Interface updates

app/models/assistant.rb

@@ -18,52 +18,98 @@ class Assistant
     @chat = chat
   end
 
-  def streamer(model)
+  class ToolCaller
-    assistant_message = AssistantMessage.new(
+    def initialize(functions: [])
-      chat: chat,
+      @functions = functions
-      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
+
+    def call_function(function_request)
+      name = function_request.function_name
+      args = JSON.parse(function_request.function_arguments)
+      fn = get_function(name)
+      result = fn.call(args)
+
+      ToolCall::Function.new(
+        provider_id: function_request.provider_id,
+        provider_call_id: function_request.provider_call_id,
+        function_name: name,
+        function_arguments: args,
+        function_result: result
+      )
+    rescue => e
+      fn_execution_details = {
+        fn_name: name,
+        fn_args: args
+      }
+
+      message = "Error calling function #{name} with arguments #{args}: #{e.message}"
+
+      raise StandardError.new(message)
+    end
+
+    private
+      attr_reader :functions
+
+      def get_function(name)
+        functions.find { |f| f.name == name }
+      end
   end
 
   def respond_to(message)
     chat.clear_error
+
     sleep artificial_thinking_delay
 
     provider = get_model_provider(message.ai_model)
 
+    tool_caller = ToolCaller.new(functions: functions)
+
+    assistant_response = AssistantMessage.new(
+      chat: chat,
+      content: "",
+      ai_model: message.ai_model
+    )
+
+    streamer = proc do |chunk|
+      case chunk.type
+      when "output_text"
+        stop_thinking
+        assistant_response.content += chunk.data
+        assistant_response.save!
+      when "response"
+        if chunk.data.function_requests.any?
+          update_thinking("Analyzing your data to assist you with your question...")
+
+          tool_calls = chunk.data.function_requests.map do |fn_request|
+            tool_caller.call_function(fn_request)
+          end
+
+          assistant_response.tool_calls = tool_calls
+          assistant_response.save!
+
+          provider.chat_response(
+            message.content,
+            model: message.ai_model,
+            instructions: instructions,
+            functions: functions.map(&:to_h),
+            function_results: tool_calls.map(&:to_h),
+            streamer: streamer
+          )
+        else
+          stop_thinking
+        end
+
+        chat.update!(latest_assistant_response_id: chunk.data.id)
+      end
+    end
+
     provider.chat_response(
-      message,
+      message.content,
+      model: message.ai_model,
       instructions: instructions,
-      available_functions: functions,
+      functions: functions.map(&:to_h),
-      streamer: streamer(message.ai_model)
+      function_results: [],
+      streamer: streamer
     )
   rescue => e
     chat.add_error(e)
@@ -78,28 +124,6 @@ class Assistant
       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
-        )
-      end
-
-      messages.each(&:save!)
-    end
-
     def instructions
       <<~PROMPT
         ## Your identity

app/models/assistant/function.rb

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

app/models/provider/llm_provider.rb

@@ -6,8 +6,12 @@ module Provider::LlmProvider
   end
 
   private
-    StreamChunk = Data.define(:type, :data)
+    StreamChunk = Data.define(:provider_type, :data)
-    ChatResponse = Data.define(:id, :messages, :functions, :model)
+    ChatResponse = Data.define(:provider_id, :model, :messages, :function_calls) do
-    Message = Data.define(:id, :content)
+      def final?
-    FunctionExecution = Data.define(:id, :call_id, :name, :arguments, :result)
+        function_calls.empty?
+      end
+    end
+    Message = Data.define(:provider_id, :content)
+    FunctionCall = Data.define(:provider_id, :provider_call_id, :name, :arguments, :result)
 end

app/models/provider/openai.rb

@@ -11,17 +11,40 @@ 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: [], previous_response_id: nil)
     with_provider_response do
-      processor = ChatResponseProcessor.new(
+      proxy_streamer = proc do |chunk|
-        client: client,
+        type = chunk.dig("type")
-        message: message,
+      end
-        instructions: instructions,
-        available_functions: available_functions,
-        streamer: streamer
-      )
 
-      processor.process
+      function_results_input = function_results.map do |fn_result|
+        {
+          type: "function_call_output",
+          call_id: fn_result[:provider_call_id],
+          output: fn_result[:result].to_json
+        }
+      end
+
+      prompt_input = [ { role: "user", content: prompt } ]
+
+      tools = functions.map do |fn|
+        {
+          type: "function",
+          name: fn[:name],
+          description: fn[:description],
+          parameters: fn[:params_schema],
+          strict: fn[:strict]
+        }
+      end
+
+      client.responses.create(parameters: {
+        model: model,
+        input: prompt_input + function_results_input,
+        instructions: instructions,
+        tools: tools,
+        previous_response_id: previous_response_id,
+        stream: streamer
+      })
     end
   end
 

app/models/provider/openai/chat_response_processor.rb

@@ -1,103 +1,101 @@
 class Provider::Openai::ChatResponseProcessor
-  def initialize(message:, client:, instructions: nil, available_functions: [], streamer: nil)
+  include Provider::Openai::Parser
+
+  def initialize(message:, function_caller:, client:, subscribers:, instructions: nil)
     @client = client
     @message = message
     @instructions = instructions
-    @available_functions = available_functions
+    @function_caller = function_caller
-    @streamer = streamer
+    @streamer = build_streamer(subscribers)
+  end
+
+  def build_streamer(subscribers)
+    ChatStreamer.new(
+      client: client,
+      function_caller: function_caller,
+      subscribers: subscribers
+    )
   end
 
   def process
-    first_response = fetch_response(previous_response_id: previous_openai_response_id)
+    raw_first_response = fetch_response(input, previous_response_id: previous_openai_response_id)
 
-    if first_response.functions.empty?
+    function_requests = extract_function_requests(raw_first_response)
-      if streamer.present?
-        streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: first_response))
-      end
 
-      return first_response
+    function_calls = function_requests.map do |function_request|
+      function_caller.fulfill_request(function_request)
     end
 
-    executed_functions = execute_pending_functions(first_response.functions)
+    first_response = build_response(raw_first_response, function_calls: function_calls)
 
-    follow_up_response = fetch_response(
+    if first_response.function_calls.empty?
-      executed_functions: executed_functions,
+      return [ first_response ]
-      previous_response_id: first_response.id
+    end
+
+    raw_follow_up_response = fetch_response(
+      input + function_caller.build_results_input(function_calls),
+      previous_response_id: first_response.provider_id,
     )
 
-    if streamer.present?
+    follow_up_response = build_response(raw_follow_up_response)
-      streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: follow_up_response))
-    end
 
-    follow_up_response
+    [ first_response, follow_up_response ]
   end
 
   private
-    attr_reader :client, :message, :instructions, :available_functions, :streamer
+    attr_reader :client, :message, :instructions, :streamer, :function_caller
 
-    PendingFunction = Data.define(:id, :call_id, :name, :arguments)
+    StreamChunk = Provider::LlmProvider::StreamChunk
+    ChatResponse = Provider::LlmProvider::ChatResponse
+    Message = Provider::LlmProvider::Message
+    FunctionCall = Provider::LlmProvider::FunctionCall
+    Error = Provider::Openai::Error
 
-    def fetch_response(executed_functions: [], previous_response_id: nil)
+    def build_response(response, function_calls: [])
-      function_results = executed_functions.map do |executed_function|
+      ChatResponse.new(
-        {
+        provider_id: extract_id(response),
-          type: "function_call_output",
+        model: extract_model(response),
-          call_id: executed_function.call_id,
+        messages: extract_messages(response).map do |msg|
-          output: executed_function.result.to_json
+          Message.new(
-        }
+            provider_id: msg[:id],
-      end
+            content: msg[:output_text]
+          )
+        end,
+        function_calls: function_calls
+      )
+    end
 
-      prepared_input = input + function_results
+    def fetch_response(input, previous_response_id: nil)
+      # raw_response = nil
 
-      # No need to pass tools for follow-up messages that provide function results
+      # internal_streamer = proc do |chunk|
-      prepared_tools = executed_functions.empty? ? tools : []
+      #   type = chunk.dig("type")
 
-      raw_response = nil
+      #   if type == "response.completed"
-
+      #     raw_response = chunk.dig("response")
-      internal_streamer = proc do |chunk|
+      #   end
-        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
 
+      #   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(StreamChunk.new(provider_type: "output_text", data: chunk.dig("delta")))
+      #     when "response.function_call_arguments.done"
+      #       streamer.call(StreamChunk.new(provider_type: "function_request", data: chunk.dig("arguments")))
+      #     when "response.completed"
+      #       normalized = normalize_chat_response(chunk.dig("response"), function_results: function_results)
+      #       streamer.call(StreamChunk.new(provider_type: "response", data: normalized))
+      #     end
+      #   end
+      # end
       client.responses.create(parameters: {
         model: model,
-        input: prepared_input,
+        input: input,
         instructions: instructions,
-        tools: prepared_tools,
+        tools: function_caller.openai_tools,
         previous_response_id: previous_response_id,
-        stream: internal_streamer
+        stream: 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
@@ -117,72 +115,4 @@ class Provider::Openai::ChatResponseProcessor
     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_streamer.rb

@@ -1,13 +1,80 @@
 # A stream proxy for OpenAI chat responses
 #
-# - Consumes an OpenAI chat response stream
+# - Consumes OpenAI stream chunks
-# - Outputs a generic "Chat Provider Stream" interface to consumers (e.g. `Assistant`)
+# - Outputs generic stream chunks to a "subscriber" (e.g. `Assistant`) if subscriber is supplied
 class Provider::Openai::ChatStreamer
+  include Provider::Openai::Parser
+
   def initialize(output_stream)
     @output_stream = output_stream
   end
 
   def call(chunk)
-    @output_stream.call(chunk)
+    output = parse_chunk(chunk)
+    output_stream.call(output) unless output.nil?
   end
+
+  private
+    attr_reader :output_stream
+
+    Chunk = Provider::LlmProvider::StreamChunk
+    Response = Provider::LlmProvider::ChatResponse
+    Message = Provider::LlmProvider::Message
+
+    def parse_chunk(chunk)
+      type = chunk.dig("type")
+
+      case type
+      when "response.output_text.delta", "response.refusal.delta"
+        build_chunk("output_text", chunk.dig("delta"))
+      when "response.function_call_arguments.done"
+        build_chunk("function_request", chunk.dig("arguments"))
+      when "response.completed"
+        handle_response(chunk.dig("response"))
+      end
+    end
+
+    def handle_response(response)
+      function_requests = extract_function_requests(response)
+
+      function_calls = function_requests.map do |function_request|
+        @function_caller.fulfill_request(function_request)
+      end
+
+      normalized_response = build_response(response, function_calls: function_calls)
+
+      build_chunk("response", normalized_response)
+    end
+
+    def build_chunk(type, data)
+      Chunk.new(
+        provider_type: type,
+        data: data
+      )
+    end
+
+    def build_response(response, function_calls: [])
+      Response.new(
+        provider_id: extract_id(response),
+        model: extract_model(response),
+        messages: extract_messages(response).map do |msg|
+          Message.new(
+            provider_id: msg[:id],
+            content: msg[:output_text]
+          )
+        end,
+        function_calls: function_calls
+      )
+    end
+
+    def fetch_response(input, previous_response_id: nil)
+      client.responses.create(parameters: {
+        model: model,
+        input: input,
+        instructions: instructions,
+        tools: function_caller.openai_tools,
+        previous_response_id: previous_response_id,
+        stream: streamer
+      })
+    end
 end

app/models/tool_call.rb

@@ -1,3 +1,13 @@
 class ToolCall < ApplicationRecord
   belongs_to :message
+
+  def to_h
+    {
+      provider_id: provider_id,
+      provider_call_id: provider_call_id,
+      name: function_name,
+      arguments: function_arguments,
+      result: function_result
+    }
+  end
 end

test/models/provider/openai_test.rb

@@ -33,16 +33,18 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
       response = @subject.chat_response(message)
 
       assert response.success?
-      assert_equal 1, response.data.messages.size
+      assert_equal 1, response.data.size
-      assert_includes response.data.messages.first.content, "Yes"
+      assert response.data.first.final?
+      assert_equal 1, response.data.first.messages.size
+      assert_includes response.data.first.messages.first.content, "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|
+      mock_subscriber = proc do |chunk|
         collected_chunks << chunk
       end
 
@@ -52,11 +54,11 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
         ai_model: @subject_model
       )
 
-      @subject.chat_response(message, streamer: mock_streamer)
+      @subject.chat_response(message, stream_subscriber: mock_subscriber)
 
-      tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
+      tool_call_chunks = collected_chunks.select { |chunk| chunk.provider_type == "function_request" }
-      text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
+      text_chunks = collected_chunks.select { |chunk| chunk.provider_type == "output_text" }
-      response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
+      response_chunks = collected_chunks.select { |chunk| chunk.provider_type == "response" }
 
       assert_equal 1, text_chunks.size
       assert_equal 1, response_chunks.size
@@ -74,15 +76,24 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
         available_functions: [ PredictableToolFunction.new(@chat) ]
       )
 
+      # Two responses: one for function requests, one follow-up for text output
       assert response.success?
-      assert_equal 1, response.data.functions.size
+      assert_equal 2, response.data.size
-      assert_equal 1, response.data.messages.size
+
-      assert_includes response.data.messages.first.content, PredictableToolFunction.expected_test_result
+      # First response has function requests / results
+      assert_not response.data.first.final?
+      assert_equal 1, response.data.first.function_calls.size
+
+      # Second response has text output that uses the function results
+      assert response.data.last.final?
+      assert_equal 0, response.data.last.function_calls.size
+      assert_equal 1, response.data.last.messages.size
+      assert_includes response.data.last.messages.first.content, PredictableToolFunction.expected_test_result
     end
   end
 
   test "streams chat response with tool calls" do
-    VCR.use_cassette("openai/chat/tool_calls") do
+    VCR.use_cassette("openai/chat/streaming_tool_calls") do
       collected_chunks = []
 
       mock_streamer = proc do |chunk|
@@ -96,16 +107,17 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
         streamer: mock_streamer
       )
 
-      text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
+      text_chunks = collected_chunks.select { |chunk| chunk.provider_type == "output_text" }
-      text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
+      tool_call_chunks = collected_chunks.select { |chunk| chunk.provider_type == "function_request" }
-      tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
+      response_chunks = collected_chunks.select { |chunk| chunk.provider_type == "response" }
-      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 2, response_chunks.count
 
-      assert_includes response_chunks.first.data.messages.first.content, PredictableToolFunction.expected_test_result
+      assert_not response_chunks.first.data.final?
+      assert response_chunks.last.data.final?
+      assert_includes response_chunks.last.data.messages.first.content, PredictableToolFunction.expected_test_result
     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
+      - Fri, 28 Mar 2025 14:23:22 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_13900312235c86dca9983c43f884612f
       Openai-Processing-Ms:
-      - '103'
+      - '1103'
       Strict-Transport-Security:
       - max-age=31536000; includeSubDomains; preload
       Cf-Cache-Status:
       - DYNAMIC
       Set-Cookie:
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-...