@
-module Provider::ExchangeRateProvider
+module Provider::ExchangeRateConcept
   extend ActiveSupport::Concern
 
+  Rate = Data.define(:date, :from, :to, :rate)
+
   def fetch_exchange_rate(from:, to:, date:)
     raise NotImplementedError, "Subclasses must implement #fetch_exchange_rate"
   end
@@ -8,7 +10,4 @@ module Provider::ExchangeRateProvider
   def fetch_exchange_rates(from:, to:, start_date:, end_date:)
     raise NotImplementedError, "Subclasses must implement #fetch_exchange_rates"
   end
-
-  private
-    Rate = Data.define(:date, :from, :to, :rate)
 end

app/models/provider/llm_concept.rb

@@ -0,0 +1,12 @@
+module Provider::LlmConcept
+  extend ActiveSupport::Concern
+
+  ChatMessage = Data.define(:id, :output_text)
+  ChatStreamChunk = Data.define(:type, :data)
+  ChatResponse = Data.define(:id, :model, :messages, :function_requests)
+  ChatFunctionRequest = Data.define(:id, :call_id, :function_name, :function_args)
+
+  def chat_response(prompt, model:, instructions: nil, functions: [], function_results: [], streamer: nil, previous_response_id: nil)
+    raise NotImplementedError, "Subclasses must implement #chat_response"
+  end
+end

app/models/provider/llm_provider.rb

@@ -1,13 +0,0 @@
-module Provider::LlmProvider
-  extend ActiveSupport::Concern
-
-  def chat_response(message, instructions: nil, available_functions: [], streamer: nil)
-    raise NotImplementedError, "Subclasses must implement #chat_response"
-  end
-
-  private
-    StreamChunk = Data.define(:type, :data)
-    ChatResponse = Data.define(:id, :messages, :functions, :model)
-    Message = Data.define(:id, :content)
-    FunctionExecution = Data.define(:id, :call_id, :name, :arguments, :result)
-end

app/models/provider/openai.rb

@@ -1,5 +1,5 @@
 class Provider::Openai < Provider
-  include LlmProvider
+  include LlmConcept
 
   # Subclass so errors caught in this provider are raised as Provider::Openai::Error
   Error = Class.new(Provider::Error)
@@ -14,17 +14,46 @@ class Provider::Openai < Provider
     MODELS.include?(model)
   end
 
-  def chat_response(message, instructions: nil, available_functions: [], streamer: nil)
+  def chat_response(prompt, model:, instructions: nil, functions: [], function_results: [], streamer: nil, previous_response_id: nil)
     with_provider_response do
-      processor = ChatResponseProcessor.new(
-        client: client,
-        message: message,
-        instructions: instructions,
-        available_functions: available_functions,
-        streamer: streamer
+      chat_config = ChatConfig.new(
+        functions: functions,
+        function_results: function_results
       )
 
-      processor.process
+      collected_chunks = []
+
+      # Proxy that converts raw stream to "LLM Provider concept" stream
+      stream_proxy = if streamer.present?
+        proc do |chunk|
+          parsed_chunk = ChatStreamParser.new(chunk).parsed
+
+          unless parsed_chunk.nil?
+            streamer.call(parsed_chunk)
+            collected_chunks << parsed_chunk
+          end
+        end
+      else
+        nil
+      end
+
+      raw_response = client.responses.create(parameters: {
+        model: model,
+        input: chat_config.build_input(prompt),
+        instructions: instructions,
+        tools: chat_config.tools,
+        previous_response_id: previous_response_id,
+        stream: stream_proxy
+      })
+
+      # If streaming, Ruby OpenAI does not return anything, so to normalize this method's API, we search
+      # for the "response chunk" in the stream and return it (it is already parsed)
+      if stream_proxy.present?
+        response_chunk = collected_chunks.find { |chunk| chunk.type == "response" }
+        response_chunk.data
+      else
+        ChatParser.new(raw_response).parsed
+      end
     end
   end
 

app/models/provider/openai/chat_config.rb

@@ -0,0 +1,36 @@
+class Provider::Openai::ChatConfig
+  def initialize(functions: [], function_results: [])
+    @functions = functions
+    @function_results = function_results
+  end
+
+  def tools
+    functions.map do |fn|
+      {
+        type: "function",
+        name: fn[:name],
+        description: fn[:description],
+        parameters: fn[:params_schema],
+        strict: fn[:strict]
+      }
+    end
+  end
+
+  def build_input(prompt)
+    results = function_results.map do |fn_result|
+      {
+        type: "function_call_output",
+        call_id: fn_result[:call_id],
+        output: fn_result[:output].to_json
+      }
+    end
+
+    [
+      { role: "user", content: prompt },
+      *results
+    ]
+  end
+
+  private
+    attr_reader :functions, :function_results
+end

app/models/provider/openai/chat_parser.rb

@@ -0,0 +1,59 @@
+class Provider::Openai::ChatParser
+  Error = Class.new(StandardError)
+
+  def initialize(object)
+    @object = object
+  end
+
+  def parsed
+    ChatResponse.new(
+      id: response_id,
+      model: response_model,
+      messages: messages,
+      function_requests: function_requests
+    )
+  end
+
+  private
+    attr_reader :object
+
+    ChatResponse = Provider::LlmConcept::ChatResponse
+    ChatMessage = Provider::LlmConcept::ChatMessage
+    ChatFunctionRequest = Provider::LlmConcept::ChatFunctionRequest
+
+    def response_id
+      object.dig("id")
+    end
+
+    def response_model
+      object.dig("model")
+    end
+
+    def messages
+      message_items = object.dig("output").filter { |item| item.dig("type") == "message" }
+
+      message_items.map do |message_item|
+        ChatMessage.new(
+          id: message_item.dig("id"),
+          output_text: message_item.dig("content").map do |content|
+            text = content.dig("text")
+            refusal = content.dig("refusal")
+            text || refusal
+          end.flatten.join("\n")
+        )
+      end
+    end
+
+    def function_requests
+      function_items = object.dig("output").filter { |item| item.dig("type") == "function_call" }
+
+      function_items.map do |function_item|
+        ChatFunctionRequest.new(
+          id: function_item.dig("id"),
+          call_id: function_item.dig("call_id"),
+          function_name: function_item.dig("name"),
+          function_args: function_item.dig("arguments")
+        )
+      end
+    end
+end

app/models/provider/openai/chat_response_processor.rb

@@ -1,188 +0,0 @@
-class Provider::Openai::ChatResponseProcessor
-  def initialize(message:, client:, instructions: nil, available_functions: [], streamer: nil)
-    @client = client
-    @message = message
-    @instructions = instructions
-    @available_functions = available_functions
-    @streamer = streamer
-  end
-
-  def process
-    first_response = fetch_response(previous_response_id: previous_openai_response_id)
-
-    if first_response.functions.empty?
-      if streamer.present?
-        streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: first_response))
-      end
-
-      return first_response
-    end
-
-    executed_functions = execute_pending_functions(first_response.functions)
-
-    follow_up_response = fetch_response(
-      executed_functions: executed_functions,
-      previous_response_id: first_response.id
-    )
-
-    if streamer.present?
-      streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: follow_up_response))
-    end
-
-    follow_up_response
-  end
-
-  private
-    attr_reader :client, :message, :instructions, :available_functions, :streamer
-
-    PendingFunction = Data.define(:id, :call_id, :name, :arguments)
-
-    def fetch_response(executed_functions: [], previous_response_id: nil)
-      function_results = executed_functions.map do |executed_function|
-        {
-          type: "function_call_output",
-          call_id: executed_function.call_id,
-          output: executed_function.result.to_json
-        }
-      end
-
-      prepared_input = input + function_results
-
-      # No need to pass tools for follow-up messages that provide function results
-      prepared_tools = executed_functions.empty? ? tools : []
-
-      raw_response = nil
-
-      internal_streamer = proc do |chunk|
-        type = chunk.dig("type")
-
-        if streamer.present?
-          case type
-          when "response.output_text.delta", "response.refusal.delta"
-            # We don't distinguish between text and refusal yet, so stream both the same
-            streamer.call(Provider::LlmProvider::StreamChunk.new(type: "output_text", data: chunk.dig("delta")))
-          when "response.function_call_arguments.done"
-            streamer.call(Provider::LlmProvider::StreamChunk.new(type: "function_request", data: chunk.dig("arguments")))
-          end
-        end
-
-        if type == "response.completed"
-          raw_response = chunk.dig("response")
-        end
-      end
-
-      client.responses.create(parameters: {
-        model: model,
-        input: prepared_input,
-        instructions: instructions,
-        tools: prepared_tools,
-        previous_response_id: previous_response_id,
-        stream: internal_streamer
-      })
-
-      if raw_response.dig("status") == "failed" || raw_response.dig("status") == "incomplete"
-        raise Provider::Openai::Error.new("OpenAI returned a failed or incomplete response", { chunk: chunk })
-      end
-
-      response_output = raw_response.dig("output")
-
-      functions_output = if executed_functions.any?
-        executed_functions
-      else
-        extract_pending_functions(response_output)
-      end
-
-      Provider::LlmProvider::ChatResponse.new(
-        id: raw_response.dig("id"),
-        messages: extract_messages(response_output),
-        functions: functions_output,
-        model: raw_response.dig("model")
-      )
-    end
-
-    def chat
-      message.chat
-    end
-
-    def model
-      message.ai_model
-    end
-
-    def previous_openai_response_id
-      chat.latest_assistant_response_id
-    end
-
-    # Since we're using OpenAI's conversation state management, all we need to pass
-    # to input is the user message we're currently responding to.
-    def input
-      [ { role: "user", content: message.content } ]
-    end
-
-    def extract_messages(response_output)
-      message_items = response_output.filter { |item| item.dig("type") == "message" }
-
-      message_items.map do |item|
-        output_text = item.dig("content").map do |content|
-          text = content.dig("text")
-          refusal = content.dig("refusal")
-
-          text || refusal
-        end.flatten.join("\n")
-
-        Provider::LlmProvider::Message.new(
-          id: item.dig("id"),
-          content: output_text,
-        )
-      end
-    end
-
-    def extract_pending_functions(response_output)
-      response_output.filter { |item| item.dig("type") == "function_call" }.map do |item|
-        PendingFunction.new(
-          id: item.dig("id"),
-          call_id: item.dig("call_id"),
-          name: item.dig("name"),
-          arguments: item.dig("arguments"),
-        )
-      end
-    end
-
-    def execute_pending_functions(pending_functions)
-      pending_functions.map do |pending_function|
-        execute_function(pending_function)
-      end
-    end
-
-    def execute_function(fn)
-      fn_instance = available_functions.find { |f| f.name == fn.name }
-      parsed_args = JSON.parse(fn.arguments)
-      result = fn_instance.call(parsed_args)
-
-      Provider::LlmProvider::FunctionExecution.new(
-        id: fn.id,
-        call_id: fn.call_id,
-        name: fn.name,
-        arguments: parsed_args,
-        result: result
-      )
-    rescue => e
-      fn_execution_details = {
-        fn_name: fn.name,
-        fn_args: parsed_args
-      }
-
-      raise Provider::Openai::Error.new(e, fn_execution_details)
-    end
-
-    def tools
-      available_functions.map do |fn|
-        {
-          type: "function",
-          name: fn.name,
-          description: fn.description,
-          parameters: fn.params_schema,
-          strict: fn.strict_mode?
-        }
-      end
-    end
-end

app/models/provider/openai/chat_stream_parser.rb

@@ -0,0 +1,28 @@
+class Provider::Openai::ChatStreamParser
+  Error = Class.new(StandardError)
+
+  def initialize(object)
+    @object = object
+  end
+
+  def parsed
+    type = object.dig("type")
+
+    case type
+    when "response.output_text.delta", "response.refusal.delta"
+      Chunk.new(type: "output_text", data: object.dig("delta"))
+    when "response.completed"
+      raw_response = object.dig("response")
+      Chunk.new(type: "response", data: parse_response(raw_response))
+    end
+  end
+
+  private
+    attr_reader :object
+
+    Chunk = Provider::LlmConcept::ChatStreamChunk
+
+    def parse_response(response)
+      Provider::Openai::ChatParser.new(response).parsed
+    end
+end

app/models/provider/openai/chat_streamer.rb

@@ -1,13 +0,0 @@
-# A stream proxy for OpenAI chat responses
-#
-# - Consumes an OpenAI chat response stream
-# - Outputs a generic "Chat Provider Stream" interface to consumers (e.g. `Assistant`)
-class Provider::Openai::ChatStreamer
-  def initialize(output_stream)
-    @output_stream = output_stream
-  end
-
-  def call(chunk)
-    @output_stream.call(chunk)
-  end
-end

app/models/provider/security_concept.rb

@@ -1,6 +1,10 @@
-module Provider::SecurityProvider
+module Provider::SecurityConcept
   extend ActiveSupport::Concern
 
+  Security = Data.define(:symbol, :name, :logo_url, :exchange_operating_mic)
+  SecurityInfo = Data.define(:symbol, :name, :links, :logo_url, :description, :kind)
+  Price = Data.define(:security, :date, :price, :currency)
+
   def search_securities(symbol, country_code: nil, exchange_operating_mic: nil)
     raise NotImplementedError, "Subclasses must implement #search_securities"
   end
@@ -16,9 +20,4 @@ module Provider::SecurityProvider
   def fetch_security_prices(security, start_date:, end_date:)
     raise NotImplementedError, "Subclasses must implement #fetch_security_prices"
   end
-
-  private
-    Security = Data.define(:symbol, :name, :logo_url, :exchange_operating_mic)
-    SecurityInfo = Data.define(:symbol, :name, :links, :logo_url, :description, :kind)
-    Price = Data.define(:security, :date, :price, :currency)
 end

app/models/provider/synth.rb

@@ -1,5 +1,5 @@
 class Provider::Synth < Provider
-  include ExchangeRateProvider, SecurityProvider
+  include ExchangeRateConcept, SecurityConcept
 
   # Subclass so errors caught in this provider are raised as Provider::Synth::Error
   Error = Class.new(Provider::Error)

app/models/tool_call/function.rb

@@ -1,4 +1,24 @@
 class ToolCall::Function < ToolCall
   validates :function_name, :function_result, presence: true
   validates :function_arguments, presence: true, allow_blank: true
+
+  class << self
+    # Translates an "LLM Concept" provider's FunctionRequest into a ToolCall::Function
+    def from_function_request(function_request, result)
+      new(
+        provider_id: function_request.id,
+        provider_call_id: function_request.call_id,
+        function_name: function_request.function_name,
+        function_arguments: function_request.function_args,
+        function_result: result
+      )
+    end
+  end
+
+  def to_result
+    {
+      call_id: provider_call_id,
+      output: function_result
+    }
+  end
 end

app/models/user_message.rb

@@ -14,9 +14,4 @@ class UserMessage < Message
   def request_response
     chat.ask_assistant(self)
   end
-
-  private
-    def broadcast?
-      true
-    end
 end

app/views/assistant_messages/_assistant_message.html.erb

@@ -17,6 +17,7 @@
 
     <div class="flex items-start mb-6">
       <%= render "chats/ai_avatar" %>
+
       <div class="prose prose--ai-chat"><%= markdown(assistant_message.content) %></div>
     </div>
   <% end %>

app/views/chats/show.html.erb

@@ -23,7 +23,7 @@
         <%= render "chats/thinking_indicator", chat: @chat %>
       <% end %>
 
-      <% if @chat.error.present? %>
+      <% if @chat.error.present? && @chat.needs_assistant_response? %>
         <%= render "chats/error", chat: @chat %>
       <% end %>
     </div>

test/models/assistant_test.rb

@@ -1,5 +1,4 @@
 require "test_helper"
-require "ostruct"
 
 class AssistantTest < ActiveSupport::TestCase
   include ProviderTestHelper
@@ -8,74 +7,109 @@ class AssistantTest < ActiveSupport::TestCase
     @chat = chats(:two)
     @message = @chat.messages.create!(
       type: "UserMessage",
-      content: "Help me with my finances",
+      content: "What is my net worth?",
       ai_model: "gpt-4o"
     )
     @assistant = Assistant.for_chat(@chat)
     @provider = mock
-    @assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
   end
 
-  test "responds to basic prompt" do
-    text_chunk = OpenStruct.new(type: "output_text", data: "Hello from assistant")
-    response_chunk = OpenStruct.new(
-    type: "response",
-    data: OpenStruct.new(
-      id: "1",
-      model: "gpt-4o",
-      messages: [
-        OpenStruct.new(
-          id: "1",
-          content: "Hello from assistant",
-        )
-      ],
-      functions: []
-    )
-  )
+  test "errors get added to chat" do
+    @assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
 
-    @provider.expects(:chat_response).with do |message, **options|
-      options[:streamer].call(text_chunk)
-      options[:streamer].call(response_chunk)
-      true
-    end
+    error = StandardError.new("test error")
+    @provider.expects(:chat_response).returns(provider_error_response(error))
 
-    assert_difference "AssistantMessage.count", 1 do
+    @chat.expects(:add_error).with(error).once
+
+    assert_no_difference "AssistantMessage.count"  do
       @assistant.respond_to(@message)
     end
   end
 
-  test "responds with tool function calls" do
-    function_request_chunk = OpenStruct.new(type: "function_request", data: "get_net_worth")
-    text_chunk = OpenStruct.new(type: "output_text", data: "Your net worth is $124,200")
-    response_chunk = OpenStruct.new(
-      type: "response",
-      data: OpenStruct.new(
-        id: "1",
-        model: "gpt-4o",
-        messages: [
-          OpenStruct.new(
-            id: "1",
-            content: "Your net worth is $124,200",
-          )
-        ],
-        functions: [
-          OpenStruct.new(
-            id: "1",
-            call_id: "1",
-            name: "get_net_worth",
-            arguments: "{}",
-            result: "$124,200"
-          )
-        ]
-      )
+  test "responds to basic prompt" do
+    @assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
+
+    text_chunks = [
+      provider_text_chunk("I do not "),
+      provider_text_chunk("have the information "),
+      provider_text_chunk("to answer that question")
+    ]
+
+    response_chunk = provider_response_chunk(
+      id: "1",
+      model: "gpt-4o",
+      messages: [ provider_message(id: "1", text: text_chunks.join) ],
+      function_requests: []
     )
 
+    response = provider_success_response(response_chunk.data)
+
     @provider.expects(:chat_response).with do |message, **options|
-      options[:streamer].call(function_request_chunk)
-      options[:streamer].call(text_chunk)
+      text_chunks.each do |text_chunk|
+        options[:streamer].call(text_chunk)
+      end
+
       options[:streamer].call(response_chunk)
       true
+    end.returns(response)
+
+    assert_difference "AssistantMessage.count", 1 do
+      @assistant.respond_to(@message)
+      message = @chat.messages.ordered.where(type: "AssistantMessage").last
+      assert_equal "I do not have the information to answer that question", message.content
+      assert_equal 0, message.tool_calls.size
     end
+  end
+
+  test "responds with tool function calls" do
+    @assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider).once
+
+    # Only first provider call executes function
+    Assistant::Function::GetAccounts.any_instance.stubs(:call).returns("test value").once
+
+    # Call #1: Function requests
+    call1_response_chunk = provider_response_chunk(
+      id: "1",
+      model: "gpt-4o",
+      messages: [],
+      function_requests: [
+        provider_function_request(id: "1", call_id: "1", function_name: "get_accounts", function_args: "{}")
+      ]
+    )
+
+    call1_response = provider_success_response(call1_response_chunk.data)
+
+    # Call #2: Text response (that uses function results)
+    call2_text_chunks = [
+      provider_text_chunk("Your net worth is "),
+      provider_text_chunk("$124,200")
+    ]
+
+    call2_response_chunk = provider_response_chunk(
+      id: "2",
+      model: "gpt-4o",
+      messages: [ provider_message(id: "1", text: call2_text_chunks.join) ],
+      function_requests: []
+    )
+
+    call2_response = provider_success_response(call2_response_chunk.data)
+
+    sequence = sequence("provider_chat_response")
+
+    @provider.expects(:chat_response).with do |message, **options|
+      call2_text_chunks.each do |text_chunk|
+        options[:streamer].call(text_chunk)
+      end
+
+      options[:streamer].call(call2_response_chunk)
+      true
+    end.returns(call2_response).once.in_sequence(sequence)
+
+    @provider.expects(:chat_response).with do |message, **options|
+      options[:streamer].call(call1_response_chunk)
+      true
+    end.returns(call1_response).once.in_sequence(sequence)
 
     assert_difference "AssistantMessage.count", 1 do
       @assistant.respond_to(@message)
@@ -83,4 +117,34 @@ class AssistantTest < ActiveSupport::TestCase
       assert_equal 1, message.tool_calls.size
     end
   end
+
+  private
+    def provider_function_request(id:, call_id:, function_name:, function_args:)
+      Provider::LlmConcept::ChatFunctionRequest.new(
+        id: id,
+        call_id: call_id,
+        function_name: function_name,
+        function_args: function_args
+      )
+    end
+
+    def provider_message(id:, text:)
+      Provider::LlmConcept::ChatMessage.new(id: id, output_text: text)
+    end
+
+    def provider_text_chunk(text)
+      Provider::LlmConcept::ChatStreamChunk.new(type: "output_text", data: text)
+    end
+
+    def provider_response_chunk(id:, model:, messages:, function_requests:)
+      Provider::LlmConcept::ChatStreamChunk.new(
+        type: "response",
+        data: Provider::LlmConcept::ChatResponse.new(
+          id: id,
+          model: model,
+          messages: messages,
+          function_requests: function_requests
+        )
+      )
+    end
 end

test/models/provider/openai_test.rb

@@ -6,16 +6,11 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
   setup do
     @subject = @openai = Provider::Openai.new(ENV.fetch("OPENAI_ACCESS_TOKEN", "test-openai-token"))
     @subject_model = "gpt-4o"
-    @chat = chats(:two)
   end
 
   test "openai errors are automatically raised" do
     VCR.use_cassette("openai/chat/error") do
-      response = @openai.chat_response(UserMessage.new(
-        chat: @chat,
-        content: "Error test",
-        ai_model: "invalid-model-that-will-trigger-api-error"
-      ))
+      response = @openai.chat_response("Test", model: "invalid-model-that-will-trigger-api-error")
 
       assert_not response.success?
       assert_kind_of Provider::Openai::Error, response.error
@@ -24,113 +19,145 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
 
   test "basic chat response" do
     VCR.use_cassette("openai/chat/basic_response") do
-      message = @chat.messages.create!(
-        type: "UserMessage",
-        content: "This is a chat test.  If it's working, respond with a single word: Yes",
-        ai_model: @subject_model
+      response = @subject.chat_response(
+        "This is a chat test.  If it's working, respond with a single word: Yes",
+        model: @subject_model
       )
 
-      response = @subject.chat_response(message)
-
       assert response.success?
       assert_equal 1, response.data.messages.size
-      assert_includes response.data.messages.first.content, "Yes"
+      assert_includes response.data.messages.first.output_text, "Yes"
     end
   end
 
   test "streams basic chat response" do
-    VCR.use_cassette("openai/chat/basic_response") do
+    VCR.use_cassette("openai/chat/basic_streaming_response") do
       collected_chunks = []
 
       mock_streamer = proc do |chunk|
         collected_chunks << chunk
       end
 
-      message = @chat.messages.create!(
-        type: "UserMessage",
-        content: "This is a chat test.  If it's working, respond with a single word: Yes",
-        ai_model: @subject_model
+      response = @subject.chat_response(
+        "This is a chat test.  If it's working, respond with a single word: Yes",
+        model: @subject_model,
+        streamer: mock_streamer
       )
 
-      @subject.chat_response(message, streamer: mock_streamer)
-
-      tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
       text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
       response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
 
       assert_equal 1, text_chunks.size
       assert_equal 1, response_chunks.size
-      assert_equal 0, tool_call_chunks.size
       assert_equal "Yes", text_chunks.first.data
-      assert_equal "Yes", response_chunks.first.data.messages.first.content
+      assert_equal "Yes", response_chunks.first.data.messages.first.output_text
+      assert_equal response_chunks.first.data, response.data
     end
   end
 
-  test "chat response with tool calls" do
-    VCR.use_cassette("openai/chat/tool_calls") do
-      response = @subject.chat_response(
-        tool_call_message,
+  test "chat response with function calls" do
+    VCR.use_cassette("openai/chat/function_calls") do
+      prompt = "What is my net worth?"
+
+      functions = [
+        {
+          name: "get_net_worth",
+          description: "Gets a user's net worth",
+          params_schema: { type: "object", properties: {}, required: [], additionalProperties: false },
+          strict: true
+        }
+      ]
+
+      first_response = @subject.chat_response(
+        prompt,
+        model: @subject_model,
         instructions: "Use the tools available to you to answer the user's question.",
-        available_functions: [ PredictableToolFunction.new(@chat) ]
+        functions: functions
       )
 
-      assert response.success?
-      assert_equal 1, response.data.functions.size
-      assert_equal 1, response.data.messages.size
-      assert_includes response.data.messages.first.content, PredictableToolFunction.expected_test_result
+      assert first_response.success?
+
+      function_request = first_response.data.function_requests.first
+
+      assert function_request.present?
+
+      second_response = @subject.chat_response(
+        prompt,
+        model: @subject_model,
+        function_results: [ {
+          call_id: function_request.call_id,
+          output: { amount: 10000, currency: "USD" }.to_json
+        } ],
+        previous_response_id: first_response.data.id
+      )
+
+      assert second_response.success?
+      assert_equal 1, second_response.data.messages.size
+      assert_includes second_response.data.messages.first.output_text, "$10,000"
     end
   end
 
-  test "streams chat response with tool calls" do
-    VCR.use_cassette("openai/chat/tool_calls") do
+  test "streams chat response with function calls" do
+    VCR.use_cassette("openai/chat/streaming_function_calls") do
       collected_chunks = []
 
       mock_streamer = proc do |chunk|
         collected_chunks << chunk
       end
 
+      prompt = "What is my net worth?"
+
+      functions = [
+        {
+          name: "get_net_worth",
+          description: "Gets a user's net worth",
+          params_schema: { type: "object", properties: {}, required: [], additionalProperties: false },
+          strict: true
+        }
+      ]
+
+      # Call #1: First streaming call, will return a function request
       @subject.chat_response(
-        tool_call_message,
+        prompt,
+        model: @subject_model,
         instructions: "Use the tools available to you to answer the user's question.",
-        available_functions: [ PredictableToolFunction.new(@chat) ],
+        functions: functions,
         streamer: mock_streamer
       )
 
       text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
-      text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
-      tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
       response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
 
-      assert_equal 1, tool_call_chunks.count
-      assert text_chunks.count >= 1
-      assert_equal 1, response_chunks.count
+      assert_equal 0, text_chunks.size
+      assert_equal 1, response_chunks.size
 
-      assert_includes response_chunks.first.data.messages.first.content, PredictableToolFunction.expected_test_result
+      first_response = response_chunks.first.data
+      function_request = first_response.function_requests.first
+
+      # Reset collected chunks for the second call
+      collected_chunks = []
+
+      # Call #2: Second streaming call, will return a function result
+      @subject.chat_response(
+        prompt,
+        model: @subject_model,
+        function_results: [
+          {
+            call_id: function_request.call_id,
+            output: { amount: 10000, currency: "USD" }
+          }
+        ],
+        previous_response_id: first_response.id,
+        streamer: mock_streamer
+      )
+
+      text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
+      response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
+
+      assert text_chunks.size >= 1
+      assert_equal 1, response_chunks.size
+
+      assert_includes response_chunks.first.data.messages.first.output_text, "$10,000"
     end
   end
-
-  private
-    def tool_call_message
-      UserMessage.new(chat: @chat, content: "What is my net worth?", ai_model: @subject_model)
-    end
-
-    class PredictableToolFunction < Assistant::Function
-      class << self
-        def expected_test_result
-          "$124,200"
-        end
-
-        def name
-          "get_net_worth"
-        end
-
-        def description
-          "Gets user net worth data"
-        end
-      end
-
-      def call(params = {})
-        self.class.expected_test_result
-      end
-    end
 end

test/vcr_cassettes/openai/chat/basic_response.yml

@@ -6,7 +6,7 @@ http_interactions:
     body:
       encoding: UTF-8
       string: '{"model":"gpt-4o","input":[{"role":"user","content":"This is a chat
-        test.  If it''s working, respond with a single word: Yes"}],"instructions":null,"tools":[],"previous_response_id":null,"stream":true}'
+        test.  If it''s working, respond with a single word: Yes"}],"instructions":null,"tools":[],"previous_response_id":null,"stream":null}'
     headers:
       Content-Type:
       - application/json
@@ -24,9 +24,9 @@ http_interactions:
       message: OK
     headers:
       Date:
-      - Wed, 26 Mar 2025 21:27:38 GMT
+      - Mon, 31 Mar 2025 20:38:55 GMT
       Content-Type:
-      - text/event-stream; charset=utf-8
+      - application/json
       Transfer-Encoding:
       - chunked
       Connection:
@@ -36,57 +36,85 @@ http_interactions:
       Openai-Organization:
       - "<OPENAI_ORGANIZATION_ID>"
       X-Request-Id:
-      - req_8fce503a4c5be145dda20867925b1622
+      - req_f99033a5841a7d9357ee08d301ad634e
       Openai-Processing-Ms:
-      - '103'
+      - '713'
       Strict-Transport-Security:
       - max-age=31536000; includeSubDomains; preload
       Cf-Cache-Status:
       - DYNAMIC
       Set-Cookie:
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-        path=/; expires=Wed, 26-Mar-25 21:57:38 GMT; domain=.api.openai.com; HttpOnly;
+      - __cf_bm=UOaolWyAE3WXhLfg9c3KmO4d_Nq6t9cedTfZ6hznYEE-1743453535-1.0.1.1-GyQq_xeRpsyxxp8QQja5Bvo2XqUGfXHNGehtQoPV.BIgyLbERSIqJAK0IEKcYgpuLCyvQdlMNGqtdBHB6r5XMPHjOSMN1bTQYJHLsvlD5Z4;
+        path=/; expires=Mon, 31-Mar-25 21:08:55 GMT; domain=.api.openai.com; HttpOnly;
         Secure; SameSite=None
-      - _cfuvid=Iqk8pY6uwz2lLhdKt0PwWTdtYQUqqvS6xmP9DMVko2A-1743024458829-0.0.1.1-604800000;
+      - _cfuvid=_zDj2dj75eLeGSzZxpBpzHxYg4gJpEfQpcnT9aCJXqM-1743453535930-0.0.1.1-604800000;
         path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
       X-Content-Type-Options:
       - nosniff
       Server:
       - cloudflare
       Cf-Ray:
-      - 9269bbb21b1ecf43-CMH
+      - 9292a7325d09cf53-CMH
       Alt-Svc:
       - h3=":443"; ma=86400
     body:
-      encoding: UTF-8
-      string: |+
-        event: response.created
-        data: {"type":"response.created","response":{"id":"resp_67e4714ab0148192ae2cc4303794d6fc0c1a792abcdc2819","object":"response","created_at":1743024458,"status":"in_progress","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"gpt-4o-2024-08-06","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"generate_summary":null},"store":true,"temperature":1.0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1.0,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}
-
-        event: response.in_progress
-        data: {"type":"response.in_progress","response":{"id":"resp_67e4714ab0148192ae2cc4303794d6fc0c1a792abcdc2819","object":"response","created_at":1743024458,"status":"in_progress","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"gpt-4o-2024-08-06","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"generate_summary":null},"store":true,"temperature":1.0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1.0,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}
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-
-        event: response.output_text.done
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-
-  recorded_at: Wed, 26 Mar 2025 21:27:39 GMT
+      encoding: ASCII-8BIT
+      string: |-
+        {
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+              "status": "completed",
+              "role": "assistant",
+              "content": [
+                {
+                  "type": "output_text",
+                  "text": "Yes",
+                  "annotations": []
+                }
+              ]
+            }
+          ],
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+          "previous_response_id": null,
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+          },
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+          "temperature": 1.0,
+          "text": {
+            "format": {
+              "type": "text"
+            }
+          },
+          "tool_choice": "auto",
+          "tools": [],
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+            },
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+          },
+          "user": null,
+          "metadata": {}
+        }
+  recorded_at: Mon, 31 Mar 2025 20:38:55 GMT
 recorded_with: VCR 6.3.1
-...

test/vcr_cassettes/openai/chat/basic_streaming_response.yml

@@ -0,0 +1,92 @@
+---
+http_interactions:
+- request:
+    method: post
+    uri: https://api.openai.com/v1/responses
+    body:
+      encoding: UTF-8
+      string: '{"model":"gpt-4o","input":[{"role":"user","content":"This is a chat
+        test.  If it''s working, respond with a single word: Yes"}],"instructions":null,"tools":[],"previous_response_id":null,"stream":true}'
+    headers:
+      Content-Type:
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+      Authorization:
+      - Bearer <OPENAI_ACCESS_TOKEN>
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+      - gzip;q=1.0,deflate;q=0.6,identity;q=0.3
+      Accept:
+      - "*/*"
+      User-Agent:
+      - Ruby
+  response:
+    status:
+      code: 200
+      message: OK
+    headers:
+      Date:
+      - Mon, 31 Mar 2025 20:38:55 GMT
+      Content-Type:
+      - text/event-stream; charset=utf-8
+      Transfer-Encoding:
+      - chunked
+      Connection:
+      - keep-alive
+      Openai-Version:
+      - '2020-10-01'
+      Openai-Organization:
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+        path=/; expires=Mon, 31-Mar-25 21:08:55 GMT; domain=.api.openai.com; HttpOnly;
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+      - _cfuvid=F6OIQe1fgGYxb6xer0VjBA1aHrf6osX7wJU6adYsMy0-1743453535321-0.0.1.1-604800000;
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+      Alt-Svc:
+      - h3=":443"; ma=86400
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+
+        event: response.in_progress
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+        event: response.output_item.added
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+
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+
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+recorded_with: VCR 6.3.1
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test/vcr_cassettes/openai/chat/error.yml

@@ -5,8 +5,7 @@ http_interactions:
     uri: https://api.openai.com/v1/responses
     body:
       encoding: UTF-8
-      string: '{"model":"invalid-model-that-will-trigger-api-error","input":[{"role":"user","content":"Error
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     headers:
       Content-Type:
       - application/json
@@ -24,7 +23,7 @@ http_interactions:
       message: Bad Request
     headers:
       Date:
-      - Wed, 26 Mar 2025 21:27:19 GMT
+      - Mon, 31 Mar 2025 20:38:55 GMT
       Content-Type:
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       Content-Length:
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       Openai-Organization:
       - "<OPENAI_ORGANIZATION_ID>"
       X-Request-Id:
-      - req_2b86e02f664e790dfa475f111402b722
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 recorded_with: VCR 6.3.1

test/vcr_cassettes/openai/chat/function_calls.yml

@@ -0,0 +1,247 @@
+---
+http_interactions:
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+    method: post
+    uri: https://api.openai.com/v1/responses
+    body:
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         event: response.output_text.delta
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         event: response.output_text.delta
-        data: {"type":"response.output_text.delta","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"delta":" $"}
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":" $"}
 
         event: response.output_text.delta
-        data: {"type":"response.output_text.delta","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"delta":"124"}
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":"10"}
 
         event: response.output_text.delta
-        data: {"type":"response.output_text.delta","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"delta":","}
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":","}
 
         event: response.output_text.delta
-        data: {"type":"response.output_text.delta","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"delta":"200"}
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":"000"}
 
         event: response.output_text.delta
-        data: {"type":"response.output_text.delta","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"delta":"."}
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":" USD"}
+
+        event: response.output_text.delta
+        data: {"type":"response.output_text.delta","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"delta":"."}
 
         event: response.output_text.done
-        data: {"type":"response.output_text.done","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"text":"Your net worth is $124,200."}
+        data: {"type":"response.output_text.done","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"text":"Your net worth is $10,000 USD."}
 
         event: response.content_part.done
-        data: {"type":"response.content_part.done","item_id":"msg_67e47003483c819290ae392b826c4910069d9116026394b6","output_index":0,"content_index":0,"part":{"type":"output_text","text":"Your net worth is $124,200.","annotations":[]}}
+        data: {"type":"response.content_part.done","item_id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","output_index":0,"content_index":0,"part":{"type":"output_text","text":"Your net worth is $10,000 USD.","annotations":[]}}
 
         event: response.output_item.done
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+        data: {"type":"response.output_item.done","output_index":0,"item":{"type":"message","id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Your net worth is $10,000 USD.","annotations":[]}]}}
 
         event: response.completed
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+        data: {"type":"response.completed","response":{"id":"resp_67eafd5ff7448192af7cd9e9dde90f5e0aa8698ee903b906","object":"response","created_at":1743453536,"status":"completed","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"gpt-4o-2024-08-06","output":[{"type":"message","id":"msg_67eafd6084ec81929a5132414ef713180aa8698ee903b906","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Your net worth is $10,000 USD.","annotations":[]}]}],"parallel_tool_calls":true,"previous_response_id":"resp_67eafd5f2ef0819290ec6bbbc5f27c8e0aa8698ee903b906","reasoning":{"effort":null,"generate_summary":null},"store":true,"temperature":1.0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1.0,"truncation":"disabled","usage":{"input_tokens":56,"input_tokens_details":{"cached_tokens":0},"output_tokens":11,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":67},"user":null,"metadata":{}}}
 
-  recorded_at: Wed, 26 Mar 2025 21:22:11 GMT
+  recorded_at: Mon, 31 Mar 2025 20:38:58 GMT
 recorded_with: VCR 6.3.1
 ...