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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
This commit is contained in:
Zach Gollwitzer
GitHub
parent
6331788b33
commit
5cf758bd03
33 changed files with 1179 additions and 624 deletions
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@ -1,5 +1,4 @@
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require "test_helper"
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require "ostruct"
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class AssistantTest < ActiveSupport::TestCase
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include ProviderTestHelper
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@ -8,74 +7,109 @@ class AssistantTest < ActiveSupport::TestCase
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@chat = chats(:two)
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@message = @chat.messages.create!(
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type: "UserMessage",
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content: "Help me with my finances",
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content: "What is my net worth?",
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ai_model: "gpt-4o"
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)
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@assistant = Assistant.for_chat(@chat)
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@provider = mock
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@assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
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end
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test "responds to basic prompt" do
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text_chunk = OpenStruct.new(type: "output_text", data: "Hello from assistant")
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response_chunk = OpenStruct.new(
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type: "response",
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data: OpenStruct.new(
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id: "1",
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model: "gpt-4o",
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messages: [
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OpenStruct.new(
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id: "1",
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content: "Hello from assistant",
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)
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],
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functions: []
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)
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)
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test "errors get added to chat" do
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@assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
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@provider.expects(:chat_response).with do |message, **options|
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options[:streamer].call(text_chunk)
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options[:streamer].call(response_chunk)
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true
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end
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error = StandardError.new("test error")
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@provider.expects(:chat_response).returns(provider_error_response(error))
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assert_difference "AssistantMessage.count", 1 do
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@chat.expects(:add_error).with(error).once
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assert_no_difference "AssistantMessage.count" do
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@assistant.respond_to(@message)
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end
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end
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test "responds with tool function calls" do
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function_request_chunk = OpenStruct.new(type: "function_request", data: "get_net_worth")
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text_chunk = OpenStruct.new(type: "output_text", data: "Your net worth is $124,200")
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response_chunk = OpenStruct.new(
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type: "response",
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data: OpenStruct.new(
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id: "1",
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model: "gpt-4o",
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messages: [
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OpenStruct.new(
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id: "1",
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content: "Your net worth is $124,200",
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)
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],
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functions: [
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OpenStruct.new(
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id: "1",
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call_id: "1",
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name: "get_net_worth",
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arguments: "{}",
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result: "$124,200"
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)
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]
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)
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test "responds to basic prompt" do
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@assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider)
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text_chunks = [
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provider_text_chunk("I do not "),
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provider_text_chunk("have the information "),
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provider_text_chunk("to answer that question")
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]
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response_chunk = provider_response_chunk(
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id: "1",
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model: "gpt-4o",
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messages: [ provider_message(id: "1", text: text_chunks.join) ],
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function_requests: []
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)
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response = provider_success_response(response_chunk.data)
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@provider.expects(:chat_response).with do |message, **options|
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options[:streamer].call(function_request_chunk)
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options[:streamer].call(text_chunk)
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text_chunks.each do |text_chunk|
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options[:streamer].call(text_chunk)
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end
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options[:streamer].call(response_chunk)
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true
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end.returns(response)
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assert_difference "AssistantMessage.count", 1 do
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@assistant.respond_to(@message)
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message = @chat.messages.ordered.where(type: "AssistantMessage").last
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assert_equal "I do not have the information to answer that question", message.content
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assert_equal 0, message.tool_calls.size
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end
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end
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test "responds with tool function calls" do
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@assistant.expects(:get_model_provider).with("gpt-4o").returns(@provider).once
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# Only first provider call executes function
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Assistant::Function::GetAccounts.any_instance.stubs(:call).returns("test value").once
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# Call #1: Function requests
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call1_response_chunk = provider_response_chunk(
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id: "1",
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model: "gpt-4o",
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messages: [],
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function_requests: [
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provider_function_request(id: "1", call_id: "1", function_name: "get_accounts", function_args: "{}")
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]
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)
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call1_response = provider_success_response(call1_response_chunk.data)
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# Call #2: Text response (that uses function results)
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call2_text_chunks = [
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provider_text_chunk("Your net worth is "),
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provider_text_chunk("$124,200")
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]
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call2_response_chunk = provider_response_chunk(
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id: "2",
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model: "gpt-4o",
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messages: [ provider_message(id: "1", text: call2_text_chunks.join) ],
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function_requests: []
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)
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call2_response = provider_success_response(call2_response_chunk.data)
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sequence = sequence("provider_chat_response")
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@provider.expects(:chat_response).with do |message, **options|
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call2_text_chunks.each do |text_chunk|
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options[:streamer].call(text_chunk)
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end
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options[:streamer].call(call2_response_chunk)
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true
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end.returns(call2_response).once.in_sequence(sequence)
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@provider.expects(:chat_response).with do |message, **options|
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options[:streamer].call(call1_response_chunk)
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true
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end.returns(call1_response).once.in_sequence(sequence)
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assert_difference "AssistantMessage.count", 1 do
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@assistant.respond_to(@message)
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@ -83,4 +117,34 @@ class AssistantTest < ActiveSupport::TestCase
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assert_equal 1, message.tool_calls.size
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end
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end
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private
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def provider_function_request(id:, call_id:, function_name:, function_args:)
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Provider::LlmConcept::ChatFunctionRequest.new(
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id: id,
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call_id: call_id,
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function_name: function_name,
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function_args: function_args
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)
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end
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def provider_message(id:, text:)
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Provider::LlmConcept::ChatMessage.new(id: id, output_text: text)
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end
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def provider_text_chunk(text)
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Provider::LlmConcept::ChatStreamChunk.new(type: "output_text", data: text)
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end
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def provider_response_chunk(id:, model:, messages:, function_requests:)
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Provider::LlmConcept::ChatStreamChunk.new(
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type: "response",
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data: Provider::LlmConcept::ChatResponse.new(
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id: id,
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model: model,
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messages: messages,
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function_requests: function_requests
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)
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)
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end
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end
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@ -6,16 +6,11 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
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setup do
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@subject = @openai = Provider::Openai.new(ENV.fetch("OPENAI_ACCESS_TOKEN", "test-openai-token"))
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@subject_model = "gpt-4o"
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@chat = chats(:two)
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end
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test "openai errors are automatically raised" do
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VCR.use_cassette("openai/chat/error") do
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response = @openai.chat_response(UserMessage.new(
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chat: @chat,
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content: "Error test",
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ai_model: "invalid-model-that-will-trigger-api-error"
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))
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response = @openai.chat_response("Test", model: "invalid-model-that-will-trigger-api-error")
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assert_not response.success?
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assert_kind_of Provider::Openai::Error, response.error
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@ -24,113 +19,145 @@ class Provider::OpenaiTest < ActiveSupport::TestCase
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test "basic chat response" do
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VCR.use_cassette("openai/chat/basic_response") do
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message = @chat.messages.create!(
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type: "UserMessage",
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content: "This is a chat test. If it's working, respond with a single word: Yes",
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ai_model: @subject_model
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response = @subject.chat_response(
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"This is a chat test. If it's working, respond with a single word: Yes",
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model: @subject_model
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)
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response = @subject.chat_response(message)
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assert response.success?
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assert_equal 1, response.data.messages.size
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assert_includes response.data.messages.first.content, "Yes"
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assert_includes response.data.messages.first.output_text, "Yes"
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end
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end
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test "streams basic chat response" do
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VCR.use_cassette("openai/chat/basic_response") do
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VCR.use_cassette("openai/chat/basic_streaming_response") do
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collected_chunks = []
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mock_streamer = proc do |chunk|
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collected_chunks << chunk
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end
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message = @chat.messages.create!(
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type: "UserMessage",
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content: "This is a chat test. If it's working, respond with a single word: Yes",
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ai_model: @subject_model
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response = @subject.chat_response(
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"This is a chat test. If it's working, respond with a single word: Yes",
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model: @subject_model,
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streamer: mock_streamer
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)
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@subject.chat_response(message, streamer: mock_streamer)
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tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
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text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
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response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
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assert_equal 1, text_chunks.size
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assert_equal 1, response_chunks.size
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assert_equal 0, tool_call_chunks.size
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assert_equal "Yes", text_chunks.first.data
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assert_equal "Yes", response_chunks.first.data.messages.first.content
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assert_equal "Yes", response_chunks.first.data.messages.first.output_text
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assert_equal response_chunks.first.data, response.data
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end
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end
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test "chat response with tool calls" do
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VCR.use_cassette("openai/chat/tool_calls") do
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response = @subject.chat_response(
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tool_call_message,
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test "chat response with function calls" do
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VCR.use_cassette("openai/chat/function_calls") do
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prompt = "What is my net worth?"
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functions = [
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{
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name: "get_net_worth",
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description: "Gets a user's net worth",
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params_schema: { type: "object", properties: {}, required: [], additionalProperties: false },
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strict: true
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}
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]
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first_response = @subject.chat_response(
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prompt,
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model: @subject_model,
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instructions: "Use the tools available to you to answer the user's question.",
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available_functions: [ PredictableToolFunction.new(@chat) ]
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functions: functions
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)
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assert response.success?
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assert_equal 1, response.data.functions.size
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assert_equal 1, response.data.messages.size
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assert_includes response.data.messages.first.content, PredictableToolFunction.expected_test_result
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assert first_response.success?
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function_request = first_response.data.function_requests.first
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assert function_request.present?
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second_response = @subject.chat_response(
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prompt,
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model: @subject_model,
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function_results: [ {
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call_id: function_request.call_id,
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output: { amount: 10000, currency: "USD" }.to_json
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} ],
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previous_response_id: first_response.data.id
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)
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assert second_response.success?
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assert_equal 1, second_response.data.messages.size
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assert_includes second_response.data.messages.first.output_text, "$10,000"
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end
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end
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test "streams chat response with tool calls" do
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VCR.use_cassette("openai/chat/tool_calls") do
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test "streams chat response with function calls" do
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VCR.use_cassette("openai/chat/streaming_function_calls") do
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collected_chunks = []
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mock_streamer = proc do |chunk|
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collected_chunks << chunk
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end
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prompt = "What is my net worth?"
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functions = [
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{
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name: "get_net_worth",
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description: "Gets a user's net worth",
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params_schema: { type: "object", properties: {}, required: [], additionalProperties: false },
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strict: true
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}
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]
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# Call #1: First streaming call, will return a function request
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@subject.chat_response(
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tool_call_message,
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prompt,
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model: @subject_model,
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instructions: "Use the tools available to you to answer the user's question.",
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available_functions: [ PredictableToolFunction.new(@chat) ],
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functions: functions,
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streamer: mock_streamer
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)
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text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
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text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
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tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
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response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
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assert_equal 1, tool_call_chunks.count
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assert text_chunks.count >= 1
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assert_equal 1, response_chunks.count
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assert_equal 0, text_chunks.size
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assert_equal 1, response_chunks.size
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assert_includes response_chunks.first.data.messages.first.content, PredictableToolFunction.expected_test_result
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first_response = response_chunks.first.data
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function_request = first_response.function_requests.first
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# Reset collected chunks for the second call
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collected_chunks = []
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# Call #2: Second streaming call, will return a function result
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@subject.chat_response(
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prompt,
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model: @subject_model,
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function_results: [
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{
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call_id: function_request.call_id,
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output: { amount: 10000, currency: "USD" }
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}
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],
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previous_response_id: first_response.id,
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streamer: mock_streamer
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)
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text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
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response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
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assert text_chunks.size >= 1
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assert_equal 1, response_chunks.size
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assert_includes response_chunks.first.data.messages.first.output_text, "$10,000"
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end
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end
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private
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def tool_call_message
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UserMessage.new(chat: @chat, content: "What is my net worth?", ai_model: @subject_model)
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end
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class PredictableToolFunction < Assistant::Function
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class << self
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def expected_test_result
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"$124,200"
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end
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def name
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"get_net_worth"
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end
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def description
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"Gets user net worth data"
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end
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end
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def call(params = {})
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self.class.expected_test_result
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end
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end
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end
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@ -6,7 +6,7 @@ http_interactions:
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body:
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encoding: UTF-8
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string: '{"model":"gpt-4o","input":[{"role":"user","content":"This is a chat
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test. If it''s working, respond with a single word: Yes"}],"instructions":null,"tools":[],"previous_response_id":null,"stream":true}'
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test. If it''s working, respond with a single word: Yes"}],"instructions":null,"tools":[],"previous_response_id":null,"stream":null}'
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headers:
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Content-Type:
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- application/json
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@ -24,9 +24,9 @@ http_interactions:
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message: OK
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headers:
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Date:
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- Wed, 26 Mar 2025 21:27:38 GMT
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- Mon, 31 Mar 2025 20:38:55 GMT
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Content-Type:
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- text/event-stream; charset=utf-8
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- application/json
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Transfer-Encoding:
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- chunked
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Connection:
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|
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@ -36,57 +36,85 @@ http_interactions:
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Openai-Organization:
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- "<OPENAI_ORGANIZATION_ID>"
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X-Request-Id:
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- req_8fce503a4c5be145dda20867925b1622
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- req_f99033a5841a7d9357ee08d301ad634e
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Openai-Processing-Ms:
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- '103'
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- '713'
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Strict-Transport-Security:
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- max-age=31536000; includeSubDomains; preload
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Cf-Cache-Status:
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- DYNAMIC
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Set-Cookie:
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