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Personal finance AI (v1) (#2022)
* AI sidebar * Add chat and message models with associations * Implement AI chat functionality with sidebar and messaging system - Add chat and messages controllers - Create chat and message views - Implement chat-related routes - Add message broadcasting and user interactions - Update application layout to support chat sidebar - Enhance user model with initials method * Refactor AI sidebar with enhanced chat menu and interactions - Update sidebar layout with dynamic width and improved responsiveness - Add new chat menu Stimulus controller for toggling between chat and chat list views - Improve chat list display with recent chats and empty state - Extract AI avatar to a partial for reusability - Enhance message display and interaction styling - Add more contextual buttons and interaction hints * Improve chat scroll behavior and message styling - Refactor chat scroll functionality with Stimulus controller - Optimize message scrolling in chat views - Update message styling for better visual hierarchy - Enhance chat container layout with flex and auto-scroll - Simplify message rendering across different chat views * Extract AI avatar to a shared partial for consistent styling - Refactor AI avatar rendering across chat views - Replace hardcoded avatar markup with a reusable partial - Simplify avatar display in chats and messages views * Update sidebar controller to handle right panel width dynamically - Add conditional width class for right sidebar panel - Ensure consistent sidebar toggle behavior for both left and right panels - Use specific width class for right panel (w-[375px]) * Refactor chat form and AI greeting with flexible partials - Extract message form to a reusable partial with dynamic context support - Create flexible AI greeting partial for consistent welcome messages - Simplify chat and sidebar views by leveraging new partials - Add support for different form scenarios (chat, new chat, sidebar) - Improve code modularity and reduce duplication * Add chat clearing functionality with dynamic menu options - Implement clear chat action in ChatsController - Add clear chat route to support clearing messages - Update AI sidebar with dropdown menu for chat actions - Preserve system message when clearing chat - Enhance chat interaction with new menu options * Add frontmatter to project structure documentation - Create initial frontmatter for structure.mdc file - Include description and configuration options - Prepare for potential dynamic documentation rendering * Update general project rules with additional guidelines - Add rule for using `Current.family` instead of `current_family` - Include new guidelines for testing, API routes, and solution approach - Expand project-specific rules for more consistent development practices * Add OpenAI gem and AI-friendly data representations - Add `ruby-openai` gem for AI integration - Implement `to_ai_readable_hash` methods in BalanceSheet and IncomeStatement - Include Promptable module in both models - Add savings rate calculation method in IncomeStatement - Prepare financial models for AI-powered insights and interactions * Enhance AI Financial Assistant with Advanced Querying and Debugging Capabilities - Implement comprehensive AI financial query system with function-based interactions - Add detailed debug logging for AI responses and function calls - Extend BalanceSheet and IncomeStatement models with AI-friendly methods - Create robust error handling and fallback mechanisms for AI queries - Update chat and message views to support debug mode and enhanced rendering - Add AI query routes and initial test coverage for financial assistant * Refactor AI sidebar and chat layout with improved structure and comments - Remove inline AI chat from application layout - Enhance AI sidebar with more semantic HTML structure - Add descriptive comments to clarify different sections of chat view - Improve flex layout and scrolling behavior in chat messages container - Optimize message rendering with more explicit class names and structure * Add Markdown rendering support for AI chat messages - Implement `markdown` helper method in ApplicationHelper using Redcarpet - Update message view to render AI messages with Markdown formatting - Add comprehensive Markdown rendering options (tables, code blocks, links) - Enhance AI Financial Assistant prompt to encourage Markdown usage - Remove commented Markdown CSS in Tailwind application stylesheet * Missing comma * Enhance AI response processing with chat history context * Improve AI debug logging with payload size limits and internal message flag * Enhance AI chat interaction with improved thinking indicator and scrolling behavior * Add AI consent and enable/disable functionality for AI chat * Upgrade Biome and refactor JavaScript template literals - Update @biomejs/biome to latest version with caret (^) notation - Refactor AI query and chat controllers to use template literals - Standardize npm scripts formatting in package.json * Add beta testing usage note to AI consent modal * Update test fixtures and configurations for AI chat functionality - Add family association to chat fixtures and tests - Set consistent password digest for test users - Enable AI for test users - Add OpenAI access token for test environment - Update chat and user model tests to include family context * Simplify data model and get tests passing * Remove structure.mdc from version control * Integrate AI chat styles into existing prose pattern * Match Figma design spec, implement Turbo frames and actions for chats controller * AI rules refresh * Consolidate Stimulus controllers, thinking state, controllers, and views * Naming, domain alignment * Reset migrations * Improve data model to support tool calls and message types * Tool calling tests and fixtures * Tool call implementation and test * Get assistant test working again * Test updates * Process tool calls within provider * Chat UI back to working state again * Remove stale code * Tests passing * Update openai class naming to avoid conflicts * Reconfigure test env * Rebuild gemfile * Fix naming conflicts for ChatResponse * Message styles * Use OpenAI conversation state management * Assistant function base implementation * Add back thinking messages, clean up error handling for chat * Fix sync error when security price has bad data from provider * Add balance sheet function to assistant * Add better function calling error visibility * Add income statement function * Simplify and clean up "thinking" interactions with Turbo frames * Remove stale data definitions from functions * Ensure VCR fixtures working with latest code * basic stream implementation * Get streaming working * Make AI sidebar wider when left sidebar is collapsed * Get tests working with streaming responses * Centralize provider error handling * Provider data boundaries --------- Co-authored-by: Josh Pigford <josh@joshpigford.com>
This commit is contained in:
Zach Gollwitzer
GitHub
parent
8e6b81af77
commit
2f6b11c18f
126 changed files with 3576 additions and 462 deletions
188
app/models/provider/openai/chat_response_processor.rb
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188
app/models/provider/openai/chat_response_processor.rb
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@ -0,0 +1,188 @@
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class Provider::Openai::ChatResponseProcessor
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def initialize(message:, client:, instructions: nil, available_functions: [], streamer: nil)
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@client = client
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@message = message
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@instructions = instructions
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@available_functions = available_functions
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@streamer = streamer
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end
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def process
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first_response = fetch_response(previous_response_id: previous_openai_response_id)
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if first_response.functions.empty?
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if streamer.present?
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streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: first_response))
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end
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return first_response
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end
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executed_functions = execute_pending_functions(first_response.functions)
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follow_up_response = fetch_response(
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executed_functions: executed_functions,
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previous_response_id: first_response.id
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)
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if streamer.present?
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streamer.call(Provider::LlmProvider::StreamChunk.new(type: "response", data: follow_up_response))
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end
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follow_up_response
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end
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private
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attr_reader :client, :message, :instructions, :available_functions, :streamer
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PendingFunction = Data.define(:id, :call_id, :name, :arguments)
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def fetch_response(executed_functions: [], previous_response_id: nil)
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function_results = executed_functions.map do |executed_function|
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{
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type: "function_call_output",
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call_id: executed_function.call_id,
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output: executed_function.result.to_json
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}
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end
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prepared_input = input + function_results
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# No need to pass tools for follow-up messages that provide function results
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prepared_tools = executed_functions.empty? ? tools : []
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raw_response = nil
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internal_streamer = proc do |chunk|
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type = chunk.dig("type")
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if streamer.present?
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case type
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when "response.output_text.delta", "response.refusal.delta"
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# We don't distinguish between text and refusal yet, so stream both the same
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streamer.call(Provider::LlmProvider::StreamChunk.new(type: "output_text", data: chunk.dig("delta")))
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when "response.function_call_arguments.done"
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streamer.call(Provider::LlmProvider::StreamChunk.new(type: "function_request", data: chunk.dig("arguments")))
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end
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end
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if type == "response.completed"
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raw_response = chunk.dig("response")
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end
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end
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client.responses.create(parameters: {
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model: model,
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input: prepared_input,
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instructions: instructions,
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tools: prepared_tools,
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previous_response_id: previous_response_id,
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stream: internal_streamer
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})
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if raw_response.dig("status") == "failed" || raw_response.dig("status") == "incomplete"
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raise Provider::Openai::Error.new("OpenAI returned a failed or incomplete response", { chunk: chunk })
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end
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response_output = raw_response.dig("output")
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functions_output = if executed_functions.any?
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executed_functions
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else
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extract_pending_functions(response_output)
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end
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Provider::LlmProvider::ChatResponse.new(
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id: raw_response.dig("id"),
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messages: extract_messages(response_output),
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functions: functions_output,
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model: raw_response.dig("model")
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)
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end
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def chat
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message.chat
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end
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def model
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message.ai_model
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end
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def previous_openai_response_id
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chat.latest_assistant_response_id
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end
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# Since we're using OpenAI's conversation state management, all we need to pass
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# to input is the user message we're currently responding to.
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def input
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[ { role: "user", content: message.content } ]
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end
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def extract_messages(response_output)
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message_items = response_output.filter { |item| item.dig("type") == "message" }
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message_items.map do |item|
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output_text = item.dig("content").map do |content|
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text = content.dig("text")
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refusal = content.dig("refusal")
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text || refusal
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end.flatten.join("\n")
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Provider::LlmProvider::Message.new(
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id: item.dig("id"),
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content: output_text,
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)
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end
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end
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def extract_pending_functions(response_output)
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response_output.filter { |item| item.dig("type") == "function_call" }.map do |item|
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PendingFunction.new(
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id: item.dig("id"),
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call_id: item.dig("call_id"),
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name: item.dig("name"),
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arguments: item.dig("arguments"),
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)
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end
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end
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def execute_pending_functions(pending_functions)
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pending_functions.map do |pending_function|
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execute_function(pending_function)
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end
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end
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def execute_function(fn)
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fn_instance = available_functions.find { |f| f.name == fn.name }
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parsed_args = JSON.parse(fn.arguments)
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result = fn_instance.call(parsed_args)
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Provider::LlmProvider::FunctionExecution.new(
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id: fn.id,
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call_id: fn.call_id,
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name: fn.name,
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arguments: parsed_args,
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result: result
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)
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rescue => e
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fn_execution_details = {
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fn_name: fn.name,
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fn_args: parsed_args
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}
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raise Provider::Openai::Error.new(e, fn_execution_details)
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end
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def tools
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available_functions.map do |fn|
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{
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type: "function",
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name: fn.name,
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description: fn.description,
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parameters: fn.params_schema,
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strict: fn.strict_mode?
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}
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end
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end
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end
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13
app/models/provider/openai/chat_streamer.rb
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app/models/provider/openai/chat_streamer.rb
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# A stream proxy for OpenAI chat responses
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#
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# - Consumes an OpenAI chat response stream
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# - Outputs a generic "Chat Provider Stream" interface to consumers (e.g. `Assistant`)
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class Provider::Openai::ChatStreamer
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def initialize(output_stream)
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@output_stream = output_stream
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end
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def call(chunk)
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@output_stream.call(chunk)
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end
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end
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