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Code Issues Releases Activity Actions

Interface updates

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This commit is contained in:
Zach Gollwitzer 2025-03-28 16:48:11 -04:00
parent 5ab59d44aa
commit 1aa7755593
10 changed files with 536 additions and 368 deletions
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128
app/models/assistant.rb
View file

@ -18,52 +18,98 @@ class Assistant
@chat = chat
end
def streamer(model)
assistant_message = AssistantMessage.new(
chat: chat,
content: "",
ai_model: model
)
class ToolCaller
def initialize(functions: [])
@functions = functions
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)
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
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
assistant_message.tool_calls = combined_tool_calls
assistant_message.save!
chat.update!(latest_assistant_response_id: chunk.data.id)
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,
message.content,
model: message.ai_model,
instructions: instructions,
available_functions: functions,
streamer: streamer(message.ai_model)
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.content,
model: message.ai_model,
instructions: instructions,
functions: functions.map(&:to_h),
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

9
app/models/assistant/function.rb
View file

@ -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

12
app/models/provider/llm_provider.rb
View file

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

41
app/models/provider/openai.rb
View file

@ -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(
client: client,
message: message,
instructions: instructions,
available_functions: available_functions,
streamer: streamer
)
proxy_streamer = proc do |chunk|
type = chunk.dig("type")
end
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

206
app/models/provider/openai/chat_response_processor.rb
View file

@ -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
@streamer = streamer
@function_caller = function_caller
@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?
if streamer.present?
streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: first_response))
function_requests = extract_function_requests(raw_first_response)
function_calls = function_requests.map do |function_request|
function_caller.fulfill_request(function_request)
end
return first_response
first_response = build_response(raw_first_response, function_calls: function_calls)
if first_response.function_calls.empty?
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
raw_follow_up_response = fetch_response(
input + function_caller.build_results_input(function_calls),
previous_response_id: first_response.provider_id,
)
if streamer.present?
streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: follow_up_response))
end
follow_up_response = build_response(raw_follow_up_response)
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)
function_results = executed_functions.map do |executed_function|
{
type: "function_call_output",
call_id: executed_function.call_id,
output: executed_function.result.to_json
}
def build_response(response, function_calls: [])
ChatResponse.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
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
prepared_tools = executed_functions.empty? ? tools : []
# internal_streamer = proc do |chunk|
# type = chunk.dig("type")
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
# if type == "response.completed"
# raw_response = chunk.dig("response")
# 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

73
app/models/provider/openai/chat_streamer.rb
View file

@ -1,13 +1,80 @@
# 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`)
# - Consumes OpenAI stream chunks
# - 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

10
app/models/tool_call.rb
View file

@ -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

48
test/models/provider/openai_test.rb
View file

@ -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_includes response.data.messages.first.content, "Yes"
assert_equal 1, response.data.size
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" }
text_chunks = collected_chunks.select { |chunk| chunk.type == "output_text" }
response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
tool_call_chunks = collected_chunks.select { |chunk| chunk.provider_type == "function_request" }
text_chunks = collected_chunks.select { |chunk| chunk.provider_type == "output_text" }
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 1, response.data.messages.size
assert_includes response.data.messages.first.content, PredictableToolFunction.expected_test_result
assert_equal 2, response.data.size
# 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.type == "output_text" }
tool_call_chunks = collected_chunks.select { |chunk| chunk.type == "function_request" }
response_chunks = collected_chunks.select { |chunk| chunk.type == "response" }
text_chunks = collected_chunks.select { |chunk| chunk.provider_type == "output_text" }
tool_call_chunks = collected_chunks.select { |chunk| chunk.provider_type == "function_request" }
response_chunks = collected_chunks.select { |chunk| chunk.provider_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

108
test/vcr_cassettes/openai/chat/basic_response.yml
View file

@ -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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path=/; expires=Wed, 26-Mar-25 21:57:38 GMT; domain=.api.openai.com; HttpOnly;
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path=/; expires=Fri, 28-Mar-25 14:53:22 GMT; domain=.api.openai.com; HttpOnly;
Secure; SameSite=None
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path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
X-Content-Type-Options:
- nosniff
Server:
- cloudflare
Cf-Ray:
- 9269bbb21b1ecf43-CMH
- 9277c8eafdbe10c3-ORD
Alt-Svc:
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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":{}}}
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